Remote sensing inversion of cultivated land fertility at county scale based on SWCI-NDVI feature space

doi:10.13287/j.1001-9332.202101.016

Abstract

Abstract: It is objective needs during utilization and management of regional cultivated land resource to use remote sensing to accurately and efficiently retrieve the status of cultivated land fertility at county level and realize the gradation of cultivated land rapidly. In this study, with Dongping County as a case, using Landsat TM satellite imagery and cultivated land fertility evaluation data, the moisture vegetation fertility index (MVFI) was constructed based on surface water capacity index (SWCI) and normalized difference vegetation index (NDVI), and then the optimal inversion model was optimized to obtain the best inversion model, which was further applied and verified at the county scale. The results showed that the correlation coefficient between MVFI and integrated fertility index (IFI) was -0.753, which could comprehensively reflect the growth of winter wheat, soil moisture and land fertility, and had clear biophysical significance. The best inversion model was the quadratic model, with high inversion accuracy. This model was suitable for the inversion of cultivated land fertility in the county. The spatial distribution and uniformity of the inversion results were similar to the results of soil fertility evaluation. The area differences between the high, medium and low grades were all less than 2.9%. This study provided a remote sensing inversion method of cultivated land fertility based on the feature space theory, which could effectively improve the evaluation efficiency and prediction accuracy of cultivated land fertility at the county scale.

Key words: cultivated land fertility, remote sensing, inversion model, feature space, Lorenz curve, Gini coefficient

LI Yin-shuai, ZHAO Geng-xing, WANG Zhuo-ran, CUI Kun, XI Xue, DOU Jia-cong. Remote sensing inversion of cultivated land fertility at county scale based on SWCI-NDVI feature space[J]. Chinese Journal of Applied Ecology, 2021, 32(1): 252-260.

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