Atmospheric Correction Method Research Articles

Semi-supervised correction model for turbulence-distorted images

Significant progress has been made in addressing turbulence distortion in recent years, but persistent challenges remain. Firstly, existing methods heavily rely on fully supervised optimization strategies and synthetic datasets, posing difficulties in effectively utilizing unlabeled real data for training. Secondly, most approaches construct networks in a straightforward manner, overlooking the representation model of phase distortion and point spread function (PSF) in spatial and channel dimensions. This oversight restricts the potential for distortion correction. To address these challenges, this paper proposes a semi-supervised atmospheric turbulence correction method based on the mean-teacher framework. Our approach imposes constraints on the unlabeled data of student networks using pseudo-labels generated by teacher networks, thereby enhancing the generalization ability by leveraging information from unlabeled data. Furthermore, we introduce to use no-reference image quality assessment criterion to select the most reliable pseudo-label for each unlabeled sample by predicting physical parameters that indicating the level of degradation. Additionally, we propose to combine sliding window-based self-attention with channel attention to facilitate local-global context interaction. This design is inspired by the representation of phase distortion and PSF, which can be characterized by coefficients and basis functions corresponding to the channel-wise representation of convolutional neural network features. Moreover, the base functions exhibit spatial correlation, akin to Zenike and Airy disks. Experimental results show that the proposed method surpasses state-of-the-art models.

Optical water characterization and atmospheric correction assessment of estuarine and coastal waters around the AERONET-OC Bahia Blanca

The site AERONET-OC Bahía Blanca (BB-AERONET-OC) is located at the mouth of the Bahía Blanca Estuary, Argentina (Southwestern Atlantic Ocean), a coastal system defined by its high suspended loads and relatively low colored dissolved organic matter. The typically high turbidity of these waters makes the BB-AERONET-OC distinctive within the AERONET-OC network stations, providing exceptional opportunities not only for the validation of atmospheric correction algorithms but also for the development of regional algorithms for coastal complex waters. A SeaWiFS Photometer Revision for Incident Surface Measurements (SeaPRISM) instrument was deployed in January 2020 in the upper rail of a Mareograph Tower, a 15 m tall structure, located 10 miles away from the coast in optically deep waters. In this work we used the remote sensing reflectance (Rrs) derived from the BB-AERONET-OC measurements along with in situ hyperspectral radiometric data to classify optical water types (OWTs). We assigned each Rrs(λ) spectra to one of the five OWTs defined by Tran et al., and OWTs were further characterized with the concentrations of optically significant components (chlorophyll-a and suspended particulate matter) and inherent optical properties (absorptions of phytoplankton, non-algal particles, and dissolved organic matter), retrieved from water samples obtained simultaneously with radiometric spectra. Based on a match-up exercise with in situ data, different schemes of atmospheric correction methods were applied to Sentinel-3 Ocean and Land Colour Instrument (OLCI) images. The operational product OLCI Level 2 European Space Agency (ESA) standard (hereafter referred to as “Standard Neural Network (NN)”) proves to be the most suitable atmospheric correction algorithm, which was then used to describe spatial patterns and temporal variability of the different OWTs in the region. The BB-AERONET-OC site is located in a sharp transition between estuarine and coastal waters that present contrasting optical conditions: OWT 4 dominates over time (73.72% of the observations), followed by OWT 3 (24.74%) and OWT 5 (1.53%). OWTs 4 and 5 are associated with turbid waters of the Bahía Blanca Estuary, especially OWT 5, which typifies the very turbid waters from the inner estuary, with the particulate load dominated by mineral sediments and detritus. OWT 3, in turn, depicts the eutrophic coastal waters of the inner shelf. The variability of OWTs and the relative contribution of organic and inorganic compounds to the suspended material would be mostly related with the prevalence of northwest winds in the area, which would drive the export of estuarine sediments to the shelf.

Spatiotemporal Variations in Biophysical Water Quality Parameters: An Integrated In Situ and Remote Sensing Analysis of an Urban Lake in Chile

This study aims to develop and implement a methodology for retrieving bio-optical parameters in a lagoon located in the Biobío region, South-Central Chile, by analyzing time series of Landsat-8 OLI satellite images. The bio-optical parameters, i.e., chlorophyll-a (Chl-a, in mg·m−3) and turbidity (in NTU) were measured in situ during a satellite overpass to minimize the impact of atmospheric distortions. To calibrate the satellite images, various atmospheric correction methods (including ACOLITE, C2RCC, iCOR, and LaSRC) were evaluated during the image preprocessing phase. Spectral signatures obtained from the scenes for each atmospheric correction method were then compared with spectral signatures acquired in situ on the water surface. In short, the ACOLITE model emerged as the best fit for the calibration process, reaching R2 values of 0.88 and 0.79 for Chl-a and turbidity, respectively. This underlies the importance of using inversion models, when processing water surfaces, to mitigate errors due to aerosols and the sun-glint effect. Subsequently, reflectance data derived from the ACOLITE model were used to establish correlations between various spectral indices and the in situ data. The empirical retrieval models (based on band combinations) yielding superior performance, with higher R2 values, were subjected to a rigorous statistical validation and optimization by applying a bootstrapping approach. From this process the green chlorophyll index (GCI) was selected as the optimal choice for constructing the Chl-a retrieval model, reaching an R2 of 0.88, while the red + NIR spectral index achieved the highest R2 value (0.79) for turbidity analysis, although in the last case, it was necessary to incorporate data from several seasons for an adequate model training. Our analysis covered a broad spectrum of dates, seasons, and years, which allowed us to search deeper into the evolution of the trophic state associated with the lake. We identified a striking eight-year period (2014–2022) characterized by a decline in Chl-a concentration in the lake, possibly attributable to governmental measures in the region for the protection and conservation of the lake. Additionally, the OLI imagery showed a spatial pattern varying from higher Chl-a values in the northern zone compared to the southern zone, probably due to the heat island effect of the northern urban areas. The results of this study suggest a positive effect of recent local regulations and serve as the basis for the creation of a modern monitoring system that enhances traditional point-based methods, offering a holistic view of the ongoing processes within the lake.

Assessment of ocean color atmospheric correction methods and development of a regional ocean color operational dataset for the Baltic Sea based on Sentinel-3 OLCI

The Baltic Sea is characterized by large gradients in salinity, high concentrations of colored dissolved organic matter, and a phytoplankton phenology with two seasonal blooms. Satellite retrievals of chlorophyll-a concentration (chl-a) are hindered by the optical complexity of this basin and the reduced performance of the atmospheric correction in its highly absorbing waters. Within the development of a regional ocean color operational processing chain for the Baltic Sea based on Sentinel-3 Ocean and Land Colour Instrument (OLCI) full-resolution data, the performance of four atmospheric correction processors for the retrieval of remote-sensing reflectance (Rrs) was analyzed. Assessments based on three Aerosol Robotic Network-Ocean Color (AERONET-OC) sites and shipborne hyperspectral radiometers show that POLYMER was the best-performing processor in the visible spectral range, also providing a better spatial coverage compared with the other processors. Hence, OLCI Rrs spectra retrieved with POLYMER were chosen as input for a bio-optical ensemble scheme that computes chl-a as a weighted sum of different regional multilayer perceptron neural nets. This study also evaluated the operational Rrs and chl-a datasets for the Baltic Sea based on OC-CCI v.6. The chl-a retrievals based on OC-CCI v.6 and OLCI Rrs, assessed against in-situ chl-a measurements, yielded similar results (OC-CCI v.6: R2 = 0.11, bias = −0.22; OLCI: R2 = 0.16, bias = −0.03) using a common set of match-ups for the same period. Finally, an overall good agreement was found between chl-a retrievals from OLCI and OC-CCI v.6 although differences between Rrs were amplified in terms of chl-a estimates.

Optimized 6S Model Atmospheric Correction Method Combined with NCEP Reanalysis Data

Optimized 6S Model Atmospheric Correction Method Combined with NCEP Reanalysis Data

An Algorithm Developed for Smallsats Accurately Retrieves Landsat Surface Reflectance Using Scene Statistics

Closed-form Method for Atmospheric Correction (CMAC) is software that overcomes radiative transfer method problems for smallsat surface reflectance retrieval: unknown sensor radiance responses because onboard monitors are omitted to conserve size/weight, and ancillary data availability that delays processing by days. CMAC requires neither and retrieves surface reflectance in near real time, first mapping the atmospheric effect across the image as an index (Atm-I) from scene statistics, then reversing these effects with a closed-form linear model that has precedence in the literature. Five consistent-reflectance area-of-interest targets on thirty-one low-to-moderate Atm-I images were processed by CMAC and LaSRC. CMAC retrievals accurately matched LaSRC with nearly identical error profiles. CMAC and LaSRC output for paired images of low and high Atm-I were then compared for three additional consistent-reflectance area-of-interest targets. Three indices were calculated from the extracted reflectance: NDVI calculated with red (standard) and substitutions with blue and green. A null hypothesis for competent retrieval would show no difference. The pooled error for the three indices (n = 9) was 0–3% for CMAC, 6–20% for LaSRC, and 13–38% for uncorrected top-of-atmosphere results, thus demonstrating both the value of atmospheric correction and, especially, the stability of CMAC for machine analysis and AI application under increasing Atm-I from climate change-driven wildfires.

Effect of atmospheric corrections on shallow sea bathymetric mapping using gaofen-2 imagery: a case study in Lingyang Reef, South China Sea

Satellite-derived bathymetry (SDB) with high spatial resolution effectively maps detailed information about shallow sea depths. Properly selecting atmospheric correction (AC) methods is crucial to obtaining precise bathymetric information from satellite data. In this work, three different AC methods (FLAASH, 6S, and DOS) were applied to GaoFen-2 imagery in Lingyang Reef, South China Sea. Water depth retrieval models, such as single-band, multiband, and band ratio models, were established by 470 points of in-situ water depth data and used for evaluating the model performances. Additionally, the optimal model was applied to depth inversion. The results show that the multiband model based on four bands performs well in this study area with R2 =0.736. The choice of AC methods significantly affects SDB, although acceptable results can be derived without these methods. Specifically, the DOS method has the highest inversion accuracy, with a mean relative error and root mean square error of 14.05% and 3.31 m, respectively. Furthermore, the inversion accuracy in various depth ranges may be primarily influenced by suspended sediment concentration and bottom-type uniformity. This study provides a valuable reference for selecting AC approaches and inversion models in high-spatial-resolution SDB in shallow seas.

Corrigendum: Ocean color atmospheric correction methods in view of usability for different optical water types

Corrigendum: Ocean color atmospheric correction methods in view of usability for different optical water types

Machine learning for cyanobacteria mapping on tropical urban reservoirs using PRISMA hyperspectral data

Urban reservoirs are important for drinking water services and urban living. However, potentially toxic cyanobacteria blooms are frequently present due to human pollution and might threaten the urban water supply. Conveniently, cyanobacteria can be monitored by remote sensing-based approaches based on the spectral features of C-Phycocyanin (PC). Furthermore, methods leveraging Machine Learning Algorithms (MLA) for PC estimation from hyperspectral data have highlighted the potential to estimate PC more accurately - even at low concentrations. Since relatively few methodologies for PC retrieval in tropical environments have been developed or validated, this research evaluated PRISMA hyperspectral data processed with three MLA (Random Forest, Extreme Gradient Boost, and Support Vector Machines) to estimate PC concentrations in the Billings reservoir, Brazil. The same MLA were used to generate PC models using Wordview-3 and Landsat-8/OLI simulated data to assess the potential gain of using hyperspectral over multispectral data. A PRISMA image was processed with three atmospheric correction methods and validated with co-located in-situ data, where the best atmospherically corrected product was used to generate synthetic Landsat-8/OLI and Worldview-3 images. The PC models were calibrated and validated through Monte Carlo simulation using field radiometric and biological data (Chlorophyll-a, PC, and phytoplankton taxonomy) collected in eight field campaigns (N = 115). The PRISMA and the synthetic multispectral images were used for a second round of models’ validation using co-located PC measurements (match-up window ± 4 h). The global PC Mixture Density Network was also applied to the PRISMA data, and the estimates were compared with the other MLA. The results showed that the standard PRISMA surface reflectance product provided the best atmospheric correction (MAE < 20% for the 500–700 nm bands), while ACOLITE and 6SV underperformed it from two to more than ten-fold. Cyanobacteria species were abundant in 96% of the taxonomical samples, even though relatively low PC concentrations were found (PC from 0 to 301.81 μg/L and median PC = 2.9 μg/L). The global Mixture Density Network sharply overestimated PC (MAE = 280% and Bias = 280%), potentially due to Billings reservoir’s low PC:Chlorophyll-a ratio relative to the original training dataset. PRISMA/Random Forest (MAE = 45%) achieved the lowest error for orbital PC estimate, while Extreme Gradient Boost outperformed the other MLA using Worldview-3 (MAE = 49%) and Landsat-8 (MAE = 74%) synthetic imagery. Therefore, the results suggest hyperspectral and multispectral orbital data aligned with MLA are feasible for monitoring PC, even for waters containing low PC concentrations and reduced PC:Chlorophyll-a ratios.

Leveraging Landsat-8/-9 underfly observations to evaluate consistency in reflectance products over aquatic environments

With an identical design and build, the Operational Land Imager-2 (OLI2) aboard Landsat-9 (L9) complements OLI observations by reducing the global revisit rate of Landsat to 8 days. This study takes advantage of near-coincident OLI2 and OLI observations obtained on 11–17 November 2021 to assess the relative quality of the standard United States Geological Survey (USGS) top-of-atmosphere (TOA) reflectance (ρt) and atmospherically corrected reflectance (aquatic reflectance; ρwAR) products over bodies of water. The TOA assessment was carried out for all the visible bands, including the panchromatic band, as well as the near-infrared (NIR) and shortwave infrared (SWIR) bands, whereas ρwAR products were analyzed in the 443, 482, 561, and 655 nm bands. The overlapping areas of OLI-OLI2 ρw product pairs were further analyzed for the rigor in corrections for the viewing geometry implemented in selected atmospheric correction (AC) processors, including SeaDAS, ACOLITE, and POLYMER, with products denoted as ρwAR, ρwac, and ρwpol, respectively. Overall, we found the OLI2-OLI ρt products to be consistent within 0.4% in the visible-near-infrared (VNIR) bands except in the green band (561 nm), where OLI2 records ∼0.8% larger values than OLI. The two SWIR bands (1610 and 2200 nm) were also found to agree within ∼2.2% and 2.1%, respectively, with OLI2 being lower in magnitude. The median differences in the standard ρw (ρwAR) were estimated to be ∼2.4%, 1.5%, 2.3%, and 2.5% in the 443, 482, 561, and 655 nm bands, respectively, which are well within the accepted differences in cross-mission data merging schemes. Further, we show that OLI2's signal-to-noise ratio (SNR) is 7–30% higher than that of OLI in all the bands, likely due to its 14-bit digitization rate, as compared to OLI's 12-bit digitization rate. Our self-consistency assessment of the AC processors for handling the differences in the view zenith angles (ΔVZA) and relative azimuth angles (ΔRAA) of OLI2 and OLI observations suggests that, overall, the processors account for the Sun-sensor geometry differently at different spectral bands. These differences (inconsistencies) amount to average median absolute percentage differences (MAPD) in ρw from 0.3 to 5.3% (e.g., ∆ρwAR443nmΔVZAΔRAA=ρwAR,OLI443+6.310−ρwAR,OLI2443+3.9−13<0.5%).More specifically, we found that SeaDAS provides the most optimal corrections for the angular variability as a function of VZA, ρwac are most consistent for different ranges of RAAs, and POLYMER performs best in the 443 nm band. We surmise that future (and other existing) AC methods should be tested with Landsat-8/-9 underfly imagery to quantify their performance for tackling the variability in imaging geometry and minimize associated uncertainties. With several missions planned for launch by the end of this decade, it is further emphasized that post-launch tandem maneuvers are essential to creating harmonized multi-mission data products and, therefore, similar operations should be considered and extended to ensure a broad range of environmental conditions are captured for comprehensive cross-mission analyses.

Applicability study of four atmospheric correction methods in the remote sensing of lake water color

Due to the complexity of spectral characteristics for inland lake water, high-precision atmospheric correction methods play an essential role in remote sensing. This research conducted atmospheric correction on the Tiande lake zone in Zhengzhou, China based on Zhuhai-1, Sentinel-2, Planet, and GF-2 images using four methods (QUAC, Dark Subtract, Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes (FLAASH) and 6S). In doing so, water reflectance can be extracted and compared with the data measured from a ground-based spectrometer. The analysis indicates that the FLAASH method has best applicability to Zhuhai-1 and Sentinel-2 images; while 6S method shows best applicability to Planet and GF-2 images. The comparison of single-band images indicates that the four atmospheric correction approaches used in different images are proven to be applicable to construction of sensitive wavebands of a water body inversion model in the Tiande lake zone. The correction results of four types of images prove that FLAASH and 6S are more applicable to lake water color remote sensing inversion.

An effective atmospheric correction method for the wide swath of Chinese GF-1 and GF-6 WFV images on lands

Accurate land surface reflectance plays an important role in the accurate inversion of surface parameters, and atmospheric correction plays a decisive role in obtaining accurate reflectance. For GF-1 WFV and GF-6 WFV images, there are two major issues to be addressed, including the spectral differences between nadir with far off-nadir pixels and the spatial variability of atmospheric components for wide imaging. Therefore, this study focuses on these two issues using the Sensor Invariant Atmospheric Correction (SIAC) method. Our results reveal that the SIAC approach improves the correlation accuracy from 0.8868 to 0.9173 for GF-1 WFV image compared with Sentinel-2 reflectance, from 0.9530 to 0.9620 for GF-6 WFV image compared with the results using FLAASH model. For alleviating wide-swathed anisotropy, the directional imaging angle is calculated with the result ranging from 5.6450° to 33.7497°. Furthermore, the atmospheric components have been inversed pixel by pixel with obvious spatial variation. And the correlation of inversed aerosol optical thickness (AOT) and total column water vapor (TCWV) with a spatial resolution of 500 m TCWV with measured results of AERONET (AErosol RObotic NETwork) observation stations are 0.9175 and 0.4442, respectively. These results reveal that the atmospheric correction method works well, which is effective for the wide swath of Chinese GF-1 WFV and GF-6 WFV images on land.

Ocean color atmospheric correction methods in view of usability for different optical water types

Satellite remote sensing allows large-scale global observations of aquatic ecosystems and matter fluxes from the source through rivers and lakes to coasts, marginal seas into the open ocean. Fuzzy logic classification of optical water types (OWT) is increasingly used to optimally determine water properties and enable seamless transitions between water types. However, effective exploitation of this method requires a successful atmospheric correction (AC) over the entire spectral range, i.e., the upstream AC is suitable for each water type and always delivers classifiable remote-sensing reflectances. In this study, we compare five different AC methods for Sentinel-3/OLCI ocean color imagery, namely IPF, C2RCC, A4O, POLYMER, and ACOLITE-DSF (all in the 2022 current version). We evaluate their results, i.e., remote-sensing reflectance, in terms of spatial exploitability, individual flagging, spectral plausibility compared to in situ data, and OWT classifiability with four different classification schemes. Especially the results of A4O show that it is beneficial if the performance spectrum of the atmospheric correction is tailored to an OWT system and vice versa. The study gives hints on how to improve AC performance, e.g., with respect to homogeneity and flagging, but also how an OWT classification system should be designed for global deployment.

Bio-geo-optical modelling of natural waters

The color of natural waters – oceanic, coastal, and inland – is determined by the spectral absorption and scattering properties of dissolved and particulate water constituents. Remote sensing of aquatic ecosystems requires a comprehensive understanding of these inherent optical properties (IOPs), their interdependencies, and their impact on ocean (water) color, i.e., remote-sensing reflectance. We introduce a bio-geo-optical model for natural waters that includes revised spectral absorption and scattering parameterizations, based on a comprehensive analysis of precisely measured IOPs and water constituents. In addition, specific IOPs of the most significant phytoplankton groups are modeled and a system is proposed to represent the optical variability of phytoplankton diversity and community structures. The model provides a more accurate representation of the relationship between bio-geo-optical properties and can better capture optical variability across different water types. Based on the evaluation both using the training and independent testing data, our model demonstrates an accuracy of within ±5% for most component IOPs throughout the visible spectrum. We also discuss the potential of this model for radiative transfer simulations and building a comprehensive synthetic dataset especially for optically complex waters. Such datasets are the crucial basis for the development of satellite-based ocean (water) color algorithms and atmospheric correction methods. Our model reduces uncertainties in ocean color remote sensing by enhancing the distinction of optically active water constituents and provides a valuable tool for predicting the optical properties of natural waters across different water types.

Closed-Form Method for Atmospheric Correction (CMAC) of Smallsat Data Using Scene Statistics

High-cadence Earth observation smallsat images offer potential for near real-time global reconnaissance of all sunlit cloud-free locations. However, these data must be corrected to remove light-transmission effects from variable atmospheric aerosol that degrade image interpretability. Although existing methods may work, they require ancillary data that delays image output, impacting their most valuable applications: intelligence, surveillance, and reconnaissance. Closed-form Method for Atmospheric Correction (CMAC) is based on observed atmospheric effects that brighten dark reflectance while darkening bright reflectance. Using only scene statistics in near real-time, CMAC first maps atmospheric effects across each image, then uses the resulting grayscale to reverse the effects to deliver spatially correct surface reflectance for each pixel. CMAC was developed using the European Space Agency’s Sentinel-2 imagery. After a rapid calibration that customizes the method for each imaging optical smallsat, CMAC can be applied to atmospherically correct visible through near-infrared bands. To assess CMAC functionality against user-applied state-of-the-art software, Sen2Cor, extensive tests were made of atmospheric correction performance across dark to bright reflectance under a wide range of atmospheric aerosol on multiple images in seven locations. CMAC corrected images faster, with greater accuracy and precision over a range of atmospheric effects more than twice that of Sen2Cor.

Satellite-Derived Bathymetry Mapping on Horseshoe Island, Antarctic Peninsula, with Open-Source Satellite Images: Evaluation of Atmospheric Correction Methods and Empirical Models

Satellite-derived bathymetry (SDB) is the process of estimating water depth in shallow coastal and inland waters using satellite imagery. Recent advances in technology and data processing have led to improvements in the accuracy and availability of SDB. The increased availability of free optical satellite sensors, such as Landsat missions and Sentinel 2 satellites, has increased the quantity and frequency of SDB research and mapping efforts. In addition, machine learning (ML)- and deep learning (DL)-based algorithms, which can learn to identify features that are indicative of water depth, such as color or texture variations, have started to be used for extracting bathymetry information from satellite imagery. This study aims to produce an initial optical image-based SBD map of Horseshoe Island’s shallow coasts and to perform a comprehensive and comparative evaluation with Landsat 8 and Sentinel 2 satellite images. Our research considers the performance of empirical SDB models (classical, ML-based, and DL-based) and the effects of the atmospheric correction methods ACOLITE, iCOR, and ATCOR. For all band combinations and depth intervals, the ML-based random forest and XGBoost models delivered the highest performance and best fitting ability by achieving the lowest error with MAEs smaller than 1 m up to 10 m depth and a maximum correlation of R2 around 0.80. These models are followed by the DL-based ANN and CNN models. Nonetheless, the non-linearity of the reflectance–depth connection was significantly reduced by the ML-based models. Furthermore, Landsat 8 showed better performance for 10–20 m depth intervals and in the entire range of (0–20 m), while Sentinel 2 was slightly better up to 10 m depth intervals. Lastly, ACOLITE, iCOR, and ATCOR provided reliable and consistent results for SDB, where ACOLITE provided the highest automation.

An atmospheric influence correction method for longwave radiation-based in-situ land surface temperature

Obtaining in-situ land surface temperature (LST) with thermal infrared or longwave radiometers for validating satellite LST, namely the radiation-based method, is one of the most widely used approaches. Due to the relatively short path lengths, the near-surface atmosphere contribution is generally ignored in radiation-based in-situ LST measuring for narrowband radiometers. However, it is necessary to investigate the influence for broadband radiometers. In this study, nine stations in China are selected to quantify the atmospheric influence. Furthermore, a correction method combining the Radiative Transfer Equation and Multi-Layer Meteorological Parameters (RTE-MLMP) is proposed to correct the influence on radiation-based in-situ LST. Results show that the influence follows diurnal patterns and seasonal differences. Depending on the station, ignoring the near-surface atmosphere influence can cause in-situ LST to be overestimated (underestimated) by up to 3.11 (1.21) K at nighttime (daytime). For the RTE-MLMP method, the key is to estimate the near-surface atmospheric transmittance (τNS) and radiance (LNS) and to establish equations for each station. Results show that the mean and standard deviation (STD) of the error are both lower than 0.001 for the estimated τNS, and lower than 0.1 W/m2 and 0.6 W/m2 for the estimated LNS, respectively. Then, the in-situ LST is corrected with the estimated τNS and LNS. The resulting LST mean and STD of its error are both lower than 0.1 K, which is significantly lower than ignoring the near-surface atmospheric influence. Our further analysis suggests that single-layer meteorological parameters can only be used when the installation height difference between the meteorological sensor and the long-wave radiometer is within 3 m. Since the correction method relies on readily available meteorological data, further research should focus on the generalizable coefficients or strategy for atmospheric correction to enhance the accuracy of radiation-based in-situ measured LST.

An Atmospheric Phase Correction Method Based on Normal Vector Clustering Partition in Complicated Conditions for GB-SAR

Atmospheric phase is the main factor affecting the accuracy of ground-based synthetic aperture radar. The atmospheric phase screen (APS) may be very complicated, due to the drastic changes in atmospheric conditions, and the conventional correction methods based on regression models cannot fit and correct it effectively. Partition correction is a feasible path to improve atmospheric phase correction (APC) accuracy for complicated APS, but the overfitting problem cannot be ignored. In this article, we propose a clustering partition method, based on the normal vector of APS, which can partition the complicated APS more reasonably, and then perform APC based on the partition results. APC, and simulation experiments on measurement data, suggests that the proposed method achieves higher accuracy than the conventional model-based methods for complicated APS and avoids severe overfitting, realizing the balance between accuracy and credibility. This article verifies the feasibility and effectiveness of using APS distribution information to guide the partition and conduct APC.

Assessment of Estimated Phycocyanin and Chlorophyll-a Concentration from PRISMA and OLCI in Brazilian Inland Waters: A Comparison between Semi-Analytical and Machine Learning Algorithms

The aim of this work is to test the state-of-the-art of water constituent retrieval algorithms for phycocyanin (PC) and chlorophyll-a (chl-a) concentrations in Brazilian reservoirs from hyperspectral PRISMA images and concurrent in situ data. One near-coincident Sentinel-3 OLCI dataset has also been considered for PC mapping as its high revisit time is a relevant element for mapping cyanobacterial blooms. The testing was first performed on remote sensing reflectance (Rrs), as derived by applying two atmospheric correction methods (6SV, ACOLITE) to Level 1 data and as provided in the corresponding Level 2 products (PRISMA L2C and OLCI L2-WFR). Since PRISMA images were affected by sun glint, the testing of three de-glint models was also performed. The applicability of Semi-Analytical (SA) and Mixture Density Network (MDN) algorithms in enabling PC and chl-a concentration retrieval was then tested over three PRISMA scenes; in the case of PC concentration estimation, a Random Forest (RF) algorithm was further applied. Regarding OLCI, the SA algorithm was tested for PC estimation; notably, only SA was calibrated with site-specific data from the reservoir. The algorithms were applied to the Rrs spectra provided by PRISMA L2C products—and those derived with ACOLITE, in the case of OLCI—as these data showed better agreement with in situ measurements. The SA model provided low median absolute error (MdAE) for PRISMA-derived (MdAE = 3.06 mg.m−3) and OLCI-derived (MdAE = 3.93 mg.m−3) PC concentrations, while it overestimated PRISMA-derived chl-a (MdAE = 42.11 mg.m−3). The RF model for PC applied to PRISMA performed slightly worse than SA (MdAE = 5.21 mg.m−3). The MDN showed a rather different performance, with higher errors for PC (MdAE = 40.94 mg.m−3) and lower error for chl-a (MdAE = 23.21 mg.m−3). The results overall suggest that the model calibrated with site-specific measurements performed better and indicates that SA could be applied to PRISMA and OLCI for remote sensing of PC in Brazilian reservoirs.

Study on the Relationship between Urban Street-Greenery Rate and Land Surface Temperature Considering Local Climate Zone.

Relationship exploration between the street-greenery rate (SGR) of different street types and land surface temperature (LST) is of great significance for realizing regional sustainable development goals. Given the lack of consideration of the local climate zone concept (LCZ), Chongqing's Inner Ring region was selected as a case to assess the relationship between SGR and LST. Firstly, the LST was retrieved based on Landsat 8 imagery, which was calibrated by the atmospheric correction method; next, the street-greenery rates of different streets were calculated based on the semantic segmentation method; finally, street types were classified in detail by introducing LCZ, and the relationship between SGR and LST was investigated. The results showed that: (1) The LST spatial distribution pattern was closely related to human activity, with the high-temperature zones mainly concentrated in the core commercial areas, dense residential areas, and industrial cluster areas; (2) The average SGR values of expressways, main trunk roads, secondary trunk roads, and branch roads were 21.70%, 22.40%, 24.60%, and 26.70%, respectively. The level of SGR will decrease when the street width increases; (3) There is a negative correlation between the SGR and the LST in most streets. Among them, the LST of secondary trunk roads in low-rise and low-density built-up areas with a south-north orientation had a strong negative correlation with the SGR. Moreover, the wider the street, the higher the cooling efficiency of plants. Specifically, the LST of streets in low-rise and low-density built-up areas with south-north orientation may decrease by 1°C when the street-greenery rate is increased by 3.57%.