Evaluation of the OPTRAM Model to Retrieve Soil Moisture in the Sanjiang Plain of Northeast China. Issue 6 (25th June 2020) - Chen, Mingxing; Zhang, Yuhu; Yao, Yunjun; Lu, Jing; Pu, Xiao; Hu, Tao; Wang, Peng
Abstract: Soil moisture is a key factor affecting crop growth and crop yield. Information on soil moisture is critical for crop growth monitoring and yield estimation. This study evaluated the OPtical TRApezoid Model (OPTRAM) on soil moisture estimates in the Sanjiang Plain, China, using Moderate Resolution Imaging Spectroradiometer (MODIS) data and in situ soil moisture data acquired from May to September in 2016 and 2017. This model was empirically calculated by parameterizing the relationship between the normalized difference vegetation index (NDVI) and shortwave infrared transformed reflectance (STR). In addition, the precipitation data were collected and used for verification of the results. According to the scatterplots of STR and NDVI, OPTRAM can estimate soil moisture from July to September, while it's inapplicable in May and June. Evaluation results indicated that OPTRAM‐based soil moisture estimates provide overall RMSE from 0.05 to 0.13 cm 3 /cm 3, bias from −0.11 to 0.06 cm 3 /cm 3, and R 2 from 0.10 to 0.50, respectively, for all investigated sites. The performance of OPTRAM compared with the precipitation data showed good agreement. In conclusion, OPTRAM could be used to estimate soil moisture with reasonable accuracy in most areas from July to September in Sanjiang Plain. Key Points: The parameters of the OPtical TRApezoid Model (OPTRAM) is inapplicable at the early stage of the crop growth period in an area of paddy field The OPTRAM soil moisture estimates show better performance in the middle and late crop growth The OPTRAM soil moisture estimates show close agreement with the precipitation, while there is a hysteresis sometimes