The land surface dynamic feedback (LSDF) information captured by time-series remote sensing data during the soil-drying process after a rainfall event provides effective covariates for digital soil mapping over low-relief areas. However, current methods used to capture LSDF require a uniform rainfall magnitude in the geographic space; a condition that is not often met for large areas. Here, we propose a LSDF construction method considering the spatial heterogeneity of rainfall magnitudes by adjusting the evaporation variables in the LSDF. For this, the relationships between evaporation and rainfall magnitudes were first established. The LSDFs from various locations for rainfall events with different magnitudes were then adjusted based on these relationships. Using a case study, the adjusted LSDFs after two rainfall events were then used to predict soil texture over a low-relief area. The results showed that the cubic polynomial model performed best when constructing the relationship between evaporation adjustment and rainfall magnitude, giving the highest R² value and a low Akaike information criterion. Adjustment to the LSDF decreases with increasing rainfall and the rate of change in the adjustment also decreases with increasing rainfall. For both rainfall events, prediction accuracies with the adjusted LSDFs were higher than those based on the original LSDFs. Furthermore, the greater the adjustment, the greater the improvement in the accuracy. We conclude that the proposed construction method for LSDF, accounting for the spatial heterogeneity of rainfall magnitudes, offers improved predictive power for digital soil mapping over large areas.

在降雨事件发生后的土壤干燥过程中，由时序遥感数据捕获的土地表面动态反馈（LSDF）信息为低洼地区的数字土壤制图提供了有效的协变量。但是，目前用于捕获低空自卫队的方法需要在地理空间内保持一致的降雨幅度。大面积不经常满足的条件。在这里，我们提出了一种通过调整LSDF中的蒸发变量来考虑降雨幅度空间异质性的LSDF构造方法。为此，首先建立了蒸发量与降雨量之间的关系。然后根据这些关系调整来自不同地点的降雨事件的不同水平的LSDF。使用案例研究 然后，在两次降雨事件之后，将经过调整的LSDFs用于预测低洼地区的土壤质地。结果表明，三次多项式模型在构造蒸发调节与降雨幅度之间的关系时表现最好，给出的最高R ²值和较低的赤池信息准则。对LSDF的调整随着降雨量的增加而减少，并且调整的变化率也随着降雨的增加而减少。对于这两个降雨事件，调整后的LSDF的预测精度均高于基于原始LSDF的预测精度。此外，调节越大，精度提高越大。我们得出结论，考虑到降雨幅度的空间异质性，拟议的LSDF构造方法为大面积数字土壤制图提供了改进的预测能力。