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

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.

Automated summary

Nitrogen and phosphorus are crucial indicators of soil nutrients in agriculture, and their accurate management is essential for ensuring food security. This study compared the capabilities of ZH-1 and Sentinel-2 satellite data in mapping soil nutrients using machine learning algorithms, and found that both datasets performed well. The results showed that Sentinel-2 data performed best in computing total nitrogen content, while the XGBoost model with ZH-1 data performed better for soil Olsen-P content.