Displays

Wetlands are important but fragile ecosystems. Half of the world’s wetlands are already lost and most of the remaining ones are in a degraded state. Such wetlands warrant immediate management and restoration works. Further, with changing land-use patterns and climate, it is essential to monitor the dynamics of such wetlands, which is in turn driven by hydrology, vegetation pattern, and geomorphology. All biogeochemical processes in the wetlands are influenced by hydropattern and water level. Understanding vegetation-hydrology nexus is an important challenge in wetland management and restoration activities. In addition, the spatial characterization of the fragmentation and shrinkage is essential to manage the wetlands.

A geostatistics-based assessment of a large floodplain wetland namely the Kaabar Tal in eastern India has been performed using multi-temporal Landsat datasets in a GIS (Geographical Information System) framework by applying linear regression method and Mann-Kendall Trend Tests. With an area of 51 km² and a total catchment size of 250 km², the Kaabar Tal is the largest wetland of the north Bihar in the East Ganga Plains of India. A historical assessment of the wetland spanning over four decades (1976-2016) has been performed by formulating a novel framework which encompasses the following six indicators: (a) pixel-wise net trend assessment of wetness and vegetation, (b) seasonal hydropattern, (c) average drying rates, (d) seasonal and annual patch dynamics (fragmentation assessment), (e) annual shoreline shrinkage rates, and (f) multi-temporal geomorphic mapping. To understand the influence of these indicators in different parts of the wetland, a sectorial approach has been followed by dividing the wetland in nine zones, and each zone was ranked from least to most degraded based on the six indicators. A linear combination of these ranks was used to decide the overall degradation rank of the zones. The different zones of Kaabar Tal were ranked in terms of increasing order of degradation. The western zone W, the most degraded zone, has suffered the highest quantum of encroachments coupled with the highest rates of shoreline shrinkage. The central zone C ranked least on the degradation scale; however, it is still degrading, and the wetness trend is ‘very severely decreasing’ while the vegetation (or eutrophication) trend is ‘severely increasing.’

The framework developed in the current work is based on the freely available satellite datasets, easily implementable remote sensing and GIS approaches, and well-known geostatistical methods. Further, the method can be adapted to analyze the hydrogeomorphic dynamics and degradation scenarios of any wetland systems, irrespective of their geographical and climatic settings.

How to cite: Singh, M. N. and Sinha, R.: Understanding wetland dynamics using geostatistics of multi-temporal Earth Observation datasets, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-288, https://doi.org/10.5194/egusphere-egu2020-288, 2020.

Proteins are the major nitrogen-containing biochemical constituents of plants. Since nitrogen (N) cannot be measured directly using remote sensing data, leaf protein content constitutes a valid proxy for this main limiting plant nutrient. In the past, mainly linear parametric algorithms, such as vegetation indices, have been employed to retrieve this non-state variable from optical reflectance data. Moreover, most studies solely relied on the relationship of chlorophyll content with nitrogen. In contrast, our study presents a hybrid model inversion scheme of a physically-based approach via protein retrieval combined with advanced machine learning regression. The leaf optical properties PROSPECT-PRO model, including the newly calibrated specific absorption coefficients (SAC) of proteins, was coupled with the canopy reflectance model 4SAIL to PROSAIL-PRO. A generic synthetic database of model input parameters with corresponding reflectance was simulated and used for training two different machine learning regression methods: a standard homoscedastic Gaussian Process (GP) and a variational heteroscedastic GP regression that accounts for signal-to-noise correlations. Both GP methods have the interesting feature of providing confidence intervals for the estimates. As part of multiple field campaigns, carried out in the scientific preparation framework of the Environmental Mapping and Analysis Program (EnMAP), spectra of maize and winter wheat were acquired to simulate EnMAP data and plant-organ-specific nitrogen measurements were destructively collected for validation. Both GP models yielded excellent performance in learning the nonlinear relationship between specific protein absorption bands and area-based above-ground N. They also performed similar or even outperformed other nonlinear nonparametric approaches. Physical validation of the estimates against in situ nitrogen measurements from leaves plus stalks yielded a root mean square error (RMSE) of 2.5 g/m². The variational heteroscedastic GP provided a more differentiated pattern of uncertainty with tighter confidence intervals within low-value regimes compared to the standard GP. The inclusion of fruit nitrogen content for validation deteriorated the results of all models, which can be explained by the inability of radiation in the optical domain to penetrate the thick tissues of maize cobs and wheat ears. Following some further validation exercises, we aim to implement GP-based algorithms for global agricultural monitoring of above-ground N derived from future satellite imaging spectroscopy data.

How to cite: Berger, K., Camps-Valls, G., Verrelst, J., Féret, J.-B., Wocher, M., and Hank, T.: Spectroscopic retrieval of above-ground crop nitrogen content with a hybrid machine learning regression method, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-401, https://doi.org/10.5194/egusphere-egu2020-401, 2020.

The classical Beer-Lambert (BL) law of exponential decay in direct transmission is widely used for modeling the photon propagation in optical media and has been employed for retrieving vegetation structure parameters (e.g. leaf area index, LAI). However, BL law assumes that these absorbing obstacles are distributed in the space independently, which is the main reason of model-observation-inconsistency and arises many studies of so-called sub- and super-exponential extinction for spatially correlated media. Discontinuous vegetation canopy is the typical case of the extinction field with spatial correlations. Because of many practical difficulties, the uncertainty of the BL law used in vegetation canopy still lacks quantitive assessment. In this paper, we carry out this task by utilizing a ray-tracing-based 3-Dimensional radiative transfer model (3D RT) to simulate the photo propagation process in real vegetation canopy scenes. We confirm that the classical BL law is only suitable for both horizontally and vertically homogenous canopy (e.g. dense grasses) and shows increasing discrepancy with the decrease of the fraction of vegetation cover (FVC). When canopy clumping occurs (FVC<1), absorbing obstacles (i.e. leaves) become to be spatially correlated and lead to a slower-than-exponential (sub-exponential) extinction with propagation distance, which will result in an underestimation of LAI when classic BL law is employed. To solve this problem, we propose a new 1st order scattering extinction model by modifying the classic BL law by introducing a pair-correlation-function. This attempt is based on the stochastic radiative transfer theory and shows good performance when compared with the reference from computer 3D simulation.

How to cite: Yan, K., Zhang, Y., Xu, X., Pu, J., and Liu, Z.: Evaluation and Refinement of the Beer-Lambert Extinction Law in the Discontinuous Vegetation Canopy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4147, https://doi.org/10.5194/egusphere-egu2020-4147, 2020.

Solar-Induced Fluorescence (SIF) data from satellites are increasingly used as a proxy for photosynthetic activity by vegetation, and as a constraint on gross primary production. The Royal Netherlands Meteorological Institute has developed an improved retrieval algorithm called SIFTER, to retrieve mid-morning (09:30 hrs local time) SIF estimates on the global scale from GOME-2 sensors on the Metop satellite series. The product is developed within the ACSAF network of EUMETSAT and a beta version is publicly available on www.temis.nl. The SIFTER algorithm improves over a previous version by using a narrower spectral window that avoids strong oxygen absorption and is less sensitive to water vapor absorption, by constructing stable reference spectra from a 6-year period (2007-2012) of atmospheric spectra over the Sahara, and by applying a latitude-dependent zero-level adjustment that accounts for biases in the product data. With SIFTER, we generate stable, good-quality SIF retrievals also in tropical regions that are known to suffer from high noise in other SIF products. Uncertainty estimates are included for individual observations, and the product is best used for mostly clear-sky scenes, and when spectral residuals remain below a certain threshold. The strength of SIFTER in the tropical regions was exploited to quantify the 2015/2016 drought in the Amazon, related to El Niño. We found that SIF was strongly suppressed over areas with anomalously high temperatures and decreased levels of soil moisture. SIF went below its climatological range starting from the end of the 2015 dry season and returned to normal levels by February 2016. A validation study is performed to assess the SIFTER quality against independent SIF and GPP products from other platforms, including SIF from OCO-2 and GOSAT, modeled GPP from MPI-BGC and eddy covariance derived, in-situ GPP measurements. SIFTER shows strong correlations (0.70 – 0.94) in the zonal distribution for each continent and in capturing seasonal patterns of SIF and GPP over different regions across the globe (0.62-0.99) when comparing to OCO-2 SIF and GPP from MPI-BGC. At ecosystem level, SIFTER was evaluated against OCO-2 SIF and EC GPP for five flux tower sites with varying biomes and geolocations. Regions with a homogeneous vegetation distribution show a higher correlation than heterogeneous regions. Overall, the results support the use of SIFTER data to be used as an independent constraint on photosynthetic activity on global and regional scales.

How to cite: Kooreman, M. L., Boersma, K. F., van Schaik, E., Mengistu, A. G., Tuinder, O. N. E., Stammes, P., Koren, G., and Peters, W.: Twelve years of SIFTER Sun-Induced Fluorescence retrievals from GOME-2 as an independent constraint on photosynthesis across continents and biomes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5421, https://doi.org/10.5194/egusphere-egu2020-5421, 2020.

Leaf area index (i.e. one-half the total green leaf area per unit of horizontal ground surface area) is a crucial parameter in carbon balancing and modeling. Forest overstory and understory layers differ in carbon and water cycle regimes and phenology, as well as in ecosystem functions. Separate retrievals of leaf area index (LAI) for these two layers would help to improve modeling forest biogeochemical cycles, evaluating forest ecosystem functions and also remote sensing of forest canopies by inversion of canopy reflectance models. The aim of this study is to compare currently available understory LAI assessment methodologies over a diverse set of greenhouse gas measurement sites distributed along a wide latitudinal and elevational gradient across Europe. This will help to quantify  the fraction of the canopy LAI which is represented by understory, since this is still the major source of uncertainty in global LAI products derived from remote sensing data. For this, we took ground photos as well as in-situ reflectance measurements of the understory vegetation at 30 ICOS (Integration Carbon Observation System) sites distributed across 10 countries in Europe. The data were analyzed by means of three conceptually different methods for LAI estimation and comprised purely empirical (fractional cover), semi-empirical (in-situ NDVI linked to the radiative transfer model FLiES), and purely deterministic (Four-scale geometrical optical model) approaches. Finally, our results are compared with global forest understory LAI maps derived from remote sensing data at 1 km resolution (Liu et al. 2017). While we found some agreement among the three methods (e.g. Pearson-correlation between empirical and semi-empirical = 0.63), we also identified sources that are particularly prone to error inclusion such as inaccurate assessment of fractional cover from ground photos. Relationships between understory LAI and long-term climate variables were weak and suggested that understory LAI at the ICOS sites is probably more strongly determined by microclimatic conditions.

Liu Y. et al. (2017): Separating overstory and understory leaf area indices for global needleleaf and deciduous broadleaf forests by fusion of MODIS and MISR data. Biogeosciences 14: 1093-1110.

How to cite: George, J.-P. and Pisek, J. and the Tobias Biermann (2), Arnaud Carrara (3), Edoardo Cremonese (4), Matthias Cuntz (5), Silvano Fares (6), Giacomo Gerosa (7), Thomas Grünwald (8), Niklas Hase (9), Michal Heliasz (2), Andreas Ibrom (10), Alexander Knohl (11), Bart Kruijt (12), Hikdeki Kobaya: Evaluation of understory LAI estimation methodologies over forest ecosystem ICOS sites across Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6674, https://doi.org/10.5194/egusphere-egu2020-6674, 2020.

In response to changes in environmental factors (e.g., temperature, radiation, soil moisture), plants emit biogenic volatile organic compounds (BVOCs). Once released in the atmosphere, BVOCs influence levels of greenhouse gases and air pollutants (e.g., methane, ozone and aerosols), thus affecting both climate and air quality. In turn, climate change may alter BVOC emissions by modifying the driving environmental conditions and by increasing the occurrence and intensity of severe stresses that alter plant functioning. To understand and better constrain the evolution of BVOC emissions under future climates, it is important to reduce the uncertainties in global and regional estimates of BVOC emissions under present climate. Part of the uncertainty in the estimates of BVOC emissions is related to the impact that water stress might have on BVOC emissions. Field campaign, in-situ and laboratory experiments investigated the effect of different regimes of water stress (short- vs. long-term) on BVOC emissions. However, these studies provide geographically scattered and uneven results. To explore the relationship between BVOC emissions and water stress globally, we use remotely sensed soil moisture and formaldehyde, a proxy of BVOC emissions. As BVOCs include a multitude of gas tracers with lifetime ranging from few hours to days, a fully characterisation of these components is virtually impossible. Nevertheless, in the continental boundary layer, formaldehyde is an intermediate by-product of the oxidation of BVOCs, it thus provides a proxy for probing local BVOC emissions, and in particular isoprene, which accounts for about 50% of the total BVOC emissions.

In the present study, retrievals of formaldehyde from the Ozone Monitoring Instrument (OMI) are combined with observations of soil moisture, biomass, aerosols, evapotranspiration, drought index, temperature and precipitation. Firstly, we look into the linear annual trend of the selected fields. Secondly, assuming formaldehyde as the dependent variable, we apply a linear mixed model analysis that extends the application of a simple linear regression model by accounting for both fixed (i.e., explained by the independent variables) and random (i.e., due to dependence in the data) effects. The analysis of the linear trend of formaldehyde concentrations shows a positive trend over the Amazon and Central Africa and a negative trend over South Africa and Australia. Over the Amazon, formaldehyde is negatively correlated with the Standardised Precipitation-Evapotranspiration Index (SPEI), a drought index that accounts for both changes in temperature and precipitation, with positive and negative values identifying wet and dry events, respectively. The outcomes of this analysis might provide new insights in the relationship between BVOC emissions and water stress and might help in improving parameterizations that link soil moisture to BVOC emissions in numerical models.

How to cite: Strada, S., Penuelas, J., Martinez, M. F., Filella, I., Yanez-Serrano, A. M., Pozzer, A., Bauwens, M., Stavrakou, T., and Giorgi, F.: Probing the relationship between formaldehyde column concentrations and soil moisture using mixed models and attribution analysis, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8263, https://doi.org/10.5194/egusphere-egu2020-8263, 2020.

In urban environments, due to climate change urban heat waves are predicted to occur more frequently. Urban vegetation and the linked evapotranspiration rate can play a mitigating role. However, a major challenge in urban hydrological modelling remains the mapping of vegetation dynamics and its role in hydrological processes in particular interception storage and evapotranspiration. Conventional mapping of vegetation usually implies intensive labor and time consuming field work. We explore the potential of different remote sensing sensors (Proba-V, Landsat, Sentinel2, Apex) to characterize the urban vegetation dynamics for hydrological modelling. The here proposed remote sensing sensors show differences in the spectral and spatial resolutions as well as in their revisit time. However, in the urban environment we need a high spatial and spectral resolution to distinguish the urban landcover and a frequent revisit time to capture seasonal vegetation dynamics. Therefore, we propose a combination of different remote sensing sensors to derive leaf area index (LAI) timeseries in the urban environment. To improve the consistency in time series generated from different remote sensing sources a harmonization of the multi-sensor time series is proposed and validated with a multi-resolution validation approach using ground-truthing LAI (BELHARMONY project). The LAI timeseries, derived from the different remote sensing sensors, are then introduced into the hydrological modelling framework for a location- and time- specific assessment of the interception storage and evapotranspiration component. The effect of the sensor differences to the LAI timeseries on the hydrological response is analyzed.

How to cite: Wirion, C., Verbeiren, B., and Sterckx, S.: Validation of seasonal time series of remote sensing derived LAI for hydrological modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9071, https://doi.org/10.5194/egusphere-egu2020-9071, 2020.

Potassium estimation on plant leaves can help monitor metabolic processes and plant health. The detailed study of hyperspectral data on leaves can therefore be a strong ally in the nutritional diagnosis of plants and can be applied to an on the go systems for precision farming application. In this study, reflectance spectra of cotton leaves were analysed for an assessment of potassium deficiency in cotton plants (Gossypium hirsutum L. ‘BRS 293’). The crop was planted in a greenhouse in the experimental area of the Federal University of Ceara (UFC), Fortaleza, Brazil.  Irrigated cotton plants were submitted to four different doses of potassium with twenty replications (n= 80) over 119 days. The following treatments were applied: 50%, 75%, 100% and 125% of the recommended potassium dose. Hyperspectral reflectance spectra data were collected using a Fieldspec ProFR 3 during full flowering, the phenological stage most demanding of potassium. Multivariate statistical techniques were applied to the raw data, the transformed data by derivative analysis, and by the technique of continuum removal. Band selection was carried out by the stepwise method in order to fit a PLSR model focused on identifying bands that are most sensitive to variations in potassium leaf concentrations. Model performance was evaluated by adjusted correlation coefficients – R²adj, root mean square error - RMSE, and residual prediction deviation - RPD. Validation results indicated that the PLSR model accounted for 82.0% of the variation in leaf potassium concentration, with a RMSE of 3.74 and RPD of 1.61. Therefore, the discrimination of potassium deficiencies in cotton using hyperspectral data was satisfactorily performed by a PLSR model composed of 13 wavelengths, of which most are commonly associated with moisture, lignin, cellulose, sugar and protein concentrations in cotton leaves.

How to cite: Teixeira, A. D. S., Oliveira, M. R. R., Moreira, L. C. J., Machado, F. L. D. C., Lopes, F. B., and Araújo, I. C. D. S.: Potassium estimation of cotton leaves based on hyperspectral reflectance, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12000, https://doi.org/10.5194/egusphere-egu2020-12000, 2020.

In this study, we investigate the performance of machine learning classification approaches and different remotely sensed data sources for identifying and mapping three types of grassland communities found in the Mongolian Steppe region (Artemisia, Caragana and grass dominated steppes). The Mongolian steppe is intensively used as pasture and provides the economic basis for approximately 1 million herders. The grassland types differ in their forage values, which is why a spatially-explicit estimation of their occurrence is of high importance. We compared different sensors, classifiers, and training-sample strategies to identify the most effective approaches for mapping these communities. Ten datasets were used: Landsat 8 OLI (30 m), pan-sharpened Landsat 8 (15 m), Landsat 8 Surface Reflectance (30 m), Sentinel 2 (10 m), Sentinel 2 (20 m), Worldview 3 (0.5 m and 1.2 m), integrated Landsat 8 and Sentinel 2 (30 m), temporal Landsat 8, and temporal Sentinel 2. The two foremost classifiers at producing high accuracy of land cover classification, SVM and RF, were applied with the same training datasets. The training samples were collected in a manner so that they could be used for different spatial resolutions (i.e., ranging from 0.5 to 30 m) with the least effect from mixed training samples and spatial autocorrelation. The results of this study indicate that remote sensing is a viable method for the identification of different grassland communities in the Mongolian Steppe region.

How to cite: Phan, T. N., Jäschke, Y., Chuluunkhuyag, O., Oyunbileg, M., Wesche, K., and Lehnert, L.: Remote sensing of grassland communities in Mongolian Steppe combining multi-source data and machine learning classification algorithms, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13399, https://doi.org/10.5194/egusphere-egu2020-13399, 2020.

Quantifying the capacity, and uncertainty, of proposed spaceborne hyperspectral imagers to retrieve atmospheric and surface state information is necessary to optimize future satellite architectures for their science value. Given the vast potential joint trade-and-environment-space, modeling key ‘globally representative’ points in this n-dimensional space is a practical solution for improving computational tractability. Given guidance from policy instruments such as the NASA Decadal Survey and the recommended Designated Target Observables, or DOs, the downselect process can be viewed as a constrained multi-objective optimization. The need to simulate imager architecture performance to achieve downselect goals has motivated the development of new mathematical models for estimating radiometric and retrieval uncertainties provided conditions analogous to real-world environments. The goals can be met with recent advances that integrate mature atmospheric inversion approaches such as Optimal Estimation (OE) that includes joint atmospheric-surface state estimation (Thompson et al. 2018) and the EnMAP end-to-end simulation tool, EeteS (Segl et al. 2012), which utilizes OE for inversions. While surface-reflectance and retrieval simulation models are normally run in isolation on local computing environments, we extend tools to enable uncertainty quantification into new representative environments and thereby increase robustness of the downselect process by providing an advanced simulation model to the broader hyperspectral imaging community in software-as-a-service (SaaS). Here, we describe and demonstrate our instrument modeling web service and corresponding hyperspectral traceability analysis (HyperTrace) library for Python. The modeling service and underlying HyperTrace OE library are deployed on the NASA DISCOVER high-performance computing (HPC) infrastructure. An intermediate HTTP server communicates between FTP and HTTP servers, providing persistent archival of model inputs and outputs for subsequent meta-analyses. To facilitate enhanced community participation, users may simply transfer a folder containing ENVI format hyperspectral imagery and a corresponding JSON metadata file to the FTP server, from which it is pulled to a NASA DISCOVER server for processing, with statistical, graphical, and ENVI-formatted results subsequently returned to the FTP server where it is available for users to download. This activity provides an expanded capability for estimating the various science values of architectures under consideration for NASA’s Surface Biology and Geology Designated Observable.

How to cite: Erickson, A., Poulter, B., Thompson, D., Okin, G., Serbin, S., Wang, W., and Schimel, D.: A software framework for optimizing the design of spaceborne hyperspectral imager architectures, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19665, https://doi.org/10.5194/egusphere-egu2020-19665, 2020.

We increasingly recognize the diversity of biological systems, in terms of taxonomy, phylogeny and function, as well as the importance of biotic interactions in shaping them. However, the diversity of abiotic factors and interactions between biotic and abiotic diversity are still understudied, despite of Alexander from Humboldt’s advocacy over 200 years ago (Schrodt et al. 2019a). As such, we have lost sight of one of fundamental concepts of Biogeosciences: holistic integrative studies of patterns and processes across the Earth’s spheres.

In the face of accelerated anthropogenic and natural change of biotic and abiotic aspects, appreciation of the interaction diversity between all spheres of the Earth is urgently needed. Yet, to date, the vast majority of studies only account for the effect of climate and, potentially, soils on biodiversity, ignoring interactions (e.g. the effect of biodiversity on soils) and other aspects of geodiversity (the range, value and dynamics of geological, geomorphological, pedological and hydrological aspects and features of the Earth’s surface and subsurface). This applies to both, primary science and the science-policy interface.

I will give a brief introduction on the state-of-the-art in geodiversity – biodiversity interaction research, discuss the importance of incorporating the diversity of abiotic factors in biodiversity and conservation studies and indicate promising avenues for further research. This includes theoretical advancements, such as the recently introduced Essential Geodiversity Variables framework (Schrodt et al. 2019b), as well as practical matters, including remote sensing (Lausch et al. 2019) and modelling approaches suitable for expanding the geo- biodiversity interaction approach across the relevant spatial and temporal scales.

F Schrodt et al. (2019a) Challenges and opportunities for biogeography—What can we still learn from von Humboldt? Journal of Biogeography

F Schrodt et al. (2019b) To advance sustainable stewardship, we must document not only biodiversity but geodiversity. PNAS 116 (33): 16155 – 16158

A Lausch et al. (2019) Linking remote sensing and geodiversity and their traits relevant to biodiversity—part I: soil characteristics. Remote sensing 11 (20): 2356-2407

How to cite: Schrodt, F.: Putting the geo back into Biogeosciences, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-20369, https://doi.org/10.5194/egusphere-egu2020-20369, 2020.

Crop mapping is necessary for a variety of application in food security and agricultural monitoring. An innovative phenology-based crop mapping method was developed to map 14 crops between 2000 and 2015. Unlike traditional mapping methods mainly based on remote-sensing data and statistic data, our method takes crop phenology as the input. Phenological metrics represent crop characteristics related to crop calendar and progress such as the timing of emergence, maturity, harvest, etc. Phenological characteristics of each crop are relatively consistent for a long period of time. Combing crop phenology, we allocated the statistical harvest areas on cropland through matching different crops to different cropping seasons in each agroecological regions, which were extracted from 16-day composite MODIS EVI (MOD13Q1) time series data in 250m spatial resolution. Here we obtained the distribution of 14 crops at the spatial resolution of 1km by 1km in 2000, 2010 and 2015, which had higher spatial resolution and higher accuracy when compared with other products. By comparing the data recorded crop types in each meteorological station, we found our method achieved higher accuracies than other methods at the same resolution. As for winter crops, the relevance between total statistical crop area and the area of different cropping seasons that extracted by remote sensing in each agroecological region was higher than 70%. Obviously, the use of crop phenology as the mapping method input improve the accuracy of crop mapping, which are convenient for analyzing the spatial and temporal change of our crops. We found that the center of gravity migration of all crops fell into three directions when analyzing the center of gravity distribution change. In addition, declining Shannon diversity index reflected that the crop richness of the same plot was decreasing.

How to cite: Wang, Y., Zuo, L., and zhang, Z.: Mapping crop distribution in China between 2000 and 2015 by fusing remote-sensing derived cropping seasons and knowledge-based crop phenology, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21674, https://doi.org/10.5194/egusphere-egu2020-21674, 2020.

Copernicus is a European Union Earth Observation program, dedicated to monitor our planet and its environment, giving free access to remote sensing data and derived Earth Observation products. For proper use in environmental monitoring and scientific applications, it is fundamental to guarantee high quality and consistency of these satellite derived products. One of the possibilities to ensure product quality is to perform quantitative comparisons of satellite derived products with the corresponding in situ observation. Two options can then be considered for ground data sources: through intensive field campaigns or making use of permanent ground stations deployed and maintained on the long term. In the first case, a large variety of variable can be assessed, but logistical challenges and financial resources limit in time and space the products validation. More over meteorological constrains often limit the number of data that can actually be used for Earth Observation products. The second option is from far the most cost effective although it is not yet possible to cover all ground variables with permanent field deployment.

To achieve these objectives of systematic and long-term data validation, the Ground-Based Observations for Validation (GBOV) service has been implemented, facilitating the use of observations from operational ground-based monitoring networks and their comparison to EO products. The service is guaranteed through 3 different components:

• Collection of multi-year ground-based observations (Reference Measurements - RMs) of high relevance for the understanding of land surface processes from more than 50 existing sites. These RMs are then upscaled to generate Land Products (LPs), in order to validate the Copernicus products. In particular, the LPs distributed through the GBOV portal are: Top of Canopy Reflectance (ToC-R), surface albedo, Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Available Radiation (FAPAR), Fraction of Covered ground (FCover), Surface Soil Moisture (SSM) and Land Surface Temperature (LST).
• Upgrade of existing sites with new instrumentation or establishing entirely new monitoring sites to close thematic or geographical gaps. In 2019 new instrumentation has been installed in three different sites: Hainich (Germany), Valencia (Spain) and Tumbarumba (Australia). Litchfield (Australia), Dahra (Senegal) and Skukuza (South Africa) will be equipped with new instrumentation in the course of 2020.
• Implementation and maintenance of a database for the distribution of the Reference Measurements and the corresponding Land Products, available through the website https://land.copernicus.eu/global/gbov. GBOV data access is completely free, after registration and acceptation of the terms of use and the data policy.

How to cite: Bai, G., Lerebourg, C., Clerici, M., Gobron, N., Muller, J.-P., Song, R., Dash, J., Brown, L., Morris, H., Lopez-Baeza, E., Albero, E., Ghent, D., and Dodd, E.: GBOV (Ground-Based Observation for Validation): A Copernicus service for validation of Land Products, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21907, https://doi.org/10.5194/egusphere-egu2020-21907, 2020.

Forests cover about 40 percent of the Earth surface and they are very important for the ecosystems. For instance, forest land highly impacts carbon dynamics, provides habitats for organisms, conserves soil and water resources, and supports human demand for timber and recreation.

This study will discuss the method of determining the deciduous and coniferous tree species in forests by using Unmanned Aerial System (UAS) or drones for distinction of old-growth and second-growth forests. The key area of research is the national park in Smolensk region in the west of Russia, it is called «Smolenskoe Poozerie».The original forests (old-growth) in this area are Pine-Spruce and Oak-Linden forests but the main part were cut down for agriculture and to fuel both industry and farms. The second-growth tree species, such as Poplar-Birch forests, have a tendency to spread to disturbed habitats and replace native tree species.

This theme is relevant because it is one of the modern methods of distinction of old-growth and second-growth forests. Drones are able to cover a relatively large area in a single flight. They operate on user demand and deliver very high resolution images. They have a huge advantage of mapping in order to analyze and monitor forest ecosystems on a tree-level, instead of on a stand-level.

How to cite: Narykova, A.: Discrimination of forest species by remote sensing in the national park "Smolenskoe Poozerie", EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-363, https://doi.org/10.5194/egusphere-egu2020-363, 2020.

Estimation of leaf photosynthetic capacity from the photochemical reflectance index and leaf pigments

Shuren Chou^1#, Bin Chen²*^#, Jing Chen^3,4*, Miaomiao Wang^2,5, Shaoqiang Wang^2,5,6, Holly Croft⁷, Qin Shi⁸

¹Space Security Center, Space Engineering University, Beijing 101416, China;

²Key Laboratory of Ecosystem Network Observation and Modeling, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China;

³School of Geographical Sciences, Fujian Normal University, Fuzhou, 350117, China

⁴Department of Geography and Planning, University of Toronto, Toronto, Canada

⁵College of Resources and Environment, University of Chinese Academy of Sciences, Beijing, China

⁶College of Geography and Information Engineering, China University of Geosciences, Wuhan, China

⁷Department of Animal and Plant Sciences, University of Sheffield, Western Bank, Sheffield S10 2TN, U.K.

⁸Institute of Botany, Jiangsu Province and Chinese Academy of Sciences, Nanjing 210014, China;

Abstract: Leaf chlorophyll content has recently been found to be a better proxy than leaf nitrogen content for leaf photosynthetic capacity in a mixed deciduous broadleaf forest. A key concept underlying the relationship between leaf photosynthetic capacity and leaf chlorophyll content was the coordinate regulation of photosynthetic components (i.e. light harvesting, photochemical, and biochemical components). In order to test this hypothesis, we measured seasonal variations in leaf nitrogen content (N_leaf), leaf photosynthetic pigments (i.e. chlorophyll (Chl_Leaf), carotenoids (Car_Leaf) and xanthophyll (Xan_Leaf)) and leaf photosynthetic capacity (i.e. the maximum rate at which ribulose bisphosphate (RuBP) is carboxylated (V_cmax25) and regenerated (J_max25) at 25 ^oC) at a paddy rice site during the growing season in 2016. We investigated the effectiveness of (N_leaf), leaf photosynthetic pigments, leaf-level photochemical reflectance index at sunny noon (PRI_{Leaf_noon}) and their possible combinations for estimating leaf photosynthetic capacities (i.e. V_cmax25 and J_max25) at a paddy rice site. Chl_Leaf was highly correlated to V_cmax25 and J_max25 (R² = 0.89 and 0.87, respectively), which were better than N_leaf (R² = 0.80 and 0.85, respectively). The products of PRI_{Leaf_noon} with leaf pigments (i.e. Chl_Leaf, Car_Leaf and Xan_Leaf) were also found to be highly correlated with V_cmax25 (R² = 0.95 to 0.96). Also, the product of leaf chlorophyll a and Car_Leaf was a good proxy for V_cmax25 (R² = 0.93). In sum, this study supported the previously findings that leaf chlorophyll content was better correlated with V_cmax25 than leaf nitrogen content. Also, combining PRI_{Leaf_noon} with leaf pigments (i.e. Chl_Leaf, Car_Leaf and Xan_Leaf) offered an additional way to estimate leaf photosynthetic capacity (i.e. V_cmax25). These findings supported the hypothesis of coordinate regulation of photosynthetic components and they would be helpful to estimation of leaf photosynthetic capacity using remote sensing data.

Keywords: seasonal variations; leaf nitrogen content; photosynthetic pigments; leaf maximum carboxylation rate

How to cite: Chou, S., Chen, B., Chen, J., Wang, M., Wang, S., Croft, H., and Shi, Q.: Estimation of leaf photosynthetic capacity from the photochemical reflectance index and leaf pigments, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1223, https://doi.org/10.5194/egusphere-egu2020-1223, 2020.

The NASA Earth Exchange (NEX) team at Ames Research Center has embarked on a collaborative effort involving scientists from NASA, NOAA, JAXA/JMA, KMA/KARI exploring the feasibility of producing EOS-quality research products from operational geostationary satellite systems for climate monitoring. The latest generation of geostationary satellites (Himawari 8/9, GOES-16/17, Fengyun-4, GeoKompsat-2A) carry sensors that closely mimic the spatial and spectral characteristics of widely used polar-orbiting, global monitoring sensors such as MODIS and VIIRS. More importantly, they provide observations as frequently as 5-15 minutes. Data from various currently operating geostationary platforms provide a geo-ring of hyper-temporal, multispectral observations. Such high frequency observations, particularly when combined with data from polar orbiters, offer exciting possibilities for improving the retrieval of geophysical variables by overcoming cloud cover, enable studies of diurnally varying phenomena over land, in the atmosphere and the oceans, and help in operational decision-making in agriculture, hydrology and disaster management. Beyond the weather-focused geo-sensors, a number of new spectrometers are scheduled to be launched in the next five years in geostationary orbit to study atmospheric pollution (GEMS, TEMPO), ocean color (GOCI) and carbon cycling (GeoCARB). This talk will highlight new research, data sets, algorithms and computational platforms that utilize data from geostationary satellites to advance our ability to monitor the environment and create climate resiliency.

How to cite: Nemani, R., Lee, T., Kalluri, S., Ichii, K., and Yeom, J.-M.: GeoNEX: Earth observations from operational geostationary satellite systems, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-2463, https://doi.org/10.5194/egusphere-egu2020-2463, 2020.

Photochemical reflectance index (PRI) as a proxy for light use efficiency (LUE) has the potential to improve the estimates of vegetation gross primary productivity (GPP) using LUE model. Solar-induced fluorescence (SIF) has increasingly been shown to be a promising approach for directly estimating GPP. However, a number of factors including the view-geometry and environmental variables, which may disassociate PRI and SIF products from photosynthesis, are important for the estimation of GPP, but rarely investigated. In this study, we observed multi-angle SIF and PRI in a maize field during the 2018 growing season, and compared the PRI-based LUE model and SIF-based linear model in estimating GPP. Our results showed that the averaged PRI and SIF using the multi-angle observations performed better than the single angle observed PRI and SIF in estimating LUE and, GPP respectively. We also found that the seasonal GPP dynamics were better captured by the SIF-based linear model (R²=0.50) than the PRI-based LUE model (R²=0.45), while the PRI-based LUE model performed better in estimating the diurnal variations of GPP (R²=0.71). Random forest analysis demonstrated that PAR and RH were of the most importance in the estimation of diurnal GPP variations using the SIF-based and the PRI-based models, respectively. The PRI-based LUE model performed better than the SIF-based model under most environmental conditions, while SIF should be a preference under clear days (Q>2). Thus, our study confirmed the importance of multi-angle observation of SIF and PRI in estimating GPP and LUE, and suggested that the environmental effects should be considered for accurately estimating GPP using SIF and PRI.

How to cite: Zhang, Q. and Chen, J.: Comparison of the Photochemical Reflectance Index and Solar-induced Fluorescence for Estimating Gross Primary Productivity, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4418, https://doi.org/10.5194/egusphere-egu2020-4418, 2020.

Weed-crop competition is a problem affecting food production leading to  significant yield losses in various crops. The use of remote sensing technologies in agriculture enables rapid, non-destructive measurements that can be used for research and agronomical management. Previous research has been conducted characterizing the spectral response of crops to the stress caused by weeds but not much progress has been achieved nor has this been fully connected to physiological processes. Understanding the spectral characteristics of this type of stress is a basic step in advancing precision agricultural technologies for managing weeds in the field. This research focuses on corn (Zea mays) with variable densities of redroot pigweed (Amaranthus retroflexus), a common weed that is known to reduce corn yields. The primary research goal is to characterize the physiological changes that occur in the corn during early growth stages in the presence of weeds of different densities. A secondary goal, is to examine the ability to detect those changes by means of proximal and remote sensing.

During June to August 2019, a field experiment was conducted in Sede – Boker, Israel. Hyperspectral reflectance measurements using an ASD spectrometer,

IR images acquired with a thermal camera and multispectral VIS-NIR images from a mounted UAV were taken. We combined the spectral measurements with physiological measurements (photosynthesis, stomatal conductance and transpiration). The data and results were integrated and analyzed to determine whether physiological differences between variable treatments can be detected by the sensing methods.  Results show that these can be observed, detected and we will provide new explanations associating the competition, spectral response and physiological processes.

How to cite: Ronay, I., Maman, S., Ephrath, J. E., Eizenberg, H., and Blumberg, D. G.: Weed-crop competition and the effect on spectral reflectance and physiological processes as demonstrated in maize, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4582, https://doi.org/10.5194/egusphere-egu2020-4582, 2020.

To mitigate the negative effects of biodiversity loss, monitoring of species and functional diversity is an important prerequisite for focused management plans. However, sampling of biodiversity during field campaigns is labor- and cost-intensive. Therefore, researchers often use proxies extracted from three-dimensional and high-resolution airborne LiDAR (Light Detection and Ranging) data of the vegetation for predicting biodiversity measures (e.g. species richness or diversity).

This study aims at (i) assessing the suitability of LiDAR observations to map species richness across 17 taxonomic groups and four trophic levels at Mount Kilimanjaro and (ii) differentiating the predictive power of LiDAR-derived structural information from what is already explained by elevation, thereby comparing the prediction potential across taxa and trophic levels.

The field data for this study were collected across 59 plots along an elevation gradient of about 4000 meters at the southern slopes of Mount Kilimanjaro using established methods to sample the selected groups of organisms. The prediction is accomplished with three consecutive steps: (1) Species richness of each taxon is estimated using Partial Least Square Regression (PLSR) with only elevation and its square as independent variables. (2) The residuals of this model are then predicted using the LiDAR-derived variables and PLSR. (3) This third model is subsequently compared to a model that uses the same LiDAR-derived variables and PLSR to predict species richness directly rather than its residuals. This procedure allows to analyze the impact of elevation versus structure on each taxon. Furthermore, the standardized study design allows to compare the predictability of species richness across the selected groups of organisms.

Results of this study show that most taxa can be best predicted by elevation, even though in most cases the structural models perform almost equally. As expected, results of the model performances of trophic levels indicate, that herbivores are influenced more by structure than decomposers and generalists.

How to cite: Ziegler, A. and the Research Group at the Kilimanjaro: Potential of LiDAR for species richness prediction at Mount Kilimanjaro , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5174, https://doi.org/10.5194/egusphere-egu2020-5174, 2020.

Many areas in Malawi undergo extreme seasonality: floods in the wet season and drought in the dry season. Each year, this extreme seasonality poses formidable challenges for local farmers to sustain their crops. Often in the dry season, farmers use the water in the surrounding seasonal wetlands (dambos) for small-scale irrigation to supplement their rainy season harvest. In Malawi, the agricultural use of wetland is growing year by year and these areas play significant roles in regulating food price shocks and price. Such intensive use of wetlands can negatively affect the sustainability of wetland eco-system and their crop production, with communities even affected by the drying up of wells. Farmers, especially small-scale farmers, will face even more challenges for sustaining wetland production, as climate changes cause more frequent occurrence of droughts as Malawi has experienced in recent years. With the increasingly intensive use of these seasonal wetlands for agricultural purpose and the expansion of wetland degradation generally across the country, more attention is required toward effective management of these wetlands through identification, mapping, monitoring and data analysis. To achieve the sustainable use of these seasonal wetlands, it is essential to establish systematic monitoring and assessment procedures. Widely used assessment protocols (i.e., WET-Health) which evaluate the wetlands based on physical indicators such as land cover, hydrology, geomorphology, soil organic matter and natural vegetation have been successfully implemented in South Africa. However, obtaining those indicators across the full length of an individual wetland, let alone all wetlands in one district in Malawi, is labour intensive and time-consuming and difficult to complete. In this research, we utilise both unmanned aerial vehicle (UAV) and satellite imageries. These data sources are being tested in nine different seasonal wetlands in central Malawi to provide an accurate derivation of key indicators such as gully formation, sedimentation, water extent, changes in land use and natural vegetation. Additionally, using satellite imageries and GIS, the condition of each individual wetland has been quantified, with land cover and the extent of inundation determined through multi-temporal data analysis. Our results can be applied across a larger area, i.e. several districts to help identify where more detailed ground assessment is needed and technical support required to improve wetland management, feeding into both policy and technical guidance which can help sustain the range of ecosystems services of these important areas.

How to cite: Ogunyomi, E., Hwang, B., and Wood, A.: Remote sensing-aid assessment of wetlands in central Malawi, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6059, https://doi.org/10.5194/egusphere-egu2020-6059, 2020.

Evapotranspiration(ET) is a critical component of the land surface energy balance system and hydrologic processes. Analysis of spatiotemporal variations and influencing factors of ET is of great importance to evaluate the growing environment for crops and to effectively use water resources, a critical base for production in research region. The traditional methods are based on point measurement, while the remote sensing provides extensive surface information. The development of remote sensing has promoted the study of regional ET.SEBAL model is based on Surface Energy Balance Algorithm for Land and its physical meaning is clear. This model was developed to show the spatial variability of surface evapotranspiration. SEBAL model was capable of being applied to large regional areas in conjunction with Moderate-resolution Imaging Spectroradiometer (MODIS) data products.According to the shortcomings of the traditional method of calculating ET, based on SEBAL model, the daily regional evapotranspiration of Anhui Province was estimated with 1km spatial resolution by using MODIS products and a few of meteorological data(temperature, wind speed) collected in meteorological stations distributed over the study area.Because of lacking observed data from the lysimeter, the results of P-M were compared with the estimation results based on SEBAL model in this research.The comparison of the evapotranspiration estimated with MODIS products and field observation showed that the former results were lower than the latter results on the whole, and demonstrated that there existed certain trend in correlation between the two results, the average relative error was different at different land surface.The ET computation method based on Remote Sensing proves that this model has strong practicality in Anhui, and it will show great potential in this field with more optimizing the model parameters.

How to cite: Wu, W.: Estimation and Analysis of Regional Evapotranspiration in the eastern province of China with Remote Sensing data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6673, https://doi.org/10.5194/egusphere-egu2020-6673, 2020.

The net primary productivity (NPP) reflects the growth or production of terrestrial vegetation and plays an important role in the carbon cycle on the earth. It quantifies the difference between the organic matter produced by photosynthesis and the loss of maintenance and growth respiration. The investigation of the spatio-temporal variation in NPP is significant for monitoring plant photosynthesis and carbon uptake in terrestrial ecosystems. In this study, the variability and trend of NPP in China during 2001-2017 are analysed using level 4 MODIS product (MOD17A2H). Additionally, to explore whether the NPP change in recent decades are related with the photosynthetically active radiation (PAR) variation caused by increasing aerosol loading, the correlation between NPP, PAR and aerosol optical depth (AOD) are analysed at national, regional, and pixel scales. The results show that the annual mean NPP shows higher values in the southeast than in the northwest. The highest NPP level above 2.5 gCm^-2day^-1 is mainly distributed in tropical humid regions, including Zhejiang, Fujian, Guangdong and western Yunnan. The NPP increases with an amplitude of 0.131 gCm^-2day^-1 during the study period. The forests have higher mean levels of NPP (1.808 gCm^-2day^-1) and larger increasing magnitudes (0.35 gCm^-2day^-1) than those of croplands and grasslands. The NPP and AOD show a negative correlation (-0.6<R<-0.2) at a significance level of 0.05 over the middle area of China. The PAR direct and diffuse components generally have positive (0<R_{PARdir_NPP}<0.6) and negative correlations (-0.6<R_{PARdif_NPP}<0) with NPP, respectively, in most of China except the northeast and Tibetan Plateau. The NPP have stronger correlations (0.215 and -0.218) with the direct and diffuse PAR in forests than in croplands and grasslands, implying that NPP is more sensitive to the change in PAR in forests than in other vegetation cover types.

How to cite: Li, X. and Cheng, W.: Spatio-temporal variation in NPP and correlation analysis with aerosol loading and PAR in China during 2001-2017, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9039, https://doi.org/10.5194/egusphere-egu2020-9039, 2020.

Monitoring vegetation photosynthetic activity and its link with the carbon cycle at a global scale is a leading breakthrough that the scientific community has been seeking in recent years. Pursuing this goal, one of the most important advances in the last decade has been the measurement of the Solar Induced Fluorescence (SIF) at a satellite scale. Current satellite-derived SIF estimations provide SIF measured at certain specific wavelengths depending on the retrieval strategy and the instrument capabilities. However, for the time being, no global observations of the total spectrally resolved and integrated SIF signal have been yet achieved. In a near-future context, spectrally resolved SIF estimations will be provided by missions such as the FLuorescence EXplorer (FLEX) from the European Space Agency.

When disentangling the total SIF contribution, emitted between 650-800 nm, from the acquired satellite signal, molecular and aerosol absorption and scattering effects must be carefully accounted for.  Particularly, within the oxygen absorption features, the characterization of the aerosol scattering effects represents the most critical step prior to the SIF estimation.

In the context of the FLEX/Sentinel-3 tandem mission concept, this work presents a novel technique that refines any a priori aerosol characterization process through the exploitation of the high spectral resolution surface apparent reflectance signal at the oxygen absorption regions. Within the absorption features, SIF contribution on satellite-derived surface apparent reflectance generates a characteristic peaky spectrum. However, the shape of these peaks can be simultaneously distorted through the atmospheric correction process due to inaccuracies in the aerosol characterization among other secondary sources. Inaccuracies in the estimation of aerosol optical thickness, Angstrom exponent, asymmetry of the scattering or single scattering albedo translate into characteristic distortions in the shape of the peaks in the apparent reflectance. This particular behaviour allows inferring the magnitude of the errors and correcting them. The presented technique improves the accuracy of any a priori aerosol retrieval.

Authors expect this study to be also of interest to other hyperspectral missions when exploiting, at high spectral resolution, information from oxygen absorption regions.

How to cite: Sabater, N., Kolmonen, P., Alonso, L., Vicent, J., Moreno, J., and Arola, A.: On the surface apparent reflectance exploitation: Entangled Solar Induced Fluorescence emission and aerosol scattering effects at oxygen absorption regions, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10014, https://doi.org/10.5194/egusphere-egu2020-10014, 2020.

Leaf area index (LAI) is one of the most important biophysical variables for regulating the physiological processes of vegetation canopy. Time series high-resolution LAI data is critical for vegetation growth monitoring, surface process simulation and global change research. However, there are no high-resolution LAI data products that are continuous in time and space. In this paper, we use MODIS LAI products and Landsat surface reflectance data to generate time series high-resolution LAI datasets from 2000 to 2018 in Saihanba based on the ensemble kalman filter, and uses time-series LAI data to monitor surface vegetation changes according to the Prophet model. Firstly, the multi-step Savitzky–Golay filtering algorithm is used to smooth the MODIS LAI data, and the upper envelope of time series LAI is generated. A dynamic model is constructed according to the trend of LAI upper envelope to provide the short-range forecast of LAI. Then the ground measured LAI data and the corresponding Landsat reflectance data are used to train a Back Propagation neural network. High-resolution LAI data from BP model is used to update the dynamic model in real time to generate high-resolution time series LAI data based on EnKF. Finally, the time series LAI data is used as the input of Prophet deep learning model to obtain the LAI time series prediction values of a certain year. Comparing the prediction results with the LAI of current year, the correlation coefficient and the root mean square error distribution maps can be obtained, a support vector machine method is used to classify the disturbed pixels and the normal pixels. The LAI time series estimation has a high accuracy of R²larger than 0.90, and RMSE less than 0.54. The disturbance monitoring results indicate that vegetation in 2009, 2010, 2013, 2014, 2015, 2017 is seriously disturbed, Variation of meteorological conditions and deforest contributes heavily to the disturbance.

How to cite: Zhou, H., Zhang, G., Wang, C., and Wang, J.: Time series 30m resolution leaf area index estimation and vegetation change monitoring in Saihanba, China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12577, https://doi.org/10.5194/egusphere-egu2020-12577, 2020.

The biogenic volatile organic compounds (BVOCs) are emitted globally at about 1100 Tg per year of which almost half of the share is entailed by isoprene. Isoprene is highly reactive in the atmosphere, and its degradation products impact the atmospheric composition through the generation of ozone (in presence of NOx typical of polluted areas) and secondary organic aerosols, and may pose a risk to human health. Isoprene is mainly emitted by plant foliage, with trees being the major contributors due to their relatively high emission factors.

In the modelling framework of biosphere-atmosphere interactions, the representation of land cover and vegetation distributions is a key aspect. We use the state-of-the-art biogenic emission model MEGAN (Guenther et al. 2012) coupled with a multi-layer canopy model MOHYCAN (Müller et al. 2008) to estimate isoprene emissions on the global scale. In its current standard version, the model uses a static plant functional type (PFT) distribution obtained from the Community Land Model (CLM4) for 2000. Our objective is to replace the static map by time-dependent PFT distributions based on satellite global land cover maps, and estimate the resulting biogenic emissions over 2001-2018. To this purpose, we use either the MODIS land cover dataset (Friedl and Sulla-Menashe, 2019), or the MODIS dataset modified to account for tree cover changes from Hansen et al. (2013). Comparisons with the ESA-CCI dataset (Poulter et al. 2015) and the FAOSTAT (www.fao.org) database are performed and the trends over large forested regions are discussed. The comparisons show a large variability in the representation of the tree cover by the available remotely-sensed datasets, leading to different spatial distributions and temporal variability in the estimated isoprene emissions. This gives a measure of the uncertainty associated to this input parameter. This work is conducted in the frame of the ALBERI project that aims at assessing links between biogenic emissions and remotely-sensed photosynthesis indicators, funded by BELSPO through the STEREO III programme.

How to cite: Opacka, B., Müller, J.-F., Stavrakou, J., Bauwens, M., and Guenther, A. B.: Evaluating the impact of different spaceborne land cover distributions on isoprene emissions and their trends using the MEGAN model., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15832, https://doi.org/10.5194/egusphere-egu2020-15832, 2020.

Vegetation and soil moisture monitoring are complicated and expensive with in-situ measurements thus remote sensing is a favorable tool to monitor changes in the land surface. Under the supervision of the European Environment Agency and the Joint Research Centre the Copernicus Global Land Service (GLS) became a prominent service providing satellite data for climatological purposes. In this study the Copernicus GLS provided leaf area index (LAI) and dry matter productivity (DMP) data are used at 1 km resolution over Europe. Based on the LAI, growing season start and length is also determined. Around 18 years of data (2000-2018) is analyzed to look for changes in vegetation. Using the CORINE land cover categories changes in vegetation parameters are also analyzed by differentiating between land cover categories. Furthermore, the ESA (European Space Agency) Climate Change Initiative soil moisture data is coupled with the changes in vegetation parameters. In the case of soil moisture, the data is available at a 0.25° resolution, therefore vegetation parameters are interpolated accordingly.

Initial results show, that the maximum value of LAI increases the most in North Europe, the increase is almost linear. Changes in LAI derived start of growing season shows an earlier start in Central Europe and a later start in North Europe. The connection between vegetation parameters and soil moisture varies based on land cover and location. The strongest correlation is found for summer soil moisture and autumn LAI for arable lands and a negative correlation is found for shrublands.    

How to cite: Breuer, H. and Szabó, A. I.: Application of Copernicus Global Land Service vegetation parameters and ESA soil moisture data to analyze changes in vegetation with respect to the CORINE database, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10633, https://doi.org/10.5194/egusphere-egu2020-10633, 2020.

Space-borne Earth Observation (EO) data depicting vegetation covered land surfaces contain insufficient information for an unambiguous interpretation of the spectral signal in terms of variables that characterize the vegetation state (e.g., leaf area index, leaf chlorophyll content and proportion of senescent material). For the retrieval of vegetation properties from EO data, an optimal estimate of the state variables needs to be found. The uncertainty of such an estimate can be reduced by combining EO data and in situ data. Information provided by citizens represents a valuable and mostly inexpensive source for in situ data. Since the quality of such data can be diverse, the consideration of uncertainties is of great importance.

In this contribution, we present a concept for the elicitation of local knowledge from citizens with respect to the application of management practices (e.g., sowing and harvesting date, irrigation) and the instantaneous state of crops. The concept includes the acquisition of in situ data as well as an uncertainty assessment (precision and/or accuracy). The latter involves a profiling in which inherent uncertainties are quantified for individual citizens. This concept was tested for agricultural fields of the Durable Environmental Multidisciplinary Monitoring Information Network (DEMMIN) test site in Northeast Germany. Within the frame of several field seminars, students were requested to assess management practices and the instantaneous state of crops. Furthermore, they assessed their own ability to create valid data. They filled in pseudonymized questionnaires from which we created corresponding datasets. At the same day, field data were collected with appropriate equipment and can be used as reference against which student estimates can be compared. The level of compliance between estimated and measured data was determined on an individual basis.

The results of this analysis will be presented. Conclusions will be drawn regarding the ability of the students to evaluate their own skills. In addition, we will demonstrate an approach for a digital ascertainment of in situ data. In the future, this approach will be used to collect in situ data for the setup of refined prior information within the frame of the Earth Observation Land Data Assimilation System (EO-LDAS).

How to cite: Truckenbrodt, S. C., Klan, F., Borg, E., Missling, K.-D., and Schmullius, C. C.: How valuable are citizen science data for a space-borne crop growth monitoring? – The reliability of self-appraisals, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13332, https://doi.org/10.5194/egusphere-egu2020-13332, 2020.

An expanding world population combined with challenges brought by climate change pose totally new scenarios for managing agricultural fields and crop production. In the last decades, a variety of ground-based, modeled, and Earth observation (EO) data have been used to characterize crop dynamics and, ultimately, estimate yield. Typically, optical vegetation indices and, in particular, metrics like their maximum peak or integral during the growing season are exploited to estimated crop yield. Also, most studies are focused on large areas with homogeneous agricultural landscapes in which cultivation/production is centred in a unique main crop (e.g. the U.S. Corn Belt or the Indian Wheat Belt). 

In this study, we study the transportability of machine learning models for crop yield estimation across different regions and the relative relevance of agro-ecological drivers (input features). We use a previous methodology presented in [1] that synergistically combined optical and microwave vegetation data for crop yield prediction. We apply this methodology, which was trained in the homogeneous area of the US Corn Belt, to the highly heterogeneous agricultural landscapes across Europe. The fragmented and diverse European agro-ecosystems poses a greater challenge for the combination of multi-sensor data, and we need to establish first which is the set of variables providing the best skill for yield estimation of the main crops grown in Europe (corn, barley and wheat) under this new scenario. Subsequently, we study whether these variables are also able to capture potential disruptions on crop dynamics deriving from extreme events and their influence in final crop production. 

[1] Synergistic Integration of Optical and Microwave Satellite Data for Crop Yield Estimation. Anna Mateo-Sanchis, Maria Piles, Jordi Muñoz-Marí, Jose E. Adsuara, Adrián Pérez-Suay and Gustau Camps-Valls. Remote Sensing of Environment 234:111460, 2019.

How to cite: Mateo Sanchis, A., Piles, M., Amorós López, J., Muñoz Marí, J., and Camps Valls, G.: Learning main drivers of crop dynamics and production in Europe, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18493, https://doi.org/10.5194/egusphere-egu2020-18493, 2020.

The light environment within vegetated landscapes is a key driver of microclimate, creating varied habitats over small spatial extents and controls the distribution of understory plant species. Modelling spatial variations of light at these scales requires finely resolved (< 1 m) information on topography and canopy properties. We demonstrate an approach to modelling spatial distributions and temporal progression of understory photosynthetically active radiation (PAR) utilising a three dimensional radiative transfer model (discrete anisotropic radiative transfer model: DART) where the scene is parameterised by drone-based data.

The study site, located in west Cornwall, UK, includes a small mixed woodland as well as isolated free-standing trees. Data were acquired from March to August 2019. Vegetation height and distribution were derived from point clouds generated from drone image data using structure-from-motion (SfM) photogrammetry. These data were supplemented by multi-temporal multispectral imagery (Parrot Sequoia camera) which were used to generate an empirical model by relating a vegetation index to plant area index derived from hemispherical photography taken over the same time period. Simulations of the 3D radiative budget were performed for the PAR wavelength interval (400 – 700 nm) using DART.

Besides maps of instantaneous above and below canopy irradiance, we provide models of daily light integrals (DLI) which are assessed against field validation measurements with PAR quantum sensors. We find relatively good agreement for simulated PAR in the woodland. The impact of simplifying assumptions regarding leaf angular distributions and optical properties are discussed. Finally, further opportunities which fine-grained drone data can provide in a radiative transfer context are highlighted.

How to cite: Fawcett, D., Bennie, J., and Anderson, K.: Modelling understory light availability in a heterogeneous landscape using drone-derived structural parameters and a 3D radiative transfer model, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19003, https://doi.org/10.5194/egusphere-egu2020-19003, 2020.

Reliable retrieval of atmospheric and surface properties from sensors deployed on satellite platforms rely on accurate simulations of the electromagnetic (EM) signal measured by such sensors. A forward radiative transfer (RT) model of the coupled atmosphere-surface system can be used to simulate how the EM signal responds to changes in atmospheric and surface properties. Realistic RT modeling is a prerequisite for solving the inverse problem, i.e. to infer atmospheric and surface parameters from the EM signals measured at the top of the atmosphere. The surface may consist of a soil-plant canopy, a snow/ice covered surface or an open water body (ocean, lake, river system). An overview will be provided of forward and inverse RT in such coupled atmosphere-surface systems. A coupled system consisting of two adjacent slabs separated by an interface across which the refractive index changes abruptly from its value in air to that in water /ice [1] will be used as an example. Several examples of how to formulate and solve inverse problems involving coupled atmosphere-water systems [2] will be provided to illustrate how solutions to the RT equation can be used as a forward model to solve practical inverse problems. Cloud screening [3], atmospheric correction [4], treatment of two-dimensional surface roughness, Earth curvature effects, and ocean bottom reflection for shallow water in coastal areas will be discussed, and the advantage of using powerful machine learning techniques to solve the inverse problem will be emphasized.

References

[1] Stamnes, K., and J. J. Stamnes, Radiative Transfer in Coupled Environmental Systems, , 2015.

[2] Stamnes, K., B. Hamre, S. Stamnes, N. Chen, Y. Fan, W. Li, Z. Lin, and J. J. Stamnes, Progress in forward-inverse modeling based on radiative transfer tools for coupled atmosphere-snow/ice-ocean systems: A review and description of the AccuRT model, , 8, 2682, 2018.

[3] Chen N., W. Li, C. Gatebe, T. Tanikawa, M. Hori, R. Shimada; T. Aoki, and K. Stamnes, New cloud mask algorithm based on machine learning methods and radiative transfer simulations, , 219, 62-71, 2018.

[4] Fan, Y., W. Li, C. K. Gatebe, C. Jamet, G. Zibordi, T. Schroeder, and K. Stamnes, Atmospheric correction and aerosol retrieval over coastal waters using multilayer neural networks, , 199, 218-240, 2017.

How to cite: Stamnes, K., Hamre, B., Stamnes, S., Chen, N., Fan, Y., Li, W., Lin, Z., and Stamnes, J.: Forward-inverse modeling based on scalar and vector radiative transfer models for coupled atmosphere-surface systems and machine learning tools, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4217, https://doi.org/10.5194/egusphere-egu2020-4217, 2020.

Wetlands are important and highly productive ecosystems in a variety of geomorphic settings ranging from inland to coastal environments. Wetlands are very dynamic in nature and are driven by the water and sediment fluxes carried by the streamlets throughout the year. Wetlands are under tremendous pressure all over the world due to various natural and anthropogenic factors, and therefore, require an immediate attention for their conservation. The available studies on wetland have given much less importance to the internal dynamics of the wetlands, which is primarily driven by hydrology and Land Use Land Cover (LULC) changes. Here, we propose to use the Optical Water Types (OWTs) concept to understand the hydrodynamics within the wetland.

The OWTs are the aquatic counterpart of terrestrial LULC classification and can be created by clustering of optically sensitive parameters like chlorophyll content, turbidity, suspended organic and inorganic matter using remote sensing reflectance, absorption, and scattering parameters. The Forel Ule (FU) color index, a visual color comparison scale of water bodies ranging from blue to cola brown (1-21), used a similar idea but is fairly limited in scope. The hyperspectral datasets have distinct absorption and reflection spectrum for various optically sensitive parameters, and therefore, they are particularly suited for this work. However, the availability of the high-resolution hyperspectral data is very limited and hence this research explores the possibility of deciphering the OWTs using multispectral datasets.

A possible approach to create OWTs is using the spectral indices of the multispectral datasets which are sensitive to the optical parameters instead of using the FU color index as a single parameter. In this work, various spectral indices which are independent and highly sensitivity to chlorophyll content, turbidity, suspended organic and inorganic matter are identified using the principal component analysis. The OWT clusters are created using the iso-cluster unsupervised classification similar to the LULC classification but the spectral indices are taken into account instead of directly using the spectral bands of satellite datasets. In this work, the Sentinel – 2A and 2B datasets are used to create independent OWT clusters of the Chilika (a Coastal wetland, along the east coast of India covering an area of 1,165 km²) and Kaabar Tal (an inland wetland in north Bihar plains, India covering an area of 51 km²) using the supervised classification method. The developed framework is very simple and robust in nature but the only disadvantage is that the clusters are variable in the temporal context. However, the temporal variations can be integrated with the spatial analysis to understand the wetland dynamics in the context of both space and time.

How to cite: Allaka, S., Singh, M., and Sinha, R.: Deciphering Optical Water Types of Wetlands using multispectral Earth observation datasets, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4773, https://doi.org/10.5194/egusphere-egu2020-4773, 2020.

Satellite phytoplankton functional type (PFT) data is assimilated into the global coupled ocean-ecosystem model MITgcm-REcoM2 for two years using a local ensemble Kalman filter. The ecosystem model has two PFTs: small phytoplankton (SP) and diatoms. Three different sets of satellite PFT data are assimilated: OC-PFT, PhytoDOAS, and SynSenPFT, which is a synergistic product combining the independent PFT products OC-PFT and PhytoDOAS. The effect of assimilating PFT data is compared with the assimilation of total chlorophyll data (TChla). This constrains both PFTs through multivariate assimilation using ensemble-estimate cross-covariances. While the assimilation of TChla already improves both PFTs individually, the assimilation of PFT data further improves the representation of the phytoplankton community. The effect is particularly large for diatoms where, compared to the assimilation of TChla, the SynSenPFT assimilation results in 57% and 67% reduction of root-mean square error (RMSE) and bias, respectively, while the correlation is increased from 0.45 to 0.54. For SP the assimilation of SynSenPFT data reduces the RMSE and bias by 14% each and increases the correlation by 30%. This shows that satellite data products beyond total chlorophyll are relevant for biogeochemical data assimilation. The separate assimilation of the PFT data products OC-PFT, SynSenPFT, and joint assimilation of OC-PFT and PhytoDOAS data lead to similar results while the assimilation of PhytoDOAS data alone leads to deteriorated SP but improved diatoms. When both OC-PFT and PhytoDOAS data are jointly assimilated, the representation of diatoms is improved compared to the assimilation of only OC-PFT. The results show slightly lower errors than when the synergistic SynSenPFT data is assimilated, which shows that the assimilation successfully combines the separate data sources.

How to cite: Nerger, L., Pradhan, H., Völker, C., Losa, S., and Bracher, A.: Global assimilation of ocean-color data of phytoplankton functional types: Impact of different datasets , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5151, https://doi.org/10.5194/egusphere-egu2020-5151, 2020.

Leaf nitrogen content is key information for ecological and agronomic processes. A number of studies aiming at estimation of leaf nitrogen content used chlorophyll content as a proxy due to a moderate to strong correlation between chlorophyll and nitrogen content during vegetative growth stages. Since leaf nitrogen content is directly linked to leaf protein content, the capacity to accurately estimate leaf protein content may improve robustness of an operational nitrogen monitoring. In the past, the introduction of proteins - as an absorbing input constituent of the PROSPECT leaf model - has been attempted numerous times. Yet, the attempts suffered from a certain number of shortcomings, including limited applicability to both fresh and dry vegetation, inaccurate definition of the specific absorption coefficients, or incomplete accounting for different constituents of leaf dry matter.

Here, we introduce PROSPECT-PRO, a new version of the PROSPECT model simulating leaf optical properties based on their biochemical properties and including protein and carbon-based constituents (CBC) as new input variables. These two additional chemical constituents correspond to two complementary constituents of LMA. Specific absorption coefficients for proteins and CBC were produced splitting LOPEX dataset into 50% for calibration and 50%for validation. Both data sets included fresh and dry samples. Our objective is to keep compatibility between PROSPECT-PRO and PROSPECT-D, the previous version of the model, and to ensure the same performances for the estimation of LMA even through its decomposition into two constituents. Therefore, the full validation consisted of two steps:

1) PROSPECT-PRO inversion using an iterative optimization approach to retrieve proteins and CBC from LOPEX data

2) Testing the compatibility with PROSPECT-D by estimating LMA as the sum of protein and CBC content from independent datasets

The capacity of PROSPECT-PRO for the accurate estimation of leaf proteins and CBC on LOPEX could be evidenced, with slightly higher performances for the estimation of fresh leaf proteins (NRMSE = 17.3%, R² = 0.75) than of dry leaf proteins (NRMSE =24.0%, R² = 0.62). Good overall performances were obtained for the estimation of CBC (NRMSE<15%, R²>0.90). Based on these results, the carbon/nitrogen ratio of leaves could be modelled accurately.

The indirect estimation of LMA through PROSPECT-PRO inversion led to similar or slightly improved results when compared to the estimation of LMA with PROSPECT-D. Hence, PROSPECT-PRO might be of particular interest for precision agriculture applications in the context of nitrogen sensing using observations of current and forthcoming satellite imaging spectroscopy missions.

How to cite: Féret, J.-B., Berger, K., de Boissieu, F., and Malenovský, Z.: PROSPECT-PRO: a leaf radiative transfer model for estimation of leaf protein content and carbon-based constituents, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5251, https://doi.org/10.5194/egusphere-egu2020-5251, 2020.

Qinghai-Tibetan Plateau (QTP), known as “the Third Pole”, has one of the most fragile ecosystems in the world. QTP is suffering from external pressures of climate change, human activities, and natural hazards. This study provides a subjective framework in assessing ecological vulnerability (EV) in QTP from 2000 to 2015 based on remote sensing and geographic information system techniques. An ecological vulnerability index (EVI) was established based on 17 indicators mainly acquired from satellite data. Principle component analysis and entropy method were used in determining indicator weights. Annual EVI were calculated based on the weighted sum of all indicators. Five vulnerability levels of potential, light, moderate, heavy and very heavy were graded to describe the spatial and temporal patterns of EVIs. Mann-Kendall trend analysis was performed over QTP during the 16 years. Results indicates QTP is suffering from an overall increasing EVI from eastern to western areas. About 10.43% of QTP has experienced significant EVI decrease, while 7.38% experienced significant increase in EVI.

How to cite: Xia, M. and Jia, K.: Evaluating ecological vulnerability over Qinghai-Tibetan Plateau based on remote sensing and geographic information systems, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6333, https://doi.org/10.5194/egusphere-egu2020-6333, 2020.

Peatlands are terrestrial carbon sinks of global significance, storing an estimated one-third of global soil carbon. Net Ecosystem Exchange (NEE) of carbon dioxide (CO₂) can however vary substantially on seasonal and inter-annual timescales, with some peatlands switching from a sink to a source of CO₂. Complex and sometimes competing processes, such as meteorology and phenology, regulate a peatland’s net carbon sink strength. Understanding seasonal and inter-annual variability in NEE requires studying these environmental controls at multiple spatial and temporal scales. The role of vegetation in regulating NEE can be particularly difficult to ascertain at the finer timescales (e.g. seasonal) and at sites with abundant plant diversity, non-uniform distribution and complex micro-topography, such as peatlands. Vegetation surveys are traditionally conducted every few years and, because of this, they might not capture the shorter-term variations that can result from meteorological anomalies such as drought. New technologies, such as Unmanned Aerial Vehicles (UAVs), offer novel opportunities to improve the temporal resolution and spatial coverage of traditional vegetation survey approaches. UAVs are a more flexible, often cheaper alternative to satellite products, which can be used to collect data at the sub-centimetre scale. Such high resolution is particularly valuable in peatland environments, which typically display strong heterogeneity at the micro-site level (< 0.5 m). We employ UAV surveys with a Parrot Sequoia multispectral camera to map vegetation and track its phenology using vegetation indices such as the Normalised Difference Vegetation Index (NDVI) over the course of two growing seasons at a temperate Scottish peatland. By combining this multispectral data with in-situ NEE measurements (closed chambers and eddy-covariance) and meteorological data, this project aims to quantify the impact of weather and phenology on carbon balance at the site. An improved understanding of these two drivers of peatland carbon cycling will allow for better prediction of the impact of climate change at the site.

How to cite: Simpson, G., Helfter, C., Nichol, C., and Wade, T.: Towers, Chambers & UAVs: Exploring the drivers of carbon sink strength at a temperate peatland, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-11175, https://doi.org/10.5194/egusphere-egu2020-11175, 2020.

Soil Water storage Capacity (SWSC) is an important quantity in the field of hydrology and agronomy to represent the hydrological functioning of a territory and/or the dynamics of a crop. SWSC spatial variability is often strong resulting from heterogeneity in texture and structure as well as soil depth. In situ measurement of SWSC is expensive, destructive and cannot be considered over a large area. Therefore, the characterization of SWSC by non-destructive methods is a mean of addressing the mapping issue. In this study we took profit of the new capacities offered by the Sentinel 2 mission, which allows characterizing relevant features in vegetation dynamic linked to stresses. In addition, yield map offers an additional source of information. Both yield and vegetation development are sensitive to several factors as the water and nitrogen supply, crop installation or pest. To isolate the influence of water supply, and therefore access parameters involved in the SWSC, an option is to delineate the effect of such factors by inverting a crop model able to simulate the observation together with the representation of most of influencing factors. The STICS crop model implemented in this study is suitable to consider interactions between carbon, nitrogen and water cycles, plant development and farming practices. The issue is then to demonstrate that parsimony in field characterization can be overcome by using satellite and yield observations to implement and invert comprehensive model such as STICS. A sensitivity analysis (Lammoglia et al. 2019) indicates that once plant variety parameters are calibrated, the parameters linked to crop installation, as the sowing depth and the sowing density, the initial soil mineral nitrogen and the SWSC are the main quantities to consider in an inversion procedure. The GLUE Bayesian method was used to retrieve the different parameters. The procedure was tested on non-irrigated winter durum wheat in a Mediterranean context in south-eastern France. The approach was evaluated in farm context 20 on heterogeneous fields over three years (2016-2018). Evaluation was made either by comparing inverted SWSC to observations and/or assessing the crop model performances on subsequent years. Soil heterogeneities are well captured by the method, but some heterogeneities interpreted as soil heterogeneities might be artefacts. A multi-year analysis is then necessary to get the permanent features that are most likely linked to soil properties. Discussion on the adding value of combining both soil vegetation dynamic (FAPAR, LAI) and yield, on the inversion strategy (calibration steps, data being considering, initialisation) and on the cost function (to reduce the impact of uncertainties on crop parameters) was made. The study has shown that LAI/FAPAR and yield observations make the use of complex model in data parsimonious context possible. In particular, the study highlights the importance of having frequent image acquisition, as it allows to capture short feature as the senescence rate which appears as an important proxy of the availability of water in the soil.

Lammoglia, A. Chanzy & M. Guerif, “Characterizing soil hydraulic properties from Sentinel 2 and STICS crop model” doi:10.1109/MetroAgriFor.2019.8909266, pp 312-316

How to cite: Chanzy, A. and Lammoglia, K.: Inverting a comprehensive crop model in parsimonious data context using Sentinel 2 images and yield map to infer soil water storage capacity., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-13447, https://doi.org/10.5194/egusphere-egu2020-13447, 2020.

Remote sensing of solar-induced chlorophyll fluorescence (SIF) is of growing interest for the scientific community due to the inherent link of SIF with vegetation photosynthetic activity. An increasing number of in situ and airborne fluorescence spectrometers has been deployed worldwide to advance the understanding and usage of SIF for ecosystem studies. Particularly, a number of sites has been instrumented with the FloX (J&B Hyperspectral Devices, Germany), an automated instrument that houses two high resolution spectrometers covering the visible and near infrared spectral regions, one specifically optimized for fluorescence retrieval, the other for plant trait estimation.

In this contribution we explore the feasibility to consistently retrieve plant traits and SIF from canopy level FloX measurements through the numerical inversion of a light version of the SCOPE model. The optimization approach was specifically adapted to work with the high- frequency time series produced by the FloX. In this context, a strategy for optimal retrieval of plant traits at daily scale is discussed, together with the implementation of an emulator of the radiative transfer model in the retrieval scheme. The retrieval strategy was applied to site measurements across Europe and the US that span a variety of natural and agricultural ecosystems.

The full spectrum of canopy SIF, the fluorescence quantum efficiency, and main plant traits controlling light absorption and reabsorption were retrieved concurrently and evaluated over the growing season in comparison with site-specific ancillary data. Improvements and challenges of this method compared to other retrievals are discussed, together with the potential of applying a similar retrieval scheme to airborne datasets acquired with e.g. the HyPlant sensor, or the reconfigured “FLEX mode” data acquired with the recently launched Sentinel-3B during its commissioning phase.

How to cite: Celesti, M., Biriukova, K., Campbell, P. K. E., Cesana, I., Cogliati, S., Damm, A., Drusch, M., Julitta, T., Middleton, E., Migliavacca, M., Miglietta, F., Panigada, C., Rascher, U., Rossini, M., Schuettemeyer, D., Tagliabue, G., van der Tol, C., Verrelst, J., Yang, P., and Colombo, R.: Exploring continuous time series of vegetation hyperspectral reflectance and solar-induced fluorescence through radiative transfer model inversion, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-14904, https://doi.org/10.5194/egusphere-egu2020-14904, 2020.

Bracken fern (Pteridium) is ranked among the most invasive species of the world (Holland & Aplin 2013). The fern’s intrusion has caused great reduction in the quantity and quality of land accessible for grazing (Birnie & Miller 1986). In some cases, farmers permanently abandon agricultural land severely invaded by bracken fern (Schneider & Geoghegan 2006).

Literature has shown that the fern also obstructs secondary forest reestablishment, and does not deliver adequate quality biomass, which improves soil nutrients regeneration (Schneider & Geoghegan 2006; Oldham et al. 2013). In some cases, bracken fern patches have excluded conifer seedlings despite several post-harvest planting efforts (Ferguson and Adams 1994), and even when seedlings do survive, bracken fern can retard seedling growth (Dimock 1964). Bracken fern spread is also a strong obstacle for re-introducing the autochthone fauna.

Empirical evidence from literature has demonstrated that spatial data on bracken fern’s spread, its life cycle and fern status cannot be accurately mapped using field surveys in the remote and inaccessible mountainous environments in many parts of the world (Mehner et al. 2004; Ngubane 2014; Odindi et al. 2014). Several studies have used available remote sensing platforms for detection and mapping bracken fern spatial distribution at various scales (e.g. Miller et al. 1990; Holland & Aplin 2013; Ngubane 2014; Singh et al. 2014).

This work concerns the feasibility of developing an EO satellite-based system capable of mapping the presence of bracken fern vegetation and monitoring its distribution in a predefined area of western highlands in Scotland.

The study considers also the impact of clouds, often present in the area of interest, and assesses the suitability of different available satellite sensors and their products (in terms of spatial, spectral and temporal resolution) as a means for achieving the objective.

The challenges encountered include problems of similarity in the spectral signatures of bracken and other vegetation species, leading to low classification accuracy. This aspect has been minimized by using an approach which considers the specific phenology of the behaviour of the vegetation of interest. Preliminary results shown summer months (June, July) as the best period during the year to monitor this area of interest, due to the minimum presence of clouds and shadow areas. Regarding the use of SAR imagery, also foreshortening  and layover effects caused in this mountainous area limit the possibility to monitor the evolution of these plants.

How to cite: Marzialetti, P., Fusilli, L., Laneve, G., and Cadau, E.: EO Satellite Based system for monitoring Bracken Fern in Scotland, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18442, https://doi.org/10.5194/egusphere-egu2020-18442, 2020.

In order to balance the economic and ecological interests, suitable farmland structures in different regions need be established which require understanding the current distribution pattern of irrigated and dry croplands, as well as the evolution rules and reasons of that. In this paper, irrigated croplands in 1985, 2000 and 2015 in Zhangjiakou city which is in the northwest from Beijing were extracted. The study area was divided into Bashang and Bxia aeras depending climate, terrain and agrotype. NDVIs and NDWIs from May to August reflecting vegetation growth and water indexes reflecting vegetation water content were adopted and decision tree classification method was employed. As a result, classification accuracies were high and meet the replying demand with 80.05% and 93.00% in Bashang and Baxia areas respectively. The classification results show that the area of irrigated lands was extended lightly, increasing about 12.73%, reached to 686127 km². Among them, there was 331438 km² converted from dryland with the proportions as 54.45%. By contrast, about 272419 km² irrigated croplands were transformed to drylands. But the plots areas of irrigated croplands were larger, showing a group development trend which is related to the large-scale development of the local vegetable industry in Bashang area. The total area of irrigated croplands was become bigger in intermontane plain around the rivers, while decreased in mountainous areas in Baxia area.

How to cite: Zhu, Z., Zuo, L., Zhang, Z., Zhao, X., Sun, F., and Pan, T.: Study on The Extraction Method and Spatial-temporal Characteristics of Irrigated Land in Zhangjiakou City, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18798, https://doi.org/10.5194/egusphere-egu2020-18798, 2020.

Introduction: The destruction of habitats has not only reduced biological diversity but also affected essential ecosystem services of the Central European cultural landscape. Therefore, in the further development of the cultural landscape and in the management of natural resources, special importance must be attached to the habitat demands of species and the preservation of ecosystem services. The study of ecosystem services has extended its influence into spatial planning and landscape ecology, the integration of which can offer an opportunity to enhance the saliency, credibility, and legitimacy of landscape ecology in spatial planning issues.

Objective: This paper proposes a methodology to detect red deer habitats for e.g. huntable game. The model is established on remote sensing based value-added information products, the derived landscape structure information and the use of spatially and temporally imprecise in-situ data (e.g. available hunting statistics). In order to realize this, four statistical model approaches were developed and their predictive performance assessed.

Methods: Altogether, our results indicate that based on the data mentioned above, modeling of habitats is possible using a coherent statistical model approach. All four models showed an overall classification of > 60% and in the best case 71,4%. The models based on logistic regression using preference data derived from 5-year hunting statistics, which has been interpreted as habitat suitability. The landscape metrics (LSM) will be calculated on the basis of the Global Forest Change dataset (HANSEN et al. 2013b ). The interpolation of landcover data into landscape-level was made with the software FRAGSTAT and the moving window approach.  Correlation analysis is used to identify relevant LSM serving as inputs; logistic regression was used to derive a final binary classifier for habitat suitability values. Three model variations with different sets of LSM are tested using the unstandardized regression coefficient. Results lead to an insight of the effect of each LSM but not on the strength of the effect. Furthermore, the predicted outcome is rather difficult to interpret as different units and scales for each LSM are used. Hence, we calculated the fourth model using the standardized regression coefficient. It harmonized the measurement units of the LSM and thus allowed a better comparison, interpretation, and evaluation.

Conclusion:  Our research reveals that applying a statistical model using coarse data is effective to identify potential red deer habitats in a significant qualitative manner. The presented approach can be analogously applied to other mammals if the relevant structural requirements and empirical habitat suitability data (e.g. home range, biotopes, and food resources) are known. The habitat preferences of red deer are best described by LSM concerning area-relation and wildlife-edge relations. Most important are edges between meadows, pastures or agricultural field and forest, as well as short paths between those elements for food resources. A large proportion of forest is important for species survival and positively influences the occurrence of red deer. Outcomes help to understand species –habitat relation and on which scale wildlife perceives the landscape. In addition, they support the practical habitat management and thus the overall species diversity.

How to cite: McKenna, A., Schultz, A., Borg, E., Neumann, M., and Mund, J.-P.: Remote sensing and GIS based ecological modelling of potential red deer habitats in the test site region DEMMIN (TERENO), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19953, https://doi.org/10.5194/egusphere-egu2020-19953, 2020.

Remote sensing of Sun-Induced chlorophyll Fluorescence (SIF) represents a growing and promising area of research in support of the upcoming ESA’s FLEX (FLuorescence EXplorer) satellite mission. For this reason, the link between SIF and photosynthetic activity has been widely explored in the recent years, as tool to characterize and monitoring terrestrial ecosystems functioning.

The SIF detection is challenging because this faint signal (which represents only few percent of the total radiance) is over imposed on the light reflected from the Earth’s surface. Decoupling these two contributions is not trivial and dedicated algorithms are needed. For this reason, a novel SIF retrieval algorithm, named SpecFit, has been developed in order to retrieve the entire SIF spectrum in the entire wavelength interval in which chlorophyll fluorescence emission occurs (670-768 nm). This novel approach is able to disentangle SIF and reflectance contributions from the total radiance spectrum emerging from the top of canopy. Nevertheless, the further interpretation of the SIF spectrum in relation to plant photosynthesis is complicated by the fact that the SIF signal is strongly influenced by several biophysical parameters, such as canopy structure and chlorophyll content that affect the leaves/canopy radiation transfer and therefore the overall remote sensed signal. 

The proposed work aims to verify first the SpecFit algorithm robustness on both simulated and field data and, second to investigate the potential of novel fluorescence indexes defined from the SIF full spectrum.   

The algorithm accuracy has been tested on a set of simulated data, obtained by coupling MODTRAN (atmosphere) and SCOPE (canopy) radiative transfer models. Scatterplots between forward simulations and retrieved SIF showed R² close to 0.98 considering all the evaluated metrics, namely: maximum of the peaks in the red and far-red and SIF spectrum integral.

The temporal series acquired during the ESA’s ATMOFlex and FLEXSense campaigns organised in an agricultural area in Braccagni (Tuscany, Italy) were, instead, used to test the algorithm on experimental measures acquired with FLOX spectrometers, from February to August on different crops (forage, alfalfa and corn). For the first time, SIF spectra observed on different vegetation species at different growing stages are presented in this work and their consistency with SIF values estimated by the more consolidated and widely used Spectral Fitting retrieval Method (SFM) are presented. The relationship found shows a linear regression slopes close to 1, intercepts approximately equal to 0 and R² higher than 0.92 are all evidences of the SpecFit accuracy.  

The final step consists in analysing the temporal evolution of novel fluorescence indexes derived from the SIF spectrum. Specifically, SpecFit SIF evaluated at 760 nm and 687 nm and normalized by the retrieved spectrum integral (SIF_SpecFit/SIF_INT) were compared to the index SIF₇₆₀/SIF₆₈₇, the latter is a proxy of the chlorophyll content. SIF₇₆₀/SIF₆₈₇ and SIF₇₆₀/SIF_INT increase linearly during the growing season due to re-absorption processes that affect both the indexes. Conversely, an inverse relationship is found between SIF₇₆₀/SIF₆₈₇and SIF₆₈₇/SIF_INT because the contribute in the visible red wavelengths to the integral become weaker at increasing biomass content. 

How to cite: Cesana, I., Cogliati, S., Celesti, M., Julitta, T., and Colombo, R.: Sun-Induced chlorophyll Fluorescence full spectrum retrieval and analysis of long-term time series , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21306, https://doi.org/10.5194/egusphere-egu2020-21306, 2020.

Three-dimensional (3D) radiative transfer (RT) modeling and simulation of the transport of radiation through earth surfaces is a challenging and difficult task. The difficulties lie in the complexity of the landscapes and also the intensive computational cost of 3D RT simulations. Current models usually work with abstract landscape elements to reduce complexity or only consider relatively small realistic scenes. In this study, a new 3D RT modeling framework (called LESS) is proposed. It employs a forward photon tracing method to simulate bidirectional reflectance factor (BRF) or flux-related data (e.g., downwelling radiation) and a backward path tracing method to generate sensor images (e.g., fisheye images) or large-scale (e.g. 1 km²) spectral images from visible to thermal infrared band. In this framework, a graphic user interface (GUI) and a set of tools are also provided to help to construct the landscape and set parameters, e.g., extracting tree crowns from airborne LiDAR data, which makes it more accessible to common users. The accuracy of LESS is evaluated with other models and field measurements in terms of directional BRF and pixel-wise comparisons. It shows that the accuracy of LESS is consistent with the reference models from RAMI model inter-comparison website (http://rami-benchmark.jrc.ec.europa.eu/HTML/Home.php) as well as field measurements. LESS has also been extended to simulate atmosphere, LiDAR and in-situ sensors. It provides as a useful tool for studying the radiative transfer process over complex forest canopies from leaf to canopy scales. The simulated datasets can be used as benchmarks for validating other physical remote sensing inversion algorithm and developing parameterized models for retrieving bio-geophysical variables of canopy. LESS can be accessed from http://lessrt.org.

How to cite: Qi, J. and Xie, D.: LESS: Large-scale remote sensing data and image simulation framework over Vegetated Areas, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21412, https://doi.org/10.5194/egusphere-egu2020-21412, 2020.