Remote Sensing Prediction Model of Cultivated Land Soil Organic Matter Considering the Best Time Window

Yiang Wang, Chong Luo, Wenqi Zhang (), Xiangtian Meng, Qiong Liu, Xinle Zhang and Huanjun Liu
Additional contact information
Yiang Wang: School of Economics and Management, Jilin Agricultural University, Changchun 130118, China
Chong Luo: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Wenqi Zhang: School of Economics and Management, Jilin Agricultural University, Changchun 130118, China
Xiangtian Meng: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Qiong Liu: School of Pubilc Adminstration and Law, Northeast Agricultural University, Harbin 150030, China
Xinle Zhang: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Huanjun Liu: Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China

Sustainability, 2022, vol. 15, issue 1, 1-15

Abstract: Soil organic matter (SOM) is very important to the quality evaluation of cultivated land, especially in fertile black soil areas. Many studies use remote sensing images combined with different machine learning algorithms to predict the regional SOM content. However, the information provided by remote sensing images in different time windows is very different. Taking Youyi Farm, a typical black soil area in Northeast China, as the research area, this study obtains all available Sentinel-2 images covering the research area from 2019 to 2021, calculates the spectral index of single-phase and multi-temporal synthesis images, takes the spectral index and band of each image as the input, and employs the random forest regression algorithm to evaluate the performance of SOM prediction using remote sensing images with different time windows. The results show that: (1) the accuracy of SOM prediction using image band and spectral index is generally improved compared to using only the band; (2) when using single-phase images, the R 2 range of SOM prediction using image band and spectral index is from 0.16 to 0.59 and the RMSE ranges from 0.82% to 1.23%; When using multi-temporal synthesis images, the R 2 range of SOM prediction using image band and spectral index is from 0.18 to 0.56 and the RMSE ranges from 0.85% to 1.19%; (3) the highest accuracy of SOM prediction using synthetic images is lower than that of single-phase images; (4) the best time window of the bare soil period in the study area is May. This study emphasizes the importance of the time window to SOM prediction. In subsequent SOM prediction research, remote sensing images with appropriate time windows should be selected first, and then the model should be optimized.

Keywords: soil organic matter; remote sensing prediction; single-phase image; multi-temporal synthesis; black soil area; Sentinel-2 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/2071-1050/15/1/469/pdf (application/pdf)
https://www.mdpi.com/2071-1050/15/1/469/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jsusta:v:15:y:2022:i:1:p:469-:d:1017039

Access Statistics for this article

Sustainability is currently edited by Ms. Alexandra Wu

More articles in Sustainability from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().