Abstract
ABSTRACT
High-standard farmland construction is an important process that can enhance food security and accelerate new-style modernization agriculture. Hyperspectral remote sensing can provide data and technical support for this type of construction to provide a reference when optimizing high-standard farmland construction areas. This study was performed in Xinzheng City, the primary grain-producing areas in Henan Province. Field sampling and indoor hyperspectral spectroscopy (350~2500 nm) were combined; spectral transformations such as continuum removal (CR) were performed after Savitzky‒Golay (SG) convolution smoothing; and the best hyperspectral bands were selected as the common index of the soil properties by correlation analysis and fuzzy clustering maximum tree. A hyperspectral inversion model was built for the panel data model of the fixed effect variable coefficient based on the ordinary least squares estimation method (OLS), including panel data describing pH, organic matter, nitrogen, phosphorus, potassium, iron, chromium, cadmium, zinc, copper, and lead of 116 samples in Xinzheng City. Results show that the panel data model is of good quality overall, and the goodness of fit is higher (
R
2
= 0.9991, F = 2195.67). The precision test results indicate that the models performed well at both description and prediction, including accurate quantification, with an RPD above 2.5. Thus, the proposed model provides an important basis for soil information management, resource evaluation, and a reference when optimizing high-standard farmland construction processes.
Publisher
Revista Brasileira de Ciencia do Solo
Reference38 articles.
1. Estimation of heavy metals in tailings and soils using hyperspectral technology: A Case study in a tin-polymetallic mining area;Bian;Bull Environ Contam Toxicol,2021
2. On the construction potential of high standard basic farmland in Gongcheng Yao autonomous county;Cai;Sci Technol Manag Land Res,2019
3. Suitability evaluation of high standard farmland construction from perspective of ecological civilization construction;Chen;Soils,2019
4. Rapid estimation of leaf nitrogen content in apple-trees based on canopy hyperspectral reflectance using multivariate methods;Chen;Infrared Phys Techn,2020
5. Reflectance spectroscopy: Quantitative analysis technidues for remote sensing applications;Clark;Journal of Geophysical Research: Solid Earth,1984