The Pearl River Delta in South China is subject to severe eutrophication, which is significantly exacerbated by the total nitrogen (TN). Remote sensing technology with large-scale synchronous observations in the Pearl River Delta can effectively monitor organic pollution. Statistical methods based on remote sensing images have been widely used in water quality parameter retrieval for inland rivers, reservoirs, and lakes, but have seldom been applied in the Pearl River Delta. TN is also a non-optically active substance, so it is difficult to retrieve TN through analysis methods. This study retrieves the concentration of total nitrogen (TN) based on Landsat8 images of the Pearl River Delta using a statistical method. The stepwise regression function is built by analyzing the TN concentration and the single-band, two-band, and three-band spectral information groups measured by an ASD FieldSpec3 spectrometer. The retrieval results show that the proposed method performs well with a small mean absolute error (MAE) (0.36 mg/L for TN) and high agreement (R² = 0.61 for TN) between the in situ data and the retrieval concentration. The results demonstrate that the concentration of TN in the east of the Pearl River Delta was higher than in the west. Dachan Bay and Shenzhen Bay had the highest TN concentrations, which were around 3.02 mg/L and 3.67 mg/L. The 750–850 nm band could be an important reference for further exploring the spectral characteristics and retrieval of TN. The retrieval method in this study is easy to implement and convenient for local TN distribution capture, which can provide a timely reference for daily water quality supervision and management in the Pearl River Delta.

The aerosol optical depth (AOD), retrieved by satellites, has been widely used to estimate ground-level PM_2.5 mass concentrations, due to its advantage of large-scale spatial continuity. However, it is difficult to obtain urban-scale pollution patterns from the coarse resolution retrieval results (e.g., 1 km, 3 km, or 10 km) at present, and little research has been conducted on PM_2.5 mass concentration retrieval from high resolution remote sensing data. In this study, a physical model is proposed based on Mie scattering theory to evaluate the PM_2.5 mass concentrations by using Landsat8 Operational Land Imager (OLI) images. First, the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) model (which can simulate the transmission process of solar radiation in the Earth-atmosphere system and calculate the radiance at the top of the atmosphere) is used to build a lookup table to retrieve the AOD of the coast and blue bands based on the improved deep blue (DB) method. Then, the Angstrom formula is used to obtain the AOD of the green and red bands. Second, the dry near-surface AOD of four bands (coast, blue, green, red) is obtained through vertical correction and humidity correction. Third, aerosol particles are divided into four types based on the standard radiation atmosphere (SRA) model, and the optical properties of different aerosol types are analyzed to derive the volume distribution of aerosol particles. Finally, the relationship between the dry near-surface AOD of each band and the volume distribution of four aerosol particles is correlated, based on Mie scattering theory, and a physical model is established between the AOD and PM_2.5 mass concentrations. Then, the distribution of PM_2.5 mass concentrations is obtained. The retrieval results show that the distribution of AOD and PM_2.5 at the urban scale in detail. The AOD results show that a reasonable relationship with a correlation coefficient (R²) of 0.66 and root mean square error (RMSE) of 0.1037 between Landsat8 OLI AOD and MODO4 DB AOD at 550 nm. The PM_2.5 retrieval results are compared with the PM_2.5 values measured by ground monitoring stations. The RMSEs for a certain day in different years, including 2017, 2018, 2019, and 2020, are 11.9470 μg/m³, 11.9787 μg/m³, 7.4217 μg/m³, and 5.4723 μg/m³, respectively. The total RMSE is 10.0224 μg/m³. The ultrahigh resolution PM_2.5 results can provide pollution details at the urban scale and support better decisions on urban atmospheric environmental governance.

The variation of sea surface temperature (SST) can change the backscatter coefficient measured by a scatterometer, resulting in a decrease in the accuracy of the sea surface wind measurement. This study proposed a new approach to correct the effect of SST on the backscatter coefficient. The method focuses on the Ku-band scatterometer HY-2A SCAT, which is more sensitive to SST than C-band scatterometers, can improve the wind measurement accuracy of the scatterometer without relying on reconstructed geophysical model function (GMF), and is more suitable for operational scatterometers. Through comparisons to WindSat wind data, we found that the Ku-band scatterometer HY-2A SCAT wind speeds are systemically lower under low SST and higher under high SST conditions. We trained a neural network model called the temperature neural network (TNNW) using HY-2A data and WindSat data. TNNW-corrected backscatter coefficients retrieved wind speed with a small systematic deviation from WindSat wind speed. In addition, we also carried out a validation of HY-2A wind and TNNW wind using European Center for Medium-Range Weather Forecasts (ECMWF) reanalysis data as a reference, and the results showed that the retrieved TNNW-corrected backscatter coefficient wind speed is more consistent with ECMWF wind speed, indicating that the method is effective in correcting SST impact on HY-2A scatterometer measurements.

Numerous machine learning models have been widely used for the spatial prediction of PM_2.5 mass concentrations in the field of remote sensing, but most studies rely on single models, limiting their ability to capture complex nonlinear relationships. Furthermore, traditional Aerosol Optical Depth (AOD) methods suffer from extensive missing values due to algorithmic limitations, hindering daily PM_2.5 mass concentration retrieval. This study first developed a hybrid random forest and extreme gradient boosting model (RF-XGBoost) to overcome single-model accuracy constraints. Subsequently, Top-of-Atmosphere (TOA) reflectance replaced conventional AOD as the hybrid model’s input. Finally, we integrated four-year (2020–2023) TOA reflectance, normalized difference vegetation index (NDVI) data, meteorological data, digital elevation model (DEM) data, and day-of-year data to develop a high-precision hybrid model specifically optimized for Jiangxi Province. The simulation results demonstrated that the hybrid RF-XGBoost model (test-R² = 0.82, RMSE = 7.25 μg/m³, MAE = 4.90 μg/m³) outperformed the single Random Forest Model by 25% and 26% in terms of the root mean square error (RMSE) and mean absolute error (MAE), respectively. The high predictive accuracy of our method confirms its effectiveness in generating reliable PM_2.5 estimates. The resulting four-year dataset also successfully delineated the characteristic seasonal PM_2.5 pattern in the region, with the highest levels in winter and the lowest in summer, alongside a clear decreasing annual trend, signifying gradual atmospheric improvement.