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. 2022 Aug 18;43(15-16):5636-5657.
doi: 10.1080/01431161.2021.2024912. Epub 2022 Mar 25.

Quantifying mangrove leaf area index from Sentinel-2 imagery using hybrid models and active learning

Nguyen An Binh  1 , Leon T Hauser  2 , Pham Viet Hoa  1 , Giang Thi Phuong Thao  1 , Nguyen Ngoc An  1 , Huynh Song Nhut  1 , Tran Anh Phuong  1 , Jochem Verrelst  3
Affiliations

Quantifying mangrove leaf area index from Sentinel-2 imagery using hybrid models and active learning

Nguyen An Binh et al. Int J Remote Sens. .

Abstract

Mangrove forests provide vital ecosystem services. The increasing threats to mangrove forest extent and fragmentation can be monitored from space. Accurate spatially explicit quantification of key vegetation characteristics of mangroves, such as leaf area index (LAI), would further advance our monitoring efforts to assess ecosystem health and functioning. Here, we investigated the potential of radiative transfer models (RTM), combined with active learning (AL), to estimate LAI from Sentinel-2 spectral reflectance in the mangrove-dominated region of Ngoc Hien, Vietnam. We validated the retrieval of LAI estimates against in-situ measurements based on hemispherical photography and compared against red-edge NDVI and the Sentinel Application Platform (SNAP) biophysical processor. Our results highlight the performance of physics-based machine learning using Gaussian processes regression (GPR) in combination with AL for the estimation of mangrove LAI. Our AL-driven hybrid GPR model substantially outperformed SNAP (R2 = 0.77 and 0.44 respectively) as well as the red-edge NDVI approach. Comparing two canopy RTMs, the highest accuracy was achieved by PROSAIL (RMSE = 0.13 m2.m-2, NRMSE = 9.57%, MAE = 0.1 m2.m-2). The successful retrieval of mangrove LAI from Sentinel-2 can overcome extensive reliance on scarce in-situ measurements for training seen in other approaches and present a more scalable applicability by relying on the universal principles of physics in combination with uncertainty estimates. AL-based GPR models using RTM simulations allow us to adapt the genericity of RTMs to the peculiarities of distinct ecosystems such as mangrove forests with limited ancillary data. These findings bode potential for retrieving a wider range of vegetation variables to quantify large-scale mangrove ecosystem dynamics in space and time.

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Conflict of interest statement

Disclosure statement No potential conflict of interest was reported by the author(s).

Figures

Figure 1
Figure 1. Workflow for mangrove leaf area index retrieval using hybrid models and active learning.
Figure 2
Figure 2. Location of study area.
Figure 3
Figure 3. Progress of active learning algorithm estimating LAI using EBD with PROSAIL (left) and INFORM (right) simulations for training.
Figure 4
Figure 4. Performance of measured vs estimated data
(a) PROSAIL(GRP), (b) INFORM(GPR), (c) PROSAIL (SNAP) and (d) red-edge NDVI.
Figure 5
Figure 5. Maps of LAI (a), Uncertainty (b) and Relative uncertainty (c) in Ngoc Hien district, Ca Mau
See this image and copyright information in PMC

References

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