An LSWI-Based Method for Mapping Irrigated Areas in China Using Moderate-Resolution Satellite Data
An LSWI-Based Method for Mapping Irrigated Areas in China Using Moderate-Resolution Satellite Data
复制标题
基于 LSWI 的中分辨率卫星数据绘制中国灌溉区地图的方法
DOI：
10.3390/rs12244181
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发表时间：
2020
期刊：
影响因子：
5
通讯作者：
Deng Yujiao
中科院分区：
文献类型：
--
作者：
Xiang Kunlun;Yuan Wenping;Wang Liwen;Deng Yujiao
准确的灌溉空间信息对于水资源管理、地表与大气之间的水交换、气候变化、水文循环、粮食安全和农业规划等各种应用至关重要。提出了一种利用统计数据、年平均降水量、中分辨率成像光谱仪（MODIS）土地覆盖类型数据和地表反射率数据提取农田灌溉信息的新方法。该方法的基础上比较的农田像元的地表水指数（LSWI）与相邻的森林像元具有相似的归一化差异植被指数（NDVI）。在我们的研究中，我们用612个参考样本（231个灌溉样本和381个非灌溉样本）在中国大陆验证了该方法，发现准确率为62.09%。统计数据验证表明，该方法对省、地两级灌溉面积空间变异的解释率分别为86.67%和58.87%。我们进一步将我们的新地图与FAO/UF，ICOF，Zhu和统计数据的现有数据集进行比较，发现与Zhu数据集的灌溉面积分布很好地吻合。结果表明，该方法是灌区制图和监测灌区年际变化的有效方法。由于该方法不依赖于训练样本，因此可以很容易地重复到其他区域。
Accurate spatial information about irrigation is crucial to a variety of applications, such as water resources management, water exchange between the land surface and atmosphere, climate change, hydrological cycle, food security, and agricultural planning. Our study proposes a new method for extracting cropland irrigation information using statistical data, mean annual precipitation and Moderate Resolution Imaging Spectroradiometer (MODIS) land cover type data and surface reflectance data. The approach is based on comparing the land surface water index (LSWI) of cropland pixels to that of adjacent forest pixels with similar normalized difference vegetation index (NDVI). In our study, we validated the approach over mainland China with 612 reference samples (231 irrigated and 381 non-irrigated) and found the accuracy of 62.09%. Validation with statistical data also showed that our method explained 86.67 and 58.87% of the spatial variation in irrigated area at the provincial and prefecture levels, respectively. We further compared our new map to existing datasets of FAO/UF, IWMI, Zhu and statistical data, and found a good agreement with the irrigated area distribution from Zhu’s dataset. Results show that our method is an effective method apply to mapping irrigated regions and monitoring their yearly changes. Because the method does not depend on training samples, it can be easily repeated to other regions.