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Temporal segmentation method for 30-meter long-term mapping of abandoned and reclaimed croplands in Inner Mongolia, China

文献类型： 外文期刊

作者： Wuyun, Deji 1 ; Sun, Liang 1 ; Chen, Zhongxin 2 ; Crusiol, Luis Guilherme Teixeira 3 ; Dong, Jinwei 4 ; Wu, Nitu 5 ; Bao, Junwei 6 ; Chen, Ruiqing 1 ; Sun, Zheng 1 ; Hasituya 5 ; Zhao, Hongwei 1 ;

作者机构： 1.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arable Land China, Beijing, Peoples R China

2.Food & Agr Org United Nations, Digitizat & Informat Div, Rome, Italy

3.Brazilian Agr Res Corp, Embrapa Soja Natl Soybean Res Ctr, Londrina, Brazil

4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Land Surface Pattern & Simulat, Beijing, Peoples R China

5.Inner Mongolia Agr Univ, Coll Grassland Resources & Environm, Hohhot, Peoples R China

6.Inner Mongolia Acad Agr & Anim Husb Sci, Inst Rural Econ & Informat, Hohhot, Peoples R China

关键词： Abandoned cropland; Agricultural natural zone; Google Earth Engine; Sample adaptive optimization; Agricultural sustainability

期刊名称：INTERNATIONAL JOURNAL OF APPLIED EARTH OBSERVATION AND GEOINFORMATION （ 影响因子：8.6； 五年影响因子：8.6 ）

ISSN： 1569-8432

年卷期： 2025 年 136 卷

页码：

收录情况： SCI

摘要： At the end of the last century, the expansion of agricultural land in the arid and semi-arid regions of northern China intensified the conflict between agricultural development and ecological protection. Accurately mapping abandoned cropland is crucial for balancing these competing interests. This research evaluates the effectiveness of an innovative remote sensing method for producing 30-meter-resolution long-term maps of abandoned and reclaimed croplands in Inner Mongolia, China, using a temporal segmentation approach developed with Google Earth Engine. The method integrates ground sample collection of major crops and inactive cropland with Normalized Difference Vegetation Index (NDVI) analysis during key growth stages, enabling precise classification of cultivation status. By employing a binary classification strategy and adaptive optimization, the efficiency of sample generation improved, providing more effective samples for the Random Forest algorithm. Cropland status maps were successfully generated for Inner Mongolia from 2000 to 2022 with annual accuracy between 97% and 99%. The Temporal Segmentation of Abandoned and Reclaimed Cropland (TSARC) method created time series maps of abandoned and reclaimed cropland at a 30-meter resolution, achieving an overall accuracy of 87.61%. The proposed remote sensing methodology reveals spatiotemporal trends in abandonment rates across arid and semi-humid regions, offering valuable insights for agricultural and environmental management in Inner Mongolia. Considering regional climatic, hydrological, and phenological conditions improves sample collection efficiency and cropland status monitoring. While designed for northern China, this method is also applicable to other single-season agricultural regions for varied agricultural land use monitoring.

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