New Methodologies for Grasslands Monitoring

Monitoring grassland areas to assess changes in their condition over time has been the subject of a lot of research at different scales. Initially the techniques focused on field-based measurements, and modelling. However, several obtained data were site specific. Based on the increase in availability of remote sensing data and products, there is an expectation that remote sensing can provide rapid and definite answers to the challenges of detecting and monitoring grassland conditions and associated changes in productivity. At the time of European Copernicus Programme, the new possibilities of satellite data from the group of Sentinel satellites give the new perspective for grasslands monitoring. The Finegrass Polish – Norwegian Project have been set to detect the biomass and its changes for grasslands in Poland and Norway applying different approaches due to different specific of the area. The results have been verified by ground measurements

A roadmap for high-resolution satellite soil moisture applications – confronting product characteristics with user requirements

Soil moisture observations are of broad scientific interest and practical value for a wide range of applications. The scientific community has made significant progress in estimating soil moisture from satellite-based Earth observation data, particularly in operationalizing coarse-resolution (25-50 km) soil moisture products. This review summarizes existing applications of satellite-derived soil moisture products and identifies gaps between the characteristics of currently available soil moisture products and the application requirements from various disciplines. We discuss the efforts devoted to the generation of high-resolution soil moisture products from satellite Synthetic Aperture Radar (SAR) data such as Sentinel-1 C-band backscatter observations and/or through downscaling of existing coarse-resolution microwave soil moisture products. Open issues and future opportunities of satellite-derived soil moisture are discussed, providing guidance for further development of operational soil moisture products and bridging the gap between the soil moisture user and supplier communities

Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data

The objectives of the study were to determine the spatial rate of CO2 flux (Net Ecosystem Exchange) and soil moisture in a wetland ecosystem applying Sentinel-1 IW (Interferometric Wide) data of VH (Vertical Transmit/Horizontal Receive—cross polarization) and VV (Vertical Transmit/Vertical Receive—like polarization) polarization. In-situ measurements of carbon flux, soil moisture, and LAI (Leaf Area Index) were carried out over the Biebrza Wetland in north-eastern Poland. The impact of soil moisture and LAI on backscattering coefficient (σ°) calculated from Sentinel-1 data showed that LAI dominates the influence on σ° when soil moisture is low. The models for soil moisture have been derived for wetland vegetation habitat types applying VH polarization (R2 = 0.70 to 0.76). The vegetation habitats: reeds, sedge-moss, sedges, grass-herbs, and grass were classified using combined one Landsat 8 OLI (Operational Land Imager) and three TerraSAR-X (TSX) ScanSAR VV data. The model for the assessment of Net Ecosystem Exchange (NEE) has been developed based on the assumption that soil moisture and biomass represented by LAI have an influence on it. The σ° VH and σ° VV describe soil moisture and LAI, and have been the input to the NEE model. The model, created for classified habitats, is as follows: NEE = f (σ° Sentinel-1 VH, σ° Sentinel-1 VV). Reasonably good predictions of NEE have been achieved for classified habitats (R2 = 0.51 to 0.58). The developed model has been used for mapping spatial and temporal distribution of NEE over Biebrza wetland habitat types. Eventually, emissions of CO2 to the atmosphere (NEE positive) has been noted when soil moisture (SM) and biomass were low. This study demonstrates the importance of the capability of Sentinel-1 microwave data to calculate soil moisture and estimate NEE with all-weather acquisition conditions, offering an important advantage for frequent wetlands monitoring

Yield Estimation using SPOT-VEGETATION Products: A Case study of Wheat in European Countries

In the period 1999-2009 ten-day SPOT-VEGETATION products of the Normalized Difference Vegetation Index (NDVI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) at 1 km spatial resolution were used in order to estimate and forecast the wheat yield over Europe. The products were used together with official wheat yield statistics to fine-tune a statistical model for each NUTS2 region, based on the Partial Least Squares Regression (PLSR) method. This method has been chosen to construct the model in the presence of many correlated predictor variables (10-day values of remote sensing indicators) and a limited number of wheat yield observations. The model was run in two different modalities: the "monitoring mode", which allows for an overall yield assessment at the end of the growing season, and the "forecasting mode", which provides early and timely yield estimates when the growing season is on-going. Performances of yield estimation at the regional and national level were evaluated using a cross-validation technique against yield statistics and the estimations were compared with those of a reference crop growth model. Models based on either NDVI or FAPAR normalized indicators achieved similar results with a minimal advantage of the model based on the FAPAR product. Best modelling results were obtained for the countries in Central Europe (Poland, North-Eastern Germany) and also Great Britain. By contrast, poor model performances characterise countries as follows: Sweden, Finland, Ireland, Portugal, Romania and Hungary. Country level yield estimates using the PLSR model in the monitoring mode, and those of a reference crop growth model that do not make use of remote sensing information showed comparable accuracies. The largest estimation errors were observed in Portugal, Spain and Finland for both approaches. This convergence may indicate poor reliability of the official yield statistics in these countries.JRC.H.4-Monitoring Agricultural Resource

Assessment of Carbon Flux and Soil Moisture in Wetlands Applying Sentinel-1 Data

The objectives of the study were to determine the spatial rate of CO2 flux (Net Ecosystem Exchange) and soil moisture in a wetland ecosystem applying Sentinel-1 IW (Interferometric Wide) data of VH (Vertical Transmit/Horizontal Receive—cross polarization) and VV (Vertical Transmit/Vertical Receive—like polarization) polarization. In-situ measurements of carbon flux, soil moisture, and LAI (Leaf Area Index) were carried out over the Biebrza Wetland in north-eastern Poland. The impact of soil moisture and LAI on backscattering coefficient (σ°) calculated from Sentinel-1 data showed that LAI dominates the influence on σ° when soil moisture is low. The models for soil moisture have been derived for wetland vegetation habitat types applying VH polarization (R2 = 0.70 to 0.76). The vegetation habitats: reeds, sedge-moss, sedges, grass-herbs, and grass were classified using combined one Landsat 8 OLI (Operational Land Imager) and three TerraSAR-X (TSX) ScanSAR VV data. The model for the assessment of Net Ecosystem Exchange (NEE) has been developed based on the assumption that soil moisture and biomass represented by LAI have an influence on it. The σ° VH and σ° VV describe soil moisture and LAI, and have been the input to the NEE model. The model, created for classified habitats, is as follows: NEE = f (σ° Sentinel-1 VH, σ° Sentinel-1 VV). Reasonably good predictions of NEE have been achieved for classified habitats (R2 = 0.51 to 0.58). The developed model has been used for mapping spatial and temporal distribution of NEE over Biebrza wetland habitat types. Eventually, emissions of CO2 to the atmosphere (NEE positive) has been noted when soil moisture (SM) and biomass were low. This study demonstrates the importance of the capability of Sentinel-1 microwave data to calculate soil moisture and estimate NEE with all-weather acquisition conditions, offering an important advantage for frequent wetlands monitoring

Drought Model DISS Based on the Fusion of Satellite and Meteorological Data under Variable Climatic Conditions

The use of effective methods for large-area drought monitoring is an important issue; hence, there have been many attempts to solve this problem. In this study, the Drought Information Satellite System (DISS) index is presented, based on the synergistic use of meteorological data and information derived from satellite images. The index allows us to monitor drought phenomena in various climatic and environmental conditions. The approach utilizes two indices for constructing a drought index: (1) the hydrothermal coefficient (HTC), which characterizes meteorological conditions across the study area over a long-term period; and (2) the temperature condition index (TCI) derived from Moderate-resolution Imaging Spectroradiometer (MODIS) data, which refers instantaneous land surface temperature (LST) to long-term extreme values. The model for drought assessment based on the DISS index was applied for generating drought index maps for Poland for the 2001–2019 vegetation seasons. The performance of the index was verified through comparison of the extent of agricultural drought to the reduction in cereal and maize yield. Analysis of variance revealed a significant relationship between the area of drought determined by the drought index and the decrease in cereal yield due to unfavorable growth conditions. The presented study proves that the proposed drought index can be an effective tool for large-area drought monitoring under variable environmental conditions

Soil Moisture in the Biebrza Wetlands Retrieved from Sentinel-1 Imagery

The objective of the study was to estimate soil moisture (SM) from Sentinel-1 (S-1) satellite images acquired over wetlands. The study was carried out during the years 2015⁻2017 in the Biebrza Wetlands, situated in north-eastern Poland. At the Biebrza Wetlands, two Sentinel-1 validation sites were established, covering grassland and marshland biomes, where a network of 18 stations for soil moisture measurement was deployed. The sites were funded by the European Space Agency (ESA), and the collected measurements are available through the International Soil Moisture Network (ISMN). The SAR data of the Sentinel-1 satellite with VH (vertical transmit and horizontal receive) and VV (vertical transmit and vertical receive) polarization were applied to SM retrieval for a broad range of vegetation and soil moisture conditions. The methodology is based on research into the effect of vegetation on backscatter (σ°) changes under different soil moisture and Normalized Difference Vegetation Index (NDVI) values. The NDVI was derived from the optical imagery of a MODIS (Moderate Resolution Imaging Spectroradiometer) sensor onboard the Terra satellite. It was found that the state of the vegetation expressed by NDVI can be described by the indices such as the difference between σ° VH and VV, or the ratio of σ° VV/VH, as calculated from the Sentinel-1 images in the logarithmic domain. The most significant correlation coefficient for soil moisture was found for data that was acquired from the ascending tracks of the Sentinel-1 satellite, characterized by the lowest incidence angle, and SM at a depth of 5 cm. The study demonstrated that the use of the inversion approach, which was applied to the newly developed models using Water Cloud Model (WCM) that includes the derived indices based on S-1, allowed the estimation of SM for wetlands with reasonable accuracy (10 vol. %). The developed soil moisture retrieval algorithms based on S-1 data are suited for wetland ecosystems, where soil moisture values are several times higher than in agricultural areas