Reconstructing daily 30 m NDVI over complex agricultural landscapes using a crop reference curve approach
Reconstructing daily 30 m NDVI over complex agricultural landscapes using a crop reference curve approach
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DOI：
10.1016/j.rse.2020.112156
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发表时间：
2020-11
影响因子：
13.5
通讯作者：
Liang Sun;F. Gao;D. Xie;Martha C. Anderson;Chen Ruiqing-;Yun Yang;Yang Yang-Yang;Zhongxin Chen
中科院分区：
文献类型：
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作者：
Liang Sun;F. Gao;D. Xie;Martha C. Anderson;Chen Ruiqing-;Yun Yang;Yang Yang-Yang;Zhongxin Chen
近年来，多传感器遥感数据融合技术得到了广泛的发展和应用，为提高高时空分辨率数据的可用性提供了一种经济可行的解决方案。然而，这些方法，一直具有挑战性的应用在高度异质性的地区，特别是在复杂的农业景观，有快速的变化在小尺度上，而功能在较大的尺度上变化较慢。在这项研究中，我们开发了一种新的方法来重建每日30米归一化植被指数（NDVI）使用图像中分辨率成像光谱仪（MODIS），陆地卫星和陆地卫星类平台，和农田数据层（CDL）。该方法利用作物参考曲线（CRC）的方法，其中一组的NDVI时间序列提取的纯MODIS像素（250米分辨率）使用CDL确定，然后用于适应Landsat的观测（30米）。CRC为基础的方法应用于一个复杂的农业景观在乔普坦河流域的海岸的马里兰州。2013年和2014年的Landsat数据以及2018年的协调Landsat和Sentinel-2（HLS）数据用于重建主要作物类型的30 m日NDVI地图。结果表明，在作物快速生长期，重建的NDVI相对误差（RE）约为6-8%，在生长缓慢的高峰期，3-5%。CRC方法的准确性优于标准的图像对为基础的数据融合算法（空间和时间自适应反射融合模型; STARFM），它产生的RE 4-9%，在缓慢增长时期和10-16%，在快速增长时期时，清晰的陆地卫星图像是稀缺的。CRC方法还比较了时间序列数据融合方法，包括谐波拟合模型和卫星数据积分（STAIR）模型。结果表明，当Landsat类图像可用性高（每年约27幅图像）时，CRC给出了类似的结果，但当可用性有限（每年少于15幅图像）时，CRC优于其他方法。重建的NDVI时间序列玉米，大豆，冬小麦/大豆和森林在30米的分辨率在子字段尺度上表现出清晰的物候模式。由此产生的30米的NDVI时间序列数据提供了有用的信息，绘制作物物候和监测作物条件在复杂的农业景观，特别是复杂的双熟地区。然而，一个准确的30米作物分类地图的输入要求限制其应用程序的地区和时期的分类是可用的。
Multi-sensor remote sensing data fusion technologies have been developed and widely applied in recent years, providing a feasible and economical solution to increase the availability of high spatial and temporal resolution data. These methods, however, have been challenging to apply in highly heterogeneous areas, especially in complex agricultural landscapes where there are rapid changes at small scales, while features at larger scales change more slowly. In this study, we developed a novel method to reconstruct daily 30 m Normalized Difference Vegetation Index (NDVI) using imagery from the Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat and Landsat-like platforms, and the Cropland Data Layer (CDL). This method utilizes a crop reference curve (CRC) approach, in which a set of NDVI time series are extracted from pure MODIS pixels (250 m resolution) identified using the CDL, and then used to fit Landsat-like observations (30 m). The CRC based method was applied over a complex agricultural landscape in the Choptank River watershed on the Eastern Shore of Maryland. Landsat data from 2013 and 2014 and Harmonized Landsat and Sentinel-2 (HLS) data from 2018 were used to reconstruct 30 m daily NDVI maps for major crop types. Results show that the relative error (RE) in reconstructed NDVI is around 6–8% during periods of rapid crop growth, and 3–5% during peak periods when growth is slow. The accuracy of the CRC method outperforms a standard image pair-based data fusion algorithm (Spatial and Temporal Adaptive Reflectance Fusion Model; STARFM), which yields RE of 4–9% in slow-growth periods and 10–16% in fast-growth periods when clear Landsat images are scarce. The CRC method was also compared with time-series data fusion methods, including a harmonic fitting model and the SaTellite dAta IntegRation (STAIR) model. The results show that CRC gives similar results when the Landsat-like image availability is high (around 27 images per year), but outperforms other methods when availability is limited (less than 15 images per year). The reconstructed NDVI time series for corn, soybean, winter wheat/soybean and forest at 30-m resolution show clear phenological patterns at the sub-field scale. The resulting 30-m NDVI timeseries data provide useful information for mapping crop phenology and monitoring crop condition in complex agricultural landscapes, especially for complex double-cropping areas. However, the input requirement of an accurate 30-m crop classification map constrains its application to areas and periods where classifications are available.