Spatiotemporal fusion method to simultaneously generate full-length normalized difference vegetation index time series (SSFIT)
Spatiotemporal fusion method to simultaneously generate full-length normalized difference vegetation index time series (SSFIT)
复制标题
DOI：
10.1016/j.jag.2021.102333
复制
发表时间：
2021-08
期刊：
影响因子：
--
通讯作者：
Yuean Qiu;Junxiong Zhou;Jin Chen;Xuehong Chen
中科院分区：
文献类型：
--
作者：
Yuean Qiu;Junxiong Zhou;Jin Chen;Xuehong Chen
植被动态监测需要高时空分辨率的归一化植被指数(NDVI)时间序列图像，近年来人们提出了多种时空数据融合方法来满足这一要求。然而，严格的数据要求和不适当的建模策略往往会限制它们的性能，特别是在可用输入数据较差的情况下。在这项研究中，我们提出了一种时空融合的方法来同时生成具有高空间分辨率和频繁覆盖的全长归一化差异植被指数时间序列(SSFIT)。它的两个显著特点是：(1)不需要无云的高空间分辨率图像；(2)同时生成多个预测日期的高空间分辨率图像。通过在两个特征区域模拟输入时间序列数据的理想和挑战条件进行了对比实验，并与4种典型的方法：时空自适应反射比融合模型(StarFM)、柔性时空数据融合(FSDAF)、FIT-FC(回归模型拟合、空间滤波和残差补偿)以及改进的FSDAF进行了比较。结果表明，在理想输入条件下(两个试验区的平均均方根误差分别为0.1037和0.0713，平均相关系数分别为0.9180和0.7875，计算时间分别为S的109%和120%)，SSFIT具有更好的整体预测精度和效率，并且在挑战条件下对可用输入数据的减少具有更强的鲁棒性。因此，预计SSFIT将扩展到各种遥感产品，并支持用于监测陆地表面动态的应用。
High spatiotemporal resolution normalized difference vegetation index (NDVI) time-series imagery is required for monitoring vegetation dynamics with dense observations and spatial details, and in recent years, many spatiotemporal data fusion methods have been proposed to fulfill this need. However, strict data requirements and inappropriate modeling strategies often limit their performance, particularly under poor conditions of available input data. In this study, we proposed a Spatiotemporal fusion method to Simultaneously generate Full-length normalized difference vegetation Index Time series (SSFIT) with a high spatial resolution and frequent coverage. The utilization of temporal information across sensors contributes to its two distinct features: (1) no cloud-free high-spatial-resolution image is required and (2) high-spatial-resolution images on multiple prediction dates are generated at the same time. Comparison experiments were conducted by simulating ideal and challenging conditions of input time-series data in two characteristic areas, and the proposed methods were also compared with four typical methods: The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM), Flexible Spatiotemporal DAta Fusion (FSDAF), Fit-FC (regression model Fitting, spatial Filtering and residual Compensation), and Improved FSDAF. The results demonstrate that SSFIT yields a better overall prediction accuracy and efficiency under ideal input conditions (average root mean square error of 0.1037 and 0.0713, average correlation coefficient of 0.9180 and 0.7875, and computation time of 109 and 120 s, computed in the two test areas) and is more robust against the decrease in available input data under challenging conditions. SSFIT is thus expected to be extended to various remote sensing products and support applications for monitoring land surface dynamics.