<!-- *** Custom HTML *** --> Mangroves not only protect the coastline from ocean waves and storms but also provide economical, ecological, and social functions for the human being. Despite the important role mangrove plays, mangrove has suffered great loss over the past decades. Mapping the mangrove extent is essential for its conservation. We examined the potential of original bands, spectral indices, including combined mangrove recognition index (CMRI) and normalized difference vegetation index (NDVI), and principal component analysis (PCA) of Sentinel-2 to map the mangrove of Dongzhaigang, China in June 2019. Random forest (RF) approach was adopted for the image classification. Results showed that the overall accuracy and kappa coefficient of the image classification can achieve 90.47% and 0.86. The area of mangrove of Dongzhaigang, China in June 2019 is 17.83 km². The importance of each variable was calculated and among all the 15 variables, the CMRI ranked first, followed by NDVI, band 3 (green), band 2 (blue), and PCA1 (the first principal component) in turn. Based on the results, we can conclude that vegetation indices and PCA are essential for mangrove extent mapping since the variable importance of these features are relatively higher in the ranking of mean decrease Gini. Also band 3 (green band) provides important information for distinguishing mangrove from land and water class. Furthermore, combining vegetation indices, PCA, and RF model can accurately delineate the extent of mangrove.

The intensification of anthropogenic activities has led to the infiltration of enormous quantities of pollutants into rivers and lakes, resulting in significant deterioration in water quality and a more prominent occurrence of eutrophication. Poyang Lake, the largest freshwater lake in China, is facing a severe challenge related to eutrophication, which seriously threatens the delivery of the ecosystem service and the safety of drinking water. To address this challenge, Landsat-8 Operational Land Imager (OLI) data for the Poyang Lake Basin (PLB) from May 2013 to December 2020 were used. Since inland water bodies with complex optical characteristics, we developed a semi-analytical algorithm to assess the trophic state of the water based on two cruise field measurements in 2016 and 2019. Combining the semi-analytical trophic level index (TLI) with an atmospheric correction model is the most suitable model for OLI images of the PLB, this model was then applied to Landsat-8 time series observations. The trends of the trophic state of water bodies in PLB were revealed, and the annual, quarterly and monthly percentages of eutrophic water bodies were calculated. Natural and anthropogenic factors were then used to explain the changes in the trophic state of the PLB waters. The main findings are as follows: (1) From the 8-year observation results, it can be seen that the variation of trophic level of water in PLB showed obviously spatial and temporal variations, characterized by higher in the north than in the south and higher in winter than in summer. (2) Temperature promoted the growth of harmful algae and plays an essential role in affecting changes in the trophic level of the water. (3) Changes in the trophic level of water bodies in PLB were mainly affected by human activities. The results of spatial and temporal variation of the trophic level of water and the driving factors in PLB can extend our knowledge of water quality degradation and provide essential references for relevant policy-making institutions.

Characterization of regrowing forests is vital for understanding forest dynamics to assess the impacts on carbon stocks and to support sustainable forest management. Although remote sensing is a key tool for understanding and monitoring forest dynamics, the use of exclusively remotely sensed data to explore the effects of different variables on regrowing forests across all biomes in Brazil has rarely been investigated. Here, we analyzed how environmental and human factors affect regrowing forests. Based on Brazil's secondary forest age map, 3060 locations disturbed between 1984 and 2018 were sampled, interpreted and analyzed in different biomes. We interpreted the time since disturbance for the sampled pixels in Google Earth Engine. Elevation, slope, climatic water deficit (CWD), the total Nitrogen of soil, cation exchange capacity (CEC) of soil, surrounding tree cover, distance to roads, distance to settlements and fire frequency were analyzed in their importance for predicting aboveground biomass (AGB) and tree cover derived from global forest aboveground biomass map and tree cover map, respectively. Results show that time since disturbance interpreted from satellite time series is the most important predictor for characterizing AGB and tree cover of regrowing forests. AGB increased with increasing time since disturbance, surrounding tree cover, soil total N, slope, distance to roads, distance to settlements and decreased with larger fire frequency, CWD and CEC of soil. Tree cover increased with larger time since disturbance, soil total N, surrounding tree cover, distance to roads, distance to settlements, slope and decreased with increasing elevation and CWD. These results emphasize the importance of remotely sensing products as key opportunities to improve the characterization of forest regrowth and to reduce data gaps and uncertainties related to forest carbon sink estimation. Our results provide a better understanding of regional forest dynamics, toward developing and assessing effective forest‐related restoration and climatic mitigation strategies.

Deteriorating water environments in plateau lakes are increasingly influenced by climate change and human activities. Water transparency, critical for understanding underwater lightfield environments, is commonly quantified as the Secchi disk depth (Z_SD, m). Despite advances in Z_SD semi-analytical model, their application in turbid plateau lakes faces challenges due to differences in quasi-analytical algorithm (QAA) and image limitations. To address these challenges, this study introduced a novel hybrid QAA model (QAA_hybrid) specifically designed to estimate Z_SD using Orbita Hyperspectral (OHS) images, a new hyperspectral image in China. The algorithm's uncertainty and image quality were evaluated and compared using the error propagation theory and noise equivalent Z_SD (NE_ZSD). Several main findings can be drawn: (1) The QAA_hybrid, categorized as moderately turbid and extremely turbid waters using a remote sensing reflectance ratio, outperformed other QAA models; (2) the new Z_SD model produced a mean absolute percentage difference (MAPD) of 10.89%, demonstrating better accuracy compared to the existing Z_SD models, and had a MAPD of 23.35% when applied to OHS images; (3) documented Z_SD from OHS images showed that Dianchi Lake had a trend of increasing from the lake center towards the shore, while Erhai Lake had a trend of decreasing from north to south. These findings emphasize the feasibility of the new ZSD semi-analytical model and OHS data in water quality monitoring, providing a reliable approach for water environmental management.

Due to the severe speckle noise of a fully polarimetric synthetic aperture radar image and the complex backscattering mechanism at the junction of different land covers, some of the pixels are easily mislabeled, especially on the edge of the land covers. To address this issue, this study presents a novel scheme that selects polarimetric features step by step to participate in classification through different classification mechanisms. Different from previous classification methods where all land covers are described by the same polarimetric features, we make a fine selection of polarimetric features for each land cover with the help of information entropy and contextual information. Among many polarimetric features, elements of the covariance matrix and the coherence matrix are selected to optimize the original classification results. The experimental results show that the proposed method can achieve good classification results, especially in the edge area of land covers. Compared to traditional classification methods, the proposed method is robust and is able to improve the overall accuracy by more than 5.58% and the kappa coefficient by more than 0.0613.