Evapotranspiration estimation using remote sensing and in-situ methods
Evapotranspiration (ET) is the key water flux at the interface of soil, vegetation and atmosphere. ET is difficult to measure directly; therefore, a range of methods have been developed within different research disciplines to estimate ET.
Remote sensing datasets are increasingly being used to provide spatially-explicit, large-scale ET estimates. While satellite datasets have been used to estimate basin- to field-scale ET, aerial platforms such as UAVs and drones are becoming popular for field-scale studies. These datasets, in combination with micrometeorological data, can be used to produce empirical models for improving ET estimates at larger scales. However, the uncertainty in ET that varies by the datasets which are used, hydro-climatic region, spatiotemporal scale, and modelling approaches, is not well understood.
Additionally, there is a range of in-situ methods such as lysimeters, sap flow, eddy covariance, scintillometers and Bowen ratio to estimate ET from ground-based measurements. However, estimating and scaling in-situ ET is prone to large method-specific uncertainties which are rarely communicated across different disciplines. This is problematic if in-situ measurements are to be compared, combined or scaled up to match the grid resolution of remote sensing products or models.
This session addresses ET estimation with both remote sensing and in-situ methods. We invite contributions that (1) assess and compare established and new in-situ and remote sensing ET estimates, (2) address uncertainty in these methods, (3) bridge spatio-temporal scales in different ET estimates (4) incorporate remote sensing and in-situ measurements into process-based modelling approaches.
Ground and Remote Sensing Observations and Modeling for Land-Atmospheric Interactions
Land-atmospheric interaction includes the land surface and atmospheric states and the mass and energy exchanges between land surface and the atmosphere. It is a key part of the Earth's weather and climate system. Studies of the land-atmosphere interaction are critical to the understanding of the Earth’s weather and climate system that is required for accurate weather and climate forecasts. These studies mainly involve ground observations, air-borne or space based remote sensing of land surface and the lower atmosphere properties, mass and energy fluxes and their dynamics, and numerical model simulations of the land-atmosphere processes. Since the 1970s, a large number of field observation experiments (such as FIFE, HAPEX/Sahel, HAPEX/MOBILHY, EFEDA, BOREAS, NOPEX, GAME, HEIFE, TIPEX, EAGLE, CAMP/Tibet, TPE and LOPEXs) have been or are currently being carried out over a wide range of different underlying land surfaces worldwide. Dozens of land process parameterization schemes or land surface models have been developed and refined. Major national and international agencies (e.g. NASA, NOAA, ESA, EUMETSAT, JAXA, CMA, JMA, KMA, etc.) have launched many satellite missions to provide continuous spatially distributed observations of land surface and atmospheric observations from local scale to regional and even global scales. Examples of these missions are EOS, Meteosat, EPS, GCOM-W, GOES, S-NPP, JPSS, FYs, SMOS, SMAP, etc. Assimilation of these observations have significantly improved understanding of the land-atmosphere interaction and in turn gradually enhanced the prediction skills of the simulation models at all of these scales. This session invites abstracts that report the development, validation and applications of these studies especially in the Third Pole Environment regions in the recent years. New development on land surface process observation, data fusion, data assimilation, hydrological hazards monitoring, climate and environmental changes at regional and global scales are especially encouraged.
Welcome to " HS6.2/AS2.5:Ground and Remote Sensing Observations and Modeling for Land-Atmospheric Interactions" session
|AttendanceMon, 04 May, 10:45–12:30 (CEST),
AttendanceMon, 04 May, 14:00–18:00 (CEST)
Remote sensing of soil moisture
We invite presentations concerning soil moisture estimation, including remote sensing, field experiments, land surface modelling and data assimilation. The technique of microwave remote sensing has made much progress toward its high potential to retrieve surface soil moisture at different scales. From local to landscape scales several field or aircraft experiments (e.g. SMAPvex) have been organised to improve our understanding of active and passive microwave soil moisture sensing, including the effects of soil roughness, vegetation, spatial heterogeneities, and topography. At continental scales a series of several passive and active microwave space sensors, including SMMR (1978-1987), AMSR (2002-), ERS/SCAT (1992-2000) provided information on surface soil moisture. Current investigations in L-band passive microwave with SMOS (2009-) and SMAP (2015-), and in active microwave with Metop/Ascat series (2006-) and Sentinel-1 open new possibilities in the quantification of the soil moisture at regional and global scales. Comparison between soil moisture simulated by land surface models, in situ observations, and remotely sensed soil moisture is also relevant to characterise regional and continental scale soil moisture dynamics (e.g., ALMIP2, GSWP3).
We encourage submissions related to soil moisture remote sensing, including:
- Field experiment, theoretical advances in microwave modelling and calibration/validation activities.
- High spatial resolution soil moisture estimation based on Sentinel-1 observations, GNSS reflections, or using novel downscaling methods.
- Inter-comparison and inter-validation between land surface models, remote sensing approaches and in-situ validation networks.
- Evaluation and trend analysis of soil moisture data record products such as the soil moisture CCI product or soil moisture re-analysis products (e.g. MERRA-Land, ERA-Land).
- Root zone soil moisture retrieval and soil moisture assimilation in land surface models as well as in Numerical Weather Prediction models.
- Application of satellite soil moisture products for improving hydrological applications such as flood prediction, drought monitoring, rainfall estimation.
Remote sensing of interactions between vegetation and hydrology
Remote sensing techniques are widely used to monitor the relationship between the water cycle and vegetation dynamics and its impact on the carbon and energy cycles. Measurements of vegetation water content, transpiration and water stress contribute to a better global understanding of the water movement in the soil-plant system. This is critical for the detection and monitoring of droughts and their impact on biomass, productivity and feedback on water, carbon and energy cycles. With the number of applications and (planned) missions increasing, this session aims to bring researchers together to discuss the current state and novel findings in the remote observation of the interactions between vegetation and hydrology. We aim to (1) discuss novel research and findings, (2) exchange views on what should be done to push the field forward, and (3) identify current major challenges.
We encourage authors to submit presentations on:
• Remote sensing data analyses,
• Modelling studies,
• New hypothesis,
• Enlightening opinions.
The chat session on Remote sensing of interactions between vegetation and hydrology will be organized according to four topics:
Monitoring of vegetation and hydrology interactions with radar
Phenology dynamics and its relation to hydrological variables
Impact of land cover on vegetation and hydrology
The use and development of indices for monitoring vegetation and water stress
More information on the presenters and moderators per topic can be found in the session materials.
We hope to meet you all in the online chat!
Tim, Julia, Brianna, Virginia and Mariette
Remote Sensing for Flood Dynamics Monitoring and Flood Mapping
The socio-economic impacts associated with floods are increasing. According to the International Disaster Database (EM-DAT), floods represent the most frequent and most impacting, in terms of the number of people affected, among the weather-related disasters: nearly 0.8 billion people were affected by inundations in the last decade (2006–2015), while the overall economic damage is estimated to be more than $300 billion. Despite this evidence, and the awareness of the environmental role of rivers and their inundation, our knowledge and accurate prediction of flood dynamics remain poor, mainly related to the lack of measurements and ancillary data at the global level.
In this context, remote sensing represents a value source of data and observations that may alleviate the decline in field surveys and gauging stations, especially in remote areas and developing countries. The implementation of remotely-sensed variables (such as digital elevation model, river width, flood extent, water level, land cover, etc.) in hydraulic modelling promises to considerably improve our process understanding and prediction. During the last decades, an increasing amount of research has been undertaken to better exploit the potential of current and future satellite observations, from both government-funded and commercial missions. In particular, in recent years, the scientific community has shown how remotely sensed variables have the potential to play a key role in the calibration and validation of hydraulic models, as well as provide a breakthrough in real-time flood monitoring applications. With the proliferation of open data and more Earth observation data than ever before, this progress is expected to increase.
We encourage presentations related to flood monitoring and mapping through remotely sensed data including: - Remote sensing data for flood hazard and risk mapping, including commercial satellite missions;
- Remote sensing techniques to monitor flood dynamics;
- The use of remotely sensed data for the calibration, or validation, of hydrological or hydraulic models;
- Data assimilation of remotely sensed data into hydrological and hydraulic models;
- Improvement of river discretization and monitoring based on Earth observations;
- River flow estimation from remote sensing;
- River and flood dynamics estimation from satellite (especially time lag, flow velocity, etc.)
Water Level, Storage and Discharge from Remote Sensing and Assimilation in Hydrodynamic Models
This session concerns measurements and estimations of water levels, water extent, water storage and water discharge of surface water bodies such as rivers, lakes, floodplains and wetlands, through combined use of remote sensing and in situ measurements. Contributions that also cover aspects on assimilation of remote sensing together with in situ data within hydrodynamic models are welcome and encouraged.
The monitoring of river water levels, river discharges, water bodies extent, storage in lakes and reservoirs, and floodplain dynamics plays a key role in assessing water resources, understanding surface water dynamics, characterizing and mitigating water related risks and enabling integrated management of water resources and aquatic ecosystems.
While in situ measurement networks play a central role in the monitoring effort, remote sensing techniques is contributing in an increasing way, as they provide near real time measurements as well as long homogeneous time series to study the impact of climate change, over various scales from local to regional and global.
During the past twenty-nine years a large number of satellites and sensors has been developed and launched allowing to quantify and monitor the extent of open water bodies (passive and active microwave, optical), the water levels (radar and laser altimetry), the global water storage and its changes (variable gravity). River discharge, a key variable of hydrological dynamics, can be estimated by combining space/in situ observations and modelling, although still challenging with available space borne techniques.
Traditional instruments contribute to long-term water level monitoring and provide baseline databases. Scientific applications of more complex technologies like the SAR altimetry on CryoSat-2 and Sentinel-3A/B missions are maturing. The future SWOT mission, to be launched in 2021, will open up many new hydrology-related opportunities.
Irrigation estimates and management from remote sensing and hydrological modelling
Agriculture is the largest consumer of water worldwide and at the same time irrigation is one of the sectors where there is one of the hugest differences between modern technology and the largely diffused ancient traditional practices. Improving water use efficiency in agriculture is an immediate requirement of human society for sustaining the global food security, to preserve quality and quantity of water resources and to reduce causes of poverties, migrations and conflicts among states, which depend on trans-boundary river basins. Climate changes and increasing human pressure together with traditional wasteful irrigation practices are enhancing the conflictual problems in water use also in countries traditionally rich in water. Saving irrigation water improving irrigation efficiency on large areas with modern technics is one of the first urgent action to do. It is well known in fact that agriculture uses large volumes of water with low irrigation efficiency, accounting in Europe for around 24% of the total water use, with peak of 80% in the Southern Mediterranean part and may reach the same percentage in Mediterranean non-EU countries (EEA, 2009; Zucaro 2014). North Africa region has the lowest per-capita freshwater resource availability among all Regions of the world (FAO, 2018).
Several recent researches are done on the optimization of irrigation water management to achieve precision farming using remote sensing information and ground data combined with water balance modelling.
In this session, we will focus on: the use of remote sensing data to estimate irrigation volumes and timing; management of irrigation using hydrological modeling combined with satellite data; improving irrigation water use efficiency based on remote sensing vegetation indices, hydrological modeling, satellite soil moisture or land surface temperature data; precision farming with high resolution satellite data or drones; farm and irrigation district irrigation management; improving the performance of irrigation schemes; irrigation water needs estimates from ground and satellite data; ICT tools for real-time irrigation management with remote sensing and ground data coupled with hydrological modelling.
Spatial Downscaling of Remotely Sensed Hydrological Cycle Components: Algorithms Development, Evaluation and Application
Accurate measurements of various hydrological cycle components (e.g. precipitation, evapotranspiration, soil moisture and water storage changes) are essential for understanding the hydrological processes and further for sustainable water resources management. Hydrological cycle components are characterized by significant variability in time and space. The conventional in-situ measurements from gauges are generally considered to be the most accurate measurements, but scientific communities are often encountered with the limited availability and capability of in-situ measurements. Specifically, the network of gauge stations is often sparse and overall the number of stations is still on decreasing trend over the globe. The point-based feature makes gauge measurements insufficient to capture spatial and temporal variability of hydrological cycle components. Therefore, alternative data sources should be investigated to fill the data gaps.
Satellite remote sensing has been shown great capability of estimating various hydrological cycle components at different temporal and spatial scales. Various communities have recognized the importance of satellite remote sensing, but they have been stressing the need for improvements in accuracy and particularly the spatial resolution because the spatial resolution of remotely sensed products is still often too coarse for many applications. To this regard, a specific topic “spatial downscaling” has emerged; over last decades, considerable efforts have been made to develop various spatial downscaling algorithms to improve the spatial resolution of remotely sensed estimates.
Machine learning and geostatistical methods have been innovatively utilized to advance the spatial downscaling in satellite remote sensing community. Together with the algorithms development in spatial downscaling, further pertinent research question arise: how to accurately evaluate the skill of downscaled remote sensing products? All current approaches for evaluation contain known limitations and, hence, there is a clear need for the development of novel procedures for fair evaluation particularly considering the limitations (e.g. representativeness and availability) of ground measurements form gauge stations.
The aim of this session is to present and discuss novel procedures in spatial downscaling of remotely sensed hydrological cycle components with emphasis on algorithms development, innovative evaluation and application of downscaled estimates.
Advances in methods and applications for satellite altimetry
Satellite altimetry provides the possibility to observe key parts of the hydrosphere, namely the ocean, ice, and continental surface water from space. Since the launch of Topex/Poseidon in 1992 the applications of altimetry have expanded from the open oceans to coastal zones, inland water, land and sea ice. Today, seven missions are in orbit, providing dense and near-global observations of surface elevation and several other parameters. Satellite altimetry has become an integral part of the global observation of the Earth‘s system and changes therein.
In recent years, new satellite altimetry missions have been launched carrying new instruments and operating in new orbits; the CryoSat-2/Sentinel-3 missions equipped with a Delay/Doppler altimeter, the Saral AltiKa mission carrying the first Ka band altimeter, and the recently launched photon counting laser altimeter on-board NASAs ICESat-2.
Fully exploiting this unprecedented availability of observables will enable new applications and results but also require novel and adapted methods of data analysis.
Across the different applications for satellite altimetry, the data analysis and underlying methods are similar and a knowledge exchange between the disciplines will be fruitful.
In this multidisciplinary altimetry session, we therefore invite contributions which discuss new methodology and applications for satellite altimetry in the fields of geodesy, hydrology, cryosphere, oceanography, and climatology.
Topics of such studies could for example be (but not limited to): creation of robust and consistent time series across sensors, validation experiments, combination of radar and laser altimetry e. g. for remote sensing of snow, classification of waveforms, application of data in a geodetic orbit, retracking, or combination with other remote sensing data sets.
A remote sensing signal acquired by a sensor system results from electromagnetic radiation (EM) interactions from incoming or emitted EM with atmospheric constituents, vegetation structures and pigments, soil surfaces or water bodies. Vegetation, soil and water bodies are functional interfaces between terrestrial ecosystems and the atmosphere. The physical types of EM used in RS has increased during the years of remote sensing development. Originally, the main focus was on optical remote sensing. Now, thermal, microwave, polarimetric, angular and quite recently also fluorescence have been added to the EM regions under study.
This has led to the definition of an increasing number of bio-geophysical variables in RS. Products include canopy structural variables (e.g. biomass, leaf area index, fAPAR, leaf area density) as well as ecosystem mass flux exchanges dominated by carbon and water exchange. Many other variables are considered as well, like chlorophyll fluorescence, soil moisture content and evapotranspiration. New modelling approaches including models with fully coupled atmosphere, vegetation and soil matrices led to improved interpretations of the spectral and spatio-temporal variability of RS signals including those of atmospheric aerosols and water vapour.
This session solicits for papers presenting methodologies and results leading to the assimilation in biogeoscience and atmospheric models of cited RS variables as well as data measured in situ for RS validation purposes. Contributions should preferably focus on topics related to climate change, food production (and hence food security), nature preservation and hence biodiversity, epidemiology, and atmospheric chemistry and pollution (stratospheric and troposphere ozone, nitrogen oxides, VOC’s, etc). It goes without saying that we also welcome papers focusing on the assimilation of remote sensing and in situ measurements in bio-geophysical and atmospheric models, as well as the RS extraction techniques themselves.
This session aims to bring together scientists developing remote sensing techniques, products and models leading to strategies with a higher (bio-geophysical) impact on the stability and sustainability of the Earth’s ecosystems.
Remote Sensing applications in the Biogeosciences
Chairperson: Frank Veroustraete & Willem Verstraeten
D530 | EGU2020-5174
Potential of LiDAR for species richness prediction at Mount Kilimanjaro
Alice Ziegler and the Research Group at the Kilimanjaro
D512 | EGU2020-288
Understanding wetland dynamics using geostatistics of multi-temporal Earth Observation datasets
Manudeo Narayan Singh and Rajiv Sinha
D515 | EGU2020-5421
Twelve years of SIFTER Sun-Induced Fluorescence retrievals from GOME-2 as an independent constraint on photosynthesis across continents and biomes
Maurits L. Kooreman, K. Folkert Boersma, Erik van Schaik, Anteneh G. Mengistu, Olaf N. E. Tuinder, Piet Stammes, Gerbrand Koren, and Wouter Peters
D516 | EGU2020-6674
Evaluation of understory LAI estimation methodologies over forest ecosystem ICOS sites across Europe
Jan-Peter George Jan Pisek and the Tobias Biermann (2), Arnaud Carrara (3), Edoardo Cremonese (4), Matthias Cuntz (5), Silvano Fares (6), Giacomo Gerosa (7), Thomas Grünwald (8) et al.
D517 | EGU2020-8263
Probing the relationship between formaldehyde column concentrations and soil moisture using mixed models and attribution analysis
Susanna Strada, Josep Penuelas, Marcos Fernández Martinez, Iolanda Filella, Ana Maria Yanez-Serrano, Andrea Pozzer, Maite Bauwens, Trissevgeni Stavrakou, and Filippo Giorgi
D518 | EGU2020-9071
Validation of seasonal time series of remote sensing derived LAI for hydrological modelling
Charlotte Wirion, Boud Verbeiren, and Sindy Sterckx
D519 | EGU2020-12000
Potassium estimation of cotton leaves based on hyperspectral reflectance
Adunias dos Santos Teixeira, Marcio Regys Rabelo Oliveira, Luis Clenio Jario Moreira, Francisca Ligia de Castro Machado, Fernando Bezerra Lopes, and Isabel Cristina da Silva Araújo
D528 | EGU2020-4418
Comparison of the Photochemical Reflectance Index and Solar-induced Fluorescence for Estimating Gross Primary Productivity
Qian Zhang and Jinghua Chen
D529 | EGU2020-4582
Weed-crop competition and the effect on spectral reflectance and physiological processes as demonstrated in maize
Inbal Ronay, Shimrit Maman, Jhonathan E. Ephrath, Hanan Eizenberg, and Dan G. Blumberg
D531 | EGU2020-6059
Remote sensing-aid assessment of wetlands in central Malawi
Emmanuel Ogunyomi, Byongjun Hwang, and Adrian Wood
End morning session
Chat time: Wednesday, 6 May 2020, 14:00–15:45
Chairperson: Willem Verstraeten Frank Veroustraete
D534 | EGU2020-10014
On the surface apparent reflectance exploitation: Entangled Solar Induced Fluorescence emission and aerosol scattering effects at oxygen absorption regions
Neus Sabater, Pekka Kolmonen, Luis Alonso, Jorge Vicent, José Moreno, and Antti Arola
D536 | EGU2020-15832
Evaluating the impact of different spaceborne land cover distributions on isoprene emissions and their trends using the MEGAN model.
Beata Opacka, Jean-François Müller, Jenny Stavrakou, Maite Bauwens, and Alex B. Guenther
D537 | EGU2020-10633
Application of Copernicus Global Land Service vegetation parameters and ESA soil moisture data to analyze changes in vegetation with respect to the CORINE database
Hajnalka Breuer and Amanda Imola Szabó
D538 | EGU2020-13332
How valuable are citizen science data for a space-borne crop growth monitoring? – The reliability of self-appraisals
Sina C. Truckenbrodt, Friederike Klan, Erik Borg, Klaus-Dieter Missling, and Christiane C. Schmullius
D539 | EGU2020-18493
Learning main drivers of crop dynamics and production in Europe
Anna Mateo Sanchis, Maria Piles, Julia Amorós López, Jordi Muñoz Marí, and Gustau Camps Valls
D540 | EGU2020-19003
Modelling understory light availability in a heterogeneous landscape using drone-derived structural parameters and a 3D radiative transfer model
Dominic Fawcett, Jonathan Bennie, and Karen Anderson
D543 | EGU2020-5151
Global assimilation of ocean-color data of phytoplankton functional types: Impact of different datasets
Lars Nerger, Himansu Pradhan, Christoph Völker, Svetlana Losa, and Astrid Bracher
D544 | EGU2020-5251
PROSPECT-PRO: a leaf radiative transfer model for estimation of leaf protein content and carbon-based constituents
Jean-Baptiste Féret, Katja Berger, Florian de Boissieu, and Zbyněk Malenovský
D547 | EGU2020-13447
Inverting a comprehensive crop model in parsimonious data context using Sentinel 2 images and yield map to infer soil water storage capacity.
André Chanzy and Karen Lammoglia
D550 | EGU2020-18798
Study on The Extraction Method and Spatial-temporal Characteristics of Irrigated Land in Zhangjiakou City
Zijuan Zhu, Lijun Zuo, Zengxiang Zhang, Xiaoli Zhao, Feifei Sun, and TianShi Pan
D551 | EGU2020-19953
Remote sensing and GIS based ecological modelling of potential red deer habitats in the test site region DEMMIN (TERENO)
Amelie McKenna, Alfred Schultz, Erik Borg, Matthias Neumann, and Jan-Peter Mund
End afternoon session