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Upscaling Remote Sensing Inversion Model of Wheat Field Cultivated Land Quality in the Huang-Huai-Hai Agricultural Region, China.
- Source :
- Remote Sensing; Dec2021, Vol. 13 Issue 24, p5095-N.PAG, 1p
- Publication Year :
- 2021
-
Abstract
- It is an objective demand for sustainable agricultural development to realize fast and accurate cultivated land quality assessment. In this paper, Tengzhou city (county-scale hilly area: scale A), Shanghe county (county-scale plain area: scale B), and Huang-Huai-Hai region (including large-scale hilly and plain area: scale C and D) were taken as research areas. Through the conversion of evaluation systems, the inversion models at the county-scale were constructed. Then, the image scale conversion was carried out based on the numerical regression method, and the upscaling inversion was realized. The results showed that: (1) the conversion models of evaluation systems (CMES) are Y = 1.021x − 4.989 (CMES<subscript>A−B</subscript>), Y = 0.801x + 16.925 (CMES<subscript>A−C</subscript>), and Y = 0.959x + 3.458 (CMES<subscript>C−D</subscript>); (2) the booting stage is the best inversion phase; (3) the back propagation neural network model based on the combination index group (CI-BPNN) is the best inversion model, with the R<superscript>2</superscript> are 0.723 (modeling set) and 0.722 (verification set). CI-BPNN and CI-BPNN-CMES<subscript>A−B</subscript> models are suitable for the hilly and plain areas at the county-scale, and the level area ratio difference is less than 4.87%. Furthermore, (4) the reflectance conversion model of short-wave infrared 2 is cubic, and the rest are quadratic. CI-BPNN-CMES<subscript>A−C</subscript> and CI-BPNN-CMES<subscript>A−C</subscript>-CMES<subscript>C−D</subscript> models realized upscaling inversion in the hilly and plain areas, with the maximum level area ratio difference being 1.60%. Additionally, (5) the wheat field quality has improved steadily since 2001 in the Huang-Huai-Hai region. This study proposes an upscaling inversion method of wheat field quality, which provides a scientific basis for cultivated land management and agricultural production in large areas. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 20724292
- Volume :
- 13
- Issue :
- 24
- Database :
- Complementary Index
- Journal :
- Remote Sensing
- Publication Type :
- Academic Journal
- Accession number :
- 154458379
- Full Text :
- https://doi.org/10.3390/rs13245095