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： Liu, Yuan 1 ; Chen, Songchao 5 ; Shen, Ge 7 ; Chen, Cheng 4 ; Cai, Zejiang 3 ; Zhu, Ji 1 ; Zhang, Xia 1 ; Shang, Guofei 1 ; Zhou, Qingbo 8 ; Bellingrath-Kimura, Sonoko Dorothea 4 ; Yu, Qiangyi 3 ; Wu, Wenbin 3 ;
作者机构： 1.Hebei GEO Univ, Sch Land Sci & Space Planning, Shijiazhuang 050031, Peoples R China
2.Hebei GEO Univ, Hebei Int Joint Res Ctr Remote Sensing Agr Drought, Shijiazhuang 050031, Peoples R China
3.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Beijing 100081, Peoples R China
4.Leibniz Ctr Agr Landscape Res ZALF, D-15374 Muncheberg, Germany
5.Zhejiang Univ, Hangzhou Global Sci & Technol Innovat Ctr, Hangzhou 311200, Peoples R China
6.Zhejiang Univ, Coll Environm & Resource Sci, Hangzhou 310058, Peoples R China
7.Zhejiang Univ Finance & Econ, Sch Publ Adm, Hangzhou 310018, Peoples R China
8.Chinese Acad Agr Sci, Agr Informat Inst, Beijing 100081, Peoples R China
9.Humboldt Univ, Fac Life Sci, D-14195 Berlin, Germany
关键词： soil pH; Sentinel-1/2 images; cropland soil; crop rotation; soil sample grouping; machine learning
期刊名称：REMOTE SENSING （ 影响因子：4.1； 五年影响因子：4.8 ）
ISSN：
年卷期： 2025 年 17 卷 9 期
页码：
收录情况： SCI
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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