环境科学
土壤图
遥感
物候学
句号(音乐)
土壤有机质
数字土壤制图
自然地理学
土壤科学
土壤水分
地理
农学
声学
生物
物理
作者
Chong Luo,Wenqi Zhang,Xinle Zhang,Huanjun Liu
出处
期刊:Catena
[Elsevier]
日期:2023-10-01
卷期号:231: 107336-107336
被引量:4
标识
DOI:10.1016/j.catena.2023.107336
摘要
Mapping of soil organic matter (SOM) in cultivated land is one of the important aspects of digital soil mapping, and its results are of great significance for agricultural precision management and carbon cycle assessment. This study takes Youyi Farm, a typical black soil area in Northeast China, as the research area and utilizes all Landsat-8 images covering the study area from April to October during 2014–2022. After masking clouds, all images were synthesized monthly. According to the local crop phenology, the period from April to October was divided into the bare soil period (April to June), peak crop growth period (July to August), and late crop growth period (September to October). Based on the random forest regression algorithm, differences in the accuracy of SOM mapping using synthetic images from different periods were evaluated, and the impact of adding environmental covariates on the SOM mapping accuracy was analyzed. The results showed that (1) when using a single-temporal synthesized image for SOM mapping, the order of accuracy was bare soil period > peak crop growth period > late crop growth period, with the synthesized image in May exhibiting the highest accuracy, with an RMSE of 0.979 %; (2) when using a multitemporal image combination for SOM mapping, the combination of optimal months (April, May, June) in the same periods can obtain the best SOM mapping accuracy, with an RMSE of 0.919 %; and (3) adding environmental covariates can effectively improve the accuracy of SOM mapping, especially when using the growing season remote sensing images, RMSE decreased from 1.136 % to 0.909 %. This study expands the applicable areas and conditions of SOM remote sensing mapping.
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