Mapping fallow fields using Sentinel-1 and Sentinel-2 archives over farming-pastoral ecotone of Northern China with Google Earth Engine
文献类型: 外文期刊
作者: Wuyun, Deji 1 ; Sun, Liang 1 ; Sun, Zheng 1 ; Chen, Zhongxin 3 ; Hou, Anhong 2 ; Teixeira Crusiol, Luis Guilherme 1 ; Reymondin, Louis 5 ; Chen, Ruiqing 1 ; Zhao, Hongwei 1 ;
作者机构: 1.Chinese Acad Agr Sci, Minist Agr & Rural Affairs Peoples Republ China, Caas Ciat Joint Lab Adv Technol Sustainable Agr I, Key Lab Agr Remote Sensing, Beijing, Peoples R China
2.Inner Mongolia Acad Agr & Anim Husb Sci, Inst Rural Econ & Informat, Hohhot, Peoples R China
3.Food & Agr Org United Nations, Digitalizat & Informat Div, Rome, Italy
4.Univ Estadual Maringa, Dept Agron, Maringa, Parana, Brazil
5.Agr Genet Inst, Hanoi Hub, Alliance Biodivers Int & Ciat Asia, Hanoi, Vietnam
关键词: Farming-pastoral ecotone in Northern China; cropland fallow; sentinel; multi-sensor; dual-polarization decomposition
期刊名称:GISCIENCE & REMOTE SENSING ( 影响因子:6.397; 五年影响因子:6.066 )
ISSN: 1548-1603
年卷期: 2022 年 59 卷 1 期
页码:
收录情况: SCI
摘要: The cropland in the farming-pastoral ecotone of Northern China is highly unstable owing to environmental restoration projects, poor soil fertility, poverty, and rural labor loss, and it is characterized by a large number of fallow fields. Mapping fallow fields in a farming-pastoral ecotone can help in evaluating the impact of complex cropland landscapes on the environment and food security. To map the fallow fields using the Sentinel-1 and Sentinel-2 archives, a multi-metric dataset of vertical transmit-vertical receive + vertical transmit-horizontal receive polarization was established. Moreover, spectral bands and vegetation index datasets were established using Google Earth Engine to classify cropped and fallow fields using the Random Forest classifier. The overall accuracies and Kappa coefficients of different datasets were assessed to examine the dataset with the highest overall accuracy in the main growing season. A 10 m resolution fallow map for 2020 was then generated based on the combined Sentinel-1 and Sentinel-2 datasets with the highest overall accuracy and Kappa coefficients were 95.82% and 0.92, respectively. In addition, the time-series characteristics of the entropy eigenvalues generated via dual-polarization decomposition were quantitatively evaluated to clarify the contribution of the Sentinel-1 synthetic-aperture radar archive to the fallow field mapping. The eigenvalues were more sensitive to the phenological characteristics of cropped and fallow fields than the original backscatter signal of the Sentinel-1 data. Moreover, the mapping method was tested at different time intervals by gradually aggregating the results across an increasing number of months to optimize the fallow field monitoring using the minimum number of observations possible within a short period. Data aggregated over August achieved the highest one-month accuracy; it was also very close to the observations from the whole growing season. The results further emphasize the influence of Sentinel-1 archives on fallow field mapping. Overall, this study clarifies the potential applicability of Sentinel archives for monitoring and mapping managing patterns of agricultural land in a region.
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