SPTS: Single Pixel in Time-Series Triangle Model for Estimating Surface Soil Moisture

Abstract

Surface soil moisture (SSM) is essential for understanding the interactions between the atmosphere and Earth’s surface. The rapid development of remote sensing technology in recent decades has provided feasible alternatives for SSM retrieval. The triangle model is one such method that uses the relationship between land surface temperature (LST) and vegetation index (VI) on a triangular space to estimate SSM. However, the traditional LST-VI triangle models inherently suffer from two major drawbacks. First, the subjective requirements for a sufficient number of pixels are characterized by a wide range of vegetation and SSM under uniform atmospheric conditions. Second, this is the need for date-to-date calibration. To overcome these limitations, the present study proposed a novel scheme of the feature space, the single pixel in time-series (SPTS) triangle model. The basic assumption of this feature space is that a given satellite pixel for cropland or grassland will undergo distinct vegetation cover and SSM status due to natural growth and soil moisture dynamics over a relatively long period. Unique triangles for 44 sites in two networks of the International Soil Moisture Network (ISMN)—the TxSon (US) dominated by grassland and REMEDHUS (Spain) dominated by cropland—were constructed based on Landsat data over a period of ~10 years (2013–2023). Compared to the traditional triangle model, the proposed model reveals enhanced skills for SSM retrieval, with a decrease in root-mean-square error (RMSE) by 13.5% (~0.050 m3/m3) over the study sites.