High resolution crop intensity mapping using harmonized Landsat-8 and Sentinel-2 data
High resolution crop intensity mapping using harmonized Landsat-8 and Sentinel-2 data
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DOI：
10.1016/s2095-3119(19)62599-2
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发表时间：
2019-12-01
影响因子：
4.8
通讯作者：
Wu Ming-quan
中科院分区：
文献类型：
--
作者：
Hao Peng-yu;Tang Hua-jun;Wu Ming-quan
增加种植密度可以提高作物产量，但也可能导致一系列环境问题，如地下水枯竭和土壤盐分增加。生成高分辨率（30米）作物密度图是用于监测这些变化的一个重要方法，但这是具有挑战性的，因为30米图像时间序列的时间分辨率低，由于卫星重访期长，云层覆盖率高。最近发射的哨兵2号卫星可提供10-60米分辨率的光学图像，从而提高30米图像时间序列的时间分辨率。这项研究使用统一的陆地卫星哨兵-2（HLS）数据来确定作物密度。采用六次多项式拟合归一化植被指数（NDVI）和增强植被指数（EVI）曲线。然后，15天的NDVI和EVI时间序列，然后从拟合曲线生成，并用于生成耕地的范围。最后，拟合VI曲线的一阶导数用于计算VI峰;使用人工定义的阈值去除假峰，并通过计数剩余VI峰的数量生成作物强度。在四个研究区域进行了测试，结果表明，从拟合曲线生成的15天时间序列可以准确地识别耕地面积。农田识别的总体准确率高于95%。此外，协调后的NDVI和EVI时间序列准确地确定了作物种植强度，非耕地，单作物周期和双作物周期的总体精度，生产者的精度和用户的精度均高于85%。NDVI优于EVI，更准确地识别双作物周期领域。
An increase in crop intensity could improve crop yield but may also lead to a series of environmental problems, such as depletion of ground water and increased soil salinity. The generation of high resolution (30 m) crop intensity maps is an important method used to monitor these changes, but this is challenging because the temporal resolution of the 30-m image time series is low due to the long satellite revisit period and high cloud coverage. The recently launched Sentinel-2 satellite could provide optical images at 10-60 m resolution and thus improve the temporal resolution of the 30-m image time series. This study used harmonized Landsat Sentinel-2 (HLS) data to identify crop intensity. The sixth polynomial function was used to fit the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) curves. Then, 15-day NDVI and EVI time series were then generated from the fitted curves and used to generate the extent of croplands. Lastly, the first derivative of the fitted VI curves were used to calculate the VI peaks; spurious peaks were removed using artificially defined thresholds and crop intensity was generated by counting the number of remaining VI peaks. The proposed methods were tested in four study regions, with results showing that 15-day time series generated from the fitted curves could accurately identify cropland extent. Overall accuracy of cropland identification was higher than 95%. In addition, both the harmonized NDVI and EVI time series identified crop intensity accurately as the overall accuracies, producer's accuracies and user's accuracies of non-cropland, single crop cycle and double crop cycle were higher than 85%. NDVI outperformed EVI as identifying double crop cycle fields more accurately.