Chromophoric dissolved organic matter (CDOM) is a significant contributor to the biogeochemical cycle and energy dynamics within aquatic ecosystems. Hence, the implementation of a systematic and comprehensive monitoring and governance framework for the CDOM in inland waters holds significant importance. This study conducted the retrieval of CDOM in Lake Khanka. Specifically, we use the GBDT (R² = 0.84) algorithm which performed best in retrieving CDOM levels and an empirical relationship based on the situ data between CDOM and dissolved organic carbon (DOC) to indicate the distribution of DOC indirectly. The performance of the CDOM-DOC retrieval scheme was reasonably good, achieving an R² value of 0.69. The empirical algorithms were utilized for the analysis of Sentinel-3 datasets from the period 2016 to 2020 in Lake Khanka. The potential factors that contributed to the sources of DOM were also analyzed with the humification index (HIX). The significant relationship between CDOM and DOC (HIX and chemical oxygen demand (COD)) indicated the potential remote sensing application of water quality monitoring for water management. An analysis of our findings suggests that the water quality of the Great Khanka is superior to that of the Small Khanka. Moreover, the distribution of diverse organic matter exhibits a pattern where concentrations are generally higher along the shoreline compared to the center of the lake. Efficient measures should be promptly implemented to safeguard the water resources in international boundary lakes such as Lake Khanka and comprehensive monitoring systems including DOM distribution, DOM sources, and water quality management would be essential for water resource protection and government management.

The traditional field-based measurements of carbon dioxide (pCO₂) for inland waters are a snapshot of the conditions on a particular site, which might not adequately represent the pCO₂ variation of the entire lake. However, these field measurements can be used in the pCO₂ remote sensing modeling and verification. By focusing on inland waters (including lakes, reservoirs, rivers, and streams), this paper reviews the temporal and spatial variability of pCO₂ based on published data. The results indicate the significant daily and seasonal variations in pCO₂ in lakes. Rivers and streams contain higher pCO₂ than lakes and reservoirs in the same climatic zone, and tropical waters typically exhibit higher pCO₂ than temperate, boreal, and arctic waters. Due to the temporal and spatial variations of pCO₂, it can differ in different inland water types in the same space-time. The estimation of CO₂ fluxes in global inland waters showed large uncertainties with a range of 1.40–3.28 Pg C y⁻¹. This paper also reviews existing remote sensing models/algorithms used for estimating pCO₂ in sea and coastal waters and presents some perspectives and challenges of pCO₂ estimation in inland waters using remote sensing for future studies. To overcome the uncertainties of pCO₂ and CO₂ emissions from inland waters at the global scale, more reliable and universal pCO₂ remote sensing models/algorithms will be needed for mapping the long-term and large-scale pCO₂ variations for inland waters. The development of inverse models based on dissolved biogeochemical processes and the machine learning algorithm based on measurement data might be more applicable over longer periods and across larger spatial scales. In addition, it should be noted that the remote sensing-retrieved pCO₂/the CO₂ concentration values are the instantaneous values at the satellite transit time. A major technical challenge is in the methodology to transform the retrieved pCO₂ values on time scales from instant to days/months, which will need further investigations. Understanding the interrelated control and influence processes closely related to pCO₂ in the inland waters (including the biological activities, physical mixing, a thermodynamic process, and the air–water gas exchange) is the key to achieving remote sensing models/algorithms of pCO₂ in inland waters. This review should be useful for a general understanding of the role of inland waters in the global carbon cycle.

Lake Xingkai is a large turbid lake composed of two parts, Small Lake Xingkai and Big Lake Xingkai, on the border between Russia and China, where it represents a vital source of water, fishing, water transport, recreation, and tourism. Chlorophyll-a (Chla) is a prominent phytoplankton pigment and a proxy for phytoplankton biomass, reflecting the trophic status of waters. Regularly monitoring Chla concentrations is vital for issuing timely warnings of this lake’s eutrophication. Owing to its higher spatial and temporal coverages, remote sensing can provide a synoptic complement to traditional measurement methods by targeting the optical Chla absorption signals, especially for the lakes that lack regular in situ sampling cruises, like Lake Xingkai. This study calibrated and validated several commonly used remote sensing Chla retrieval algorithms (including the two-band ratio, three-band method, four-band method, and baseline methods) by applying them to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images in Lake Xingkai. Among these algorithms, the four-band model (FBA), which removes the absorption signal of detritus and colored dissolved organic matter, was the best-performing model with an R² of 0.64 and a mean absolute percentage difference of 38.26%. With the FBA model applied to OLCI images, the monthly and spatial distributions of Chla in Lake Xingkai were studied from 2016 to 2022. The results showed that over the seven years, the Chla concentrations in Small Lake Xingkai were higher than in Big Lake Xingkai. Unlike other eutrophic lakes in China (e.g., Lake Taihu and Lake Chaohu), Lake Xingkai did not display a stable seasonal Chla variation pattern. We also found uncertainties and limitations of the Chla algorithm models when using a larger satellite zenith angle or applying it to an algal bloom area. Recent increases in anthropogenic nutrient loading, water clarity, and warming temperatures may lead to rising phytoplankton biomass in Lake Xingkai, and the results of this study can be applied for the satellite-based monitoring of its water quality.

The Secchi disk depth (SDD) is a measure of water clarity appropriate to assess a lake’s trophic level. Optical satellite remote sensing has been widely used to detect water SDD at regional, continental, or global scales, with the Google Earth Engine (GEE) providing an excellent platform for fast and large-scale monitoring of SDD for global lakes. Hence, this study evaluates whether retrieving SDD for global lakes is possible by applying machine learning algorithms using Landsat top-of-atmosphere reflectance (TOA) embedded in GEE. Eleven algorithms were calibrated (5498 samples), and their performances were validated (2749 samples) with a global dataset of Landsat TOA reflectance and in situ SDD match-ups. The results indicated that the smallest modeling error was derived from Gradient Boosting Decision Tree (GBDT) ( R ² = 0.98, RMSE = 0.27 m, MAPE = 10.52%, MAE = 0.07 m), followed by XGBoost (XGB) and Swarm Optimization-Random Forest (PSO-RF). Furthermore, it was demonstrated that combining GBDT, XGB, PSO-RF, and Landsat TOA reflectance provides a robust way to monitor SDD across global lakes. Eventually, global SDD maps for 2020 were produced using Landsat OLI TOA imagery, with SDD changing range from 0.01 to 13.1 m (mean = 3.3 m). The machine learning models in this study can be applied to archived Landsat imagery to assess SDD trends of global lakes and capture annual or seasonal variations.