Search

Sentinel-3 synergy (SYN) VEGETATION (VGT) products were designed to provide continuity to the SPOT/VEGETATION (SPOT VGT) base products archive. Since the PROBA-V mission acted as a gap filler between SPOT VGT and Sentinel-3, and in principle, a continuous series of data products from the combined data archives of SPOT VGT (1998–2014), PROBA-V (2013–2020) and Sentinel-3 SYN VGT (from 2018 onwards) are available to users, the consistency of Sentinel-3 SYN VGT with both the latest SPOT VGT (VGT-C3) and PROBA-V (PV-C2) archives is highly relevant. In past years, important changes have been implemented in the SYN VGT processing baseline. The archive of SYN VGT products is therefore intrinsically inconsistent, leading to different consistency levels with SPOT VGT and PROBA-V throughout the years. A spatio-temporal intercomparison of the combined time series of VGT-C3, PV-C2 and Sentinel-3 SYN VGT 10-day NDVI composite products with an external reference from LSA-SAF, and an intercomparison of Sentinel-3 SYN V10 products with a climatology of VGT-C3 resp. PV-C2 for three distinct periods with different levels of product quality have shown that the subsequent processing baseline updates have indeed resulted in better-quality products. It is therefore essential to reprocess the entire Sentinel-3 SYN VGT archive; a uniform data record of standard SPOT VGT, PROBA-V and Sentinel-3 SYN VGT products, spanning over 25 years, would provide valuable input for a wide range of applications. [ABSTRACT FROM AUTHOR]

The HYPERNETS project developed a new hyperspectral radiometer (HYPSTAR®) integrated in automated networks of water (WATERHYPERNET) and land (LANDHYPERNET) bidirectional reflectance measurements for satellite validation. In this paper, the feasibility of using LANDHYPERNET surface reflectance data for vicarious calibration of multispectral (Sentinel-2 and Landsat 8/9) and hyperspectral (PRISMA) satellites is studied. The pipeline to process bottom of atmosphere (BOA) surface reflectanceHYPERNETS data to band-integrated top of atmosphere (TOA) reflectances and compare them to satellite observations is detailed. Two LANDHYPERNET sites are considered in this study: the GobabebHYPERNETS site in Namibia (GHNA) and Princess Elizabeth Base in Antarctica (PEAN). 36 near-simultaneous match-ups within 1 h are found where HYPERNETS and satellite data pass all quality checks. For the Gobabeb HYPERNETS site, agreement towithin 5% is found with Sentinel-2 and Landsat 8/9. The differenceswith PRISMA aresmaller than 10%. For theHYPERNETS Antarctica site, there are also a number of match-ups-with good agreement to within5%for Landsat 8/9. The majority show notable disagreement, i.e., HYPERNETS being over 10% different compared to satellite. This is due to small-scale irregularities in the wind-blown snow surface, and their shadows cast by the low Sun. A study comparing the HYPERNETS measurements against a bidirectional reflectance distribution function (BRDF) model is recommended. Overall, we confirm data from radiometrically stable HYPERNETS sites with sufficient spatial and angular homogeneity can successfully be used for vicarious calibration purposes. [ABSTRACT FROM AUTHOR]

Long-term, accurate, stable, and continuous aerosol records from space are a major requirement for climate and atmospheric environment research. Due to the limited period span of the single satellite mission, solving the problem requires the combination of multiple satellite missions. In this study, a novel aerosol retrieval algorithm, named enhanced Land General Aerosol Retrieval (e-LaGA), for Visible/Infrared Imager Radiometer Suite (VIIRS) was developed to continue Moderate-resolution Imaging Spectro-radiometer (MODIS) aerosol retrieval. e-LaGA utilizes a Surface Reflectance (SR) relationship model map to more accurately estimate the SR parameter, optimizes the priori aerosol-type map, and improves the multi-band retrieval strategy using the residual-interpolation approach. Additionally, e-LaGA expands the retrieval ability of the traditional Dark Target (DT) algorithm over bright surfaces, and can more accurately retrieve Aerosol Optical Depth (AOD), Fine-mode AOD (AODF), and Fraction (FMF). The validation using global 533 AERONET sites shows that the volume of matchups of AOD (AODF) is approximately 80000, the correlation coefficient (R) is 0.902 (0.879) and the fraction of meeting the expected error envelope (±0.05 ± 0.15τ) is 0.806 (0.804). The inter-comparison indicates that the accuracy of e-LaGA AOD retrievals is comparable to seven commonly used AOD products. The validation performance and spatial distribution pattern of e-LaGA AODF and FMF are in good agreement with the POLDER (Polarization and Directionality of the Earth's Reflectances) products. e-LaGA is also applied to MODIS sensors. The VIIRS e-LaGA AOD, AODF, and FMF retrievals have high consistency with MODIS e-LaGA. Their average bias of AOD is 0.006, which is smaller than 0.024 for the Deep Blue and 0.011 for the DT. These preliminary results demonstrate the robustness of the e-LaGA algorithm and its potential for establishing a long-term climate record of total and fine-mode aerosol by combining multiple satellite missions, which is expected to reduce the uncertainty of climate change research. [ABSTRACT FROM AUTHOR]

Landsat data have been used to derive forest canopy structure, height, and volume using machine learning models, i.e., giving computers the ability to learn from data and make decisions and predictions without being explicitly programmed, with training data provided by ground measurement or airborne lidar. This study explored the potential use of Landsat reflectance and airborne lidar data as training data to estimate canopy heights in the Brazilian Amazon forest and examined the impacts of Landsat reflectance products at different process levels and sample spatial autocorrelation on random forest modeling. Specifically, this study assessed the accuracy of canopy height predictions from random forest regression models impacted by three different Landsat 8 reflectance product inputs (i.e., USGS level 1 top of atmosphere reflectance, USGS level 2 surface reflectance, and NASA nadir bidirectional reflectance distribution function (BRDF) adjusted reflectance (NBAR)), sample sizes, training/test split strategies, and geographic coordinates. In the establishment of random forest regression models, the dependent variable (i.e., the response variable) was the dominant canopy heights at a 90 m resolution derived from airborne lidar data, while the independent variables (i.e., the predictor variables) were the temporal metrics extracted from each Landsat reflectance product. The results indicated that the choice of Landsat reflectance products had an impact on model accuracy, with NBAR data yielding more trustful results than the other products despite having higher RMSE values. Training and test split strategy also affected the derived model accuracy metrics, with the random sample split (randomly distributed training and test samples) showing inflated accuracy compared to the spatial split (training and test samples spatially set apart). Such inflation was induced by the spatial autocorrelation that existed between training and test data in the random split. The inclusion of geographic coordinates as independent variables improved model accuracy in the random split strategy but not in the spatial split, where training and test samples had different geographic coordinate ranges. The study highlighted the importance of data processing levels and the training and test split methods in random forest modeling of canopy height. [ABSTRACT FROM AUTHOR]

The use of satellite remote sensing has considerably improved scientific understanding of the heterogeneity of Amazonian rainforests. However, the persistent cloud cover and strong Bidirectional Reflectance Distribution Function (BRDF) effects make it difficult to produce up-to-date satellite image composites over the huge extent of Amazonia. Advanced pre-processing and pixel-based compositing over an extended time period are needed to fill the data gaps caused by clouds and to achieve consistency in pixel values across space. Recent studies have found that the multidimensional median, also known as medoid, algorithm is robust to outliers and noise, and thereby provides a useful approach for pixel-based compositing. Here we describe Landsat-7 and Landsat-8 composites covering all Amazonia that were produced using Landsat data from the years 2013–2021 and processed with Google Earth Engine (GEE). These products aggregate reflectance values over a relatively long time, and are, therefore, especially useful for identifying permanent characteristics of the landscape, such as vegetation heterogeneity that is driven by differences in geologically defined edaphic conditions. To make similar compositing possible over other areas and time periods (including shorter time periods for change detection), we make the workflow available in GEE. Visual inspection and comparison with other Landsat products confirmed that the pre-processing workflow was efficient and the composites are seamless and without data gaps, although some artifacts present in the source data remain. Basin-wide Landsat-7 and Landsat-8 composites are expected to facilitate both local and broad-scale ecological and biogeographical studies, species distribution modeling, and conservation planning in Amazonia.

The Fluorescence Explorer (FLEX) satellite will carry the high-resolution Fluorescence Imaging Spectrometer (FLORIS) that measures the complete fluorescence spectrum emitted by chlorophyll of terrestrial vegetation. This small signal must be validated. One validation approach is comparing the fluorescence signal retrieved from satellite-based measurements with ground based measurements. However, the difference in spatial resolution of the satellite and ground-based instruments and a geolocation mismatch will result in differences in the detected signal and thus, in uncertainties of the validation strategy. In a case study, we identify a representative ground site for validating the fluorescence signal by analyzing surface reflectance measurements from an aeroplane.We define requirements of representativeness for a validation ground site in vegetated areas. Based on those requirements, we identify a suitable position within a case study in central Italy using surface reflectance data from the airborne High-Performance Airborne Imaging Spectrometer (HyPlant) measured in summer 2018. The representativeness is quantified by the relative difference between the single HyPlant pixel representing a ground-based measurement and the averaged signal of several HyPlant pixels that mimics a FLORIS pixel. With this measure, we quantify the validation uncertainty due to spatial resolution and geolocation mismatch. The effect of the temporal evolution of the surface properties on the validation uncertainty due to spatial resolution is investigated.We select the ground site position by minimizing the validation uncertainty due to spatial resolution. Especially for wavelengths larger than 700 nm, this uncertainty is smaller than 2 % for all different reference areas. The largest differences between ground-based like measurement and satellite-like measurement of the surface reflectance is due to geolocation mismatch. The uncertainty due the geolocation mismatch is very large for wavelengths smaller than 720 nm and moderate for wavelengths larger than 720 nm. Thus, the surface reflectance at the chosen position for the validation site is not homogeneous enough for validation purpose. Considering a reference area of 13.5 × 13.5 m2, we quantify temporal stable and small uncertainties for the spectral range between 720 and 800 nm. For an all-embracing validation of the surface reflectance of vegetated areas, the chosen site is not appropriate.

ABSTRACTThe recently launched Landsat-9 has an important mission of working together with Landsat-8 to reduce the revisit period of Landsat Earth observations to eight days. This requires the data of Landsat-9 to be highly consistent with that of Landsat-8 to avoid bias caused by data inconsistency when the two satellites are simultaneously used. Therefore, this study evaluated the consistency of the surface reflectance (SR) and land surface temperature (LST) data between Landsat-8 and Landsat-9 based on five test sites from different parts of the world using synchronized underfly image pairs of both satellites. Previous cross-comparisons have demonstrated high consistency between the spectral bands of Landsat-8 and Landsat-9, with differences of around 1%. However, it is unclear whether this low deviation will be amplified in subsequent multiband calculations. It is also necessary to determine whether the difference is consistent across different land cover types. Therefore, this study used a three-level cross-comparison approach to specifically examine these concerns. Besides the commonly used band-by-band comparison, which served as the first-level comparison in this study, this approach included a second-level comparison based on the calculations of several indicators and a third-level comparison based on a composite index calculated from the indicators obtained in the second-level comparison. This three-level approach will examine whether the difference found in the first-level per-band comparison would change after the subsequent calculations in the second- and third-level comparisons. The Remote Sensing based Ecological Index (RSEI) was used for this approach because it is a composite index integrating four indicators. The results of this three-level comparison show that the first-level per-band comparison exhibited high consistency between the two satellites’ SR data, with an average absolute percent change (PC) of 1.88% and an average R2 of 0.957 across six bands in the five test sites. This deviation increased to 2.21% in the third-level composite index-based comparison, with R2 decreasing to 0.956. This indicates that after complex calculations, the deviation between the bands of the two satellites was amplified to some extent. However, when analyzing specific land cover types, notable differences emerged between the two satellites for the water category, with an average absolute PC ranging from 18% to 35% and an R2 of lower than 0.6. Additionally, there were also nearly 5% differences for the built-up land category, with an average R2 value of lower than 0.7. The comparison of LST data between both satellites also reveals that the Landsat-9 LST is on average 0.24°C lower than Landsat-8 LST across the five test areas but can be 0.58°C lower in built-up land-dominated areas and 0.42°C higher in desert environments. Overall, the SR and LST data between Landsat-8 and Landsat-9 are consistent. However, their performance varies depending on different land cover types. Caution is needed particularly for water-related research when utilizing both satellites simultaneously. Significant discrepancies may also arise in the areas characterized by deserts and built-up lands.

ABSTRACTWater quality deterioration in the Main Ethiopian Rift Lakes is one of the problems affecting the health and socioeconomic development of the area. This study was aimed to develop a method to assess the water quality change of Lake Beseka using satellite image reflectance data and observed data. Multi-temporal Landsat imagery and three variables were used to assess the physicochemical environment: pH, EC, and TDS. Linear regression and correlation were accomplished between observed water quality parameters and surface reflectance of different band operations. The best-fitted linear regression equation for pH was found on SWIR1 while for EC and TDS, the blue/green band ratio was highly fitted. The Pearson coefficients of correlation for pH, EC, and TDS were − 0.79, 0.86, and 0.85, respectively. The RMSE and p-value for validation analysis were 0.25/0.005, 548/0.003, and 367/0.004 for pH, EC, and TDS, respectively. The estimated pH, EC, and TDS were higher in the central portion of the lake grouped under brackish water and lower in the southwestern, southern, and northeastern shores. The estimated EC and TDS for the years 2018 and 2021 were relatively lower than in 2007 and 2023. The spatiotemporal lake water variations were due to anthropogenic and geogenic factors including hot springs and groundwater discharge to the lake, water-level rise and depth variation, and evaporation. These findings are helpful for understanding linear regression equations that developed from surface reflectance and observed data, which are essential to estimating and monitoring water quality change in a cost-effective and time-saving manner.

Landsat data correction using the Land Surface Reflectance Code (LaSRC) has been proposed as the basis for the atmospheric correction of smallsats. While atmospheric correction can enhance smallsat data, the Landsat/LaSRC pathway delays output and may constrain accuracy and utility. The alternative, the Closed-form Method for Atmospheric Correction (CMAC), developed for smallsat application, provides surface reflectance derived solely from scene statistics. In a prior paper, CMAC closely agreed with LaSRC software for correction of the four VNIR bands of Landsat-8/9 images for conditions of low to moderate atmospheric effect over quasi-invariant warehouse-industrial targets. Those results were accepted as surrogate surface reflectance to support analysis of CMAC and LaSRC reliability for surface reflectance retrieval in two contrasting environments: shortgrass prairie and barren desert. Reliability was defined and tested through a null hypothesis: the same top-of-atmosphere reflectance under the same atmospheric condition will provide the same estimate of surface reflectance. Evaluated against the prior surrogate surface reflectance, the results found decreasing error with increasing wavelength for both methods. From 58 comparisons across the four bands, the LaSRC average absolute error ranged from 0.59% (NIR) to 50.30% (blue). CMAC provided reliable results: error was well constrained from 0.01% (NIR) to 0.98% (blue). [ABSTRACT FROM AUTHOR]

Tropical peatlands store copious amounts of carbon (C) and play a critical role in the global C cycle. However, this C store is vulnerable to natural and anthropogenic disturbances, leading these ecosystems to become weaker C sinks or even net C sources. Variabilities in water table (WT) greatly influence the magnitude of greenhouse gas flux in these biomes. Despite its importance in C cycling, observations of the spatiotemporal dynamics of tropical peatland WT are limited in spatial extent and length. Here, we use in situ WT measurements from tropical peatlands in Indonesia, Malaysia, and Peru to evaluate the satellite‐based Optical Trapezoid Model (OPTRAM). The model uses the pixel distribution in the shortwave infrared transformed reflectance and normalized difference vegetation index (NDVI) space to calculate indices that are then compared against in situ WT data. 30‐m resolution Landsat 7 and Landsat 8 images were utilized for model parameterization. We found OPTRAM to best capture tropical peatland WT dynamics in minimally forested and non‐forested areas (low to intermediate NDVI) (0.7 < R < 1) using the "best pixel" approach (the pixel with the highest Pearson‐R correlation value). In areas with relatively higher NDVI, OPTRAM index did not correlate with WT (average R of −0.04 to 0.24), likely due to trees being less sensitive to WT fluctuations. OPTRAM shows potential for reliably estimating tropical peatland WT without the need for direct measurements, which is challenging due to site remoteness and harsh conditions. Plain Language Summary: Tropical peatlands store copious amounts of carbon in their soil and biomass. The position of the water table (WT) relative to the soil surface plays a large role in how much greenhouse gases (GHGs) (i.e., carbon dioxide, methane, and nitrous oxide) is emitted or taken up and may also give insight into areas most vulnerable to fire. Therefore, estimating tropical peatland WT fluctuations over space and time is essential for understanding the global carbon and GHG budgets. Due to harsh environmental conditions and site remoteness, direct measurements of tropical peatland WT are limited in spatial extent and length. As a result, other methods must be developed to estimate the spatiotemporal variability of this parameter. Through manipulation of satellite surface reflectance, we demonstrate the feasibility of estimating fluctuations in tropical peatland WT from space. This method is tested on sites spanning Peru, Indonesia, and Malaysia. We find that WT fluctuations are accurately quantified over areas that are minimally forested or non‐forested. Our findings provide a pathway to map tropical peatland WT from space leading to improved understanding of WT‐dependent processes such as GHG emissions and fire risk, both of which contribute greatly to global climate change. Key Points: OPTRAM is capable of accurately capturing the temporal variability of tropical peatland water tableOPTRAM performs optimally over minimally forested and non‐forested areas [ABSTRACT FROM AUTHOR]

This paper demonstrates the use of a novel, autonomous hyperspectral surface reflectance data collected at Wytham Woods, United Kingdom (WWUK) as part of the LANDHYPERNET network for the validation of multispectral data from Sentinel-2, Landsat 8 and Landsat 9. The deployment of the HYPSTAR instrument at the site and the corresponding quality control of the data products is described. In addition, a methodology based upon metrological principles is outlined showing the propagation of uncertainties from the LANDHYPERNET and satellite data products to enable conformity testing of the satellite products using the satellite mission requirements. A total of 9 matchups are found for satellite validation at the site, where there is a cloud-free satellite scene and a corresponding LANDHYPERNET sequence, which has passed all quality checks, within two hours of the overpass. An analysis of the impact of the spatial variability of the site is presented and can account for up to 40% of the uncertainty associated with the in-situ surface reflectance data. There is no systematic bias in the Bottom-Of-Atmosphere reflectance data obtained from the LANDHYPERNET data in comparison to the satellite data. In the best case, differences of less than 2% are found for certain spectral bands. However, in the worst cases, relatively large differences are found which exceed 100%, this is affected by the relatively low reflectance values found in the visible bands. These differences could be caused by the spatial and temporal mismatch between the in-situ and satellite measurement, or due to shadowing caused by the flux tower. Further data quality control and assurance is needed to best choose data sets suitable for satellite validation. Incorporating a Bidirectional Reflectance Distribution Function model into the processing chain for the forest canopy is recommended. Overall, although there are areas to further characterise, the site provides a useful benchmark for which to develop techniques for validation of satellite surface reflectance products over a challenging environment. [ABSTRACT FROM AUTHOR]

During the launch and path to its final orbit, the Landsat 9 satellite performed a once in a mission lifetime maneuver as it passed beneath Landsat 8, resulting in near coincident data collection. This maneuver provided ground validation teams from across the globe the opportunity of collecting surface in situ data to compare directly to Landsat 8 and Landsat 9 data. Ground validation teams identified surface targets that would yield reflectance and/or thermal values that could be used in Landsat Level 2 product validation and set out to collect at these locations using surface validation methodologies the teams developed. The values were collected from each team and compared directly with each other across each of the different bands of both Landsat 8 and 9. The results proved consistency across the Landsat 8 and 9 platforms and also agreed well in surface reflectance underestimation of the Coastal Aerosol, Blue, and SWIR2 bands. [ABSTRACT FROM AUTHOR]

Urban surfaces play a crucial role in shaping the Urban Heat Island (UHI) effect by absorbing and retaining significant solar radiation. This paper explores the potential of high-resolution satellite imagery as an alternative method for characterizing urban surfaces to support UHI mitigation strategies in urban redevelopment plans. We utilized Landsat images spanning the past 40 years to analyze trends in Land Surface Temperature (LST). Additionally, WorldView-3 (WV3) imagery was acquired for surface characterization, and the results were compared with ground truth measurements using the ASD FieldSpec 4 spectroradiometer. Our findings revealed a strong correlation between satellite-derived surface reflectance and ground truth measurements across various urban surfaces, with Root Mean Square Error (RMSE) values ranging from 0.01 to 0.14. Optimal characterization was observed for surfaces such as bituminous membranes and parking with cobblestones (RMSE < 0.03), although higher RMSE values were noted for tiled roofs, likely due to aging effects. Regarding surface albedo, the differences between satellite-derived data and ground measurements consistently remained below 12% for all surfaces, with the lowest values observed in high heat-absorbing surfaces like bituminous membranes. Despite challenges on certain surfaces, our study highlights the reliability of satellite-derived data for urban surface characterization, thus providing valuable support for UHI mitigation efforts. [ABSTRACT FROM AUTHOR]

Topography complicates the illumination distribution over rugged terrains and hinders the applications of surface reflectance data over mountainous areas. Topographic correction is an essential process to remove the topographic effects in surface reflectance data. This study proposed a Physical model and image Simulation-based topographic Correction method (PSC) for atmospherically corrected surface reflectance images. In contrast with traditional methods, the proposed approach explicitly estimates the illumination distribution over rugged terrains based on image information and then corrects the topographic effects following physical laws. The proposed method was validated and compared with existing well-performed topographic correction methods, including path length correction (PLC), sun-canopy-sensor correction with c factor (SCSC), C correction (CC), and statistical empirical correction (SE). Simulated and satellite data obtained at different times and geolocations are used for evaluation and comparison. The results demonstrated that most existing methods face challenges in removing the biases induced by topography in surface reflectance images. The PLC method failed to obtain reasonable results for faint illumination conditions when the sun zenith angle is high. SE showed relatively poor performance in terms of physical consistency and outlier percentage. Besides, all the traditional methods failed in the correction of cast shadow regions. Comparably, our method consistently demonstrated superior performance in physical consistency, cast shadow correction, and outlier percentage across various regions, encompassing different sun zenith angles and illumination conditions. The proposed method offers an effective and physically consistent approach for the topographic correction of surface reflectance images, facilitating multi-temporal applications over mountainous areas. [ABSTRACT FROM AUTHOR]

Copyright of Revista Tecno Lógicas is the property of Instituto Tecnologico Metropolitano and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012–2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha−1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (−25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels. [ABSTRACT FROM AUTHOR]

In recent years, Geoscience Australia has undertaken a successful continental-scale validation program, targeting Landsat and Sentinel analysis-ready data surface reflectance products. The field validation model used for this program was successfully built upon earlier studies, and the measurement uncertainties associated with these protocols have been quantified and published. As a consequence, the Australian earth observation community was well-prepared to respond to the United States Geological Survey (USGS) call for collaborators with the 2021 Landsat 8 (L8) and Landsat 9 (L9) underfly. Despite a number of challenges, seven validation datasets were captured across five sites. As there was only a single 100% overlap transit across Australia, and the country was amidst a strong La Niña climate cycle, it was decided to deploy teams to the two available overpasses with only 15% side lap. The validation sites encompassed rangelands, chenopod shrublands, and a large inland lake. Apart from instrument problems at one site, good weather enabled the capture of high-quality field data allowing for meaningful comparisons between the radiometric performance of L8 and L9, as well as the USGS and Australian Landsat analysis-ready data processing models. Duplicate (cross-calibration) spectral sampling at different sites provides evidence of the field protocol reliability, while the off-nadir view of L9 over the water site has been used to better compare the performance of different water and atmospheric correction processing models. [ABSTRACT FROM AUTHOR]

Satellite remote sensing, particularly using the MODIS orbital platform, is crucial for large-scale lake monitoring, allowing the observation of optically active components with suitable spatial and temporal resolution for lakes with surfaces greater than 40 hectares. In this context, the objective of this article was to propose a methodology that improves the retrieval and monitoring of data related to Total Suspended Solids (TSS) in large lakes. The methodology employed involved defining a baseline to map the spatiotemporal dynamics of TSS using MODIS band 1, which generates information in the infrared spectrum and is centered at 645 nm. The method was tested in the Porto Primavera Reservoir (PPR), Brazil. To validate the model, two fieldwork campaigns were conducted in the PPR, where radiometric and water quality data were collected. An empirical model was fitted between reflectance and the TSS data set (r = 0.93, R2 = 0.85, p

The HYPERNETS project developed a new hyperspectral radiometer (HYPSTAR®) integrated in automated networks of water (WATERHYPERNET) and land (LANDHYPERNET) bidirectional reflectance measurements for satellite validation. In this paper, the feasibility of using LANDHYPERNET surface reflectance data for vicarious calibration of multispectral (Sentinel-2 and Landsat 8/9) and hyperspectral (PRISMA) satellites is studied. The pipeline to process bottom of atmosphere (BOA) surface reflectance HYPERNETS data to band-integrated top of atmosphere (TOA) reflectances and compare them to satellite observations is detailed. Two LANDHYPERNET sites are considered in this study: the Gobabeb HYPERNETS site in Namibia (GHNA) and Princess Elizabeth Base in Antarctica (PEAN). 36 near-simultaneous match-ups within 1 h are found where HYPERNETS and satellite data pass all quality checks. For the Gobabeb HYPERNETS site, agreement to within 5% is found with Sentinel-2 and Landsat 8/9. The differences with PRISMA are smaller than 10%. For the HYPERNETS Antarctica site, there are also a number of match-ups with good agreement to within 5% for Landsat 8/9. The majority show notable disagreement, i.e., HYPERNETS being over 10% different compared to satellite. This is due to small-scale irregularities in the wind-blown snow surface, and their shadows cast by the low Sun. A study comparing the HYPERNETS measurements against a bidirectional reflectance distribution function (BRDF) model is recommended. Overall, we confirm data from radiometrically stable HYPERNETS sites with sufficient spatial and angular homogeneity can successfully be used for vicarious calibration purposes.

Sentinel-3 synergy (SYN) VEGETATION (VGT) products were designed to provide continuity to the SPOT/VEGETATION (SPOT VGT) base products archive. Since the PROBA-V mission acted as a gap filler between SPOT VGT and Sentinel-3, and in principle, a continuous series of data products from the combined data archives of SPOT VGT (1998–2014), PROBA-V (2013–2020) and Sentinel-3 SYN VGT (from 2018 onwards) are available to users, the consistency of Sentinel-3 SYN VGT with both the latest SPOT VGT (VGT-C3) and PROBA-V (PV-C2) archives is highly relevant. In past years, important changes have been implemented in the SYN VGT processing baseline. The archive of SYN VGT products is therefore intrinsically inconsistent, leading to different consistency levels with SPOT VGT and PROBA-V throughout the years. A spatio-temporal intercomparison of the combined time series of VGT-C3, PV-C2 and Sentinel-3 SYN VGT 10-day NDVI composite products with an external reference from LSA-SAF, and an intercomparison of Sentinel-3 SYN V10 products with a climatology of VGT-C3 resp. PV-C2 for three distinct periods with different levels of product quality have shown that the subsequent processing baseline updates have indeed resulted in better-quality products. It is therefore essential to reprocess the entire Sentinel-3 SYN VGT archive; a uniform data record of standard SPOT VGT, PROBA-V and Sentinel-3 SYN VGT products, spanning over 25 years, would provide valuable input for a wide range of applications.

A comparison between the ASI-PRISMA (Agenzia Spaziale Italiana-PRecursore IperSpettrale della Missione Applicativa) DLR-EnMAP (German Aerospace Center—Environmental Mapping and Analysis Program) data and field spectrometer measurements has been performed. The test site, located at the "Sale 'e Porcus" pond (hereafter SPp) in Western Sardinia, Italy, offers particularly homogenous characteristics, making it an ideal location not only for experimentation but also for calibration purposes. Three remote-sensed data acquisitions have been performed by these agencies (ASI and DLR) starting on 14 July 2023 and continuing until 22 July 2023. The DLR-EnMAP data acquired on 22 July overestimates both that of the ASI-PRISMA and the 14 July DLR-EnMAP radiance in the VNIR region, while all the datasets are close to each other, up to 2500 nm, for all considered days. The average absolute mean difference between the reflectance values estimated by the ASI-PRISMA and DLR-EnMAP, in the test area, is around 0.015, despite the small difference in their time of acquisition (8 days); their maximum relative difference value occurs at about 2100 nm. In this study, we investigate the relationship between the averaged ground truth value of reflectance, acquired by means of a portable ASD FieldSpec spectoradiometer, characterizing the test site and the EO reflectance data derived from the official datasets. FieldSpec measurements confirm the quality of both the ASI-PRISMA and DLR-EnMAP's reflectance estimations. [ABSTRACT FROM AUTHOR]

Accurate monitoring of air pollution is crucial to human health and the global environment. In this research, the various multispectral satellite data, including MODIS AOD/SR, Landsat 8 OLI, and Sentinel-2, together with the two most commonly used machine-learning models, viz. multi-layer backpropagation neural network (MLBPN) and random forest (RF), have been employed to analyze the spatiotemporal distributions of the primary air pollutant from 2019 to 2022 in Guanzhong Region, China. In the conducted experiments, the RF-based model, using the MODIS AOD data, has generally demonstrated the "optimal" estimation performance for the ground-surface concentrations of the primary air-pollutants. Then, the "optimal" estimation model has been employed to analyze the spatiotemporal distribution of the various air pollutants—in terms of temporal distribution, the annual average concentrations of PM2.5, PM10, NO2, and SO2 in the research area showed a decreasing trend from 2019 to 2022, while the annual average concentration of CO remained relatively stable and the annual average concentration of O3 slightly increased; in terms of the spatial distribution, the air pollution presents a gradual increase from west to east in the research area, with the distribution of higher concentrations in the center of the built-up areas and lower in the surrounding rural areas. The proposed estimation model and spatiotemporal analysis can provide reliable methodologies and data support for the further study of the air pollution characteristics in the research area. [ABSTRACT FROM AUTHOR]

This paper demonstrates the use of a novel, autonomous hyperspectral surface reflectance data collected at Wytham Woods, United Kingdom (WWUK) as part of the LANDHYPERNET network for the validation of multispectral data from Sentinel-2, Landsat 8 and Landsat 9. The deployment of the HYPSTAR instrument at the site and the corresponding quality control of the data products is described. In addition, a methodology based upon metrological principles is outlined showing the propagation of uncertainties from the LANDHYPERNET and satellite data products to enable conformity testing of the satellite products using the satellite mission requirements. A total of 9 matchups are found for satellite validation at the site, where there is a cloud-free satellite scene and a corresponding LANDHYPERNET sequence, which has passed all quality checks, within two hours of the overpass. An analysis of the impact of the spatial variability of the site is presented and can account for up to 40% of the uncertainty associated with the in-situ surface reflectance data. There is no systematic bias in the Bottom-Of-Atmosphere reflectance data obtained from the LANDHYPERNET data in comparison to the satellite data. In the best case, differences of less than 2% are found for certain spectral bands. However, in the worst cases, relatively large differences are found which exceed 100%, this is affected by the relatively low reflectance values found in the visible bands. These differences could be caused by the spatial and temporal mismatch between the in-situ and satellite measurement, or due to shadowing caused by the flux tower. Further data quality control and assurance is needed to best choose data sets suitable for satellite validation. Incorporating a Bidirectional Reflectance Distribution Function model into the processing chain for the forest canopy is recommended. Overall, although there are areas to further characterise, the site provides a useful benchmark for which to develop techniques for validation of satellite surface reflectance products over a challenging environment.

Atmospheric correction eliminates corruption in reflectance captured by satellite images due to atmospheric elements like gases, aerosols, and water vapours. Existing physics-based approaches employ radiative transfer models constructed using lookup tables computed for different atmospheric parameters. However, these approaches are computationally expensive and rely on estimates of parameters that are difficult to sense accurately. This paper proposes a deep learning model as an alternative to physics-based approaches. We present an end-to-end deep neural network trained on seasonally and spatially rich Landsat 8 satellite images without explicit atmospheric parameterization along with our analysis and its validation. We validate the model's effectiveness vis-a-vis Landsat 8's Land Surface Reflectance Code – LaSRC results in $RMSE \sim 0.042$ R M S E ∼ 0.042 , $SSIM \sim 0.97$ S S I M ∼ 0.97 , and correlation coefficient $r \sim 0.99$ r ∼ 0.99. For ground measurements by RadCalNet, the proposed model has an $RMSE \sim 0.053$ R M S E ∼ 0.053 , $SSIM \sim 0.90$ S S I M ∼ 0.90 , and $r \sim 0.88$ r ∼ 0.88. The results show that the model accurately predicts surface reflectance and correlates highly with reference data. [ABSTRACT FROM AUTHOR]

Surface reflectance measurement is an integral part of the vicarious calibration of satellite sensors and the validation of satellite-derived top-of-atmosphere (TOA) and surface reflectance products. A well-known practice for estimating surface reflectance is to conduct a field campaign with a spectrometer and a calibration panel, which is labor-intensive and expensive. To address this issue, the Radiometric Calibration Network, RadCalNet, has been developed, which automatically collects surface reflectance over several selected sites. Neither of these approaches can continuously track the atmosphere, which limits their ability to compensate for atmospheric transmittance change during target measurement. This paper presents the dual-spectrometer approach that uses a stationary spectrometer dedicated to continuously tracking changes in atmospheric transmittance by staring at a calibrated reference panel while the mobile spectrometer measures the target. Simultaneous measurement of the reflectance panel and target help to transfer calibration from the stationary spectrometer to the mobile spectrometer and synchronize the measurements. In this manner, atmospheric transmittance changes during target measurement can be tracked and used to reduce the variability of the target surface reflectance. This paper uses field measurement data from combined field campaigns between different calibration groups at Brookings, South Dakota, and Landsat 8 and Landsat 9 underfly efforts over Coconino National Forest, Arizona, and Guymon, Oklahoma. Preliminary results show that even in a clear sky condition, where atmospheric transmittance changes are minimal, the precision of target surface reflectance estimated using the dual-spectrometer approach is 2–6% better than the single-spectrometer approach. The dual-spectrometer approach shows the potential for a substantial improvement in the precision of the target spectral profile when the atmospheric transmittance is changing rapidly during field measurement. Results show that during non-optimal atmospheric conditions, the dual-spectrometer approach improved the precision of the surface reflectance by 50–60% compared to the single-spectrometer approach across most spectral regions. The ability to estimate surface reflectance more precisely using the dual-spectrometer approach in different atmospheric conditions improves the vicarious calibration of optical satellite sensors and the validation of both TOA and surface reflectance products. [ABSTRACT FROM AUTHOR]

Alternaria blight is a destructive disease of mustard crops that causes significant economic loss and usually warrants extensive pesticide application. Characterization and estimation of pest severity for better management is the need of the hour to increase the scope of advisories to the farmers. In this study, statistical approaches like multiple stepwise linear regression, principal component regression and partial least square regression have been employed to detect the disease severity of Alternaria blight in mustard leaves using hyperspectral reflectance data. The predicted values of diseased leaves in all the regression models were significantly reduced vis-à-vis healthy mustard leaves. The results of this study indicate that it is possible to detect the disease severity of Alternaria blight in mustard crops using hyperspectral reflectance data. [ABSTRACT FROM AUTHOR]

Retrieval of terrestrial aerosol optical depth (AOD) has been a challenge for satellite Earth observations, mainly due to the difficulty of estimating surface reflectance caused by land-atmosphere coupling. Current satellite AOD retrieval products have low spatial resolution under complex surface processes. In this study, based on our previous studies of AOD retrieval, 17 we further improved the estimation method of surface reflectance by establishing an error correction model and then obtained a more accurate AOD. A lookup table is constructed using the Second Simulation of Satellite Signal in the Solar Spectrum (6S) to obtain high-precision retrieval of AOD. The retrieval accuracy of the algorithm is verified by AERONET (Aerosol Robotic Network) observations. The results indicate that the retrieved AOD based on the improved method of this study has advantages in fewer missing AOD pixels and finer spatial resolution, as compared to the MODIS AOD product and our previous estimation method. Among the nine MISR angles, the optimal correlation coefficient (R) of retrieved AOD and observed AOD can reach 0.89. Root mean square error (RMSE) and relative mean bias (RMB) can reach a minimum26 values of 0.20 and 0.32, respectively. This study will help to further improve the accuracy of retrieving multi-angle AOD at large spatial scales. [ABSTRACT FROM AUTHOR]

The GaoFen-1 wide field of view (GF-1 WFV) has produced level 1 digital number data globally; however, most applications have focused on China, and data quality outside China has not been validated. This study presents a preliminary assessment of the 2020 GF-1 WFV surface reflectance data for Nepal, Azerbaijan, Kenya, and Sri Lanka along "the Belt and Road" route using Sentinel-2 Multi-Spectral Instrument (MSI), Landsat-8 Operational Land Image (OLI), and Moderate Resolution Imaging Spectroradiometer (MODIS) data. A method for obtaining the GF-1 WFV surface reflectance data was also proposed, with steps including atmospheric correction, cross-radiation calibration, and bidirectional reflectance distribution function correction. The results showed that WFV surface reflectance data was not significantly different from MSI, OLI, and MODIS surface reflectance data. In the visible and near-infrared bands, for most landcover types, the bias was less than 0.02, and the precision and root mean square error were less than 0.04. When the landcover types were forest and water, the MSI, OLI, and MODIS surface reflectance data were higher than that of WFV in the near-infrared band. The results of this study provide a basis for assessing the global application potential of GF-1 WFV. [ABSTRACT FROM AUTHOR]

The validation of satellite remote sensing surface reflectance products is aimed at comparing the reflectance pixel values of products with ground measurement values at the pixel scale. Due to the existence of surface heterogeneity, we cannot obtain the satellite pixel scale truth value through ground sampling, and only the satellite relative pixel scale truth value that closely approximates it can be acquired. The process of converting the point-scale spectrum of ground sampling into a pixel-scale spectrum will produce certain errors, known as point-to-pixel-scale conversion uncertainty, which is closely related to the type of sample area and the ground sampling method. In this study, we conducted research on the uncertainty of point-to-pixel-scale conversion generated via different ground sampling methods in the upscaling process. We utilized unmanned aerial vehicle (UAV) hyperspectral images to invert the surface reflectance spectral curves of wheat, corn, bare soil, and soybeans at the pixel scale, and simulate the ground measurement spectra and satellite pixel scale ground truth of different sampling methods, so as to realize the quantitative calculation of the uncertainty of the ground truth at the satellite pixel scale. On this basis, we analyzed in depth the effects of the sampling method, measurement height, and number of spectra on the scale conversion uncertainty. The research results show that airborne hyperspectral images can accurately simulate the spectra of ground measurements, and can be used as an effective means of ground spectral sampling and uncertainty analysis. When using the systematic sampling method, the more the sampling points, the smaller the uncertainty. However, the uncertainty of scale conversion tends to stabilize when the number of sampling points is increased to a certain quantity. As the height of ground measurement increases, the number of spectra within the elementary sampling unit (ESU) increases, leading to smaller scale conversion uncertainties. The research results of this study will provide support for the subsequent optimization of ground sampling methods and the improvement of measurement efficiency and measurement accuracy. [ABSTRACT FROM AUTHOR]

Landsat data correction using the Land Surface Reflectance Code (LaSRC) has been proposed as the basis for the atmospheric correction of smallsats. While atmospheric correction can enhance smallsat data, the Landsat/LaSRC pathway delays output and may constrain accuracy and utility. The alternative, the Closed-form Method for Atmospheric Correction (CMAC), developed for smallsat application, provides surface reflectance derived solely from scene statistics. In a prior paper, CMAC closely agreed with LaSRC software for correction of the four VNIR bands of Landsat-8/9 images for conditions of low to moderate atmospheric effect over quasi-invariant warehouse-industrial targets. Those results were accepted as surrogate surface reflectance to support analysis of CMAC and LaSRC reliability for surface reflectance retrieval in two contrasting environments: shortgrass prairie and barren desert. Reliability was defined and tested through a null hypothesis: the same top-of-atmosphere reflectance under the same atmospheric condition will provide the same estimate of surface reflectance. Evaluated against the prior surrogate surface reflectance, the results found decreasing error with increasing wavelength for both methods. From 58 comparisons across the four bands, the LaSRC average absolute error ranged from 0.59% (NIR) to 50.30% (blue). CMAC provided reliable results: error was well constrained from 0.01% (NIR) to 0.98% (blue).

Urban surfaces play a crucial role in shaping the Urban Heat Island (UHI) effect by absorbing and retaining significant solar radiation. This paper explores the potential of high-resolution satellite imagery as an alternative method for characterizing urban surfaces to support UHI mitigation strategies in urban redevelopment plans. We utilized Landsat images spanning the past 40 years to analyze trends in Land Surface Temperature (LST). Additionally, WorldView-3 (WV3) imagery was acquired for surface characterization, and the results were compared with ground truth measurements using the ASD FieldSpec 4 spectroradiometer. Our findings revealed a strong correlation between satellite-derived surface reflectance and ground truth measurements across various urban surfaces, with Root Mean Square Error (RMSE) values ranging from 0.01 to 0.14. Optimal characterization was observed for surfaces such as bituminous membranes and parking with cobblestones (RMSE < 0.03), although higher RMSE values were noted for tiled roofs, likely due to aging effects. Regarding surface albedo, the differences between satellite-derived data and ground measurements consistently remained below 12% for all surfaces, with the lowest values observed in high heat-absorbing surfaces like bituminous membranes. Despite challenges on certain surfaces, our study highlights the reliability of satellite-derived data for urban surface characterization, thus providing valuable support for UHI mitigation efforts.

During the launch and path to its final orbit, the Landsat 9 satellite performed a once in a mission lifetime maneuver as it passed beneath Landsat 8, resulting in near coincident data collection. This maneuver provided ground validation teams from across the globe the opportunity of collecting surface in situ data to compare directly to Landsat 8 and Landsat 9 data. Ground validation teams identified surface targets that would yield reflectance and/or thermal values that could be used in Landsat Level 2 product validation and set out to collect at these locations using surface validation methodologies the teams developed. The values were collected from each team and compared directly with each other across each of the different bands of both Landsat 8 and 9. The results proved consistency across the Landsat 8 and 9 platforms and also agreed well in surface reflectance underestimation of the Coastal Aerosol, Blue, and SWIR2 bands.

Winter cover crops are planted during the fall to reduce nitrogen losses and soil erosion and improve soil health. Accurate estimations of winter cover crop performance and biophysical traits including biomass and fractional vegetative groundcover support accurate assessment of environmental benefits. We examined the comparability of measurements between ground-based and spaceborne sensors as well as between processing levels (e.g., surface vs. top-of-atmosphere reflectance) in estimating cover crop biophysical traits. This research examined the relationships between SPOT 5, Landsat 7, and WorldView-2 same-day paired satellite imagery and handheld multispectral proximal sensors on two days during the 2012–2013 winter cover crop season. We compared two processing levels from three satellites with spatially aggregated proximal data for red and green spectral bands as well as the normalized difference vegetation index (NDVI). We then compared NDVI estimated fractional green cover to in-situ photographs, and we derived cover crop biomass estimates from NDVI using existing calibration equations. We used slope and intercept contrasts to test whether estimates of biomass and fractional green cover differed statistically between sensors and processing levels. Compared to top-of-atmosphere imagery, surface reflectance imagery were more closely correlated with proximal sensors, with intercepts closer to zero, regression slopes nearer to the 1:1 line, and less variance between measured values. Additionally, surface reflectance NDVI derived from satellites showed strong agreement with passive handheld multispectral proximal sensor-sensor estimated fractional green cover and biomass (adj. R2 = 0.96 and 0.95; RMSE = 4.76% and 259 kg ha−1, respectively). Although active handheld multispectral proximal sensor-sensor derived fractional green cover and biomass estimates showed high accuracies (R2 = 0.96 and 0.96, respectively), they also demonstrated large intercept offsets (−25.5 and 4.51, respectively). Our results suggest that many passive multispectral remote sensing platforms may be used interchangeably to assess cover crop biophysical traits whereas SPOT 5 required an adjustment in NDVI intercept. Active sensors may require separate calibrations or intercept correction prior to combination with passive sensor data. Although surface reflectance products were highly correlated with proximal sensors, the standardized cloud mask failed to completely capture cloud shadows in Landsat 7, which dampened the signal of NIR and red bands in shadowed pixels.

The Landsat global consolidated data archive now exceeds 50 years. In recognition of the need for consistently processed data across the Landsat satellite series, the U.S. Geological Survey (USGS) initiated collection-based processing of the entire archive that was processed as Collection 1 in 2016. In preparation for the data from the now successfully launched Landsat 9, the USGS reprocessed the Landsat archive as Collection 2 in 2020. This paper describes the rationale for, and the contents and advancements provided by Collection 2, and highlights the differences between the Collection 1 and Collection 2 products. Notably, the Collection 2 products have improved geolocation and, for the first time, the USGS provides a global inventory of Level 2 surface reflectance and surface temperature products. Also for the first time, the USGS used a commercial cloud computing architecture to efficiently process the archive and enable direct cloud access of the Landsat products. The paper concludes with discussion of likely improvements expected in Collection 3 in preparation for the Landsat Next mission that is planned for launch in the early 2030s.

Maps of aeolian sand reactivation and wind erosion are needed for monitoring and amelioration of land degradation in Thar Desert. Conventionally, wind erosion is mapped here through visual observation of the patterns of sand colour on satellite image false colour composites (FCCs), which is highly subjective. We present here a satellite sensor–based digital mapping method for unbiased and reliable regional-scale monitoring. Called the 'Aeolian Sand Reactivation Index with broadband emissivity' (ASRI_bbe), the method exploits the surface reflectance and emissivity properties from MODIS sensors that are available as 8-day summaries. Reconstruction of the ASRI_bbe pattern in the desert from mid-March to mid-June during the years 2000 to 2011 revealed that the moderate to high reactivation areas mostly occur along some discreet WSW-ENE-oriented patches through the central part of the desert, that are driven by the dominant SW wind, which in turn appears to be related to the summer atmospheric turbulence over the region. A sharp rise in sand reactivation following a drought year, a gradual decline in the severity of sand reactivation over the mapping period, especially due to a fall in wind strength, and the growing signatures of a lull in activity during the first and/or the third week of May due to sporadic rains associated with the Western Disturbances, are the other major findings. [ABSTRACT FROM AUTHOR]

Copyright of Geography & Geographic Information Science is the property of Geography & Geo-Information Science Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In the Mediterranean area, vegetation dynamics and phenology analysed over a long time can have an important role in highlighting changes in land use and cover as well as the effect of climate change. Over the last 30 years, remote sensing has played an essential role in bringing about these changes thanks to many types of observations and techniques. Satellite images are to be considered an important tool to grasp these dynamics and evaluate them in an inexpensive and multidisciplinary way thanks to Landsat and Sentinel satellite constellations. The integration of these tools holds a dual potential: on the one hand, allowing us to obtain a longer historical series of reflectance data, while on the other hand making data available with a higher frequency even within a specific timeframe. The study aims to conduct a comprehensive cross-comparison analysis of long-time-series pixel values in the Mediterranean regions. For this scope comparisons between Landsat-7 (ETM+), Landsat-8 (OLI), and Sentinel-2 (MSI) satellite sensors were conducted based on surface reflectance products. We evaluated these differences using Ordinary Least Squares (OLS) and Major Axis linear regression (RMA) analysis on points extracted from over 15,000 images across the Mediterranean basin area from 2017 to 2020. Minor but consistent differences were noted, necessitating the formulation of suitable adjustment equations to better align Sentinel-2 reflectance values with those of Landsat-7 or Landsat-8. The results of the analysis are compared with the most-used harmonization coefficients proposed in the literature, revealing significant differences. The root-mean-square deviation, the mean difference and the orthogonal distance regression (ODR) slope show an improvement of the parameters for both models used (OLS and RMA) in this study. The discrepancies in reflectance values leads to corresponding variations in the estimation of biophysical parameters, such as NDVI, showing an increase in the ODR slope of 0.3. Despite differences in spatial, spectral, and temporal characteristics, we demonstrate that integration of these datasets is feasible through the application of band-wise regression corrections for a sensitive and heterogeneous area like those of the Mediterranean basin area. [ABSTRACT FROM AUTHOR]

With the launch of Landsat-9 on 27 September 2021, Landsat continues its fifty-year continuity mission of providing users with calibrated Earth observations. It has become a requirement that an underflight experiment be performed during commissioning to support sensor cross-calibration. In this most recent experiment, Landsat-9 flew under Landsat-8 for nearly three days with over 50% ground overlap, from 13 to 15 November 2021. To address the scarcity of reference data that are available to support calibration and validation early-on in the mission, a ground campaign was planned and executed by the Rochester Institute of Technology (RIT) on 14 November 2021 to provide full spectrum measurements for early mission comparisons. The primary experiment was conducted in the Outer Banks, North Carolina at Jockey's Ridge Sand Dunes. Full-spectrum ground-based measurements were acquired with calibrated reference equipment, while a novel Unmanned Aircraft System (UAS)-based platforms acquired hyperspectral visible and near-infrared (VNIR)/Short-wave infrared (SWIR) imagery data and coincident broadband cooled thermal infrared (TIR) imagery. Results of satellite/UAS/ground comparisons were an indicator, during the commissioning phase, that Landsat-9 is behaving consistently with Landsat-8, ground reference, and UAS measurements. In the thermal infrared, all measurements agree to be within 1 K over water and to within 2 K over sand, which represents the most challenging material for estimating surface temperature. For the surface reflectance product(s), Landsat-8 and -9 are in good agreement and only deviate slightly from ground reference in the SWIR bands; a deviation of 2% in the VNIR and 5–8% in the SWIR regime. Subsequent longer-term studies indicate that Landsat 9 continues to perform as expected. The behavior of Thermal Infrared Sensor-2 (TIRS-2) against reference is also shown for the first year of the mission to illustrate its consistent performance. [ABSTRACT FROM AUTHOR]

The correction of atmospheric effects on optical remote sensing products is an essential component of Analysis Ready Data (ARD) production lines. The MAJA processor aims at providing accurate time series of surface reflectances over land for satellite missions, such as Sentinel-2, Venμs, and Landsat 8. The Centre d'Études Spatiales de la Biosphère (CESBIO) and the Centre National d'Études Spatiales (CNES) share a common effort to maintain, validate, and improve the MAJA processor, using state-of-the-art ground measurement sites, and participating in processor inter-comparisons, such as the Atmospheric Correction Intercomparison Exercise (ACIX). While contributing to the second ACIX-II Land validation exercise, it was found that the candidate MAJA dataset could not adequately be compared to the main reference dataset. MAJA reflectances were corrected for adjacency and topography effects while the reference dataset was not, excluding MAJA from a part of the performance metrics of the exercise. The first part of the following study aims at providing complementary performance assessment to ACIX-II by reprocessing MAJA surface reflectances without adjacency nor topographic correction, allowing for an un-biased full resolution comparison with the reference Sentinel-2 dataset. The second part of the study consists of validating MAJA against surface reflectance measurements time series of up to five years acquired at three automated stations. Both approaches provide extensive insights on the quality of MAJA Sentinel-2 Level 2 products. [ABSTRACT FROM AUTHOR]

As a critical component of many remote sensing satellites and model validation, pixel-scale surface quantitative parameters are often affected by scale effects in the acquisition process, resulting in deviations in the accuracy of image scale parameters. Consequently, various successive scale conversion methods have been proposed to correct the errors caused by scale effects. In this study, we propose ResTransformer, a deep learning model for scale conversion of surface reflectance using UAV images, which fully extracts and fuses the features of UAV images in the sample area and sample points and establishes a high-dimensional nonlinear spatial correlation between sample points and sample area in the target sample area, so that the scale conversion of surface reflectance at the pixel-scale can be completed quickly and accurately. We collected and created a dataset of 500k samples to verify the accuracy and robustness of the model with other traditional scale conversion methods. The results show that the ResTransformer deep learning model works best, providing average MRE, average MRSE, and correlation coefficient R values of 0.6440%, 0.7460, and 0.99911, respectively, and the baseline improvements compared with the Simple Average method are 92.48%, 92.45%, and 16.59%, respectively. The ResTransformer model also shows the highest robustness and universality and can adapt to surface pixel-scale conversion scenarios with different sizes, heterogeneous sample areas, and arbitrary sampling methods. This method provides a promising, highly accurate, and robust method for converting pixel-scale surface reflectance scale. [ABSTRACT FROM AUTHOR]

Surface reflectance is an important indicator for the physical states of the Earth's surface. The Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance product at 500 m resolution (MOD09A1) includes seven spectral bands and has been widely used to derive many high-level parameter products, such as leaf area index (LAI) and fraction of absorbed photosynthetically active radiation (FAPAR). However, the MODIS surface reflectance product at 250 m resolution (MOD09Q1) is only available for the red and near-infrared (NIR) bands, which greatly limits its applications. In this study, a downscaling reflectance convolutional neural network (DRCNN) is proposed to downscale the surface reflectance of the MOD09A1 product and derive 250 m surface reflectance in the blue, green, shortwave infrared (SWIR1, 1628–1652 nm) and shortwave infrared (SWIR2, 2105–2155 nm) bands for generating high-level parameter products at 250 m resolution. The surface reflectance of the MOD09A1 and MOD09Q1 products are preprocessed to obtain cloud-free continuous surface reflectance. Additionally, the surface reflectance in the blue, green, SWIR1 and SWIR2 bands from the preprocessed MOD09A1 product were upsampled to obtain surface reflectance in the corresponding bands at 1 km resolution. Then, a database was generated from the upsampled surface reflectance and the preprocessed MOD09A1 product over the On Line Validation Exercise (OLIVE) sites to train the DRCNN. The surface reflectance in the blue, green, SWIR1 and SWIR2 bands from the preprocessed MOD09A1 product and the surface reflectance in the red and NIR bands from the preprocessed MOD09Q1 product were entered into the trained DRCNN to obtain the surface reflectance in the blue, green, SWIR1 and SWIR2 bands at 250 m resolution. The downscaled surface reflectance from the DRCNN were compared with the surface reflectance from the MOD09A1 product and Landsat 7. The results show that the DRCNN can effectively downscale the surface reflectance of the MOD09A1 product to generate the surface reflectance at 250 m resolution. [ABSTRACT FROM AUTHOR]

There is an increasing number of on-orbit earth observation sensors in the solar domain (visible to middle infrared). While most the sensors are designed with apparent analogous bandpasses, they often exhibit differences in terms of Relative Spectral Response (RSR). Although the differences are well characterized, there is a need for techniques to adjust for the effect of these differences on surface reflectance after accounting for calibration, directional, and atmospheric effects. This research evaluated eight bandpass adjustment techniques using synthetic and actual remote sensing observations on seven widely used sensors. The scope of the study is global. The eight evaluated techniques were: a) spectral band adjustment factor technique (SBAF, i.e., one-coefficient regression), b) linear fit technique (i.e., two-coefficient regression), c) three Adaptive SBAF techniques (ASBAF), which are defined according to three different cost functions and apply a target-dependent SBAF, d) two artificial neural network approaches using (i) a single band (ANN-1B) and (ii) all bands (ANN-NB) as inputs, and e) a Classification and Regression Tree method (CART) using all bands as inputs. All these techniques require a calibration phase, which was carried out separately using three hyperspectral data sets: (i) USGS spectral measurements, (ii) simulated reflectance by the ProSail radiative transfer model, and (iii) real-world Hyperion observations. The full data set is publically available. The evaluation using the three hyperspectral data sets alone showed that the techniques with a single band as input (SBAF, LIN, and ANN-1B) generated higher uncertainty than the techniques with all bands as input (ASBAF, ANN-N, CART). The ASBAF techniques performed well with a few differences among the three ASBAF variants. The ANN-N was the technique that performed the best. The evaluation based on actual remote sensing data sets (Landsat-8 and Sentinel-2 data were used) suggested that all techniques reduced the deviation between the two data sets. Nonetheless, the deviation between the two data sets caused by factors other than RSR differences did not permit a clearcut comparison of the techniques. Some recommendations regarding the techniques to select for a particular application are provided. Finally, this research introduces the concept of RSR difference index (RDI) to quantify the spectral differences between two spectral bands. [ABSTRACT FROM AUTHOR]

In recent years, Geoscience Australia has undertaken a successful continental-scale validation program, targeting Landsat and Sentinel analysis-ready data surface reflectance products. The field validation model used for this program was successfully built upon earlier studies, and the measurement uncertainties associated with these protocols have been quantified and published. As a consequence, the Australian earth observation community was well-prepared to respond to the United States Geological Survey (USGS) call for collaborators with the 2021 Landsat 8 (L8) and Landsat 9 (L9) underfly. Despite a number of challenges, seven validation datasets were captured across five sites. As there was only a single 100% overlap transit across Australia, and the country was amidst a strong La Niña climate cycle, it was decided to deploy teams to the two available overpasses with only 15% side lap. The validation sites encompassed rangelands, chenopod shrublands, and a large inland lake. Apart from instrument problems at one site, good weather enabled the capture of high-quality field data allowing for meaningful comparisons between the radiometric performance of L8 and L9, as well as the USGS and Australian Landsat analysis-ready data processing models. Duplicate (cross-calibration) spectral sampling at different sites provides evidence of the field protocol reliability, while the off-nadir view of L9 over the water site has been used to better compare the performance of different water and atmospheric correction processing models.