Mingjian Zhai

Accurate retrieval of water quality parameters in nearshore lake environments using medium- and high-resolution remote sensing imagery is often constrained by substantial radiative interference from high-reflectance terrestrial features at the water–land boundary. To investigate the mechanisms underlying this adjacency effect, this study employed an inverse Monte Carlo (MC) radiative transfer model to quantitatively simulate the adjacency radiance under the combined influence of atmospheric conditions, observation geometry, and surface properties. Key contributing factors were identified through sensitivity analysis. In addition, a lake morphology index was introduced to characterize the spatial superposition effect of radiative diffusion caused by shoreline structure. Consistent with previous studies, the simulations confirmed the dominant contribution of terrestrial radiance to adjacency effects in nearshore waters and further quantified how this effect varies with environmental factors and lake morphological features. The intensity and spatial pattern of adjacency radiance are jointly influenced by multiple environmental factors and lake morphological features. Based on these findings, a parameterized adjacency effect correction model was developed by coupling multi-factor influences with morphological constraints, enabling effective correction of nearshore remote sensing reflectance. Validation using representative lakes demonstrated that the model significantly reduces retrieval errors in nearshore reflectance and decreases the computational cost of pixel-level MC simulation by approximately 90%. The proposed method is well-suited for correcting adjacency effects in medium- and high-resolution imagery of lakes with complex shorelines, providing high-quality remote sensing reflectance for the accurate inversion of nearshore water quality parameters.

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.

The vegetation cover of forests and grasslands in mountain regions plays a crucial role in regulating climate at both regional and global scales. Thus, it is necessary to develop accurate methods for estimating and monitoring fractional vegetation cover (FVC) in mountain areas. However, the complex topographic and climate factors pose significant challenges to accurately estimating the FVC of mountain forests and grassland. Existing remote sensing products, FVC retrieval methods, and FVC samples may fail to meet the required accuracy standards. In this study, we propose a method based on spatio-temporal transfer learning for the retrieval of FVC in mountain forests and grasslands, using the mountain region of Huzhu County, Qinghai Province, as the study area. The method combines simulated FVC samples, Sentinel-2 images, and mountain topographic factor data to pre-train LSTM and 1DCNN models and subsequently transfer the models to HJ-2A/B remote sensing images. The results of the study indicated the following: (1) The FVC samples generated by the proposed method (R² = 0.7536, RMSE = 0.0596) are more accurate than those generated by the dichotomy method (R² = 0.4997, RMSE = 0.1060) based on validation with ground truth data. (2) The LSTM model performed better than the 1DCNN model: the average R² of the two models was 0.9275 and 0.8955; the average RMSE was 0.0653 and 0.0735. (3) Topographic features have a significant impact on FVC retrieval results, particularly in relatively high-altitude mountain regions (DEM > 3000 m) or non-growing seasons (May and October). Therefore, the proposed method has better potential in FVC fine spatio-temporal retrieval of high-resolution mountainous remote sensing images.

The spatial representativeness of the in-situ data is an important prerequisite for ensuring the reliability and accuracy of remote sensing product retrieval and verification. Limited by the collection cost and time window, it is essential to simultaneously collect multiple water parameter data in water tests. In the shipboard measurements, sampling design faces problems, such as heterogeneity of water quality multi-parameter spatial distribution and variability of sampling plan under multiple constraints. Aiming at these problems, a water multi-parameter sampling design method is proposed. This method constructs a regional multi-parameter weighted space based on the single-parameter sampling design and performs adaptive weighted fusion according to the spatial variation trend of each water parameter within it to obtain multi-parameter optimal sampling points. The in-situ datasets of three water parameters (chlorophyll a, total suspended matter, and Secchi-disk Depth) were used to test the spatial representativeness of the sampling method. The results showed that the sampling method could give the sampling points an excellent spatial representation in each water parameter. This method can provide a fast and efficient sampling design for in-situ data for water parameters, thereby reducing the uncertainty of inversion and the validation of water remote sensing products.

Validation is a crucial technique used to strengthen the application capabilities of earthobservation satellite data and solve the quality problems of remote-sensing products. Observing land-surface parameters in the field is one of the key steps of validation. Therefore, the demand for long-term stable validation stations has gradually increased. However, the current location-selection procedure of validation stations lacks a systematic and objective evaluation system. In this research, a data-driven selection of a land product validation station (DSS-LPV) based on Machine Learning is proposed. Firstly, we construct an evaluation indicator system in which all factors affecting the location of validation stations are divided into surface characteristics, atmospheric conditions and the social environment. Then, multi-scale evaluation grids are constructed and indicators are allocated for spatial evaluation. Finally, four Machine Learning (ML) methods are used to learn the established reliable stations, and different data-driven scoring models are constructed to explore the intrinsic relationship between evaluation indicators and station locations. In this article, the reliability of DSS-LPV is effectively validated by the example of China using the national-level land product validation station that has been established. After a comparison between the four ML models, the random forest (RF) with the highest accuracy was selected as the modeling method of DSS-LPV. The correlation between the regression value of test stations and the target value is 0.9133. The average score of test stations is 0.8304. The test stations are generally located within the calculated hot-spot area of the score density map, which means that it is highly consistent with the location of the built stations. Research results indicate that DSS-LPV is an effective method that can provide a reasonable geographical distribution of the stations. The location-selection results can provide scientific decision-making support for the construction of land product validation stations.