A soil moisture experiment for validating high-resolution satellite products and monitoring irrigation at agricultural field scale

Abstract

Validating the satellite soil moisture products is always an active research topic for the application of the products and improvement of the retrieval algorithms, attracting extensive attention. Nevertheless, seldom existing validation activities focus on the validation of high-resolution soil moisture products at the fine scale. To this end, an experiment was conducted in the middle stream of the Heihe River Basin in northwestern China in August to October of 2021, aiming to validate high-resolution satellite remote sensing products of soil moisture. The paper introduces the design, composite, and preliminary results of the experiment. A soil moisture observation network was established with two kinds of sensors (CS616 and Stevens Hydra Probe) validated against soil core measurements. Several synchronized campaigns were performed, and data were collected to validate the SMAP/Sentinel-1 L2 Radiometer/Radar 30-Second Scene 3 and 1 km EASE-Grid Soil Moisture (SPL2SMAP_S) products. Besides, an optical trapezoid model (OPTRAM) and collected Sentinel-2 data were applied to estimate soil moisture and to map irrigated area. Preliminary analyses show that: 1) Steven probes perform best, with an RMSE = 0.040 m³m⁻³ and ubRMSE = 0.034 m³m⁻³; 2) Both the SPL2SMAP_S products at 3 km and 1 km show large RMSE (0.128 m³m⁻³ for 3 km and 0.158 m³m⁻³ for 1 km) and ubRMSE (0.115 m³m⁻³ for 3 km and 0.158 m³m⁻³ for 1 km); 3) The OPTRAM retrievals over bare surface present relatively smaller RMSE (0.06 m³m⁻³) and ubRMSE (0.057 m³m⁻³), while retrievals over vegetated croplands present a relatively large RMSE/ubRMSE (0.083/0.083 m³m⁻³), and the retrievals can identify the irrigated area at field scale. Overall, the experiment provides fruitful methodologies and datasets for the validation of high-resolution remote sensing products, benefiting the development and improvement of soil moisture retrieval algorithms and products to support irrigation scheduling and management at a precision agricultural scale in the future.