Hydrological processes drive the transport of phosphorus (P) from soil to surface water. This study is the first to use space-based observations to examine P storage in watershed lakes. Based on the vertical distribution characteristics of the total P (TP) concentration in multiple eutrophic lakes, a remote sensing estimation method for water column integrated P storage in eutrophic lakes was proposed using machine learning. The results showed that the TP profile followed a quadratic distribution that was primarily influenced by chlorophyll-a in shallow water and suspended particulate matter (SPM) in deep water. Based on this observation, a water column TP mass estimation algorithm was developed using extreme gradient boosting (XGBoost) to estimate surface TP, combined with an adjusted floating algae index (FAI) and near-infrared band. The algorithm achieved$R^{\mathbf {2}}\ge 0.6$, with the error increasing with depth. Then, water depth and lake spatial information were added to the algorithm, the average P storage of 35 large lakes in the Jianghuai region was calculated as 5347 t, and the lake area explains 85% of the P storage. The modeled P storage in Lake Taihu and Chaohu exhibited increasing trends that were mainly driven by the water level. This study is the first to observe lake P storage from space and to help elicidate the P cycle in shallow eutrophic lakes. At present, the Yangtze River Basin exports large amounts of P, lakes reduce P loss in the basin and enrich, and there is still great potential for the recycling and utilization of P resources.

Moderate-high resolution satellite missions provide an opportunity to capture subtle spatial variability in lakes; however, the sparsity of time series for individual satellite instruments cannot monitor temporal variation in the lake environment. To date, studies on the joint observations of chlorophyll-a (Chl-a) in inland lakes from multiple missions have been poorly reported. Here, we generated a harmonized Chl-a dataset for the lakes in the Yunnan–Guizhou Plateau in China from 2013 to 2022 by the Landsat 8/9 and Sentinel-2A/B virtual constellation. This study first examined the performance of four atmospheric correction processors to derive remote sensing reflectance (Rrs) from Landsat 8/9 Operational Land Imager (OLI) and Sentinel-2A/B multispectral instrument (MSI) images. We determined that the dark spectral fitting algorithm generated better Rrsthan the other processors, e.g., Rrs(561) mean absolute percentage error (MAPE)=15.2%, Rrs(665) MAPE=27.5%, and Rrs(704) MAPE=25.7%. OLI-derived Rrsat five visible and near-infrared bands showed satisfactory agreement with MSI (slope=0.94, MAPE=11.8%). The mixed density network outperformed the six state-of-the-art algorithms and other two machine learning models in retrieving Chl-a [MSI: MAPE=31.4% (N=109), OLI: MAPE=38.0% (N=74)]. The satisfactory agreement of Chl-a retrievals between the synchronous MSI and OLI images (N=2,293,821, MAPE=34.6%) supported the establishment of the virtual constellation. MSI- and OLI- derived Chl-a in nine major lakes in the studied area exhibited apparent seasonal variability from 2013 to 2022, particularly after 2017. Results highlight a solution to establish the Landsat/Sentinel-2 virtual constellation for improving the spatial and temporal resolutions of a database of lake water quality.

The Visible Infrared Imaging Radiometer Suite (VIIRS) instrument was launched to continue the legacy of the MODerate Resolution Imaging Spectroradiometer (MODIS). Despite recent studies demonstrating the use of VIIRS observations over inland waters, VIIRS has not been widely used to generate water quality products (e.g., chlorophyll-a (Chl-a), suspended particulate matter (SPM)) in relatively large turbid lakes. This study examines the quality of VIIRS-derived remote sensing reflectance (Rrs) from four different atmospheric-correction processors with matchups from 13 lakes sized between 107 km2and 2573 km2across the eastern plain of China. NOAA’s operational Rrsoutperforming Rrsretrieved by other state-of-the-art algorithms were shown to contain mean uncertainties of 57%, 33%, 20%, 28% for Rrs(486), Rrs(551), Rrs(671), and Rrs(745), respectively, which induced ~55% uncertainty in satellite-retrieved SPM and Chl-afrom recently developed algorithms in the studied lakes. A deep neural network was developed for simultaneous retrievals of Chl-aand SPM from VIIRS Rayleigh-corrected reflectance to improve accuracy. The model with satisfactory accuracy (mean uncertainty of 28% for Chl-aand 20% for SPM) outperformed other machine learning approaches and nearly halved uncertainties compared to those obtained from satellite-derived Rrsproducts. Within the 2012-2020 period, high-quality VIIRS-derived Chl-aand SPM across 61 lakes in eastern China had evident interannual variability in SPM but insignificant temporal variations in Chl-a. This study provides validated, high-quality, basin-scale VIIRS-derived Chl-aand SPM products in eastern China during the past decade. Our results offer a strategy for improving regional water quality products from VIIRS data.

Coarse-resolution sensors have been used operationally to monitor floating algal blooms with near daily revisit in coastal and inland waters. Most of the current methods in estimating fractional floating algae cover (FAC) were based on the linear pixel un-mixing assumption. In this study, a new FAC model following logistic curve was developed and applied to multisensor satellite data in two large shallow eutrophic lakes, Lake Taihu and Lake Chaohu, in China. The results indicated that after resampling to 250 m, match-up pairs of MODIS (Moderate Resolution Imaging Spectroradiometer), VIIRS (The Visible and Infrared Imager/Radiometer Suite), GOCI (Geostationary Ocean Color Imager) and OLCI (Ocean and Land Color Instrument) possessed consistent Rayleigh corrected reflectance (Rrc) and FAI (floating algae index) or AFAI (alternative floating algae index). The FAC model was developed based on the simulated AFAI data of GOCI using point spread function (PSF) and bloom percent derived from OLI (Operational Land Imager), and then was applied to MODIS, VIIRS, and OLCI. Compared with the linear pixel un-mixing method, the FAC model reflects the asymptotic reflectance saturation in the NIR (near infrared) band with the accumulation of blooms. Besides, the equivalent bloom area (EBA) of GOCI was validated using OLI matched pairs with UPD 37.6% (N=39, R2=0.96). The spatial-temporal dataset of FAC (2002-2020) shows that Lake Taihu and Lake Chaohu experienced severe algal blooms after 2010, partly resulting from the higher frequency of multisensor data. This study provides a method in building a lasting and comparable FAC dataset using multisensors.

The coastal zone imager (CZI) onboard HY-1C satellite provides a new data source to monitor the lake environments. Here, we provided a preliminary evaluation for the applications of CZI on inland lakes and a comparison with the in situ, Landsat-8 operational land imager (OLI), and Sentinel-2 multispectral instrument (MSI) measurements. First, the in-orbit signal-to-noise ratios (SNRs) were estimated based on homogenous ocean pixels. SNRs of CZI reached ~200:1 in the visible bands and ~150:1 at the near-infrared bands, which are comparable with the OLI and slightly higher than those of the MSI. Then, the performance of 6SV and fast line-ofsight atmospheric analysis of hypercubes (FLAASH) models on the retrievals of remote sensing reflectance (Rrs) from CZI measurements was evaluated. 6SV-derived Rrs showed higher accuracy than that of FLAASH, validated by the synchronous in situ (R2≃ 0.50, absolute percent difference (APD) ≃20%) and OLI-derived Rrs (R2≃ 0.75, APD ≃5%). Finally, the abilities of CZI to observe cyanobacterial bloom, suspended particular matter (SPM), and chlorophyll-a (Chla) were assessed. CZIderived, the area of cyanobacterial bloom and SPM, showed good agreements with the results yielded by the OLI and MSI data on May 24, 2019, in Lake Taihu (R2= 0.68, root-mean-square error (RMSE) = 9.68 mg/L, APD = 13.52% for SPM). While CZI only has four wide bands, Chla derived by CZI using an empirical algorithm was relatively consistent with MSI-derived values. CZI demonstrated a decent performance in monitoring the environments of large lakes, and it is expected to add the bands for atmospheric correction and further works in the small-medium lakes in the future.