An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites

Wang, Jing; Song, Guangqin; Liddell, Michael; Morellato, Patricia; Lee, Calvin K. F.; Yang, Dedi; Alberton, Bruna; Detto, Matteo; Ma, Xuanlong; Zhao, Yingyi; Yeung, Henry C. H.; Zhang, Hongsheng; Ng, Michael; Nelson, Bruce W.; Huete, Alfredo; Wu, Jin

doi:10.1016/j.rse.2022.113429

Title: An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites

Journal Article · Fri Jan 06 00:00:00 EST 2023 · Remote Sensing of Environment

DOI:https://doi.org/10.1016/j.rse.2022.113429· OSTI ID:1913816

Wang, Jing ^[1]; Song, Guangqin ^[2]; Liddell, Michael ^[3]; Morellato, Patricia ^[4]; Lee, Calvin K. F. ^[2];

^[5]; Alberton, Bruna ^[6]; Detto, Matteo ^[7]; Ma, Xuanlong ^[8]; Zhao, Yingyi ^[2]; Yeung, Henry C. H. ^[2]; Zhang, Hongsheng ^[2]; Ng, Michael ^[2]; Nelson, Bruce W. ^[9]; Huete, Alfredo ^[10]; Wu, Jin ^[11]

Sun Yat-Sen Univ., Shenzhen, Guangzhou (China); University of Hong Kong (China)
University of Hong Kong (China)
James Cook University, Cairns, QLD (Australia)
São Paulo State University (UNESP), Rio Claro, SP (Brazil)
Brookhaven National Lab. (BNL), Upton, NY (United States)
São Paulo State University (UNESP), Rio Claro, SP (Brazil); Instituto Tecnologico Vale, Belem, PA (Brazil)
Princeton Univ., NJ (United States)
Lanzhou Univ. (China); International Research Center of Big Data for Sustainable Development Goals, Beijing (China); East China Normal Univ. (ECNU), Shanghai (China)
National Institute for Amazon Research (INPA), Manaus (Brazil)
Univ. of Technology Sydney, NSW (Australia)
Univ. of Hong Kong, Pokfulam, Hong Kong (China)

In tropical forests, leaf phenology signals leaf-on/off status and exhibits considerable variability across scales from a single tree-crown to the entire forest ecosystem. Such phenology signals importantly regulate large-scale biogeochemical cycles and regional climate. PlanetScope CubeSats data with a 3-m resolution and near-daily global coverage provide an unprecedented opportunity to monitor both fine- and ecosystem-scale phenology variability along large environmental gradients. However, a scalable method that accurately characterizes leaf phenology from PlanetScope with biophysically meaningful metrics remains lacking. Here we developed an index-guided, ecologically constrained autoencoder (IG-ECAE) method to automatically derive a deciduousness metric (i.e. percentage of tree canopies with leaf-off status within an image pixel) from PlanetScope. The IG-ECAE first estimated the reflectance spectra of leafy/leafless canopies based on their spectral indices characteristics, then used the derived reflectance spectra to guide an autoencoder deep learning method with additional ecological constraints to refine the reflectance spectra, and finally used linear spectral unmixing to estimate the relative abundance of leafless canopies (or deciduousness) per PlanetScope image pixel. We tested the IG-ECAE method at 16 tropical forest sites spanning multiple continents and a large precipitation gradient (1470-2819 mm year^-1). Among these sites, we evaluated the PlanetScope-derived deciduousness with corresponding measures derived from WorldView-2 (n=9 sites) and local phenocams (n=9 sites). Our results show that PlanetScope-derived deciduousness agrees: 1) with that derived from WorldView-2 at the patch level (90m×90m) with r²=0.89 across all sites; and 2) with that derived from phenocams to quantify ecosystem-scale seasonality with r² ranging from 0.62-0.96. These results demonstrate the effectiveness and scalability of IG-ECAE in characterizing the wide variability in deciduousness across scales from pixels to forest ecosystems, and from a single date to the full annual cycle, indicating the potential for using high-resolution satellites to track the large-scale phenological patterns and response of tropical forests to climate change.

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Research Organization:: Brookhaven National Laboratory (BNL), Upton, NY (United States)

Sponsoring Organization:: USDOE Office of Science (SC), Biological and Environmental Research (BER); National Natural Science Foundation of China (NSFC); Hong Kong Research Grant Council; São Paulo Research Foundation (FAPESP); Shenzhen Science and Technology Program; Natural Science Foundation of Gansu Province, China; International Research Center of Big Data for Sustainable Development Goals

Grant/Contract Number:: SC0012704; 31922090; 17316622; 2013/50155-0; 2019/11835-2; 2019/16191-6; 202011159154; 42171305; 21JR7RA499; CBAS2022DF006; 12300519; 17201020; 17300021; C1013-21GF; C7004-21GF; NSFC-RGC N-HKU76921

OSTI ID:: 1913816

Report Number(s):: BNL-223881-2023-JAAM

Journal Information:: Remote Sensing of Environment, Vol. 286; ISSN 0034-4257

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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