Accurate and high spatiotemporal resolution water quality data are critical for the effective management of marine and coastal ecosystems. However, accurate atmospheric correction under high solar zenith angles (SZA) remains a challenge, introducing substantial uncertainties in satellite-derived water quality indicators (WQI) under high SZA. With an attempt to fill the gap, this study evaluated three types of strategies for satellite retrieval of suspended particulate matter (SPM) and chlorophyll-a (Chl-a) concentrations from top-of-atmosphere reflectance (ρt), Rayleigh-corrected reflectance (ρrc) and remote sensing reflectance (Rrs), respectively. The models, named XGBWQI, based on three types of remote sensing data were tested with in-situ data and compared with the Geostationary Ocean Color Imager (GOCI) standard algorithms. Results showed that: (i) ρt-based XGBWQI had the best accuracy (R2= 0.90 and MAPD = 14.65% for SPM, R2= 0.85 and MAPD = 5.34% for Chl-a); (ii) model testing results with in-situ data also confirmed the advantage of ρt-based XGBWQI over other models (R2= 0.88, MAPD = 26.9% and MRPD =11.8% for SPM, R2= 0.78, MAPD = 43.3% and MRPD = -15.5% for Chl-a); and (iii) the XGBWQI models obtained more valid WQI values for GOCI images under high SZA and successfully revealed the diurnal variations of a red tide event in the Yellow Sea and the SPM dynamics in the East China Sea. Therefore, ρt-based XGBWQI models were recommended as the best strategy for satellite retrievals of WQI under high SZA. The methods can serve as an effective tool in retrieving WQI in coastal waters under high SZA, and thus contribute to better and high-frequency water quality monitoring.

Tropical cyclones (TCs), with an intensive wind pump impact, induce sea surface temperature cooling (SSTC) on the upper ocean. SSTC is a pronounced indicator to reveal TC evolution and oceanic conditions. However, there are few effective methods for accurately approximating the amplitude of the spatial structure of TC-induced SSTC. This study proposes a novel explainable machine learning framework to model and interpret the amplitude of the spatial structure of SSTC over the northwest Pacific (NWP). In particular, 12 predictors related to TC characteristics and pre-storm ocean states are considered as inputs. A composite analysis technique is used to characterize the amplitude of the spatial structure of SSTC across the TC track. Extreme gradient boosting (XGBoost) is utilized to predict the amplitude of SSTC from the 12 predictors. To better interpret the ocean-atmosphere interaction, a SHapely Additive explanations (SHAP) method is further employed to identify the contributions of predictors in determining the amplitude of the TC-induced SSTC, bringing the attribute-oriented explainability to the proposed method. Results showed that the proposed method could accurately predict the amplitude of the spatial structure of SSTC for different TC intensity groups and outperforms a numerical model. The proposed method also serves as an effective tool for reconstructing composite maps of both interannual and seasonal evolutions of SSTC spatial structure. The study offers insight into applying machine learning to model and interpret the responses of oceanic conditions triggered by extreme weather conditions (e.g., TCs).

Accurate satellite retrieval of oceanic particulate organic nitrogen (PON) concentrations could contribute to a better and more comprehensive understanding of global marine biogeochemical processes. However, no satisfactory satellite PON retrieval model could be found in the literature. In an attempt to develop applicable PON models, large diverse matchups of synchronous global oceanic in situ PON measurements and ocean color satellite data were used to develop PON retrieval models using the Gaussian process regression (GPR) method for SeaWiFS, Terra-Moderate Resolution Imaging Spectroradiometer (MODIS), MERIS, Aqua-MODIS, and SNPP-Visible Infrared Imaging Radiometer Suite (VIIRS), respectively. The GPR PON models were compared with polynomial PON models based on single bio-optical properties or band index, and further used to retrieve spatiotemporal variations of global oceanic PON concentrations. Combined with the satellite-derived particulate organic carbon (POC) products, the possibility of deriving the POC to nitrogen ratio (POC:PON) was further explored. Results showed that GPR PON models, with$R^{2}$, root mean square error (RMSE), and mean absolute percentage error (MAPE) ranging from 0.76 to 0.87, 0.15 to 0.18, and 9.19% to 12.59%, respectively, had a comparable performance for both Case-1 and Case-2 waters and outperformed the polynomial PON models. The global PON concentrations derived from different satellite missions were generally consistent, with their mean relative differences all less than 14.70% between Aqua-MODIS and the other four missions. The GPR-derived POC:PON was acceptable, with most relative errors within ±40% compared to in situ POC:PON. The applicable satellite PON retrieval models and the readily available satellite PON products should be helpful for studying oceanic PON dynamics and ecological processes in marine biogeochemical cycles.

In recent years, several novel satellite platforms and sensors have been proposed for the Earth Radiation Budget (ERB). Simulating the sensor-measured signals could be helpful for optimizing the settings of sensors and exploring their potential in ERB. The anisotropic factor, depicting the anisotropy of Earth’s radiation, is essential in the simulation. However, developing angular distribution models involves complex procedures of data preparation, processing, and modeling. This study, targeting at simplifying the procedure of simulating the signals of ERB sensors, proposed a suit of models for estimating the longwave anisotropic factors directly from the Earth’s radiative fluxes. The models were developed with CERES/Terra data sensed in rotating azimuth plane (RAP) mode during 2000-2005 and the artificial neural network (ANN) algorithm, and tested with 12 monthly of CERES/Terra data collected in RAP and cross-track mode during 2021-2022, respectively. Models were developed for 10 scene types based on Earth’s surface types, and compared with the operational ANN ADMs. Results showed that the longwave anisotropic factors were accurately estimated with the correlation coefficient (r) varying between 0.84 and 0.98 and MAPE within 1.20% for the test dataset, and the approach proposed in this study had comparable performance with the ANN ADMs. With the estimated anisotropic factors, the sensor-measured radiances were accurately retrieved with r=1.00 and MAPE=0.53%. Therefore, the proposed approach is promising in accurate and efficient simulations of novel ERB platforms and sensors like the Moon-based Earth Radiation.

Extreme precipitation events have caused severe societal, economic and environmental impacts through the disasters of floods, flash-floods and landslides. However, the coarse-resolution of satellite-derived precipitation data makes it difficult to quantitatively capture certain fine-scale heavy rainfall process. Therefore, to improve the spatial resolution and accuracy of satellite-based precipitation extremes, a downscaling-calibration scheme based on eXtreme Gradient Boosting (XGBoost_DC) was proposed in this study, where the XGBoost algorithm was applied in both downscaling and calibration procedures. The performance of XGBoost_DC was evaluated with other two comparative methods, in which XGBoost was only used in either downscaling (XGBoost_Spline) or calibration (Spline_XGBoost) process. The results showed that: (i) XGBoost_DC achieved the best performance, as it obtained the highest accuracy and well reproduced the occurrence and the spatial distribution of precipitation during typhoon events. (ii) XGBoost_DC could capture the spatial variations of the precipitation. Although Spline_XGBoost obtained results only slightly worse than the XGBoost_DC, it significantly underestimated the spatial variability. (iii) the model assessment between the XGBoost_DC and Spline_XGBoost illustrated the essential contribution of XGBoost algorithm in downscaling process, and improved our understanding of the capability of machine learning algorithm in reproducing spatial variance of precipitation. These findings imply that our proposed downscaling-calibration scheme can be applied for generating high-resolution and high-quality precipitation extremes during typhoon events, which would benefit the water and flood management, as well as other various applications in hydrological and meteorological modelling.

The Ocean and Land Color Instrument (OLCI) on Sentinel-3 is one of the most advanced ocean color satellite sensors for aquatic environment monitoring. However, limited studies have been focused on a comprehensive assessment of atmospheric correction (AC) methods for OLCI. In an attempt to fill the gap, this study evaluated seven different AC methods for OLCI using global automaticin situobservations from Aerosol Robotic Network-Ocean Color (AERONET-OC). Results showed that the POLYnomial-based algorithm applied to MERIS (POLYMER) had the best performance for bands with wavelength ≤ 443 nm, and the SeaDAS method based on 779 and 865 nm was the best for longer spectral bands; however, SeaDAS (SeaWiFS Data Analysis System) processing algorithm based on 779 and 1020 nm, as well as 865 and 1020 nm, obtained degraded AC performance; Case 2 Regional CoastColor (C2RCC) also produced large uncertainties; Baseline AC (BAC) method might be better than SeaDAS method; and simple subtraction method was the worst except for turbid waters. POLYMER and C2RCC underestimated high remote sensing reflectance (Rrs) at red and green bands; SeaDAS method based on 779 and 865 nm held an advantage for clear waters over the other two band combinations, while their difference turned small for turbid waters. AC uncertainties generally impacted the performance of chlorophyll retrievals. POLYMER outperformed other methods for chlorophyll retrieval. This study provides a good reference for selecting a suitable AC method for aquatic environment monitoring with Sentinel-3 OLCI.

As the only natural satellite of the Earth, the Moon provides vital location resources and supportive environment for Earth observations, and the Moon-based Earth observation (MEO) has unparalleled advantages in global climate change and large-scale phenomena. The ocean plays an important role in regulating climate and global water and carbon cycle. With an attempt to explore the feasibility of MEO-based marine environment monitoring, this study aimed to investigate the observing geometry and revisiting frequency of the MEO-based ocean color remote sensing and further to explore its quantitative application potentials. Results showed that MEO-based ocean color remote sensing, capturing the Earth on an hourly basis, could observe most part of the ocean for over five times per day; however, both solar zenith angle and view zenith angle were high at high-latitude regions; atmospheric reflectance accounted for most of sensor-measured signal, especially at high solar and view zenith angle, while surface-reflected glint reflectance was also notable at low solar zenith angle; and the remote sensing reflectance retrieved from MEO-based ocean color remote sensing could be used for chlorophyll retrieval. In further studies, more efforts should be paid on how to accurately retrieve remote sensing reflectance at high solar and view zenith angle, which would improve the application capability of MEO for polar regions. Overall, this study demonstrated the great potentials of MEO-based ocean color remote sensing, and MEO would be a new observing perspective and long-term consistent data source for marine environment monitoring.

Accurate removal of atmospheric interference and precise retrieval of water-leaving reflectance is decisive for subsequent water color applications. As follow-up satellite of Envisat, Sentinel-3 will provide valuable observations of the earth. This study aims to adapt the shortwave infrared extrapolation (SWIRE) atmospheric correction algorithm for Sentinel-3 to derive remote sensing reflectance of turbid waters, and validation it using our in -situ data in Pearl River Estuary. Results showed that SWIRE algorithm could effectively remove atmospheric perturbations, and produced more accurate remote sensing reflectance over complex waters of PRE than NIR and SWIR algorithms.