Suspended sediments have profound impacts on marine primary productivity and the ecological environment. The Yellow River estuary and its vicinity waters, with a high dynamic range of suspended sediment concentration (SSC), have important eco-environmental functions for the sustainable development in this region. The multispectral imager (MI) on board China’s first Sustainable Development Goals Science Satellite 1 (SDGSAT-1) features seven high-resolution bands (10 m). This study employs multispectral imagery obtained from SDGSAT-1 with single-band and band-ratio models to monitor the SSC in the Yellow River estuary and its vicinity waters. The results show that SDGSAT-1 images can be used to estimate the SSC in the Yellow River estuary and its vicinity waters. The overall pattern of the SSC exhibits a notable pattern of higher concentrations in nearshore areas and lower concentrations in offshore areas, and the retrieved SSC can attain values surpassing 1000 mg/L in nearshore areas. The R² values of both the single-band and the band-ratio models for SSC inversion exceed 0.7. The single-band model R(854) demonstrates superior performance, achieving the highest R² value of 0.93 and the lowest mean absolute percentage error (MAPE) of 44.04%. The single-band model based on SDGSAT-1 R(854) tends to outperform the band-ratio models for waters with algal blooms, which may be used for inversions of SSC and/or suspended particulate matter (SPM) in the waters full of algal blooms and suspended sediments. The monitoring results by SDGSAT-1 suggest that the complex SSC distributions in the Yellow River estuary and its vicinity waters were highly impacted by the river sediments discharge, tide, currents and wind-induced waves.

Ulva pertusa (U. pertusa) is a benthic macroalgae in submerged conditions, and it is relatively difficult to monitor with the remote sensing approaches for floating macroalgae. In this work, a novel remote-sensing approach is proposed for monitoring the U. pertusa green tide, which applies a deep learning method to high-resolution RGB images acquired with unmanned aerial vehicle (UAV). The results of U. pertusa extraction from semi-simultaneous UAV, Landsat-8, and Gaofen-1 (GF-1) images demonstrate the superior accuracy of the deep learning method in extracting U. pertusa from UAV images, achieving an accuracy of 96.46%, a precision of 94.84%, a recall of 92.42%, and an F1 score of 0.92, surpassing the algae index-based method. The deep learning method also performs well in extracting U. pertusa from satellite images, achieving an accuracy of 85.11%, a precision of 74.05%, a recall of 96.44%, and an F1 score of 0.83. In the cross-validation between the results of Landsat-8 and UAV, the root mean square error (RMSE) of the portion of macroalgae (POM) model for U. pertusa is 0.15, and the mean relative difference (MRD) is 25.01%. The POM model reduces the MRD in Ulva pertusa area extraction from Landsat-8 imagery from 36.08% to 6%. This approach of combining deep learning and UAV remote sensing tends to enable automated, high-precision extraction of U. pertusa, overcoming the limitations of an algae index-based approach, to calibrate the satellite image-based monitoring results and to improve the monitoring frequency by applying UAV remote sensing when the high-resolution satellite images are not available.

Red-Green-Blue (RGB) images (or videos) captured by consumer-level unmanned aerial vehicle (UAV) cameras are widely used in high-resolution remote observations. However, digital number (DN) values of these RGB images usually have a nonlinear relationship with the incident radiance, which reduces the accuracy of quantitative remote sensing of macroalgae. To solve this problem, we proposed an improved processing procedure for UAV RGB images (or videos) based on camera response functions (CRF). The CRF was utilized to convert the DN values into energy values (E values) which demonstrate a linear relationship with the incident radiance. When the DN values were replaced by their corresponding E values to calculate the reflectance of green macroalgae under different illumination intensities, the errors in reflectance were reduced by ∼21%; for the corresponding green macroalgae indices such as the Red-Green band virtual baseline Floating green Algae Height (RG-FAH), the E value-based RG-FAH demonstrate more resistance to the impacts of sun-glints; and, the E values were further applied to estimate the coverage portion of macroalgae (POM, %) in RGB videos, the illumination-induced deviations of the POM were effectively reduced by up to 33.06%, showing an advantage in quantitative estimation of macroalgae biomass. The results of applications to UAV RGB images show that the E values have significant suitability in estimating POM across diverse green macroalgae species and various algae indices, suggesting promising potentials of the proposed processing procedure with E-based photo and/or video RGB images in monitoring aquatic plants and environment.

The Yellow River, China’s second-largest river, is renowned for its high sediment content. In response to the potential impacts of climate change on Yellow River water resources and water environmental management, an advanced monitoring and forecasting system for water and sediment throughout the entire Yellow River basin—from its source to the sea—is urgently needed. In this paper, based on the current status of water and sediment monitoring technologies, we proposed an integrated remote sensing monitoring network that combines satellite remote sensing, drone remote sensing, and ground-based wireless automatic monitoring networks, aiming to achieve the digital monitoring of water and sediment across the entire Yellow River basin, from its upper reaches to its estuary in the Bohai Sea. By utilizing ground-based in situ hyperspectral stations for sediment source areas in the upper reaches, such as the Qingshui River basin in Ningxia, and satellite remote sensing for midstream processes in the Xiaolangdi reservoir before the flood season in 2023, as well as downstream monitoring at the Yellow River estuary, this paper demonstrates the novelty and efficiency of the space–air–ground integrated remote sensing monitoring technology.

Chlorophyll-a (Chl-a) and chemical oxygen demand (COD) are key indicators for water quality evaluation. In previous research on the inversion of Chl-a and COD concentrations using hyperspectral data, disparities in hyperspectral data types have constrained the universality of the inversion models. To solve this problem, in this study, synchronous in situ hyperspectral data and water samples were collected from 308 stations within the river networks of Zhongshan City. Four inversion models, support vector regression (SVR), random forest (RF), backpropagation neural network (BPNN), and one-dimensional convolutional neural network (1D-CNN), were established using the original reflectance (R), remote sensing reflectance (Rrs), and their normalized forms as inputs. To evaluate the robustness of the models, their performance was assessed via cross-reflectance type validation. For example, a model was trained using R data and then tested with Rrs data. The results show that using the normalized hyperspectral data for modeling not only improves the accuracy of the inversion results of Chl-a and COD concentrations, but also effectively unifies different types of hyperspectral data, thereby improving the versatility of the inversion model. This study provides a reference for constructing a general water quality inversion model based on hyperspectral data.