Fu Bolin, Li Sunzhe, Lao Zhinan, Yuan Bingyan, Liang Yiyin, He Wen, Sun Weiwei, He Hongchang
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China.
College of Geomatics and Geoinformation, Guilin University of Technology, Guilin 541006, China.
Sci Total Environ. 2023 Nov 25;901:165963. doi: 10.1016/j.scitotenv.2023.165963. Epub 2023 Aug 4.
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 (R = 0.670) for water Chla in karst wetland; (3) Multi-sensor weighted integration retrieval methods improved the Chla estimation accuracy (R = 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 R = 0.669) than that of methods from UAV to Planet platform. The transfer learning methods obtained the highest estimation accuracy of Chla (R = 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/m 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.
中国是世界上碳酸盐岩分布最广的国家之一。喀斯特湿地是碳酸盐岩地区特殊且重要的生态系统。叶绿素a（Chla）浓度是水体富营养化的关键指标，能够定量评估喀斯特湿地的水质状况。然而，喀斯特湿地水体的光谱反射特征尚不清楚，这给Chla的遥感反演带来了巨大挑战。本研究率先利用基于野外的全光谱高光谱数据揭示喀斯特湿地水体的光谱响应特征，并确定Chla的敏感光谱波段。我们还进一步评估了分析光谱仪（ASD）、无人机（UAV）和行星Scope（Planet）等多平台光谱数据对Chla的反演性能。通过将偏最小二乘法与自适应集成算法相结合，我们针对中国最大的喀斯特湿地提出了两种多传感器加权集成策略和两种迁移学习框架来估算水体Chla。结果表明：（1）在500 - 850nm范围内，喀斯特湿地水体的光谱反射率总体比内陆水体高0.001 - 0.105，水体Chla的敏感光谱范围集中在603 - 778nm；（2）无人机影像在喀斯特湿地水体Chla反演精度上优于ASD和Planet数据，反演精度最高（R = 0.670）；（3）多传感器加权集成反演方法提高了Chla估算精度（R = 0.716）。不同权重的集成反演方法比等权重方法具有更好的Chla估算精度；（4）从ASD到无人机平台的迁移学习方法比从无人机到Planet平台的方法具有更好的反演性能（平均R = 0.669）。当目标域中训练数据与测试数据的比例为7:3时，迁移学习方法获得了最高的Chla估算精度（R = 0.814）。与多传感器加权集成反演方法相比，迁移学习方法在预测值与测量值之间的绝对残差分布<20.957mg/m时具有更高的估算精度，这表明迁移学习更适合利用多平台和多传感器数据估算喀斯特湿地水体中的Chla。研究结果为喀斯特湿地的保护和可持续发展提供了科学依据。
