Sentinel-1 Radar Data Assessment to Estimate Crop Water Stress

Abstract

Water is an important component in agricultural production for both yield quantity and quality. Although all weather conditions are driving factors in the agricultural sector, the precipitation in rainfed agriculture is the most limiting weather parameter. Water deficit may occur continuously over the total growing period or during any particular growth stage of the crop. Optical remote sensing is very useful but, in cloudy days it becomes useless. Radar penetrates the cloud and collects information through the backscattering data. Normalized Difference Vegetation Index (NDVI) was extracted from Landsat 8 satellite data and used to calculate Crop Coefficient (Kc). The FAO-Penman-Monteith equation was used to calculate reference evapotranspiration (ETo). NDVI and Land Surface Temperature (LST) were calculated from satellite data and integrated with air temperature measurements to estimate Crop Water Stress Index (CWSI). Then, both CWSI and potential crop evapotranspiration (ETc) were used to calculate actual evapotranspiration (ETa). Sentinel-1 radar data were calibrated using SNAP software. The relation between backscattering (dB) and CWSI was an inverse relationship and R 2 was as high as 0.82.

Statistical Exploration of SENTINEL-1 Data, Terrain Parameters, and in-situ Data for Estimating the Near-Surface Soil Moisture in a Mediterranean Agroecosystem

“…We are aware that both soil moisture and the SAR backscatter are influenced by more factors than addressed in our study. For instance, the soil surface roughness was not considered, although it is reported a major limiting factor for active microwave soil moisture RS (e.g., Sahebi et al, 2002;Schuler et al, 2002;Walker et al, 2004;Wang et al, 2016;El-Shirbeny and Abutaleb, 2017). As no tillage and storm events, commonly known to largely influence the backscatter (e.g., Baghdadi et al, 2008;Dalla Rosa et al, 2012), were notice and recorded, respectively, during our short observation period (i.e., 18 days), we assumed that the surface roughness did not vary and ignored it, particularly as its field survey was considered not feasible.…”

“…ML approaches are increasingly used to estimate θ in agricultural watersheds across humid to arid climate regimes and at different scales (e.g., Quesney et al, 2000;Hachani et al, 2019). They are reported to improve hydrological modeling in wetlands (Dabrowska-Zielinska et al, 2018) and to support the monitoring of water tables dynamics (Asmuß et al, 2019), and the estimation of crop water stress (El-Shirbeny and Abutaleb, 2017), and the water use efficiency (Efremova et al, 2019).…”

Multi-temporal Crop Type and Field Boundary Classification with Google Earth Engine

“…Nevertheless, the lack of benchmarks and various metrics makes it difficult to compare performance. Various optical and radar-based data from Landsat 7, Landsat 8, Sentinel-1, and Planet have been analysed for yield forecasts [1,2]. The combined use of data from Landsat 8 and Sentinel-2 improves the temporal resolution for 2016, before Sentinel-2B was launched.…”

Mapping and Monitoring Wetland Dynamics Using Thermal, Optical, and SAR Remote Sensing Data

“…The relation between radar and optical/thermal data can be significant for better understanding and managing wetlands. Several studies have investigated the relation between different satellite data in different land covers [16]. However, the relation between SAR values and land surface temperature values within a wetland area has not been a subject of a delicate investigation.…”