Synergetic use of DEM derivatives, Sentinel-1 and Sentinel-2 data for mapping soil properties of a sloped cropland based on a two-step ensemble learning method

Zhenwang Li; Feng Liu; Xiuyuan Peng; Bangguo Hu; Xiaodong Song

doi:10.1016/j.scitotenv.2023.161421

Synergetic use of DEM derivatives, Sentinel-1 and Sentinel-2 data for mapping soil properties of a sloped cropland based on a two-step ensemble learning method

Sci Total Environ. 2023 Mar 25:866:161421. doi: 10.1016/j.scitotenv.2023.161421. Epub 2023 Jan 5.

Authors

Zhenwang Li¹, Feng Liu², Xiuyuan Peng³, Bangguo Hu⁴, Xiaodong Song⁵

Affiliations

¹ Jiangsu Key Laboratory of Crop Genetics and Physiology/Jiangsu Key Laboratory of Crop Cultivation and Physiology, Agricultural College, Yangzhou University, Yangzhou 225009, China.
² State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China.
³ Information Research Institute, Liaoning Academy of Agricultural Sciences, Shenyang 110161, China.
⁴ Beijing Deep Blue Space Remote Sensing Technology Co., Ltd, Beijing 100101, China.
⁵ State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, Nanjing 210008, China. Electronic address: xdsong@issas.ac.cn.

PMID: 36621491
DOI: 10.1016/j.scitotenv.2023.161421

Abstract

Understanding the spatial variability of soil organic matter (SOM), soil total nitrogen (STN), soil total phosphorus (STP), and soil total potassium (STK) is important to support site-specific agronomic management, food production, and climate change adaptation. High-resolution remote sensing imageries have emerged as an innovative solution to investigate the spatial variation in agricultural soils with machine learning (ML) algorithms. However, the predictive power of the individual and combined effects of Sentinel-1 (S1) synthetic aperture radar (SAR) and Sentinel-2 (S2) multispectral images for mapping soil properties, especially STN, STP, and STK, have rarely been investigated. Moreover, single ML model may achieve unstable performance for predicting multiple soil properties due to strong spatial heterogeneity. This study explored the combine use of S1, S2, and DEM derivatives to map SOM, STN, STP, and STK content of a sloped cropland of northeastern China. A two-step method with a weighted sum of four ML models was proposed to improve the accuracy and robustness in predicting multiple soil properties. Our results showed that single ML model has various performance in predicting the four soil properties. The optimal ML models could explain approximately 56 %, 53 %, 56 % and 37 % of the variability of SOM, STN, STP, and STK, respectively. Using the weights estimated through a 10-fold cross-validation procedure, the two-step ensemble learning model was retrained and showed more robust performance than the four ML models, in which the prediction accuracy was improved by 2.38 %, 1.40 %, 3.52 %, and 3.29 % for SOM, STN, STP, and STK, respectively. Our results also showed that the optical S2 derived features, especially the two S2 short-wave infrared bands, enhanced vegetation index, and soil adjusted vegetation index, were more important for soil property prediction than S1 data and DEM derivatives. Compared with individual sensor, a combination of S1 and S2 data yielded more accurate predictions of STN and STP but not for SOM and STK. The results of this study highlight the potential of high-resolution S1 and S2 data and the two-step method for soil property prediction at farmland scale.

Keywords: Digital soil mapping; Machine learning model; Optical Sentinel-2; Sentinel-1 SAR; Two-step ensemble learning.