Multi-sensor and multi-platform retrieval of water chlorophyll a concentration in karst wetlands using transfer learning frameworks with ASD, UAV, and Planet CubeSate reflectance data

Bolin Fu; Sunzhe Li; Zhinan Lao; Bingyan Yuan; Yiyin Liang; Wen He; Weiwei Sun; Hongchang He

doi:10.1016/j.scitotenv.2023.165963

Multi-sensor and multi-platform retrieval of water chlorophyll a concentration in karst wetlands using transfer learning frameworks with ASD, UAV, and Planet CubeSate reflectance data

Sci Total Environ. 2023 Nov 25:901:165963. doi: 10.1016/j.scitotenv.2023.165963. Epub 2023 Aug 4.

Authors

Bolin Fu¹, Sunzhe Li², Zhinan Lao², Bingyan Yuan², Yiyin Liang², Wen He³, Weiwei Sun⁴, Hongchang He²

Affiliations

¹ College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China. Electronic address: fubolin@glut.edu.cn.
² College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China.
³ Guangxi Key Laboratory of Plant Conservation and Restoration Ecology in Karst Terrain, Guangxi Institute of Botany, Guangxi Zhuang Autonomous Region and Chinese Academy of Sciences, Guilin 541006, China.
⁴ Department of Geography and Spatial Information Techniques, Ningbo University, Ningbo 315211, China. Electronic address: sunweiwei@nbu.edu.cn.

PMID: 37543316
DOI: 10.1016/j.scitotenv.2023.165963

Abstract

China has one of the widest distributions of carbonate rocks in the world. Karst wetland is a special and important ecosystem of carbonate rock regions. Chlorophyll-a (Chla) concentration is a key indicator of eutrophication, and could quantitatively evaluate water quality status of karst wetland. However, the spectral reflectance characteristics of the water bodies of karst wetland are not yet clear, resulting in remote sensing retrieval of Chla with great challenges. This study is a pioneer in utilizing field-based full-spectrum hyperspectral data to reveal the spectral response characteristics of karst wetland water body and determine the sensitive spectral bands of Chla. We further evaluated the Chla retrieval performance of multi-platform spectral data between Analytical Spectral Device (ASD), Unmanned aerial vehicle (UAV), and PlanetScope (Planet). We proposed two multi-sensor weighted integration strategies and two transfer learning frameworks for estimating water Chla from the largest karst wetland in China by combing a partial least square with adaptive ensemble algorithms. The results showed that: (1) In the range of 500-850 nm, the spectral reflectance of water bodies in the karst wetland was overall 0.001-0.105 higher than the inland water bodies, and the sensitive spectral ranges of water Chla focus on 603-778 nm; (2) UAV images outperformed ASD and Planet data, and produced the highest inversion accuracy (R² = 0.670) for water Chla in karst wetland; (3) Multi-sensor weighted integration retrieval methods improved the Chla estimation accuracy (R² = 0.716). Integration retrieval methods with the different weights produced the better Chla estimation accuracy than that of methods with the equal weights; (4) The transfer learning methods from ASD to UAV platform provided the better retrieval performance (the average R² = 0.669) than that of methods from UAV to Planet platform. The transfer learning methods obtained the highest estimation accuracy of Chla (R² = 0.814) when the ratio of the training and test data in the target domain was 7:3. The transfer learning methods produced the higher estimation accuracies with the distribution of the absolute residuals between predicted and measured values <20.957 mg/m³ compared to the multi-sensor weighted integration retrieval methods, which demonstrated that transfer learning is more suitable for estimating Chla in karst wetland water bodies using multi-platform and multi-sensor data. The results provide a scientific basis for the protection and sustainable development of karst wetlands.

Keywords: Adaptive ensemble machine learning; Chlorophyll a estimation; Karst wetland; Spectral reflectance properties; Transfer learning; Weighted integration retrieval.