数字农科院2.0

Unveiling the Effects of Crop Rotation on Cropland Soil pH Mapping: A Remote Sensing-Based Soil Sample Grouping Strategy

文献类型： 外文期刊

作者： Yuan Liu;Songchao Chen;Ge Shen;Cheng Chen;Zejiang Cai;Ji Zhu;Xia Zhang;Guofei Shang;Qingbo Zhou;Sonoko Dorothea Bellingrath-Kimura;Qiangyi Yu;Wenbin Wu

作者机构：

关键词： crop rotation;cropland soil;machine learning;Sentinel-1/2 images;soil pH;soil sample grouping

期刊名称： Remote Sensing

ISSN： 2072-4292

年卷期： 2025 年 17 卷 9 期

页码：

收录情况： SCIE（2025版） ; ; EI（2025版）

摘要： Crop rotation affects soil pH by disturbing H+ production and consumption within soil–crop systems, primarily through fertilization, irrigation, cropping, and harvest. Studies have shown that crop rotation improves soil organic matter prediction. However, simply incorporating crop rotation may not significantly improve soil pH prediction, because the spatial variability in soil pH is lower and the way crop rotation influences pH is different. To quantify the extent to which crop rotation improves soil pH mapping, we introduced the strategy of grouping soil samples by crop rotation and modeling separately. We chose a typical multiple-cropping region suffering soil acidification in Southern China, where the complex crop rotation was mapped by Sentinel-1/2 time series and a legend featuring three main systems (i.e., paddy, vegetable, and orchard) and nine subsystems. This crop rotation map was then combined with other variables to derive multiple combinations and predict soil pH. Based on the best combination, we further assessed the grouping strategy. The results showed that simply incorporating crop rotation in one joint model was useful but could not obtain the expected accuracy, with a root mean squared error (RMSE) of 0.66 and an R2 of 0.36. The individual statistical accuracies were quite low for the vegetable and orchard rotations, with an RMSE of 0.77/0.70 and an R2 of 0.30/−0.04. Grouping soil samples by crop rotation significantly enhanced soil pH predictability with a decrease in the RMSE of 15% and an increase in the R2 of 53%. The results proved that grouping by crop rotation can fit and optimize the sub-models after learning the characteristics of the rotation subsamples, offering a way for improving digital mapping of soil pH over heterogeneous agricultural landscapes.

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