Mapping of soil organic matter in a typical black soil area using Landsat-8 synthetic images at different time periods

• Published in: Catena (Giessen) Vol. 231; p. 107336
• Main Authors: Luo, Chong, Zhang, Wenqi, Zhang, Xinle, Liu, Huanjun
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 01.10.2023
• Subjects: Bare soil period; Crop growth period; Environmental covariates; Landsat-8; Soil organic matter; Synthesized images; Soil organic matter; Environmental covariates; Landsat-8; Synthesized images; Crop growth period; Bare soil period
• ISSN: 0341-8162; 1872-6887
• DOI: 10.1016/j.catena.2023.107336

•We evaluated the potential of using a combination of multitemporal bare soil and crop growth images for SOM prediction.•Remote sensing images during the bare soil period are more suitable for SOM mapping.•The accuracy of SOM mapping can be improved by combining images with appropriate periods.•The use of appropriate multitemporal crop growth images combined with environmental covariates has great potential in SOM mapping. 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.