Toward 30 m Fine-Resolution Land Surface Phenology Mapping at a Large Scale Using Spatiotemporal Fusion of MODIS and Landsat Data

Abstract

Satellite-retrieved land surface phenology (LSP) is a first-order control on terrestrial ecosystem productivity, which is critical for monitoring the ecological environment and human and social sustainable development. However, mapping large-scale LSP at a 30 m resolution remains challenging due to the lack of dense time series images with a fine resolution and the difficulty in processing large volumes of data. In this paper, we proposed a framework to extract fine-resolution LSP across the conterminous United States using the supercomputer Tianhe-2. The proposed framework comprised two steps: (1) generation of the dense two-band enhanced vegetation index (EVI2) time series with a fine resolution via the spatiotemporal fusion of MODIS and Landsat images using ESTARFM, and (2) extraction of the long-term and fine-resolution LSP using the fused EVI2 dataset. We obtained six methods (i.e., AT, FOD, SOD, RCR, TOD and CCR) of fine-resolution LSP with the proposed framework, and evaluated its performance at both the site and regional scales. Comparing with PhenoCam-observed phenology, the start of season (SOS) derived from the fusion data using six methods of AT, FOD, SOD, RCR, TOD and CCR obtained r values of 0.43, 0.44, 0.41, 0.29, 0.46 and 0.52, respectively, and RMSE values of 30.9, 28.9, 32.2, 37.9, 37.8 and 33.2, respectively. The satellite-retrieved end of season (EOS) using six methods of AT, FOD, SOD, RCR, TOD and CCR obtained r values of 0.68, 0.58, 0.68, 0.73, 0.65 and 0.56, respectively, and RMSE values of 51.1, 53.6, 50.5, 44.9, 51.8 and 54.6, respectively. Comparing with the MCD12Q2 phenology, the satellite-retrieved 30 m fine-resolution LSP of the proposed framework can obtain more information on the land surface, such as rivers, ridges and valleys, which is valuable for phenology-related studies. The proposed framework can yield robust fine-resolution LSP at a large-scale, and the results have great potential for application into studies addressing problems in the ecological environmental at a large scale.

Pansharpening and spatiotemporal image fusion method for remote sensing

“…The ESTARFM and EVI2 models accurately forecast the spatial distribution features of the red bands and NIR bands. The fusion of images using the proposed reference image selection rule yields similar results to the actual ones [189].…”

“…Large-scale multispectral and multitemporal observation data may be obtained using remote sensing, making it a useful technique for comprehensive monitoring of glaciers, forest fires caused by climate change and land surface phenology [187][188][189]. Satellite-retrieved land surface phenology (LSP) is vital for monitoring the ecological environment as well as human and societal sustainable development since it plays a key role in energy exchange, the Earth's water cycle, and the carbon balance [190,191].…”

Adaptability Evaluation of the Spatiotemporal Fusion Model of Sentinel-2 and MODIS Data in a Typical Area of the Three-River Headwater Region

“…The experimental results showed that the accuracy of direct vegetation index data fusion was higher, and in-depth studies such as surface vegetation cover can be conducted on this basis. The existing studies mostly applied the fusion model to Landsat and MODIS data [34], and the study of grassland cover change and toxic weed invasion in the TRHR also focused on medium-and low-resolution remote sensing data [35]. In contrast, Sentinel-2 had a higher spatial resolution compared to Landsat data, and the accuracy of monitoring the surface vegetation cover and change was also higher.…”