An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites
Abstract
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 frommore »
- Authors:
-
- 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)
- Publication Date:
- Research Org.:
- Brookhaven National Laboratory (BNL), Upton, NY (United States)
- Sponsoring Org.:
- 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
- OSTI Identifier:
- 1913816
- Report Number(s):
- BNL-223881-2023-JAAM
Journal ID: ISSN 0034-4257
- 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
- Resource Type:
- Accepted Manuscript
- Journal Name:
- Remote Sensing of Environment
- Additional Journal Information:
- Journal Volume: 286; Journal ID: ISSN 0034-4257
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
- Subject:
- 54 ENVIRONMENTAL SCIENCES; tropical forests; carbon cycles; environmental gradient; deciduousness; multi-scale remote sensing; machine learning
Citation Formats
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, and Wu, Jin. An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites. United States: N. p., 2023.
Web. doi:10.1016/j.rse.2022.113429.
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. An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites. United States. https://doi.org/10.1016/j.rse.2022.113429
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, and Wu, Jin. Fri .
"An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites". United States. https://doi.org/10.1016/j.rse.2022.113429. https://www.osti.gov/servlets/purl/1913816.
@article{osti_1913816,
title = {An ecologically-constrained deep learning model for tropical leaf phenology monitoring using PlanetScope satellites},
author = {Wang, Jing and Song, Guangqin and Liddell, Michael and Morellato, Patricia and Lee, Calvin K. F. and Yang, Dedi and Alberton, Bruna and Detto, Matteo and Ma, Xuanlong and Zhao, Yingyi and Yeung, Henry C. H. and Zhang, Hongsheng and Ng, Michael and Nelson, Bruce W. and Huete, Alfredo and Wu, Jin},
abstractNote = {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.},
doi = {10.1016/j.rse.2022.113429},
journal = {Remote Sensing of Environment},
number = ,
volume = 286,
place = {United States},
year = {Fri Jan 06 00:00:00 EST 2023},
month = {Fri Jan 06 00:00:00 EST 2023}
}
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- Remote Sensing of Environment, Vol. 255
Spatial patterns and temporal dynamics in savanna vegetation phenology across the North Australian Tropical Transect
journal, December 2013
- Ma, Xuanlong; Huete, Alfredo; Yu, Qiang
- Remote Sensing of Environment, Vol. 139
Combining spectral and spatial information to map canopy damage from selective logging and forest fires
journal, October 2005
- Souza, Carlos M.; Roberts, Dar A.; Cochrane, Mark A.
- Remote Sensing of Environment, Vol. 98, Issue 2-3
Mapping Crop Phenology in Near Real-Time Using Satellite Remote Sensing: Challenges and Opportunities
journal, March 2021
- Gao, Feng; Zhang, Xiaoyang
- Journal of Remote Sensing, Vol. 2021
Mechanistic evidence for tracking the seasonality of photosynthesis with solar-induced fluorescence
journal, May 2019
- Magney, Troy S.; Bowling, David R.; Logan, Barry A.
- Proceedings of the National Academy of Sciences
Quantifying Leaf Phenology of Individual Trees and Species in a Tropical Forest Using Unmanned Aerial Vehicle (UAV) Images
journal, June 2019
- Park, John Y.; Muller-Landau, Helene C.; Lichstein, Jeremy W.
- Remote Sensing, Vol. 11, Issue 13
Canopy near-infrared reflectance and terrestrial photosynthesis
journal, March 2017
- Badgley, Grayson; Field, Christopher B.; Berry, Joseph A.
- Science Advances, Vol. 3, Issue 3
Remote-Sensing-Based Water Balance for Monitoring of Evapotranspiration and Water Stress of a Mediterranean Oak–Grass Savanna
journal, May 2020
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- Water, Vol. 12, Issue 5
Overview of the radiometric and biophysical performance of the MODIS vegetation indices
journal, November 2002
- Huete, A.; Didan, K.; Miura, T.
- Remote Sensing of Environment, Vol. 83, Issue 1-2
Endmember variability in Spectral Mixture Analysis: A review
journal, July 2011
- Somers, Ben; Asner, Gregory P.; Tits, Laurent
- Remote Sensing of Environment, Vol. 115, Issue 7