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

Yongjian Ruan, Baozhen Ruan, Xinchang Zhang (), Zurui Ao, Qinchuan Xin (), Ying Sun and Fengrui Jing
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Yongjian Ruan: School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Baozhen Ruan: School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Xinchang Zhang: School of Geography and Remote Sensing, Guangzhou University, Guangzhou 510006, China
Zurui Ao: Beidou Research Institute, Faculty of Engineering, South China Normal University, Foshan 528000, China
Qinchuan Xin: School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
Ying Sun: School of Geography and Planning, Sun Yat-Sen University, Guangzhou 510275, China
Fengrui Jing: Geoinformation and Big Data Research Lab, Department of Geography, University of South Carolina, Columbia, SC 29208, USA

Sustainability, 2023, vol. 15, issue 4, 1-19

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.

Keywords: land surface phenology (LSP); 30 m fine-resolution; Landsat-MOD09Q1; PhenoCam; ESTARFM; supercomputer Tianhe-2 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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