Construction of land surface dynamic feedback for digital soil mapping considering the spatial heterogeneity of rainfall magnitude
Construction of land surface dynamic feedback for digital soil mapping considering the spatial heterogeneity of rainfall magnitude
复制标题
考虑降雨量空间异质性的数字土壤测绘地表动态反馈构建
DOI：
10.1016/j.catena.2020.104576
复制
发表时间：
2020
期刊：
影响因子：
6.2
通讯作者：
Feng Liu
中科院分区：
文献类型：
--
作者：
Canying Zeng;Feng Qi;A-Xing Zhu;Feng Liu
降雨后土壤干燥过程中时序遥感数据获取的地表动态反馈（LSDF）信息为低起伏地区土壤数字化制图提供了有效的协变量。然而，目前用于捕获LSDF的方法需要在地理空间中具有均匀的降雨强度；在大范围内不经常满足的条件。本文通过调整LSDF中的蒸发变量，提出了一种考虑降雨强度空间异质性的LSDF构建方法。为此，首次建立了蒸发量与降雨量之间的关系。然后根据这些关系对不同地点不同量级降雨事件的lsdf进行调整。通过实例研究，利用两次降雨事件后调整后的lsdf来预测低起伏地区的土壤质地。结果表明，三次多项式模型在构建蒸发调整与雨量之间的关系时效果最好，str2值最高，赤池信息准则较低。对LSDF的调整随降雨量的增加而减小，调整的变化率也随降雨量的增加而减小。对于这两个降水事件，使用调整后的lsdf的预测精度都高于基于原始lsdf的预测精度。此外，调整幅度越大，精度提高幅度越大。研究结果表明，考虑降雨量级空间异质性的LSDF构建方法可提高大面积数字土壤制图的预测能力。
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 highestR2value 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.