Construction of a high-precision cultivated land identification model based on machine learning-using Zhangye City， Gansu Province as an example

doi:10.11686/cyxb2024110

Abstract

Abstract:

Cultivated land is a vital foundation resource for agricultural production and ensuring food security. Accurate identification of cultivated land is of great significance for the conservation of cultivable land resources and the sustainable development of agricultural production. In order to construct a high-precision cultivated land identification model， this study used Sentinel-1/2 data together with the spatial cloud computing platform and built combinations of different feature types. Through feature importance analysis， cultivated land identification features were then evaluated to identify the optimal feature set. Random Forest （RF）， support vector machine （SVM）， and classification and regression tree （CART） models were employed to identify the cultivated land in Zhangye City， Gansu Province for the year 2021. Simultaneously， the classification accuracy of each classifier was compared and analyzed. The results show that using a combination of vegetation index features， radar features， and topographic features improved the classification accuracy to 91.32%； Features that performed well in cultivated land identification in the study area included elevation， radar polarization channel VH， and normalized difference water index （NDWI）. In the cultivated land identification of Zhangye City， RF algorithm demonstrates clear advantages， with an overall accuracy of 90.04% and a Kappa coefficient of 0.79. Based on the RF model， the cultivated land area associated with Zhangye City is estimated to be 585000 ha， accounting for 15.4% of the total area. The methodology developed in this study achieves accurate identification of cultivated land in Zhangye City and offers a tool for cultivated land mapping in the region.

Key words: identification of cultivated land, machine learning, random forest, Sentinel

Jing-jing MAI, Qi-sheng FENG, Rui-jing WANG, Sen-yao FENG, Zhe-ren JIN, Zhong-xue ZHANG, Tian-gang LIANG, Jia-ming JIN. Construction of a high-precision cultivated land identification model based on machine learning-using Zhangye City， Gansu Province as an example[J]. Acta Prataculturae Sinica, 2025, 34(2): 149-162.

Figures/Tables 9

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编号Number	特征类型组合Feature type combinations
1	光谱Spectrum
2	光谱+雷达Spectrum+synthetic aperture radar （SAR）
3	光谱+植被指数Spectrum+vegetation index
4	植被指数+雷达Vegetation index+SAR
5	光谱+植被指数+雷达Spectrum+vegetation index+SAR
6	植被指数+雷达+土壤Vegetation index+SAR+soil
7	植被指数+雷达+地形Vegetation index+SAR+topography
8	植被指数+雷达+地形+土壤Vegetation index+SAR+topography+soil
9	全部特征类型组合All feature types

编号Number	特征类型组合Feature type combinations
1	光谱Spectrum
2	光谱+雷达Spectrum+synthetic aperture radar （SAR）
3	光谱+植被指数Spectrum+vegetation index
4	植被指数+雷达Vegetation index+SAR
5	光谱+植被指数+雷达Spectrum+vegetation index+SAR
6	植被指数+雷达+土壤Vegetation index+SAR+soil
7	植被指数+雷达+地形Vegetation index+SAR+topography
8	植被指数+雷达+地形+土壤Vegetation index+SAR+topography+soil
9	全部特征类型组合All feature types

识别特征Feature	特征重要性Feature importance
海拔Elevation	8771.81
VH，雷达特征中的VH极化Synthetic aperture radar features VH polarization	8210.86
归一化水指数Normalized difference water index， NDWI	8128.18
简单比值Sample ratio， SR	7979.18
VV，雷达特征中的VV极化Synthetic aperture radar features VV polarization	7978.85
归一化物候指数Normalized difference phenology index， NDPI	7947.32
土壤调整植被指数Soil adjusted vegetation index， SAVI	7896.25

识别特征Feature	特征重要性Feature importance
海拔Elevation	8771.81
VH，雷达特征中的VH极化Synthetic aperture radar features VH polarization	8210.86
归一化水指数Normalized difference water index， NDWI	8128.18
简单比值Sample ratio， SR	7979.18
VV，雷达特征中的VV极化Synthetic aperture radar features VV polarization	7978.85
归一化物候指数Normalized difference phenology index， NDPI	7947.32
土壤调整植被指数Soil adjusted vegetation index， SAVI	7896.25

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