Displays

Observations from the C-band scatterometers ERS ESCAT and Metop ASCAT have been used to monitor vegetation dynamics predominantly in agricultural areas and grasslands (Schroeder et al., 2016, Vreugdenhil et al., 2016, Vreugdenhil et al., 2017). In particular, the slope  between the measured radar backscatter and the incidence angle of the observations has been found to reflect structural changes in the vegetation (e.g., size and orientation of stems and leaves) and vegetation water content, as well as deficits therein, as for example during an extensive drought period in North American grasslands (Steele-Dunne 2019).

Often, a peak in the slope time series is observed during spring. This peak occurs predominantly in regions covered by deciduous broadleaf forests (DBF), and recurs in most years around the beginning of April. We carried out a detailed study of the causes of such spring peaks over Austria by comparing the timing of the peaks to phenology observations of leaf emergence, leaf area index and temperature conditions. The comparison showed a good agreement between the timing of the ASCAT spring peaks and the reference datasets, even in regions with low coverage of DBF, with a median absolute difference between the peak in ASCAT and the reference datasets of less than 14 days for grid cells with at least 10% DBF (Pfeil et al., in prep.).

In this presentation, we assess if similar spring peaks occur in passive microwave satellite observations. Therefore we investigate the spring behavior of vegetation optical depth (VOD) time series from the radiometers AMSR-E and AMSR2 over DBF and find similar peaks, which are less pronounced but occur very close in time to the ASCAT peaks. It can thus be said that the spring peak is not a sensor-dependent phenomenon, but reflects the sensitivity of C-band microwave sensors to leaf development in deciduous trees. In summary, the results of the study suggest that spring water uptake in deciduous trees manifests in active and passive C-band microwave observations, as it causes increased scattering from the bare twigs and branches, followed by an attenuation of the twigs- and branches scattering by the emerging leaves.

References

• Schroeder, R., McDonald, K. C., Azarderakhsh, M., & Zimmermann, R. (2016). ASCAT MetOp-A diurnal backscatter observations of recent vegetation drought patterns over the contiguous US: An assessment of spatial extent and relationship with precipitation and crop yield. Remote sensing of environment, 177, 153-159.
• Steele-Dunne, S. C., Hahn, S., Wagner, W., & Vreugdenhil, M. (2019). Investigating vegetation water dynamics and drought using Metop ASCAT over the North American Grasslands. Remote Sensing of Environment, 224, 219-235.
• Vreugdenhil, M., Dorigo, W. A., Wagner, W., De Jeu, R. A., Hahn, S., & Van Marle, M. J. (2016). Analyzing the vegetation parameterization in the TU-Wien ASCAT soil moisture retrieval. IEEE Transactions on Geoscience and Remote Sensing, 54(6), 3513-3531.
• Vreugdenhil, M., Hahn, S., Melzer, T., Bauer-Marschallinger, B., Reimer, C., Dorigo, W. A., & Wagner, W. (2017). Assessing vegetation dynamics over mainland Australia with metop ASCAT. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 10(5), 2240-2248.

How to cite: Pfeil, I., Wagner, W., Vreugdenhil, M., Forkel, M., and Dorigo, W.: C-band microwave sensors reflect the spring water uptake of temperate deciduous broadleaf trees, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-9955, https://doi.org/10.5194/egusphere-egu2020-9955, 2020.

Grasslands cover almost one third of the world’s terrestrial surface. In Alpine environments grassland vegetation fulfills various key environmental purposes such as water reservoir, slope stabilizer and carbon sink or fodder for livestock. At the same time Alpine regions are more affected by climatic changes than other geographic zones, potentially resulting in earlier green-up phases or an elevated exposure to drought events, hampering the growth and vitality of grassland vegetation. The scope of this study is to build an algorithm capable of biomass estimation using Support Vector Machine approach on hyperspectral and Synthetic Aperture Radar (SAR) data. To that purpose, field campaigns were carried out during 2017 and 2019 in Val Mazia (South Tyrol, Italy), where hyperspectral spectroradiometer samples were collected, as well as leaf area index (LAI), soil moisture, and above ground biomass measurements. Copernicus Sentinel-1 IW SAR backscattering data were used to complete the dataset.

The spectroradiometer was used to simulate the hyperspectral data of the Italian Space Agency (ASI)’s PRISMA mission, launched on 22 March 2019. Since the number of bands is larger than the number of samples, a prediction approach based on machine learning risks to model noise. The following two solutions were tested and compared: (i) the number of bands was reduced by resampling the data to match specifications of Copernicus Sentinel-2 Multispectral Instrument (MSI), and (ii) the data was simulated using the PROSPECT model, increasing the sample size.

In the first case correlation R² of 0.37 was found. Discrepancies were observed for high biomass values, which could be explained by the small number of samples available shortly before harvest. To mitigate this effect, data were simulated for high biomass based on field average values and standard deviation within each date. R² increased to 0.71 in this case, confirming the above mentioned hypothesis regarding the dataset representativeness.

In the case of PROSPECT model, the parameters were found by iterating each one within ranges defined in the bibliography, until the spectral signatures matched the field observations. The resulting parameters were the input for data simulation. A genetic algorithm feature selection was run to reduce the number of features, discarding those with little or redundant information followed by an SVR model applied to the most sensitive bands resulting in an R² of 0.53. These initial results will be used as a basis for future investigations to improve the prediction model, for example by extending the dataset with new field campaigns, including more simulated data at biomass peak, as made with Sentinel-2 resampled dataset, or by adding further input variables, such as leaf area index. Furthermore, the procedure will be performed for fresh biomass and water content estimations.

The results obtained pave the way for future implementation of the tested algorithms on PRISMA hyperspectral and COSMO-SkyMed X-band SAR data in the future.

This research is part of the ongoing project ‘Development of algorithms for estimation and monitoring of hydrological parameters from satellite and drone’, funded by ASI under grant agreement n.2018-37-HH.0.

How to cite: Chiarito, E., Cigna, F., Cuozzo, G., De Gregorio, L., Fontanelli, G., Paloscia, S., Rossi, M., Santi, E., Tapete, D., and Notarnicola, C.: Biomass estimation based on hyperspectral and SAR data: an experimental study in South Tyrol, Italy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21439, https://doi.org/10.5194/egusphere-egu2020-21439, 2020.

Root zone soil moisture (θ_rz) is a crucial component of the hydrological cycle and provides information for drought monitoring, irrigation scheduling, and carbon cycle modeling. During vegetation conditions, estimation of θ_rz thru radar has so far only focused on retrieving surface soil moisture using the soil component of the total backscatter (σ_soil), which is then assimilated into physical hydrological models. The utility of the vegetation component of the total backscatter (σ_veg) has not been widely explored and is commonly corrected for in most soil moisture retrieval methods. However, σ_veg provides information about vegetation water content. Furthermore, it has been known in agronomy that pre-dawn leaf water potential is in equilibrium with that of the soil. Therefore soil water status can be inferred by examining  the vegetation water status. In this study, our main goal is to determine whether changes in root zone soil moisture (Δθ_rz) shows corresponding changes in vegetation backscatter (Δσ_veg) at pre-dawn. We utilized Sentinel-1 (S1) descending pass and in situ soil moisture measurements from 2016-2018 at two soil moisture networks (Raam and Twente) in the Netherlands. We focused on corn and grass which are the most dominant crops at the sites and considered the depth-averaged θ_rz up to 40 cm to capture the rooting depths for both crops. Dubois’ model formulation for VV-polarization was applied to estimate the surface roughness parameter (H_rms) and σ_soil during vegetated periods. Afterwards, the Water Cloud Model was used to derive σ_veg by subtracting σ_soil from S1 backscatter (σ_tot). To ensure that S1 only measures vegetation water content, rainy days were excluded to remove the influence of intercepted rainfall on the backscatter. The slope of regression lines (β) fitted over plots of Δσ_veg against Δθ_rz were used investigate the dynamics over a growing season. Our main result indicates that Δσ_veg - Δθ_rz relation is influenced by crop growth stage and changes in water content in the root zone. For corn, changes in β’s over a growing season follow the trend in a crop coefficient (K_c) curve, which is a measure of crop water requirements. Grasses, which are perennial crops, show trends corresponding to the mature crop stage. The correlation between soil moisture (Δθ) at specific soil depths (5, 10, 20, and 40 cm) and Δσ_veg matches root growth for corn and known rooting depths for both corn and grass. Dry spells (e.g. July 2018) and a large increase in root zone water content in between two dry-day S1 overpass (e.g. from rainfall) result in a lower β, which indicates that Δσ_veg does not match well with Δθ_rz. The influence of vegetation on S1 backscatter is more pronounced for corn, which translated to a clearer Δσ_veg - Δθ_rz relation compared to grass. The sensitivity of Δσ_veg to Δθ_rz in corn means that the analysis may be applicable to other broad leaf crops or forested areas, with potential applications for monitoring  periods of water stress.

How to cite: Carranza, C., van Emmerik, T., and van der Ploeg, M.: Detecting changes in root zone soil moisture from radar vegetation backscatter, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-1005, https://doi.org/10.5194/egusphere-egu2020-1005, 2020.

Vegetation Optical Depth (VOD) is directly related to Vegetation Water Content (VWC), which can be used in different applications including crop health monitoring, water resources management and drought detection. Moreover, VOD is used to account for the attenuating effect of vegetation in soil moisture retrieval using microwave remote sensing.

Commonly, to retrieve soil moisture and VOD from microwave remote sensing, VWC is considered to be vertically homogeneous and relatively static.  However, nonuniform vertical distribution of water inside the vegetation may lead to unrealistic retrievals in agricultural areas. Therefore, it is important to improve the understanding of the relation between vegetation optical depth and distribution of bulk vegetation water content during the entire growing season.

The goal of this study is to investigate the effect of different factors such as phenological stage, different crop elements and nonuniform distribution of internal vegetation water content on VOD. Backscatter data were collected every 15 minutes using a tower-based, fully polarimetric, L-band radar. The methodology of Vreugdenhil et al. [1] was adapted to estimate VOD from single-incidence angle backscatter data in each polarization.

In order to characterize the vertical distribution of VWC, pre-dawn destructive sampling was conducted three times a week for a full growing season. VWC could therefore be analyzed by constituent (leaf, stem, ear) or by height.

A temporal correlation analysis showed that the relation between VOD and VWC during the growing season is not constant. The assumed linear relationship is only valid during the vegetative growth stages for corn.  Furthermore, the sensitivity of VOD to various plant components (leaf, stem and ear) varies between phenological stages and depends on polarization.

Improved understanding of VOD can contribute to improved consideration of vegetation in soil moisture retrieval algorithms. More importantly, it is essential for the interpretation of VOD data in a wide range of vegetation monitoring applications.

[1] M. Vreugdenhil,W. A. Dorigo,W.Wagner, R. A. De Jeu, S. Hahn, andM. J. VanMarle, “Analyzing the vegetation parameterization in the TU-Wien ASCAT soil moisture retrieval,” IEEE Transactions on Geoscience and Remote Sensing, vol. 54, no. 6, pp. 3513–3531, 2016.

How to cite: Khabbazan, S., Gao, G., Vermunt, P., Steele-Dunne, S., Judge, J., and Vreugdenhil, M.: Interpreting Vegetation Optical Depth (VOD) using detailed destructive vegetation sampling of a corn canopy, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8161, https://doi.org/10.5194/egusphere-egu2020-8161, 2020.

Arctic greening trends are well documented at various scales (Fraser et al., 2011; Tremblay et al., 2012; Bjorkman et al., 2018). In this context, Remote Sensing offers a unique tool for estimating the high latitude vegetation evolution in the relatively long-term, i.e. the Landsat archive since the 80’s. Spectral indices derived from visible and infra-red wavelengths provide relations that can be used to quantify vegetation dynamics, we will combine the well-used Normalized Difference Vegetation Index (NDVI) and the recent Normalized Anthocyanins Reflectance Index (Bayle et al., 2019), using red-edge spectral band (690 to 710 µm) from Sentinel-2, to better quantify vegetation change over 30 years.

The application area is located in Nunavik, northern Québec (Canada), and concerns the George River catchment (565 km length, 41 700 km²). This large river basin covers vegetation from boreal forest (South) to arctic tundra (North). Local study sites stem from the Kangiqsualujjuaq village (Ungava Bay) to 300 km south, along the main river and its tributaries.

NDVI: surface reflectance Landsat bands were gathered for three years 1985, 2000 and 2015 (respectively Landsat missions 5, 7 and 8). For each period of interest, the best August cloud-free scenes were chosen and merged to create a cloud free mosaic covering the study area. NDVI bands were calculated and compared after cloud and water masking. NDVI trends were compared between the main vegetation types following the newly released “Ecological mapping of the vegetation of northern Quebec” (MRNFP, 2018). Centroid of polygons within the main vegetation types of the map were used to classify the NDVI results and assess changes per type. Results of NDVI time evolution revealed a clear greening trend at the river basin scale. Although greening was observed across the whole latitudinal gradient, the relative NDVI increase was stronger on the northern half of the study area, mostly covered with tundra and subarctic vegetation. Both shrublands and sparsely vegetated zones dominated by rocks had the greatest relative NDVI increase. This is likely caused by improved growth of established prostrate vegetation over the past 30 years in response to increasing temperatures trend.

NARI: greening trends in the Eastern Canadian Arctic have been partly attributed to increases in shrub cover (Myers-smith et al., 2011) and specifically to Betula glandulosa (e.g. Tremblay et al., 2012). Such land cover changes alter species competition (Shevtosa et al., 1997) and soil thermal regime (Domine et al., 2015; Paradis et al., 2016). Transformations in biotic and abiotic conditions reduce the fruit productivity of low stature shrubs of the Ericaceae family (Lussier 2017), which in turn is expected to impact animal (Prescott and Richard 2013) and human populations (Lévesque et al., 2013; Boulanger-Lapointe et al., 2019). An innovative method has been developed in the French Alps to detect the late-fall reddening of shrub leaves and map shrublands (Bayle et al., 2019). Quantifying NARI dynamics related to NDVI dynamics could allow to gain a better understanding of species composition change related to current landscape transformation.

How to cite: Dedieu, Dr. J.-P., Housset, J., Bayle, A., Lévesque, E., and Gérin-Lajoie, J.: Greening dynamics and shrubland extent from remote sensing data using NDVI and NARI Indices: case study of the George River basin (Nunavik, Canada)., EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3861, https://doi.org/10.5194/egusphere-egu2020-3861, 2020.

Mediterranean grasslands are an essential component of rural economy as the primary source of fodder for livestock in extensive areas. These annual grasslands present an escape mechanism to cope with the long summer dry season and the recurrent water scarcity events of the Mediterranean climate, completing their life cycle before serious soil and plant water deficits develop. It results in a close link between grass phenology and soil water dynamics. In this work we have explored this relationship using satellite and ground remote sensing (Sentinel-2 (S2) and a terrestrial digital camera) and ground measurements of hydrological variables.

The terrestrial photography was used as a field validator, grass greenness was assessed using the Green Chromatic Coordinate Index (GCC) and key phenological dates were extracted from the variation of this index during a calibration period (December 2017 to May 2019). The evolution of GCC index was highly correlated with soil moisture (SM) dynamic, which is consistent with the water-limited condition of the ecosystem. Some other variables, including vapor pressure deficit, solar radiation, and minimum, medium and maximum air temperatures were inversely correlated with greenness. Rainfall, although positively correlated, presented the lowest coefficient of all analyzed variables. The capability of SM and S2-NDVI to predict the phenology of the grass canopy was assessed by fitting a double-logistic function to the variables time-series and extracting the phenological parameters start of season (SOS), peak of season (POS) and end of season (EOS) using the 50% amplitude method. The comparison with the terrestrial camera resulted in differences less than 10 days for all phenological dates parameters studied (representing less than 5% error within a grass cycle). The behavior of S2-NDVI and SM relationship during four growing seasons was analyzed. It pointed out the synchronized seasonality shown in this system by the vegetation greenness, measured here by the NDVI, and the soil moisture. The higher agreement was found at the beginning and the end of the dry season, with stage changes estimated first by SM, followed by NDVI with a delay between 3 to 10 days. These results highlight the close relationship between these phenological parameters and the soil moisture dynamic under the study conditions, and the capability of satellite data to track these parameters.

How to cite: Gómez-Giráldez, P. J., Pérez-Palazón, M. J., Polo, M. J., and González-Dugo, M. P.: Exploring the relationship between phenology and hydrology using Sentinel-2 and terrestrial photography in Mediterranean grasslands, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18215, https://doi.org/10.5194/egusphere-egu2020-18215, 2020.

Vegetation dynamics are determined by a multitude of hydro-meteorological variables, and this interplay changes in space and time. Due to its complexity, it is still not fully understood at large spatial scales. This knowledge gap contributes to increased uncertainties in future climate projections because large-scale photosynthesis is influencing the exchange of energy and water between the land surface and the atmosphere, thereby potentially impacting near-surface weather. In this study, we explore the relative importance of several hydro-meteorological variables for vegetation dynamics. For this purpose, we infer the correlations of anomalies in temperature, precipitation, soil moisture, VPD, surface net radiation and surface downward solar radiation with respective anomalies of photosynthetic activity as inferred from Sun-Induced chlorophyll Fluorescence (SIF). To detect changing hydro-meteorological controls across different climate conditions, this global analysis distinguishes between climate regimes as determined by long-term mean aridity and temperature. The results show that soil moisture was the most critical driver with SIF in the simultaneous correlation with dry and warm conditions, while temperature and VPD was both influential on cold and wet regimes during the study period 2007-2018. We repeat our analysis by replacing the SIF data with NDVI, as a proxy for vegetation greenness, and find overall similar results, except for surface net radiation expanding controlled regions on cold and wet regimes. As the considered hydro-meteorological variables are inter-related, spurious correlations can occur. We test different approaches to investigate and account for this phenomenon. The results can provide new insight into mechanisms of vegetation-water-energy interactions and contribute to improve dynamic global vegetation models.

How to cite: Li, W., Migliavacca, M., Luo, Y., and Orth, R.: Which hydro-meteorological variables control large-scale photosynthesis?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15936, https://doi.org/10.5194/egusphere-egu2020-15936, 2020.

By changing matter and energy exchange, biogeochemical process and geophysical process, land use and land cover changes have crucial effects on the earth system modelling. Previous studies have focused on reconstructing the land use and land cover change to be a continuous changing process over time considering human and natural factors. The real land cover change processes have rarely been taken into consideration in the simulation of earth system. Using Gong global land cover mapping products (1985-2015) and the Lawrence land cover dataset (default) in CESM, this study have quantitatively compared the differences in plant function types (PFT) between two products. The results show the land cover changes in default dataset are slowly changing processes with little variation from year to year. In contrast, the Gong global mapping products express a noticeable drastic change tendency between adjacent years. Driving the model with different land cover datasets, our results indicates that globally land evapotranspiration (ET) is dramatically impacted by the land cover changes, especially in areas with distinct tree changes. Also the land cover change can cause a certain proportion variation in soil water (-50%-65%) and runoff (-60%-60%, even >90% in some special grid points) in a global scale. This study estimates the substantial effect land use and land cover changes can have on the land surface hydrological process in earth system modelling.

How to cite: Ni, S. and Lu, H.: Assess the impacts of different land cover datasets on land surface hydrological process in Community Earth System Model (CESM), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12236, https://doi.org/10.5194/egusphere-egu2020-12236, 2020.

Water security depends on forests. Forest return less water to soil compared with grasslands or cultivation land because of their higher contribution to atmospheric moisture content via evapotranspiration. Water infiltration and retention are encouraged in forest soil by root systems. They are vital for reducing soil erosion and reducing drought by capturing fog water, reducing surface water run-off and promoting groundwater recharge. Deforestation and Afforestation in Korea Peninsula may cause change of water yield on precipitation storage and erosion control. This study is focus on how much water is contented in the changing land cover, especially forests. SWAT (Soil and Water Assessment Tool) model needs some data for simulation of water yield for example DEM, climatic data, land cover, soil data, etc. In this study, evaluation of water yield was performed at two time, 2005 and 2018 using SWAT model. Land cover was classified by using GEE (Google Earth Engine) which is useful tool for classification about enormous data. Through GEE, we got the two land cover maps, 2005 and 2018, these data were used for input data in SWAT model. Soil data is used by FAO Soil. To calibrate result data, we controlled some parameters like soil depth, porous volume which have stronger correlation between forests and soil properties. We can find that forest can store more water than other classes such as city, agriculture, and so on. In this study, we quantitatively estimated the water content by changing land cover. This study present functional positive effects of forests to store water. This study can be used in preparing various forest strategy in South Korea. Above all, this result maybe useful background data for supporting North Korea in afforestation.

How to cite: Ko, Y., Jo, H.-W., Lee, S., Lee, H., Lim, C.-H., Kim, J., and Lee, W.-K.: Evaluation of Forest Water Storage by changing Land Cover in Korea Peninsula , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21216, https://doi.org/10.5194/egusphere-egu2020-21216, 2020.

Remote sensing techniques are widely used to evaluate the biophysical status of vegetation, including water stress caused by soil water deficit. Based on the nominal links between water stress condition, transpiration and canopy temperature in the vegetation, numerous studies have used a trapezoidal relationship between Land Surface Temperature (LST) and Normalized Difference Vegetation Index (NDVI) over vegetated surfaces to develop the water stress metric, in which the level of stress could be identified by the spatial location of the pixels on the spectral space (Goetz and Goetz 1997; Lambin, Lambin, and Ehrlich 1996; Nemani et al. 1993; Nemani and Running 1989; Price 1990; Sandholt, Rasmussen, and Andersen 2002). However, the amount of change in canopy temperature could also vary spatially by the canopy water status at that time. Thus, LST-NDVI alone cannot construct an efficient metric to see the spatial patterns of water stress at ecosystem level unless they are coupled with water status of vegetation at that moment. This study hypothesizes that a metric which can combine LST-NDVI information with an indicator for canopy water status could give more accurate estimations of the real-time vegetation water stress. The remotely sensed plant canopy water status indicator (a metric based on canopy reflection in the Short-Wave Infrared region (SWIR)) could add the canopy water status information to the LST-NDVI based indices, which may better explain spatial/temporal water stress condition in the plants especially in densely forested areas where signal saturation is a major issue. In this study, the third-dimensional information of SWIR has been combined with LST-NDVI spectral space to create a new remotely sensed vegetation water stress index, TVWSI (Temperature Vegetation Water Stress Index) which seems to be more realistic to capture stress dynamics at large scale. 

Sixty grids (2 km X 2 km) each containing 16 pixels of daily MODIS-reflectance (band 1 – band 7, 500 m spatial resolution) and 4 pixels of daily MODIS-LST (1 km spatial resolution) were chosen over forested areas in Victoria representing most of the bioregions as classified by the Interim Biogeographic Regionalisation for Australia (IBRA7). From 2002 to 2018 daily TVWSI values of each grid were evaluated against the modelled daily available soil moisture content in the top 1 m of the soil profile, and rainfall data, from the Australian Bureau of Meteorology (BOM). TVWSI performed better than other dryness indices mentioned in the literature. A high correlation was obtained between TVWSI vs. soil moisture and TVWSI vs. rainfall with a coefficient of determination value of 0.6 (p<0.001) and 0.61 (p<0.001) respectively when data were combined spatially and temporally. Even improved correlations ranging (0.4-0.7, p<0.001) were obtained for individual grids over the mentioned period. While correlation ranging (0.15-0.48, p<0.001) were obtained using dryness indices like Perpendicular Drought Index (PDI), Modified PDI (MPDI), Temperature Vegetation Dryness Index (TVDI) and Vegetation Supply Water Index (VSWI). The result shows that the TVWSI can capture real-time ecosystem water stress well and the metric could be an efficient input parameter for many hydrological, drought and fire prediction models.

How to cite: Joshi, R. C., Ryu, D., Sheridan, G. J., and Lane, P. N. J.: A new Remote Sensing-based vegetation water stress index: -Temperature Vegetation Water Stress Index (TVWSI), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12308, https://doi.org/10.5194/egusphere-egu2020-12308, 2020.

The IPCC presented accelerated climate change and an increase in abnormal climate phenomena in the 21st century. This abnormal climate increases the frequency and intensity of extreme precipitation, resulting in changes in the water balance, such as precipitation and evaporation. Droughts are caused by prolonged water shortages, and it usually occurs in areas with subaverage rainfall. Drought is difficult to point precisely at the start and end, so its monitoring and forecasting are important to prepare for damage and mitigate impact. And although various satellite-based drought indices are being developed and used, it is still difficult to define drought quantitatively and to select a drought index suitable for the local situation. Currently, the drought indices used in Republic of Korea include SPI, which deals only with the water supply, and SPEI using the simple difference between precipitation and evapotranspiration. However, no standardized system of drought monitoring suitable for agricultural drought situations, such as the supply, consumption and impact of vegetation, has been established. However, it does not have a standardized system for monitoring drought agricultural drought suitable for situations such as the supply and demand of water and the impact on vegetation. this study tried to shows a new drought index that best expresses the drought in Korean cropland using long-term satellite data.

How to cite: Yun, Y.-S. and Lee, Y.-W.: Development of satellite-based Surface water stress index considering surface water balance, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-18800, https://doi.org/10.5194/egusphere-egu2020-18800, 2020.

A significant increase in irrigated area has been observed in India since the green revolution, however the influence of irrigation on vegetation health, land surface temperature (LST), and vegetation drought remains to be explored in detail. We develop a high-resolution (250m) remotely sensed data of enhanced vegetation index (EVI) and LST from Moderate Resolution Imaging Spectroradiometer (MODIS) at 8-day temporal resolution for the period 2000-2019 for India. We quantify the role of irrigation in the modulation of EVI, LST, and vegetation stress. The results show significantly higher EVI (p-value < 0.05) and cooler LST (1-2 K) in the irrigation dominated regions during the crop-growing season over the Indo-Gangetic Plain. A poor correlation between vegetation and meteorological drought (Standardized Precipitation Evapotranspiration Index-SPEI and Standardized Precipitation Index-SPI) was found in highly irrigated regions due to irrigation. While irrigation resulted in an elevated vegetation growth, it has caused groundwater depletion in Indo-Gangetic Plain. Simulations (with and without irrigation) using Noah land surface model coupled with the weather research and forecasting (WRF) show cooling due to irrigation that is consistent with the observational evidence.

How to cite: Krishnankutty Ambika, A. and Mishra, V.: Strong linkage between irrigation and land surface cooling in India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21154, https://doi.org/10.5194/egusphere-egu2020-21154, 2020.