Chlorophyll-a (Chl-a) is an important characterized parameter of lakes. Monitoring it accurately through remote sensing is thus of great significance for early warnings of water eutrophication. Sentinel Multispectral Imager (MSI) images from May to September between 2020 and 2021 were used along with in-situ measurements to estimate Chl-a in Lake Chagan, which is located in Jilin Province, Northeast China. In this study, the extreme gradient boosting (XGBoost) and Random Forest (RF) models, which had similar performances, were generated by six single bands and six band combinations. The RF model was then selected based on the assessments (R² = 0.79, RMSE = 2.51 μg L⁻¹, MAPE = 9.86%), since its learning of the input features in the model conformed to the bio-optical properties of Case 2 waters. The study considered Chl-a concentrations in Lake Chagan as a seasonal pattern according to the K-Nearest-Neighbors (KNN) classification. The RF model also showed relatively stable performance for three seasons (spring, summer and autumn) and it was applied to map Chl-a in the whole lake. The research presents a more reliable machine learning (ML) model with higher precision than previous empirical models, as shown by the effects of the input features linked with the biological mechanisms of Chl-a. Its robustness was revealed by the temporal and spatial distributions of Chl-a concentrations, which were consistent with in-situ measurements in the map. This research was capable of revealing the current ecological situation in Lake Chagan and can serve as a reference in remote sensing of inland lakes.

Lake ecosystem eutrophication is a crucial water quality issue that can be efficiently monitored with remote sensing. GF-6 WFV with a high spatial and temporal resolution provides a comprehensive record of the dynamic changes in water quality parameters in a lake. In this study, based on GF-6 WFV images and the field sampling data of Xingkai Lake from 2020 to 2021, the accuracy of three machine learning models (RF: random forest; SVR: support vector regression; and BPNN: back propagation neural network) was compared by considering 11 combinations of surface reflectance in different wavebands as input variables for machine learning. We mapped the spatiotemporal variations of Chl-a concentrations in Xingkai Lake from 20192021 and integrated machine learning algorithms to demonstrate that RF obtained a better degree of derived-fitting (Calibration: N = 82, RMSE = 0.82 μg/L, MAE = 0.57 μg/L, slope = 0.94, and R² = 0.98; Validation: N = 40, RMSE = 2.12 μg/L, MAE = 1.58 μg/L, slope = 0.91, R² = 0.89, and RPD = 2.98). The interannual variation from 2019 to 2021 showed that the Chl-a concentration in Xingkai Lake was low from June to July, while maximum values were observed from October to November, thus showing significant seasonal differences. Spatial distribution showed that Chl-a concentrations were higher in Xiao Xingkai Lake than in Da Xingkai Lake. Nutrient inputs (N, P) and other environmental factors such as high temperature could have an impact on the spatial and temporal distribution characteristics of Chl-a, therefore, combining GF-6 WFV satellite images with RF could realize large-scale monitoring and be more effective. Our results showed that remote-sensing-based machine learning algorithms provided an effective method to monitor lake eutrophication as well as technical support and methodological reference for inland lake water quality parameter inversion.

Remote sensing technology has the potential to enhance the lake’s large-scale and long-term dynamic monitoring capabilities significantly. High-quality in-situ datasets are essential for improving the accuracy and reliability of remote sensing retrieval of lake ecosystems. This dataset provides satellite-ground synchronized in-situ data on water multi-parameters for typical lakes in China spanning the period between 2020 and 2023. It includes quality-checked water optical parameters (remote sensing reflectance (Rrs), chlorophyll-a (Chl-a), total suspended matter (TSM) and Secchi disk depth (SDD)), and water surface temperature (WST) data. It encompasses 586 sampling points across 18 lakes. The dataset exhibits two significant highlights: Firstly, synchronous observations from multiple satellites are coordinated during the data collection effectively supporting the retrieval and validation of water remote sensing products. Secondly, it encompasses diverse data types, collecting synchronous measurements of Rrs and various parameters. This dataset will continuously update, substantially enhancing regional and global lake monitoring capabilities through satellite remote sensing data.

To obtain more abundant target information for vegetation remote sensing research, the wide field of view (WFV) of the GaoFen-6 (GF-6) satellite utilizes eight bands, including four new bands. Unfortunately, it finds the unavailable reference bands from well-calibrated sensors to implement radiometric cross-calibration of the WFV sensor for these new bands, which severely affects the radiometric quality estimation and the radiometric degradation detection of WFV. Therefore, taking the Dunhuang radiometric calibration site (DRCS) in China as the radiometric transfer platform and treating the moderate-resolution imaging spectroradiometer as a reference sensor, a novel radiometric cross-calibration method for WFV is proposed. The validation results show that compared with the official calibration coefficients (OCCs) published by the China centre for resources satellite data and application (CRESDA), the proposed method can obtain reliable radiometric cross-calibration results for each band even without available corresponding reference bands. Moreover, the influences of the interpolation method and the spectral band adjustment factor (SBAF) correction on the calibration results are discussed. The total radiometric cross-calibration uncertainty is less than 4.33%.

Remote sensing (RS)-derived vegetation indices (VIs) with medium and high spatial resolution have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring at local or global scales. Before they can be used as reliable inputs for other research, conducting in situ measurements for validation is very critical. However, the spatial heterogeneity due to the diversity of land cover and its spatial organization in the landscape increases the uncertainty of validation, so design of optimal sampling is an important basis for the reliability of the validation. In this paper, we propose an integrative stratified sampling strategy (INTEG-STRAT) based on normalized difference vegetation index (NDVI) data as prior knowledge. The basic idea is to realize a sampling optimization by determining the optimal combination of the spatial sampling method (e.g., simple random sampling (SRS), spatial system sampling (SYS), stratified sampling, generalized random tessellation stratified (GRTS), balanced acceptance sampling (BAS)) and spatial stratification scheme with an objective rule. The objective rule in this paper is to minimize the root mean square error (RMSE) of 10-fold cross validation between estimated values (sample are not included) and the corresponding values on prior knowledge. Relative precision, correlation coefficient, and RMSE are used to compare the effectiveness of the proposed sampling strategy with each sampling method without considering sampling optimization. After comparing, we find that the INTEG-STRAT requires fewer samples to become stable and has higher accuracy. At site 1, when the correlation coefficient between NDVI image and the simulated NDVI surface reached 80%, INTEG-STRAT needed only 70 sampling points while other methods require more sampling points. At the same time, INTEG-STRAT strategy has a smaller RMSE between the estimated values and the corresponding values on prior knowledge image. In general, INTEG-STRAT is an effective method in the selection of representative samples to support the validation of vegetation indices products with medium and high spatial resolution.

The monitoring of particulate organic carbon (POC) flux at the bottom of the euphotic layer in global ocean using remote sensing satellite data plays an important role in clarifying and evaluating the ocean carbon cycle. Based on the in situ POC flux data, this paper evaluated various estimation models. The global ocean POC flux from 2003 to 2018 was calculated using the optimal model, and its temporal and spatial variation characteristics were analyzed. In general, the annual average of global ocean POC flux is about 8.5–14.3 ${\mathrm{Gt}\mathrm{C}\mathrm{yr}}^{-1}$ for period of 2003–2018. In the spatial dimension, the POC flux in the mid-latitude ocean (30–60°) is higher than that in the low-latitude (0–30°). The POC flux in Continental Margins with water depth less than 2000 m accounted for 30% of global ocean, which should receive more attention in global carbon cycle research. In the time dimension, the global POC flux decreases year by year generally, but the POC flux abnormally decreases during El Niño and increases during La Niña. In addition, due to global warming, sea ice melting, and bipolar sea area expansion, POC flux in high-latitude oceans (60–90°) is increasing year by year.