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

Author(s):Show less -

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

• Document type: Journal article
• Source: REMOTE SENSING OF ENVIRONMENT; v. 286, p. 16-pg., 2023-01-06.

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. We developed an index -guided, ecologically constrained autoencoder (IG-ECAE) method to automatically derive a deciduousness metric (percentage of upper 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 charac-teristics, 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 against 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 (90 m x 90 m) with r2 = 0.89 across all sites; and 2) with that derived from phenocams to quantify ecosystem-scale sea-sonality with r2 ranging from 0.62 to 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. (AU)

• FAPESP's process: 14/00215-0 - Remote phenology and leaf exchange patterns towards a sazonality gradient
• Grantee: Bruna de Costa Alberton
• Support Opportunities: Scholarships in Brazil - Doctorate
• FAPESP's process: 13/50155-0 - Combining new technologies to monitor phenology from leaves to ecosystems
• Grantee: Leonor Patricia Cerdeira Morellato
• Support Opportunities: Research Program on Global Climate Change - University-Industry Cooperative Research (PITE)
• FAPESP's process: 19/16191-6 - Coupled phenocam - satellite surveillance of changing seasonally dry environments in the Southern Hemisphere
• Grantee: Leonor Patricia Cerdeira Morellato
• Support Opportunities: Regular Research Grants
• FAPESP's process: 16/01413-5 - Using leaf phenology patterns to investigate net ecosystem exchange controls in tropical vegetations
• Grantee: Bruna de Costa Alberton
• Support Opportunities: Scholarships abroad - Research Internship - Doctorate
• FAPESP's process: 19/11835-2 - Phenology of deciduous dry forests: a comparison at multiple spatial scales using phenocams and drones
• Grantee: Leonor Patricia Cerdeira Morellato
• Support Opportunities: Regular Research Grants