Correction of the multi-temporal soil line consistency for the inversion of soil moisture content

Gaining large-scale, temporally continuous surface soil moisture information through remote sensing techniques is of significant importance for crop irrigation management and farmland drought early warning. The commonly used remote sensing soil moisture inversion method often utilizes the modified perpendicular dryness index (MPDI) in conjunction with ground-measured data to construct models. However, this method lacks universality, as each temporal phase of the image inversion requires the acquisition of corresponding ground-measured data for calibration, resulting in a high dependence on ground-measured data. To reduce the dependence on ground-measured data when estimating soil moisture using optical remote sensing data, this study firstly analyzed the feature space differences and the variation laws of soil line slopes using 54 temporal phases of Sentinel-2 T50SKH scenes from 2020 to 2021. The study also derived and explained the influence of soil line slope variations on soil moisture inversion, revealing the fundamental reason for the need for ground-measured data calibration for each period in the MPDI soil moisture inversion. Based on this, a multi-temporal soil line consistency correction method and a re-modified perpendicular drought index (RPDI) were proposed. Soil moisture inversion was conducted using RPDI, and the effectiveness was validated in the study area. The main conclusions are as follows: 1) The analysis of the feature space of the Sentinel-2 satellite's continuous two-year scenes indicated that the feature spaces of most temporal phases were incomplete. Theoretically existing completely bare or extremely wet points may not be present in a single image, leading to long-term fluctuations in soil line slopes across different temporal phases. For the modified perpendicular dryness index (MPDI), the soil lines used for the calculation of MPDI in different temporal phases were different, which could only represent the relative dryness or wetness of each phase. This was the fundamental reason why ground-measured data calibration was required for each temporal phase when using MPDI for soil moisture inversion. 2) Through theoretical derivation of the MPDI changes caused by soil line slope variations, it was found that the changes in the inversion equation's slope were mainly to offset the fluctuations in soil line slopes. For the same region, since soil texture did not change over time, if a stable soil line slope could be obtained, then the slope of the linear regression equation between soil moisture and MPDI would also remain stable, and calibration would not be necessary for each temporal phase. 3) A soil line consistency correction method and RPDI were proposed. Since the calculation of this index was based on a unified theoretical soil line, it stabilized the regression relationship between RPDI and soil moisture. Validation results showed that a unified regression equation could be used between RPDI and ground-measured soil moisture (the unbiased root mean square error of the calibration set was 2.88%, R² was 0.49, and the unbiased root mean square error of the validation set was 3.05%, R² was 0.54). When applying the regression equation obtained from the calibration set to other temporal phases, it was not significantly different from the MPDI regression equations established for each phase separately in terms of R² and unbiased root mean square error. This indicated that RPDI no longer depended on ground-measured data and could achieve direct comparison across different temporal phases, significantly reducing the dependence on ground-measured data for multi-temporal soil moisture estimation using optical remote sensing. In conclusion, this research offered a substantial contribution to the utilization of optical remote sensing data for large-scale, continuous soil moisture inversion. The introduced RPDI method not only simplified the process of soil moisture estimation but also fortified the resilience of soil moisture retrieval. Furthermore, it significantly boosted the efficiency and precision of soil moisture monitoring. As a result, it held great value for the implementation of sound irrigation strategies and the timely issuance of drought warnings.