Estimation of the Total Soil Nitrogen Based on a Differential Evolution Algorithm from ZY1-02D Hyperspectral Satellite Imagery
Author:
Zhang Rongrong1, Cui Jian23, Zhou Wenge1, Zhang Dujuan4, Dai Wenhao1, Guo Hengliang4, Zhao Shan1
Affiliation:
1. School of Geoscience and Technology, Zhengzhou University, Zhengzhou 450001, China 2. Henan Institute of Geological Survey, Zhengzhou 450001, China 3. National Engineering Laboratory Geological Remote Sensing Center for Remote Sensing Satellite Application, Zhengzhou 450001, China 4. National Supercomputing Center in Zhengzhou, Zhengzhou University, Zhengzhou 450001, China
Abstract
Precise fertilizer application in agriculture requires accurate and dependable measurements of the soil total nitrogen (TN) concentration. Henan Province is one of the most important grain-producing areas in China. In order to promote the development of precision agriculture in Henan Province, this study took the high-standard basic farmland construction area in central Henan Province as the research area. Using single-phase images acquired from the ZY1-02D satellite hyperspectral sensor on 28 January 2021 (with a spatial resolution of 30 m × 30 m, a spectral range that covered 400–2500 nm, and a revisit period of 3 days) for spectral reflectance transformation and feature spectral band extraction. Based on multiple representation models, such as multiple linear regression, partial least squares regression, and support vector machine (SVM), all bands, feature bands, feature band combinations, and differential evolution (DE) algorithms were used to extract the secondary characteristic variables of the combination of characteristic bands, which were used as model inputs to estimate the content of TN in the study area. It was found that (1) the spectral reflectance transformation could help to improve the accuracy of prediction by reducing the interference from noise in the model, but the optimal spectral transformation method differed between different models and even between the training and test sets of the same model; (2) the estimation accuracy of the TN content model based on the minimum shrinkage and feature selection operator of the feature band was usually better than that of the full band, the feature combination band contained more effective information related to the TN content, and the combination of the DE algorithm and the SVM model achieved a better estimation accuracy for secondary feature extraction and TN content estimation of the feature combination band; and (3) ZY1-02D hyperspectral satellite data have the potential for the dynamic and non-destructive monitoring of regional TN content.
Funder
Major Science and Technology Special Projects in Henan Province Science and Technology Tackling Plan of Henan Province
Subject
Agronomy and Crop Science
Reference56 articles.
1. Li, H., Wang, J., Zhang, J., Liu, T., Acquah, G.E., and Yuan, H. (2022). Combining Variable Selection and Multiple Linear Regression for Soil Organic Matter and Total Nitrogen Estimation by DRIFT-MIR Spectroscopy. Agronomy, 12. 2. Nitrogen Use Efficiency for Sugarcane-Biofuel Production: What Is Next?;Otto;BioEnergy Res.,2016 3. Land Use Change and Nitrogen Enrichment of A Rocky Mountain Watershed;Kaushal;Ecol. Appl.,2006 4. Peng, Y., Wang, L., Zhao, L., Liu, Z., Lin, C., Hu, Y., and Liu, L. (2021). Estimation of Soil Nutrient Content Using Hyperspectral Data. Agriculture, 11. 5. Vis-SWIR spectral prediction model for soil organic matter with different grouping strategies;Bao;CATENA,2020
|
|