Mapping Soil Available Nitrogen Using Crop-Specific Growth Information and Remote Sensing

Xinle Zhang, Yihan Ma, Shinai Ma, Chuan Qin, Yiang Wang (), Huanjun Liu, Lu Chen and Xiaomeng Zhu
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Xinle Zhang: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Yihan Ma: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Shinai Ma: State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Chuan Qin: College of Information Technology, Jilin Agricultural University, Changchun 130118, China
Yiang Wang: State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Huanjun Liu: State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Lu Chen: State Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun 130102, China
Xiaomeng Zhu: College of Information Technology, Jilin Agricultural University, Changchun 130118, China

Agriculture, 2025, vol. 15, issue 14, 1-22

Abstract: Soil available nitrogen (AN) is a critical nutrient for plant absorption and utilization. Accurately mapping its spatial distribution is essential for improving crop yields and advancing precision agriculture. In this study, 188 AN soil samples (0–20 cm) were collected at Heshan Farm, Nenjiang County, Heihe City, Heilongjiang Province, in 2023. The soil available nitrogen content ranged from 65.81 to 387.10 mg kg −1 , with a mean value of 213.85 ± 61.16 mg kg −1 . Sentinel-2 images and normalized vegetation index (NDVI) and enhanced vegetation index (EVI) time series data were acquired on the Google Earth Engine (GEE) platform in the study area during the bare soil period (April, May, and October) and the growth period (June–September). These remote sensing variables were combined with soil sample data, crop type information, and crop growth period data as predictive factors and input into a Random Forest (RF) model optimized using the Optuna hyperparameter tuning algorithm. The accuracy of different strategies was evaluated using 5-fold cross-validation. The research results indicate that (1) the introduction of growth information at different growth periods of soybean and maize has different effects on the accuracy of soil AN mapping. In soybean plantations, the introduction of EVI data during the pod setting period increased the mapping accuracy R 2 by 0.024–0.088 compared to other growth periods. In maize plantations, the introduction of EVI data during the grouting period increased R 2 by 0.004–0.033 compared to other growth periods, which is closely related to the nitrogen absorption intensity and spectral response characteristics during the reproductive growth period of crops. (2) Combining the crop types and their optimal period growth information could improve the mapping accuracy, compared with only using the bare soil period image (R 2 = 0.597)—the R 2 increased by 0.035, the root mean square error (RMSE) decreased by 0.504%, and the mapping accuracy of R 2 could be up to 0.632. (3) The mapping accuracy of the bare soil period image differed significantly among different months, with a higher mapping accuracy for the spring data than the fall, the R 2 value improved by 0.106 and 0.100 compared with that of the fall, and the month of April was the optimal window period of the bare soil period in the present study area. The study shows that when mapping the soil AN content in arable land, different crop types, data collection time, and crop growth differences should be considered comprehensively, and the combination of specific crop types and their optimal period growth information has a greater potential to improve the accuracy of mapping soil AN content. This method not only opens up a new technological path to improve the accuracy of remote sensing mapping of soil attributes but also lays a solid foundation for the research and development of precision agriculture and sustainability.

Keywords: soil available nitrogen; crop types; soil nutrient mapping; precision agriculture; Sentinel-2 (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: 2025
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