Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon)
Harmonization of Landsat and Sentinel 2 for Crop Monitoring in Drought Prone Areas: Case Studies of Ninh Thuan (Vietnam) and Bekaa (Lebanon)
复制标题
DOI：
10.3390/rs12020281
复制
发表时间：
2020-01-01
期刊：
影响因子：
5
通讯作者：
Ribbe, Lars
中科院分区：
文献类型：
--
作者：
Nguyen, Minh D.;Baez-Villanueva, Oscar M.;Ribbe, Lars
在农场一级适当的卫星作物监测应用往往需要接近每日的中高空间分辨率图像。正在进行的不同卫星任务哨兵2号（欧空局）和大地卫星7/8号（美国航天局）的数据相结合，在全球范围内提供了这一前所未有的机会;然而，这很少得到执行，因为这些程序需要大量数据和计算。这项研究开发了一个强大的流处理，用于协调谷歌地球引擎云平台中的Landsat 7，Landsat 8和Sentinel 2，将Google Cloud中一致的数据结构，内置功能和计算能力的优势联系起来。协调的表面反射率图像是为2018-2019年期间贝卡（黎巴嫩）和宁顺（越南）的两个农业计划生成的。我们评估了协调所需的几个预处理步骤的性能，包括图像配准、双向反射分布函数校正、地形校正和波段调整。我们发现，Landsat 8和Sentinel 2图像之间的配准误差从宁顺（越南）的10 m到贝卡（黎巴嫩）的32 m不等，如果不进行处理，将对最终协调数据集的质量产生很大影响。对贝卡地区一对重叠的L 8-S2图像的分析表明，在协调后，所有波段之间的空间相关性都得到了极大的改善。最后，我们展示了密集协调数据集在作物制图和监测中的应用。谐波（傅立叶）分析，以适应检测到的单峰，双峰和三峰形状的时间NDVI模式在宁顺省在一个作物年。推导出的相位和振幅值的作物周期相结合的最大NDVI作为R-G-B假合成图像。最终图像能够以明亮的颜色（高相位和振幅）突出显示农田，而非作物区域以灰色/深色（低相位和振幅）显示。统一数据集（空间分辨率为30米）沿着使用的谷歌地球引擎脚本供公众使用。
Proper satellite-based crop monitoring applications at the farm-level often require near-daily imagery at medium to high spatial resolution. The combination of data from different ongoing satellite missions Sentinel 2 (ESA) and Landsat 7/8 (NASA) provides this unprecedented opportunity at a global scale; however, this is rarely implemented because these procedures are data demanding and computationally intensive. This study developed a robust stream processing for the harmonization of Landsat 7, Landsat 8 and Sentinel 2 in the Google Earth Engine cloud platform, connecting the benefit of coherent data structure, built-in functions and computational power in the Google Cloud. The harmonized surface reflectance images were generated for two agricultural schemes in Bekaa (Lebanon) and Ninh Thuan (Vietnam) during 2018-2019. We evaluated the performance of several pre-processing steps needed for the harmonization including the image co-registration, Bidirectional Reflectance Distribution Functions correction, topographic correction, and band adjustment. We found that the misregistration between Landsat 8 and Sentinel 2 images varied from 10 m in Ninh Thuan (Vietnam) to 32 m in Bekaa (Lebanon), and posed a great impact on the quality of the final harmonized data set if not treated. Analysis of a pair of overlapped L8-S2 images over the Bekaa region showed that, after the harmonization, all band-to-band spatial correlations were greatly improved. Finally, we demonstrated an application of the dense harmonized data set for crop mapping and monitoring. An harmonic (Fourier) analysis was applied to fit the detected unimodal, bimodal and trimodal shapes in the temporal NDVI patterns during one crop year in Ninh Thuan province. The derived phase and amplitude values of the crop cycles were combined with max-NDVI as an R-G-B false composite image. The final image was able to highlight croplands in bright colors (high phase and amplitude), while the non-crop areas were shown with grey/dark (low phase and amplitude). The harmonized data sets (with 30 m spatial resolution) along with the Google Earth Engine scripts used are provided for public use.