Mapping Cropland Soil Nutrients Contents Based on Multi-Spectral Remote Sensing and Machine Learning

Wenjie Zhang, Liang Zhu (), Qifeng Zhuang (), Dong Chen and Tao Sun
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Wenjie Zhang: College of Geomatics Science and Technology, Nanjing Tech University, Nanjing 211816, China
Liang Zhu: State Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, China
Qifeng Zhuang: College of Geomatics Science and Technology, Nanjing Tech University, Nanjing 211816, China
Dong Chen: College of Geomatics Science and Technology, Nanjing Tech University, Nanjing 211816, China
Tao Sun: College of Geomatics Science and Technology, Nanjing Tech University, Nanjing 211816, China

Agriculture, 2023, vol. 13, issue 8, 1-19

Abstract: Nitrogen (N) and phosphorus (P) are primary indicators of soil nutrients in agriculture. Accurate management of these nutrients is essential for ensuring food security. High-resolution, multi-spectral remote sensing images can provide crucial information for mapping soil nutrients at the field scale. This study compares the capabilities of ZH-1 and Sentinel-2 satellite data, along with different spectral indices, in mapping soil nutrients (total N and Olsen-P) using two machine learning algorithms, random forest (RF) and XGBoost (XGB). Two agricultural fields in Suihua City were selected as the study areas for this investigation. The results showed that Sentinel-2 data performed best in computing the total N content in soil using the RF model ( R 2 = 0.74, RMSE = 0.10 g/kg). However, for the soil Olsen-P content, the XGBoost model performed better with ZH-1 data ( R 2 = 0.75, RMSE = 9.79 mg/kg) than the RF model. This study demonstrates that both ZH-1 and Sentinel-2 satellite data perform well in terms of accurately mapping soil total N and Olsen-P contents using machine learning. Due to its higher spectral and spatial resolution, ZH-1 remote sensing data provides more detailed information on soil nutrient content during Olsen-P inversion and exhibits comparable accuracy.

Keywords: Sentinel-2; ZH-1; random forest; extreme gradient boosting; soil fertility; digital mapping (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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