Developing a unified chlorophyll-a (Chla) inversion algorithm for cross-water types is a significant challenge owing to the insufficiency of input features and training samples. Although machine learning algorithms can build a consistent model for different trophic waters, the accuracy of the inversion is dependent on the quality of the extended features. Here, we designed a novel hybrid framework called CHLNET, which combines...

The high-precision estimation of mangrove leaf area index (LAI) using a deep learning regression algorithm (DLR) always requires a large amount of training sample data. However, it is difficult for LAI field measurements to collect a sufficient amount of sample data in mangrove wetlands. To tackle this challenge, this paper proposed an approach for expanding training samples and quantitatively evaluated the performance of esti...