Evaluation of soil moisture estimation techniques based on Sentinel-1 observations over wheat fields

Soil moisture (SM) is a key variable in agriculture and its monitoring is essential. SM determines the amount of water available to plants, having a direct impact on the development of crops, on the forecasting of crop yields and on the surveillance of food security. Microwave remote sensing offers a great potential for estimating SM because it is sensitive to the dielectric characteristics of observed surface that depend on surface soil moisture. The objective of this study is the evaluation of three change detection methodologies for SM estimation over wheat at the agricultural field scale based on Sentinel-1 time series: Short Term Change Detection (STCD), TU Wien Change Detection (TUWCD) and Multitemporal Bayesian Change Detection (MTBCD). Different methodological alternatives were proposed for the implementation of these techniques at the agricultural field scale. Soil moisture measurements from eight experimental wheat fields were used for validating the methodologies. All available Sentinel-1 acquisitions were processed and the eventual benefit of correcting for vegetation effects in backscatter time series was evaluated. The results were rather variable, with some experimental fields achieving successful performance metrics (ubRMSE -0.05 m3/m3) and some others rather poor ones (ubRMSE > 0.12 m3/ m3). Evaluating median performance metrics, it was observed that both TUWCD and MTBCD methods obtained better results when run with vegetation corrected backscatter time series (ubRMSE-0.07 m3/m3) whereas STCD produced similar results with and without vegetation correction (ubRMSE-0.08 m3/m3). The soil moisture content had an influence on the accuracy of the different methodologies, with higher errors observed for drier conditions and rain-fed fields, in comparison to wetter conditions and irrigated fields. Taking into account the spatial scale of this case study, results were considered promising for the future application of these techniques in irrigation management.

Automated summary

Soil moisture is a crucial variable in agriculture, affecting crop development, yield forecasting, and food security. Microwave remote sensing shows potential for estimating soil moisture, especially with the use of Sentinel-1 time series data. This study evaluates three change detection methodologies for soil moisture estimation over wheat fields, with varying results. The accuracy of the methodologies is influenced by soil moisture content and field conditions. Overall, the results are considered promising for future application in irrigation management.