Medium Resolution Imaging Spectrometer Research Articles

Analysis of spatial and temporal variations of vegetation NPP and TWS in the Yangtze River Basin

Net primary productivity (NPP) is an important parameter reflecting vegetation growth, and water is one of the necessary factors for vegetation growth. Investigating the mutual influence between NPP and water is significant for ensuring the stable development of the ecological environment. This study focuses on the Yangtze River Basin (YRB) as the research area, and based on medium-resolution imaging spectrometer (MODIS) data, climate data, and gravity recovery and climate experiment (GRACE) data, the spatiotemporal evolution characteristics of vegetation NPP and terrestrial water storage (TWS) in the YRB from 2000 to 2022 are explored and analyzes the mutual influence of NPP with climate factors and TWS. The results show that vegetation NPP (4.10 gC·m−2·a−1) and TWS (0.55 mm) in the YRB have exhibited an increasing trend from 2000 to 2022, with a strong correlation between the two, which is related to recent environmental policies. Analysis of the impact of climate factors on NPP reveals that temperature and TWS significantly positively impact NPP changes. Furthermore, comparisons between NPP and TWS indicate that changes in TWS substantially promote plant growth. In addition, the comparison between NPP and TWS indicates that changes in TWS have an important promoting effect on plant growth. Surface water (SWS) and soil water (SM) have a significant promoting effect on plant growth, but with a strong lag, while the consumption of groundwater (GWS) has been promoting plant growth without significant lag.

Developing a novel index for detection of optically shallow waters using multispectral satellite imagery and radiative transfer modelling

ABSTRACT Optical remote sensing of water quality has been problematic due to contamination of remote sensing observations by the sea-bottom effect over complex shallow or very clear waters. This has resulted in providing misleading information on the estimation of Water Constituent Concentrations (WCCs) retrieved from satellite images. In this research, we used Radiative Transfer (RT) Modelling to develop a simple and innovative index named the Sea-Bottom Effect Index (SEBEI) to readily determine water pixels contaminated by the sea-bottom effect, (hereafter named Optically Shallow Waters, (OSWs)) from remote sensing observations. To define the SEBEI, we initially assessed the influence of the sea-bottom on simulated water-leaving reflectance (R rs ) using RT Water-Sea-Bottom (WSB) modelling. This evaluation encompassed a range of water depths, seabed albedos, and WCCs (i.e. Chlorophyll-a (Chla) and Suspended Particulate Matter (SPM)). Next, we detected the most sensitive wavelengths (i.e. 750 nm, 810 nm, and 900 nm) to the seabed contribution on the simulated R rs spectra at the water surface level and developed the SEBEI. We validated the accuracy of the proposed SEBEI over a time series of MEdium Resolution Imaging Spectrometer (MERIS) and Ocean and Land Colour Instrument (OLCI) images captured during low and high tidal phases over shallow waters of the Dutch Wadden Sea, the Netherlands. The results indicate that the SEBEI effectively distinguishes between OSWs and Optically Deep Waters (ODWs) using satellite images (with an R2 value of ≥ 0.97 and a Root Mean Square Error (RMSE) of ≤ 0.65). The SEBEI can serve as an intermediate solution for detecting OSWs in multispectral and hyperspectral satellite images worldwide. It eliminates the need for ancillary datasets like bathymetry maps and significantly enhances the reliability of WCC maps produced over complex shallow coastal waters.

A novel water optical types framework for Chinese inland waters with the application of multitype satellite sensor

ABSTRACT Optical Water Type (OWT) analysis is crucial for comprehending water composition and quality, key factors in assessing water quality over extensive areas. However, China’s inland waters lack a standardized system for such analysis. To quantitatively analyze the classification results, our study compared three K-means clustering methods, for analyzing 1310 spectral data from various Chinese lakes and reservoirs, thereby addressing this gap. The innovative split-merge K-means method identified 13 distinct OWTs that more closely adhere to the principles of minimizing intra-class distance and maximizing inter-class distance. These were categorized into four groups: clear water, turbid water, eutrophic water, and special type water. Additionally, we developed a method based on Spectral Angle Distance (SAD) to evaluate the classification capabilities of 12 satellite sensors. The results show that Sentinel-3 OLCI (Ocean and Land Color Instrument), MERIS (Medium Resolution Imaging Spectrometer), and Sentinel-2 MSI (Multispectral Instrument) have the best water classification capabilities, making them well-suited for large-scale monitoring of OWT changes. Conversely, other sensors, such as the Sustainable Development Scientific Satellite-1 (SDGSAT-1), Landsat-8, GaoFen-6, GaoFen-1, GaoFen-2, Landsat-5, Landsat-7, Moderate Resolution Imaging Spectroradiometer (MODIS), and HuanJing-1, necessitate the consolidation of water types for effective categorization, indicative of their more limited classification capabilities.

A new approach to quantify chlorophyll-a over inland water targets based on multi-source remote sensing data

Chlorophyll-a (Chl-a) concentration is a reliable indicator of phytoplankton biomass and eutrophication, especially in inland waters. Remote sensing provides a means for large-scale Chl-a estimation by linking the spectral water-leaving signal from the water surface with in situ measured Chl-a concentrations. Single-sensor images cannot meet the practical needs for long-term monitoring of Chl-a concentrations due to cloud cover and satellite operational lifetimes. However, quantifying long-term inland water Chl-a concentrations using multi-source remote sensing data remains a problem, as improper input of satellite reflectance products will affect the accuracy of Chl-a over inland waters, as well as existing models cannot meet the need for multi-source remote sensing data to retrieve high precision Chl-a. To explore these problems towards a solution, four reflectance data derived from Ocean and Land Colour Instrument (OLCI), MultiSpectral Instrument (MSI), and Operational Land Imager (OLI) were evaluated against in situ measurements of Erhai Lake. Reflectance data from these sensors were assessed to determine their consistency. Results indicate that R_rhos products (i.e., surface reflectance, a semi-atmospheric correction reflectance) that controlled for the atmospheric diffuse transmittance were highly correlated with the measured reflectance values. The in situ reflectance also confirmed the higher fidelity of satellite reflectance in the green-red band. Subsequently, a new extreme gradient boosting (XGB) model applied to multi-source remote sensing data is proposed to estimate long-term inland water Chl-a concentrations. Comparative experiments showed the XGB model with R_rhos products outperformed other solutions, providing accurate estimates for daily, monthly, and long-term trends in Erhai Lake. The XGB model was finally processed 3954 R_rhos reflectance data derived from OLCI, ENVISAT Medium Resolution Imaging Spectrometer (MERIS), MSI, and OLI sensors, mapping Chl-a concentrations in Erhai Lake over a 20-year period. This study could serve as a reference for the long-term Chl-a monitoring using multi-source remote sensing data to support inland lake management and future water quality evaluation.

Uncertainty of atmospheric scattering functions relevant for Satellite Ocean Colour Radiometry in European Seas

ABSTRACT Uncertainty assessment of Ocean Colour Radiometry (OCR) usually relies on the comparison between satellite marine reflectance and concurrent in situ radiometric measurements. However, when dealing with the standard atmospheric correction (AC), uncertainty of marine reflectance originates fundamentally from two sources: (i) uncertainty of the Level-1 calibration at top of the atmosphere, and (ii) uncertainty of the atmospheric scattering functions computed by the AC (atmospheric path reflectance and transmittance). The aim of this alternative work is thus to assess the uncertainty of these scattering functions, thanks to inherent optical properties of aerosols extracted from the AErosol Robotic NETwork (AERONET) and ingested in a radiative transfer code. The study is conducted for the MEdium Resolution Imaging Spectrometer (MERIS), over three AERONET sites selected in European seas (Aqua Alta Oceanographic Tower, AAOT; Gustav Dalen Tower, GDT; Lampedusa) and Lanai in the Pacific Ocean as a reference. We develop an uncertainty budget based on the statistical analysis of the errors between MERIS and AERONET-derived data. At 443 nm, uncertainty of MERIS atmospheric path reflectance due to random effects is estimated around 2%, while that of total atmospheric transmittance is between 0.6% and 1%. Propagation of these uncertainties to OCR is achieved following metrological principles and varies between match-ups and sites due to the distinctive contribution of the marine signal to the total radiometry: uncertainty of MERIS marine reflectance at 443 nm is 10.8% (±1.1%) at Lanai, 18.3% (±4.6%) at Lampedusa, 29.4% (±12.2%) at AAOT and up to 68.7% (±127.4%) at GDT (i.e. with a significant intra-site variability). The methodology is a first step aimed at deriving a pixel-by-pixel uncertainty budget of OCR, instead of providing overall estimates as generally done in the OC community. Limitations due to uncertainties in the AERONET data are also discussed, with a tentative quantification.

Sentinel-2 time series: a promising tool in monitoring temperate species spring phenology

Abstract The changes in vegetation phenology observed in recent decades may have significant implications for carbon, water, and energy fluxes as well as ecosystem productivity. Phenology is an important indicator of a changing environment, and studying species-specific phenology can help understand their responses to varying climate conditions. While satellite-based phenology provides long-term information for large areas, it describes different traits than ground-based phenology. Nonetheless, emerging research on remote sensing-based phenology has shown its usefulness in determining inter- and intra-specific differences, as well as interannual changes. In this study, we examined the potential of the Sentinel-2 time series from 2018 to 2022 in assessing the spring phenology of seven temperate tree species. Three vegetation indices were calculated, namely, the Medium Resolution Imaging Spectrometer (MERIS) Terrestrial Chlorophyll Index, the Normalized Difference Vegetation Index, and the Enhanced Vegetation Index. We examined interspecific variations in the start of the season (SOS) and interannual variability. Additionally, we explored intraspecific variations in spring phenology among common beech stands across an elevation gradient. Our findings reveal substantial differences in Sentinel-2-derived phenology across species, with larch, birch, and hornbeam displaying an earlier SOS compared with oak and alder. The latest SOS dates were observed in 2021, whereas the earliest occurred in 2018 and the difference exceeded 20 days. It corresponded to the diverse spring meteorological conditions in the examined years, with the highest heat accumulation in 2018 and the lowest in 2021. Additionally, the three examined indices showed different phenological curves, with MERIS Terrestrial Chlorophyll Index exhibiting distinct patterns with a delayed start of the spring increase compared with the other two indices. The relationship between elevation and SOS dates was generally positive, but there was high variability in some years. The earliest SOS dates occurred at ~600 m a.s.l. and larger interannual variations were observed for stands located on the highest elevations. These results underscore the potential of Sentinel-2 in monitoring the spring phenology of temperate species, offering insights into inter- and intra-specific variations, as well as year-to-year changes. In the future, longer Sentinel-2 time series will improve estimates and facilitate the tracking of long-term changes. Such advancements hold relevance for forest management, carbon budget predictions, and understanding ecosystem dynamics.

Phenological stage and vegetation index for predicting corn yield under rainfed environments.

Uncrewed aerial systems (UASs) provide high temporal and spatial resolution information for crop health monitoring and informed management decisions to improve yields. However, traditional in-season yield prediction methodologies are often inconsistent and inaccurate due to variations in soil types and environmental factors. This study aimed to identify the best phenological stage and vegetation index (VI) for estimating corn yield under rainfed conditions. Multispectral images were collected over three years (2020-2022) during the corn growing season and over fifty VIs were analyzed. In the three-year period, thirty-one VIs exhibited significant correlations (r ≥ 0.7) with yield. Sixteen VIs were significantly correlated with the yield at least for two years, and five VIs had a significant correlation with the yield for all three years. A strong correlation with yield was achieved by combining red, red edge, and near infrared-based indices. Further, combined correlation and random forest an alyses between yield and VIs led to the identification of consistent and highest predictive power VIs for corn yield prediction. Among them, leaf chlorophyll index, Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index and modified normalized difference at 705 were the most consistent predictors of corn yield when recorded around the reproductive stage (R1). This study demonstrated the dynamic nature of canopy reflectance and the importance of considering growth stages, and environmental conditions for accurate corn yield prediction.

Identifying lakes at risk of toxic cyanobacterial blooms using satellite imagery and field surveys across the United States

Harmful algal blooms caused by cyanobacteria are a threat to global water resources and human health. Satellite remote sensing has vastly expanded spatial and temporal data on lake cyanobacteria, yet there is still acute need for tools that identify which waterbodies are at-risk for toxic cyanobacterial blooms. Algal toxins cannot be directly detected through imagery but monitoring toxins associated with cyanobacterial blooms is critical for assessing risk to the environment, animals, and people. The objective of this study is to address this need by developing an approach relating satellite imagery on cyanobacteria with field surveys to model the risk of toxic blooms among lakes. The Medium Resolution Imaging Spectrometer (MERIS) and United States (US) National Lakes Assessments are leveraged to model the probability among lakes of exceeding lower and higher demonstration thresholds for microcystin toxin, cyanobacteria, and chlorophyll a. By leveraging the large spatial variation among lakes using two national-scale data sources, rather than focusing on temporal variability, this approach avoids many of the previous challenges in relating satellite imagery to cyanotoxins. For every satellite-derived lake-level Cyanobacteria Index (CI_cyano) increase of 0.01 CI_cyano/km2, the odds of exceeding six bloom thresholds increased by 23–54 %. When the models were applied to the 2192 satellite monitored lakes in the US, the number of lakes identified with ≥75 % probability of exceeding the thresholds included as many as 335 lakes for the lower thresholds and 70 lakes for the higher thresholds, respectively. For microcystin, the models identified 162 and 70 lakes with ≥75 % probability of exceeding the lower (0.2 μg/L) and higher (1.0 μg/L) thresholds, respectively. This approach represents a critical advancement in using satellite imagery and field data to identify lakes at risk for developing toxic cyanobacteria blooms. Such models can help translate satellite data to aid water quality monitoring and management.

A data-driven approach to flag land-affected signals in satellite derived water quality from small lakes

The land-affected signal in remotely sensed radiance from nearshore waters is a common problem for remote sensing, introducing uncertainty in atmospheric correction and subsequent water quality constituent concentration estimates. This study proposes a new method for identifying effects of land on satellite remote sensing of water quality. The new optical water types (OWT) containing the land-affected signal were derived from POLYMER-corrected imagery of the Medium Resolution Imaging Spectrometer in reduced resolution (MERIS RR) and Sentinel-3 Ocean and Land Colour Instrument (OLCI). These were then applied, as part of a larger set of existing OWTs corresponding to the variability observed in natural waters, to satellite images. The ability to identify pixels containing both water and land, and those contaminated with radiance from adjacent land, was evaluated. Our test sites include dark lakes of varying size in Sweden (Lakes Rusken, Bolmen, Ringsjön, and Ivösjön) where the classification showed high sensitivity to land near the lake shore. The land-affected signal is shown to lead to underestimations of chlorophyll-a concentration and Forel-Ule colour indices, and overestimations of turbidity in these lakes, which can be corrected after masking out the land-affected pixels. The land-affected signal is strongest in summer, both NDVI and sun zenith angle covaried with the seasonal variation of land-affected signal. Further, the results confirmed that satellite images with coarser spatial resolution are more prone to land-affected signal compared to images with finer spatial resolution, for small inland water bodies. We propose a data-driven approach for water quality processing with ‘land-affected water types’ as an effective way to improve the lake optical water quality monitoring from water colour sensors.

A Novel Algorithm of Haze Identification Based on FY3D/MERSI-II Remote Sensing Data

Since 2013, frequent haze pollution events in China have been attracting public attention, generating a demand to identify the haze areas using satellite observations. Many studies of haze recognition algorithms are based on observations from space-borne imagers, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), the Visible Infrared Imaging Radiometer Suite (VIIRS) and the Advanced Himawari Imager (AHI). Since the haze pixels are frequently misidentified as clouds in the official cloud detection products, these algorithms mainly focus on recovering them from clouds. There are just a few studies that provide a more precise distinction between haze and clear pixels. The Medium Resolution Imaging Spectrometer II (MERSI-II), the imager aboard the FY-3D satellite, has similar bands to those of MODIS, hence, it appears to have equivalent application potential. This study proposes a novel MERSI haze mask (MHAM) algorithm to directly categorize haze pixels in addition to cloudy and clear ones. This algorithm is based on the fact that cloudy and clear pixels exhibit opposing visible channel reflectance and infrared channel brightness temperature characteristics, and clear pixels are relative brighter, and as well as this, there is a positive difference between their apparent reflectance values, at 0.865 μm and 1.64 μm, respectively, over bright surfaces. Compared with the Aqua/MODIS and MERSI-II official cloud detection products, these two datasets treat the dense aerosol loadings as certain clouds, possible clouds and possible clear pixels, and they treat distinguished light or moderate haze as possible clouds, possible clear pixels and certainly clear pixels, while the novel algorithm is capable of demonstrating the haze region’s boundary in a manner that is more substantially consistent with the true color image. Using the PM2.5 (particle matter with a diameter that is less than 2.5 μm) data monitored by the national air quality monitoring stations as the test source, the results indicated that when the ground-based PM2.5 ≥ 35 μg/cm3 is considered to be haze days, the samples with the recognition rate that is higher than 85% accounted for 72.22% of the total samples. When PM2.5 ≥ 50 μg/cm3 is considered as haze days, 83.33% of the samples had an identification rate that was higher than 85%. A cross-comparison with similar research methods showed that the method proposed in this study had better sensitivity to bright surface clear and haze areas. This study will provide a haze mask for subsequent quantitative inversion of aerosol characteristics, and it will further exert the application benefits of MERSI-II instrument aboard on FY3D satellite.

A novel red‐edge spectral index for retrieving the leaf chlorophyll content

Abstract The leaf chlorophyll content (Chlleaf) is a crucial vegetation parameter in carbon cycle modelling and agricultural monitoring at local, regional and global scales. The red‐edge spectral region is sensitive to variations in Chlleaf. An increasing number of sensors are capable of sampling red‐edge bands, providing opportunities to estimate Chlleaf. However, the contributions of canopy/foliar/soil factors are always combined in the reflectance signal, which limits the generalizability of vegetation index (VI)‐based Chlleaf inversions. This study aims to propose a new red‐edge chlorophyll index to decouple the effects of the canopy and soil background from the Chlleaf estimation. The chlorophyll sensitive index (CSI) was proposed, and the regression equations between the CSI and Chlleaf were acquired using PROSAIL (PROSPECT + SAIL) and the 4‐Scale‐PROSPECT model. Sensitivity analyses showed that the CSI is resistant to variations in the canopy structure and soil background. Validation results obtained using 308 ground‐measured samples over nine sites world‐wide revealed that CSI improves the Chlleaf retrieval accuracy (root mean square error (RMSE = 9.39 μg cm−2) compared with the existing Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI; RMSE = 13.00 μg cm−2). Moreover, the CSI method steadily achieves a highly accurate inversion under different LAI and Chlleaf conditions. Based on the CSI regression method, a Chlleaf product with a 30‐m/10‐day resolution across China was generated. The CSI is sensitive to Chlleaf but resistant to canopy structure and soil moisture parameters, and it has the potential to explicitly retrieve leaf‐scale biochemistry in ecosystem modelling and ecological applications.

Validation of Copernicus Sentinel-3/OLCI Level 2 Land Integrated Water Vapour product

Abstract. Validation of the Integrated Water Vapour (IWV) from Sentinel-3 Ocean and Land Colour Instrument (OLCI) was performed as a part of the “ESA/Copernicus Space Component Validation for Land Surface Temperature, Aerosol Optical Depth and Water Vapour Sentinel-3 Products” (LAW) project. High-spatial-resolution IWV observations in the near-infrared spectral region from the OLCI instruments aboard the Sentinel-3A and Sentinel-3B satellites provide continuity with observations from MERIS (Medium Resolution Imaging Spectrometer). The IWV was compared with reference observations from two networks: GNSS (Global Navigation Satellite System) precipitable water vapour from the SuomiNet network and integrated lower tropospheric columns from radio-soundings from the IGRA (Integrated Radiosonde Archive) database. Results for cloud-free matchups over land show a wet bias of 7 %–10 % for OLCI, with a high correlation against the reference observations (0.98 against SuomiNet and 0.90 against IGRA). Both OLCI-A and OLCI-B instruments show almost identical results, apart from an anomaly observed in camera 3 of the OLCI-B instrument, where observed biases are lower than in other cameras in either instrument. The wavelength drift in sensors was investigated, and biases in different cameras were found to be independent of wavelength. Effect of cloud proximity was found to have almost no effect on observed biases, indicating that cloud flagging in the OLCI IWV product is sufficiently reliable. We performed validation of random uncertainty estimates and found them to be consistent with the statistical a posteriori estimates, but somewhat higher.

Data fusion system for monitoring water quality: Application to chlorophyll-a in Baltic sea coast

We present an operational system for multi-sensor data fusion implemented at the Finnish Environment Institute. The system uses Ensemble Kalman filter and smoother algorithms, which are often used for probabilistic analysis of multi-sensor data. Uncertainty and spatial and temporal correlations present in the available observation data are accounted for to obtain accurate and realistic results. To test the data fusion system, daily chlorophyll-a concentration has been modelled across northern shoreline of Gulf of Finland over the period of August 1st – October 31st 2011. Chlorophyll-a data from routine monitoring stations, ferrybox measurements, and data derived from Medium Resolution Imaging Spectrometer (MERIS) instrument on board the ENVISAT satellite has been used as input. The data fusion system demonstrates the use of existing and well-known Ensemble Kalman filtering and smoothing methods for improving water quality monitoring programs and for ensuring compliance with ecological standards.

An optical water classification and quality control model (OC_QC model) for spectral diffuse attenuation coefficient

Spectral diffuse attenuation coefficient of down-welling irradiance (Kd(λ)) is a fundamental optical parameter for water color and of significance for heat budget calculation, photosynthesis modeling and carbon sequestration. Uncertainties exist in both in situ measured and satellite derived Kd(λ), therefore, evaluation of its data quality is essential. In addition, optical water classification of Kd(λ) is still limited. To address these issues, we proposed an optical water classification and quality control model (OC_QC model) based on a globally collected high-quality in situ Kd(λ) data. The model consists of the reference Kd(λ) spectra for 7 optical water types (OWT, ranging from clear open ocean water to highly turbid water) and a scoring system. An individual Kd(λ) spectrum can be assigned an OWT and quality control score (QC score) with this model. The performance of the OC_QC model is tested with independent in situ Kd(λ) spectra from the turbid coastal water (Bohai Sea) and clear oceanic water (Indian Ocean). Questionable or erroneous Kd(λ) spectra can be identified, suggesting the model’s robustness. The model was also evaluated with the Medium Resolution Imaging Spectrometer (MERIS) Level-2 and the Moderate Resolution Imaging Spectroradiometer (MODIS) Level-3 Kd(λ) data in the Bohai Sea, Yellow Sea, East China Sea and South China Sea. Satellite derived maps clearly showed the spatial and temporal variations of OWTs and QC scores, and the regions of low-quality Kd(λ) were reasonably extracted. Global maps of Kd(λ)-based optical water types and corresponding QC scores were also obtained with OC_QC model. With the quality controlled Kd(λ) data, the retrieval accuracy of Secchi disk depth was improved. The retrieval accuracies of Chlorophyll-a concentration and total seawater absorption coefficient were also improved with the Kd(λ) optical water classification based algorithms. The OC_QC model would be valuable for evaluating Kd(λ) data (especially the operational global Kd(λ) products), investigation of water optical properties, and development and merging of bio-optical algorithms.

Satellites quantify the spatial extent of cyanobacterial blooms across the United States at multiple scales

Previous studies indicate that cyanobacterial harmful algal bloom (cyanoHAB) frequency, extent, and magnitude have increased globally over the past few decades. However, little quantitative capability is available to assess these metrics of cyanoHABs across broad geographic scales and at regular intervals. Here, the spatial extent was quantified from a cyanobacteria algorithm applied to two European Space Agency satellite platforms—the MEdium Resolution Imaging Spectrometer (MERIS) onboard Envisat and the Ocean and Land Colour Instrument (OLCI) onboard Sentinel-3. CyanoHAB spatial extent was defined for each geographic area as the percentage of valid satellite pixels that exhibited cyanobacteria above the detection limit of the satellite sensor. This study quantified cyanoHAB spatial extent for over 2,000 large lakes and reservoirs across the contiguous United States (CONUS) during two time periods: 2008–2011 via MERIS and 2017–2020 via OLCI when cloud-, ice-, and snow-free imagery was available. Approximately 56% of resolvable lakes were glaciated, 13% were headwater, isolated, or terminal lakes, and the rest were primarily drainage lakes. Results were summarized at national-, regional-, state-, and lake-scales, where regions were defined as nine climate regions which represent climatically consistent states. As measured by satellite, changes in national cyanoHAB extent did have a strong increase of 6.9% from 2017 to 2020 (|Kendall’s tau (τ)| = 0.56; gamma (γ) = 2.87 years), but had negligible change (|τ| = 0.03) from 2008 to 2011. Two of the nine regions had moderate (0.3 ≤ |τ| < 0.5) increases in spatial extent from 2017 to 2020, and eight of nine regions had negligible (|τ| < 0.2) change from 2008 to 2011. Twelve states had a strong or moderate increase from 2017 to 2020 (|τ| ≥ 0.3), while only one state had a moderate increase and two states had a moderate decrease from 2008 to 2011. A decrease, or no change, in cyanoHAB spatial extent did not indicate a lack of issues related to cyanoHABs. Sensitivity results of randomly omitted daily CONUS scenes confirm that even with reduced data availability during a short four-year temporal assessment, the direction and strength of the changes in spatial extent remained consistent. We present the first set of national maps of lake cyanoHAB spatial extent across CONUS and demonstrate an approach for quantifying past and future changes at multiple spatial scales. Results presented here provide water quality managers information regarding current cyanoHAB spatial extent and quantify rates of change.

Hyperspectral reconstruction method for optically complex inland waters based on bio-optical model and sparse representing

For better use of well-performed water quality parameter estimation models and the comprehensive use of multi-source remote sensing data, hyperspectral reconstruction is urgently needed in the remote sensing of optically complex inland waters. In this study, we proposed a bio-optical–based hyperspectral reconstruction (BBHR) algorithm to generate hyperspectral above-surface remote-sensing reflectance (Rrs) data ranging in wavelength from 400 to 800 nm. One core advantage of the BBHR method is its in situ data independency, which theoretically renders the algorithm universal. The other advantage is its ability to reconstruct hyperspectral Rrs for the 400–800 nm spectral range, which facilitates the construction of more high accuracy chlorophyll-a concentration (Cchla) estimation models for optically complex waters. The reconstruction was tested by employing six widely used multispectral sensors: the Medium Resolution Imaging Spectrometer, (MERIS), Sentinel-3 Ocean and Land Color Instrument (S3 OLCI), Sentinel-2 Multispectral Instrument (S2 MSI), Geostationary Ocean Color Imager (GOCI), Visible Infrared Imaging Radiometer Suite (VIIRS), and Moderate Resolution Imaging Spectroradiometer (MODIS). The model performance was validated by using a ASD FieldSpec spectroradiometer-measured hyperspectral dataset containing 1396 samples and a satellite-in-situ match-up dataset with 66 samples. The results show that the proposed BBHR method exhibits satisfactory performance. The average mean absolute percentage error (MAPE), root mean square error (RMSE), R2 and bias indices of the BBHR-reconstructed Rrs over all spectral bands of the six multispectral sensors were 3.27%, 8.86 × 10−4 sr−1, 0.98, and − 6.53 × 10−5 sr−1, respectively. In the field Cchla estimation experiment that contained 391 samples (mean Cchla is 25.42 ± 16.37 μg/L), the BBHR algorithm improved the MAPE and RMSE indices of multispectral data from 0.47 and 12.80 μg/L to 0.42 and 10.16 μg/L, respectively. For the satellite image match-up dataset (66 samples), the BBHR method decreased the MAPE and RMSE indices of multispectral images from 0.51 and 12.94 μg/L to 0.32 and 8.01 μg/L, respectively. The proposed algorithm outperformed the other two high-accuracy models in terms of spectral fidelity and Cchla estimation. In addition, the BBHR method shows great potential for the multi-source monitoring of inland water bodies. This could improve the accuracy and robustness of the reconstruction when semi-synchronized multi-source data are input and increase the consistency of multi-source data when non-synchronized multi-source data are provided. Our results revealed that BBHR is a trustworthy algorithm that offers hyperspectral Rrs data and facilitates the remote monitoring of turbid inland waterbodies.

A semianalytical algorithm for estimating particulate composition in inland waters based on Sentinel-3 OLCI images

The composition of suspended particles is a key factor in determining the underwater light field, which is of great significance for understanding the variability in the optical properties of water bodies. In this study, the ratio of the phytoplankton absorption coefficient to the backscattering coefficient at wavelength of 681 nm (aph(681)/bb(681)) was found to be an optimal optical indicator of the ratio of chlorophyll-a to the total suspended matter concentration (Chla/TSM), a parameter indicating the particulate composition. Therefore, a semianalytical algorithm was proposed to estimate Chla/TSM from remote sensing reflectance (Rrs(λ)) at 681 nm and 754 nm on Sentinel-3 Ocean and Land Color Instrument (OLCI) images. The validation dataset collected from 11 inland lakes and 3 reservoirs in China and 2 inland lakes in America was used to evaluate the algorithm’s performance. The evaluation results demonstrated that the proposed algorithm could have favorable performance in inland waters. Furthermore, comparison with two other state-of-the-art algorithms (Sun_13 and NTD675) showed that this proposed algorithm had higher estimation accuracy, with an overall winning rate (OWR) of 60%, an unbiased mean absolute percentage error (UMAPE) reduction from 72.95% to 46.44%, a root mean square error (RMSE) decline from 1.42 µg/mg to 0.83 µg/mg and a normalized root mean square error (NRMSE) decline from 11.36% to 6.62%. This algorithm was successfully applied to acquire the Chla/TSM tempo-spatial variation using the OLCI images of Lake Taihu from 2016 to 2019. It was found that the algorithm developed based on OLCI images can be applied to satellite sensors with similar bands, such as Medium-Resolution Imaging Spectrometer (MERIS), and Sentinel-2 multispectral instrument (MSI), etc. As a simple and effective algorithm, the proposed algorithm has the potential to monitor changes in Chla/TSM in inland waters on a global scale.

Merging of the Case 2 Regional Coast Colour and Maximum-Peak Height chlorophyll-a algorithms: validation and demonstration of satellite-derived retrievals across US lakes

Water quality monitoring is relevant for protecting the designated, or beneficial uses, of water such as drinking, aquatic life, recreation, irrigation, and food supply that support the economy, human well-being, and aquatic ecosystem health. Managing finite water resources to support these designated uses requires information on water quality so that managers can make sustainable decisions. Chlorophyll-a (chl-a, µg L−1) concentration can serve as a proxy for phytoplankton biomass and may be used as an indicator of increased anthropogenic nutrient stress. Satellite remote sensing may present a complement to in situ measures for assessments of water quality through the retrieval of chl-a with in-water algorithms. Validation of chl-a algorithms across US lakes improves algorithm maturity relevant for monitoring applications. This study compares performance of the Case 2 Regional Coast Colour (C2RCC) chl-a retrieval algorithm, a revised version of the Maximum-Peak Height (MPH(P)) algorithm, and three scenarios merging these two approaches. Satellite data were retrieved from the MEdium Resolution Imaging Spectrometer (MERIS) and the Ocean and Land Colour Instrument (OLCI), while field observations were obtained from 181 lakes matched with U.S. Water Quality Portal chl-a data. The best performance based on mean absolute multiplicative error (MAEmult) was demonstrated by the merged algorithm referred to as C15−M10 (MAEmult = 1.8, biasmult = 0.97, n = 836). In the C15−M10 algorithm, the MPH(P) chl-a value was retained if it was > 10 µg L−1; if the MPH(P) value was ≤ 10 µg L−1, the C2RCC value was selected, as long as that value was < 15 µg L−1. Time-series and lake-wide gradients compared against independent assessments from Lake Champlain and long-term ecological research stations in Wisconsin were used as complementary examples supporting water quality reporting requirements. Trophic state assessments for Wisconsin lakes provided examples in support of inland water quality monitoring applications. This study presents and assesses merged adaptations of chl-a algorithms previously reported independently. Additionally, it contributes to the transition of chl-a algorithm maturity by quantifying error statistics for a number of locations and times.

A Semianalytical Algorithm for Estimating Water Transparency in Different Optical Water Types from MERIS Data

Water transparency (or Secchi disk depth: ZSD) is a key parameter of water quality; thus, it is very important to routinely monitor. In this study, we made four efforts to improve a state-of-the-art ZSD estimation algorithm that was developed in 2019 on the basis of a new underwater visibility theory proposed in 2015. The four efforts were: (1) classifying all water into clear (Type I), moderately turbid (Type II), highly turbid (Type III), or extremely turbid (Type IV) water types; (2) selecting different reference wavelengths and corresponding semianalytical models for each water type; (3) employing an estimation model to represent reasonable shapes for particulate backscattering coefficients based on the water type classification; and (4) constraining likely wavelength range at which the minimum diffuse attenuation coefficient (Kd(λ)) will occur for each water type. The performance of the proposed ZSD estimation algorithm was compared to that of the original state-of-the-art algorithm using a simulated dataset (N = 91,287, ZSD values 0.01 to 44.68 m) and an in situ measured dataset (N = 305, ZSD values 0.3 to 16.4 m). The results showed a significant improvement with a reduced mean absolute percentage error (MAPE) from 116% to 65% for simulated data and from 32% to 27% for in situ data. Outliers in the previous algorithm were well addressed in the new algorithm. We further evaluated the developed ZSD estimation algorithm using medium resolution imaging spectrometer (MERIS) images acquired from Lake Kasumigaura, Japan. The results obtained from 19 matchups revealed that the estimated ZSD matched well with the in situ measured ZSD, with a MAPE of 15%. The developed ZSD estimation algorithm can probably be applied to different optical water types due to its semianalytical features.

Direct emission spectroscopy of exoplanets with the medium resolution imaging spectrometer on board JWST MIRI

Context. The Medium Resolution Spectrometer (MRS) of the Mid-Infrared Instrument (MIRI) on board the James Webb Space Telescope (JWST) will give access to mid-infrared (mid-IR) spectra (5–28 microns) while retaining spatial information. With the unparalleled sensitivity of JWST and the MIRI detectors, the MRS has the potential to revolutionise our understanding of giant exoplanet atmospheres. Aims. Molecular mapping is a promising detection and characterisation technique used to study the spectra of directly imaged exoplanets. We aim to examine the feasibility and application of this technique to MRS observations. Methods. We used the instrument simulator MIRISIM to create mock observations of resolved star and exoplanet systems. As an input for the simulator, we used stellar and planet parameters from literature, with the planet spectrum being modelled with the radiative transfer code petitRADTRANS. After processing the raw data with the JWST pipeline, we high pass filter the data to account for the stellar point spread function, and used a forward modelling approach to detect the companions and constrain the chemical composition of their atmospheres through their molecular signatures. Results. We identified limiting factors in spectroscopic characterisation of directly imaged exoplanets with the MRS and simulated observations of two representative systems, HR8799 and GJ504. In both systems, we could detect the presence of multiple molecules that were present in the input model of their atmospheres. We used two different approaches with single molecule forward models, used in literature, that are sensitive to detecting mainly H2O, CO, CH4, and NH3, and a log-likelihood ratio test that uses full atmosphere forward models and is sensitive to a larger number of less dominant molecular species. Conclusions. We show that the MIRI MRS can be used to characterise widely separated giant exoplanets in the mid-IR using molecular mapping. Such observations would provide invaluable information for the chemical composition of the atmosphere, complementing other JWST observing modes, as well as ground-based observations.