Chromophoric dissolved organic matter (CDOM) is crucial in the biogeochemical cycle and carbon cycle of aquatic environments. However, in inland waters, remotely sensed estimates of CDOM remain challenging due to the low optical signal of CDOM and complex optical conditions. Therefore, developing efficient, practical and robust models to estimate CDOM absorption coefficient in inland waters is essential for successful water environment monitoring and management. We examined and improved different machine learning algorithms using extensive CDOM measurements and Landsat 8 images covering different trophic states to develop the robust CDOM estimation model. The algorithms were evaluated via 111 Landsat 8 images and 1708 field measurements covering CDOM light absorption coefficient a(254) from 2.64 to 34.04 m⁻¹. Overall, the four machine learning algorithms achieved more than 70% accuracy for CDOM absorption coefficient estimation. Based on model training, validation and the application on Landsat 8 OLI images, we found that the Gaussian process regression (GPR) had higher stability and estimation accuracy (R² = 0.74, mean relative error (MRE) = 22.2%) than the other models. The estimation accuracy and MRE were R² = 0.75 and MRE = 22.5% for backpropagation (BP) neural network, R² = 0.71 and MRE = 24.4% for random forest regression (RFR) and R² = 0.71 and MRE = 24.4% for support vector regression (SVR). In contrast, the best three empirical models had estimation accuracies of R² less than 0.56. The model accuracies applied to Landsat images of Lake Qiandaohu (oligo-mesotrophic state) were better than those of Lake Taihu (eutrophic state) because of the more complex optical conditions in eutrophic lakes. Therefore, machine learning algorithms have great potential for CDOM monitoring in inland waters based on large datasets. Our study demonstrates that machine learning algorithms are available to map CDOM spatial-temporal patterns in inland waters.

Total phosphorus (TP) is the main limiting factor of eutrophication for most inland waters globally. However, the combination of the limited temporal-spatial ranges of traditional manual sampling, poor spectral resolutions, and weather-vulnerable satellite observations, have yielded great data gaps in TP dynamics in short-lived, extreme episodic, or unpredictable pollution. Hence, a novel ground-based hyperspectral proximal sensing system (GHPSs) with a maximum observation frequency of 20 s and a spectral resolution of 1 nm between 400 and 900 nm was developed for automatic, real-time and continuous observation of TP. Focusing on the GHRSs, a TP machine learning model was developed and validated with ideal accuracy (R² = 0.97, RMSE = 0.017 mg·L⁻¹, MAPE = 12.8%) using 377 pairs of in situ TP measurements collected from Fuchunjiang Reservoir (FR), Liangxi River (LR), and Lake Taihu (LT). Second-scale TP results showed a low-value stable period followed by a sharp change period in LT during 29–31 October and 1–3 November, respectively. The exponential increase (R² = 0.65, p < 0.05) on 1 November and the two complete variations with peak values of 0.32 mg·L⁻¹ and 0.42 mg·L⁻¹ were recorded in LT on 2 and 3 November, respectively. Simultaneously, a significant decrease (R² = 0.57, p < 0.05) over the observation days was observed in LR and no obvious change was observed in FR. High consistency between the GHPSs spectrum data standardized at 574 nm and the measured reflectance in different weather demonstrated the accuracy of the GHPSs spectrum data (R² > 0.99, slop = 0.98). Short and rapid TP changes were observed within one day in LT and LR based on GHPSs minute scale monitoring, which highlighted the importance of high frequency observations of TP. Several advantages of real-time, high accuracy and wide applicability to complex weather were highlighted for the GHPSs for TP monitoring compared to traditional equipment. Therefore, there are potential applications of the GHPSs in the integrated space-air-ground TP monitoring, as well as emergency monitoring and early-warning systems in the future, and it can raise our awareness of the dynamics and driving mechanisms of water quality for inland waters.