Ebinur Lake is a shallow lake and vulnerable to strong winds, which can lead to drastic changes in Suspended Particulate Matter (SPM). High spatial and temporal resolution images are therefore urgently needed for SPM monitoring over the Ebinur Lake. Hence, a high-efficiency inversion model of estimating SPM from high-resolution images using machine learning is essential to increase the amount of extracted information through band combinations quadratic optimization. This study aims to evaluate the capability of the PlanetScope images and four machine learning approaches for estimating SPM of the Ebinur Lake. The specific objectives include: (1) to obtain the sensitive bands and band combinations for SPM using correlation analysis; (2) to quadratically optimize the combination pattern of sensitive bands using a linear model; and (3) to compare the accuracy of traditional linear model and machine learning models in estimating SPM. The results of the study confirm that after linear model quadratic optimization, the band combinations of B3*B4, (B2+B3)/(B2-B3), (B3+B4)*(B3+B4), and (B3-B2)/(B2/B3) have higher accuracy than that of the single band model. By inputting the preferred four-band combinations into the Partial Least Squares (PLS), Random Forest (RF), Extreme Gradient Boosting (XGBoost), Gradient Boosting Decision Tree (GBDT) and Categorical Boosting (CatBoost) models, the performance of the SPM inversion based on PlanetScope images is better than the traditional linear model. Validation of the inversion maps with observations further indicates that the CatBoost model performed the best.

The variability in the quality of water that runs along the course of a river, flowing out of a mountain pass, through an agricultural oasis and into a lake, has been a key topic of research in recent years. In this study, the characteristics of dissolved organic matter (DOM) along the river flow, and its relationship with water quality, were analyzed using the Canadian water quality index (CWQI), parallel factor (PARAFAC) and self-organizing map (SOM). The study results include: (1) The conclusion of field sampling along the lower reaches of the Kaidu River and laboratory measurements of water quality parameters, using CWQI to determine the water quality index of the lower Kaidu River, ranging between 59.58 and 93.47. The water quality of the lower reaches of the Kaidu River generally ranges between moderate and good, and can meet the water use requirements of Class II water function standards. (2) The DOM composition of the river predominantly contained three fluorescence components, while the three fluorescence indices of the water body varied less in different river sections. Based on the SOM training model, the fluorescence intensity of the C1 component was larger among the three fluorescence components, followed by the C2 component, and the smallest fluorescence intensity of the C3, which was dominated by humic-like substances, with a high authigenic origin and humification degree. (3) The fluorescence index and fluorescence components were correlated with water quality parameters, and it was found that C1, C2 and C3 were negative and correlated significantly with SO42- and Total-dissolved solids (TDS) concentrations; FI, HIX and BIX showed strong positive correlations with SAL and Cu and negative correlations with dissolved oxygen (DO). This study provides a scientific basis for surface water quality monitoring and water quality pollution management in the Kaidu River.

The monitoring of global soil moisture is crucial for understanding the hydrological cycle and managing terrestrial water resources. The Chinese-French Oceanography Satellite (CFOSAT), equipped with the first sector-beam rotary scanning microwave scatterometer (CSCAT), provides a novel opportunity for global soil moisture mapping. However, the capability of CFOSAT's Ku-band for soil moisture retrieval remains underexplored and lacks systematic evaluation. In this study, an Adaptive Backscatter Change Tracking (ABCT) algorithm is designed to retrieve absolute soil moisture from CFOSAT's CSCAT measurements. The ABCT algorithm assumes a stable roughness, where changes in backscattering are primarily attributed to soil moisture variation based on a logarithmic relationship. It incorporates a vegetation influence coefficient, which quantifies how vegetation impacts the backscatter signal This coefficient adaptively scales with changes in the Normalized Difference Vegetation Index (NDVI) to adjust the backscattering appropriately to include the effect of vegetation growth or decay. This allows the algorithm to isolate changes in the backscatter signal that are due to soil moisture while minimizing the false readings from vegetation growth or wilting. The CFOSAT ABCT algorithm's performance was evaluated against extensive in-situ soil moisture data, demonstrating a robust correlation, with the Vertical-Vertical Polarization Ascending Orbit (VV Asc) result showing the highest accuracy, indicated by Pearson's correlation coefficient (R) of 0.68 and unbiased root mean squared error (ubRMSE) of 0.057 m3/m3. Comparative analysis with the Advanced Scatterometer (ASCAT) data revealed that, while the ABCT algorithm's correlation was slightly lower than that of the official EUMETSAT H SAF product, it notably improved the bias and ubRMSE metrics. This study underscores that the CFOSAT ABCT soil moisture retrieval algorithm and product are a valuable addition to global soil moisture mapping, complementing existing satellite missions or sensors such as SMAP, SMOS, ASCAT, AMSR2 and FY-3/MWRI.