Soil moisture content estimate with drying process segmentation using shortwave infrared bands

Soil moisture content (SMC) has been widely focused and studied in various fields of research, including atmosphere-land water/energy exchange, drought, and vegetation-related topics. Many indices and models have been proposed to illustrate the reflectance features changing with SMC, and to estimate SMC from the reflectance of narrow bands and broadbands, using lab, field, airborne and satellite data. These methods/indices were mostly developed for all levels of SMCs, from saturated to air-dry; however, we found that a single relationship mapping saturated to air-dry can lead to a false impression of accurate estimation. The inconsistency of a regression relationship between spectral reflectance features and SMCs over the whole drying process results in a low estimation accuracy for relatively low SMCs. In this study, a segmentation method is proposed with the goal of a more accurate SMC estimate. The whole drying process of three soil samples, representing varied soil characteristics, is monitored, and the transition points are determined by evaporation rate change (as stage-1 drying with high SMC and stage-2 drying with low SMC). An index, the shortwave normalized index (SNI), is built for SMC estimation, and the SNI index trend during drying is supported by a radiative transfer-based model. As validated, the SNI index is capable of SMC estimates, and the segmentation method improves estimation accuracy astonishingly compared with current methods.

Automated summary

Soil moisture content (SMC) has been widely studied in various fields. Although many indices and models have been proposed to estimate SMC from reflectance data, there is an issue with accuracy for lower SMC levels. A segmentation method is introduced in this study to improve SMC estimation accuracy, which shows promising results compared to current methods.