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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]

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]

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]

Atmospheric correction of airborne hyperspectral imaging spectroscopy (AHIS) to obtain high-quality surface reflectance is the prerequisite for remote sensing applications. Over the last decades, different atmospheric correction methods have been developed based on radiative transfer models (RTMs), however, the relative performances of different algorithms are unclear. Automated operational atmospheric correction methods to process large-volume AHIS data in a high-accurate and high-throughput manner are still lacking. Therefore, this study proposed an operational atmospheric correction pipeline for deriving surface reflectance from AHIS data. To ensure the accuracy and efficiency of the pipeline, we focused on three specific aspects: (1) selecting a suitable RTM for the development of atmospheric lookup tables (LUTs) by comparing the commercial MODerate resolution atmospheric TRANsmission (MODTRAN) and open-sourced Library for Radiative TRANsfer (LibRadTRAN) models, where the widely-used software, Atmospheric/Topographic Correction for Airborne Imagery (ATCOR), was used as benchmarks; (2) identifying key atmospheric correction parameters and determining suitable sources for parameter retrievals including AHIS, Moderate Resolution Imaging Spectroradiometer (MODIS), and AErosol RObotic NETwork (AERONET); and (3) testing the performance of using machine learning emulators to speed up the RTM-based atmospheric correction. Results indicate that (1) atmospheric correction based on MODTRAN LUTs can produce surface reflectance accurately with mean absolute errors < 0.05 and cosine similarities > 0.98 compared to field measurements, which is comparable to the software ATCOR and slightly outperforms the LibRadTRAN LUTs; (2) sobol global sensitivity analysis demonstrates that in the atmospheric correction, visibility and water vapor are two key parameters that can be accurately derived from AHIS in contrast to MODIS or AERONET data; and (3) Random Forest emulators can produce accurate estimations of surface reflectance with mean absolute errors < 0.03 and cosine similarities > 0.98 for higher processing efficiency and determine a suitable set of wavelengths for retrieving atmospheric visibility and water vapor. The proposed atmospheric correction pipeline also improved the four-stream radiative transfer theory for airborne applications by considering adjacent effects from airborne surrounding pixels and can also be applied for atmospheric correction of hyperspectral data from spaceborne missions. [ABSTRACT FROM AUTHOR]

The Earth surface Mineral dust source InvesTigation (EMIT) is an imaging spectrometer launched to the International Space Station in July 2022 to measure the mineral composition of Earth's dust-producing regions. We present a systematic accuracy assessment of the EMIT surface reflectance product in two parts. First, we characterize the surface reflectance product's overall performance using multiple independent vicarious calibration field experiments with hand-held and automated field spectrometers. We find that the EMIT surface reflectance product has a standard error of ± 1. 0 % in absolute reflectance units for temporally coincident observations. Discrepancies rise to ± 2. 7 % for spectra acquired at different dates and times of day, which we attribute mainly to changes in solar geometry. Second, we develop an error budget that explains the differences between EMIT and in-situ field spectrometer data. We find that uncertainties in spatial footprints, field spectroscopy, and the EMIT-reported measurement were sufficient to explain discrepancies in most cases. Our approach did not detect any systematic calibration or reflectance errors in the timespan considered. Together, these findings demonstrate that a space-based imaging spectrometer can acquire high-quality spectra across a wide range of observational and atmospheric conditions. • We highlight the EMIT imaging spectrometer's surface reflectance product performance. • The reflectance error at a typical arid land site is ± 1. 0 % for coincident observations. • Our error budget captures variability between EMIT and in-situ reflectance values. [ABSTRACT FROM AUTHOR]

As climate change intensifies, trees face heightened drought risks, impacting future forest composition. This study compares the climate sensitivity and resilience of black pine (Pinus nigra) across its north-south distribution range, guiding adaptive forest management amidst changing environmental conditions. Tree-ring cores from 211 P. nigra trees across seven sites in northern distribution (Slovenia) and three different sites in southern distribution range (Western Turkey), including the subspecies P. nigra subsp. nigra and P. nigra subsp. pallasiana, were analyzed. We investigated climate-growth dynamics, evaluating temperature and precipitation correlations with tree-ring width indices, and analyzed resilience indices. Leveraging remote sensing data, disparities in surface reflectance and photosynthetic activity were assessed. Our findings reveal distinct climate-growth patterns between southern and northern P. nigra populations, with precipitation predominantly shaping growth in the south and both precipitation and temperature influencing growth in the north. Given the site-specific conditions of each population, resilience indices further suggest that P. nigra subsp. pallasiana exhibits stronger drought tolerance. Sites in the northern range show the lowest resistance due to precipitation limitation. Temperature-limited sites demonstrate the highest resilience, indicating potential long-term effects of drought on tree growth. Our findings enhance our understanding of the climate-growth responses and resilience mechanisms in two subpopulations of Pinus nigra in Southern Europe. • We compared P. nigra from northern and southern distribution limits. • Southern sites were strictly precipitation limited. • Northern sites exhibited mixed precipitation and temperature signal. • We observed enhanced drought-coping mechanisms in P. nigra subsp. pallasiana. • Surface reflectance displayed a clear north-south spatial pattern. [ABSTRACT FROM AUTHOR]

• Water surface reflectance measured using a UAV with multispectral camera. • Images were radiometrically corrected to obtain the true water surface reflectance. • An estimator algorithm developed for surface Chl- a estimation based on NGRDI. • Spatial and temporal variation of Chl- a mapped during an algal bloom event. • Complement to manual sampling for fisheries management and emergency response. In subtropical coastal waters, the explosive growth of phytoplankton under favorable conditions can lead to water discolouration and massive fish kills. Manual field sampling and laboratory analysis of chlorophyll- a concentration (Chl- a) as an indicator to algal biomass, is resources intensive and time consuming, delaying responses to disastrous harmful algal blooms. Cloudy weather often precludes the use of satellite images for water quality and algal bloom monitoring. This study aims at developing an estimator algorithm for quantitative mapping of surface Chl- a for coastal waters, based on surface reflectance measurement from an Unmanned Aerial Vehicle (UAV) with a five-band multispectral camera. The surface reflectance is obtained from calibrated multispectral images which are radiometric-corrected against incoming solar radiation. It is found that Chl- a has an inverse correlation with the Normalized Green-Red Difference Index (NGRDI). A regression estimator model for Chl- a from NGRDI is developed, showing excellent performance for fish farms in coastal waters with different characteristics. The technology is demonstrated for mapping the spatial and temporal variation of Chl- a during an algal bloom, offering a useful complement to traditional field monitoring for fisheries management and emergency response. [ABSTRACT FROM AUTHOR]

Total suspended matter (TSM) has a crucial impact on light propagation in the water column, and often co-varies with nutrients, heavy metal and micropollutant fluxes. The objective of this study was to explore the feasibility of mapping TSM in lakes (area > 1 ha) across China using Landsat surface reflectance product embedded in Google Earth Engine. We conducted 44 sampling campaigns at 423 natural and manmade lakes across China, and determined TSM from 2036 water samples collected during 2011–2020. Landsat surface reflectance was matched with water sampling events within ±7 days of satellite overpasses, yielding 1908 matched pairs. We divided the TSM dataset into nine subsets, with eight subsets dedicated to building of simple regression and Random Forest (RF) models, and the remaining subset used to validate model performance. Regression analysis indicated strong associations between TSM concentration and both the Red band (Band 4; R2 = 0.76, RMSE = 21.4 mg/L) and the suspended matter index [(Nir + Red)/2); R2 = 0.71, RMSE = 17.1 mg/L]. The RF model outperformed the Red band model as indicated by higher coefficient of determination for model calibration (R2 = 0.95) and validation (R2 = 0.81). We also assembled samples (N = 1703) from lakes in different continents, and both the RF (RMSE = 16.04 mg/L) and Red band (RMSE = 18.9 mg/L) models exhibited acceptable performance. The models were further evaluated using a time series of TSM records from six large lakes in the USA and Japan (in situ TSM collected during 1988–2018). Finally, we used 460 scenes of Landsat/OLI images mainly acquired in 2019 to map TSM in lakes across China. Results showed considerable regional variability (TSM range: 0.12–860 mg/L), with lakes in Northeast China (44.6 mg/L), East China (38.8 mg/L) and Xinjiang-Inner Mongolia (39.7 mg/L) exhibiting much higher TSM concentration than lakes in the Yungui Plateau (17.62 mg/L) and the Tibetan Plateau (5.31 mg/L). Results confirmed the stability and spatial transferability of both models. Given the relationships among TSM and various indicators (e.g. nutrients, water clarity, chlorophyll-a) of the trophic state of aquatic ecosystems, these models would greatly facilitate TSM monitoring in lakes, and provide water resources managers with additional tools to assess the impact of water protection measures. [ABSTRACT FROM AUTHOR]

• An ensemble of spectro-temporal and spectro-spatial predictions for Landsat SR was proposed. • The proposed method has higher accuracy than traditional Landsat SR prediction algorithms. • The proposed method could accurately restore SR for areas with abrupt land cover changes. The cloud-induced contaminations limit the availability of Earth observation data acquired by Landsat satellites. In the field of cloud-contaminated image prediction and restoration, abrupt land cover change is an important but usually unconsidered issue. Based on the existing Time Series Models and Continuous Change Detection algorithm (TSM-CCD), an enhanced TSM-CCD algorithm (eTSM-CCD) is proposed in this paper to improve Landsat surface reflectance (SR) predictions based on all available Landsat data. The proposed eTSM-CCD method integrates time-series model estimation in the temporal domain and similar pixel replacement in the spatial domain to both address the issue of abrupt land cover changes and improve the prediction accuracy of SR. The proposed method was applied to Landsat time-series data in two study areas in the United States. The results indicated that the proposed method achieved higher overall and band-wise (especially NIR band) prediction accuracies than the original TSM-CCD algorithm. Specifically, the accuracies were improved more significantly for land cover types with distinct phenological characteristics at the peak of the growing season. In addition, compared to classical spatiotemporal prediction algorithms, such as modified neighborhood similar pixel interpolator (MNSPI) and enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM), the proposed method also yielded higher prediction accuracies and performed much better in predicting abrupt changes in land cover. The eTSM-CCD algorithm proposed in this paper is promising for the prediction and restoration of surface reflectance, especially in the vegetated areas during the peak of the growing season or in areas undergoing abrupt land cover changes. [ABSTRACT FROM AUTHOR]

Remote sensing satellite Earth Observing Systems (EOS) provide a variety of products for monitoring Earth surface processes at varying spatial and spectral resolutions. Combining information from high and medium spatial resolution images is valuable for monitoring ground cover and vegetation status in cropland, grassland, forests, and other natural settings. However, coupling information from different EOS requires compensating for atmospheric and view angle effects before integrating comparable surface reflectance (SR) values. The objectives of this study were i) to assess how different atmospheric constituents affect the atmospheric correction results in Sentinel-2 and WorldView-3 imagery, ii) to establish a relationship with field spectra measurements, and iii) to develop an empirical approach to ensure that SR values extracted from different EOS can be normalized for use in monitoring vegetation and land cover status. We compared surface reflectance values derived from Sentinel-2 images corrected with Sen2Cor, MODTRAN or FLAASH atmospheric correction approaches for the visible-to-near infrared regions. Additionally, this information was compared to SR values extracted from WorldView-3 imagery acquired from the same dates and location (Central Spain) and corrected with MODTRAN and FLAASH approaches. Assessment of the atmospheric correction was conducted by comparing satellite image SR with ground-truth spectra acquired with a FieldSpec hand-held spectroradiometer. The results emphasized the importance of using common atmospheric parameters collected from ancillary data sources (i.e. MODIS Atmosphere & Land products) to ensure a reliable SR comparison. When compared to field-collected spectral data, SR from corrected Sentinel-2 push-broom imagery showed a reliable match (<4% difference in the visible bands and <0.52% difference in the near infrared bands). However, SR imagery from the pointable WorldView-3 instrument showed significant deviation, likely resulting from the effects of steep off-nadir acquisition angles (24.6° to 39.1°) combined with surface anisotropy. The magnitude and sign of the deviation in SR differed depending on the vegetation type, wavelength and sun-surface-sensor geometry. Therefore, it was necessary to account for angular effects to ensure reliable comparisons of imagery from the different EOS. In this study, an empirical angular correction approach was developed based on calibrating each WorldView-3 band against the ground-truth spectra. This correction allowed for the accurate signal normalization of WorldView-3 and Sentinel-2 imagery SR in the visible-to-near infrared regions. [ABSTRACT FROM AUTHOR]

Combined use of different satellite sensors are known to improve retrievals of aerosol optical depth (AOD). In this study, we propose a new method for retrieving Multi-angle Imaging SpectroRadiometer (MISR) AOD data supported by Moderate Resolution Imaging Spectroradiometer (MODIS) data in Jiangsu Province, China, over the period of 2016–2018 using MODIS L1B, bidirectional reflectance distribution function (BRDF), MISR 1B2T, and ground-measured AOD data. This method is based on the surface reflectance determined by the MODIS V5.2 algorithm. Through the observation angle and spectral conversion between different sensors, the MISR AOD can be obtained. The correlation coefficient (R) and root-mean-square error (RMSE) between the retrieved MISR and ground-measured AOD data varied between different seasons. The accuracy of the MISR AOD retrieval was notably improved after correcting the MISR surface reflectance. Therefore, the method proposed in this study is feasible for the retrieval of MISR AOD supported by MODIS data, and will be applicable to atmospheric environmental monitoring over large areas in the future. [ABSTRACT FROM AUTHOR]

• A method was developed to measure the solar albedo of rough surface layers with large particle sizes. • The albedo of rough surface layers was theoretically studied by the discrete-particle model. • The albedo of rough surface layers was mainly affected by solar conditions and particle properties. • The methods could be used to measure and calculate the albedo of crushed-rock layers or gravel piles. Albedo is an important parameter for many research fields, including solar energy, building energy conservation, road engineering, regional and global climate, environmental protection, and other fields. The solar albedo of rough surface is always lower than the reflectance of homogeneous and flat surface due to the irregular and nonuniform surface properties. It is difficult to determine the solar albedo of rough surface in laboratory, especially for the surface with large particle sizes or cavities. Here a method was developed to measure the solar albedo of rough surface layers based on discrete-particle model in consideration of particle sizes, diurnal variations, and solar and season conditions. Besides, the solar albedo was theoretically studied by the discrete-particle model with and without cavities, which shows good performance. The relationship between the solar albedo of rough surface and the reflectance of flat surface was also studied. This developed methods can be applied to many engineering fields, such as the crushed-rock layers of embankments in permafrost regions and the cool roofing of buildings in tropical regions. The results also showed that the rough surfaces with higher reflective coatings can increase the solar albedo with greater values, which may be expected to raise the cooling performance of above engineering applications. [ABSTRACT FROM AUTHOR]

Abstract Hundreds of millions of hectares of tropical forest have been selectively logged, either legally or illegally. Methods for detecting and monitoring tropical selective logging using satellite data are at an early stage, with current methods only able to detect more intensive timber harvest (>20 m3 ha−1). The spatial resolution of widely available datasets, like Landsat, have previously been considered too coarse to measure the subtle changes in forests associated with less intensive selective logging, yet most present-day logging is at low intensity. We utilized a detailed selective logging dataset from over 11,000 ha of forest in Rondônia, southern Brazilian Amazon, to develop a Random Forest machine-learning algorithm for detecting low-intensity selective logging (<15 m3 ha−1). We show that Landsat imagery acquired before the cessation of logging activities (i.e. the final cloud-free image of the dry season during logging) was better at detecting selective logging than imagery acquired at the start of the following dry season (i.e. the first cloud-free image of the next dry season). Within our study area the detection rate of logged pixels was approximately 90% (with roughly 20% commission and 8% omission error rates) and approximately 40% of the area inside low-intensity selective logging tracts were labelled as logged. Application of the algorithm to 6152 ha of selectively logged forest at a second site in Pará, northeast Brazilian Amazon, resulted in the detection of 2316 ha (38%) of selective logging (with 20% commission and 7% omission error rates). This suggests that our method can detect low-intensity selective logging across large areas of the Amazon. It is thus an important step forward in developing systems for detecting selective logging pan-tropically with freely available data sets, and has key implications for monitoring logging and implementing carbon-based payments for ecosystem service schemes. Graphical abstract Unlabelled Image Highlights • We develop a new method to map forest disturbance from selective logging. • We focus on low-intensity logging (<15 m3 ha−1) to advance current detection methods. • Detailed logging records were used to train Random Forest models with Landsat data. • We detected approximately 40% of selectively logged areas at two test sites in Brazil. • Satellite imagery acquired closest in time to logging events provided best detection. [ABSTRACT FROM AUTHOR]

Abstract The Harmonized Landsat and Sentinel-2 (HLS) project is a NASA initiative aiming to produce a Virtual Constellation (VC) of surface reflectance (SR) data acquired by the Operational Land Imager (OLI) and Multi-Spectral Instrument (MSI) aboard Landsat 8 and Sentinel-2 remote sensing satellites, respectively. The HLS products are based on a set of algorithms to obtain seamless products from both sensors (OLI and MSI): atmospheric correction, cloud and cloud-shadow masking, spatial co-registration and common gridding, bidirectional reflectance distribution function normalization and spectral bandpass adjustment. Three products are derived from the HLS processing chain: (i) S10: full resolution MSI SR at 10 m, 20 m and 60 m spatial resolutions; (ii) S30: a 30 m MSI Nadir BRDF (Bidirectional Reflectance Distribution Function)-Adjusted Reflectance (NBAR); (iii) L30: a 30 m OLI NBAR. All three products are processed for every Level-1 input products from Landsat 8/OLI (L1T) and Sentinel-2/MSI (L1C). As of version 1.3, the HLS data set covers 10.35 million km2 and spans from first Landsat 8 data (2013); Sentinel-2 data spans from October 2015. The L30 and S30 show a good consistency with coarse spatial resolution products, in particular MODIS Collection 6 MCD09CMG products (overall deviations do not exceed 11%) that are used as a reference for quality assurance. The spatial co-registration of the HLS is improved compared to original Landsat 8 L1T and Sentinel-2A L1C products, for which misregistration issues between multi-temporal data are known. In particular, the resulting computed circular errors at 90% for the HLS product are 6.2 m and 18.8 m, for S10 and L30 products, respectively. The main known issue of the current data set remains the Sentinel-2 cloud mask with many cloud detection omissions. The cross-comparison with MODIS was used to flag products with most evident non-detected clouds. A time series outlier filtering approach is suggested to detect remaining clouds. Finally, several time series are presented to highlight the high potential of the HLS data set for crop monitoring. Highlights • HLS is a surface reflectance record from the Landsat/Sentinel-2 Virtual Constellation. • HLS products cover 10 million km2, and correct for spectral and angular sensors differences. • HLS v1.3 products are consistent with MODIS products with a deviation lower than 11%. • The HLS relative spatial co-registration is around 0.62 pixels. • Sentinel-2 cloud mask is not optimal and is the main HLS v1.3 known issue. [ABSTRACT FROM AUTHOR]

Abstract A practical atmospheric correction algorithm, called Coupled Moderate Products for Atmospheric Correction (CMPAC), was developed and implemented for the Multispectral Camera (MUX) on-board the China-Brazil Earth Resources Satellite (CBERS-4). This algorithm uses a scene-based processing and sliding window technique to derive MUX surface reflectance (SR) at continental scale. Unlike other optical sensors, MUX instrument imposes constraints for atmospheric correction due to the absence of spectral bands for aerosol estimation from imagery itself. To overcome this limitation, the proposed algorithm performs a further processing of atmospheric products from Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors as input parameters for radiative transfer calculations. The success of CMPAC algorithm was fully assessed and confirmed by comparison of MUX SR data with the Landsat-8 OLI Level-2 and Aerosol Robotic Network (AERONET)-derived SR products. The spectral adjustment was performed to compensate for the differences of relative spectral response between MUX and OLI sensors. The results show that MUX SR values are fairly similar to operational Landsat-8 SR products (mean difference < 0.0062, expressed in reflectance). There is a slight underestimation of MUX SR compared to OLI product (except the NIR band), but the error metrics are typically low and scattered points are around the line 1:1. These results suggest the potential of combining these datasets (MUX and OLI) for quantitative studies. Further, the robust agreement of MUX and AERONET-derived SR values emphasizes the quality of moderate atmospheric products as input parameters in this application, with root-mean-square deviation lower than 0.0047. These findings confirm that (i) CMPAC is a suitable tool for estimating surface reflectance of CBERS MUX data, and (ii) ancillary products support the application of atmospheric correction by filling the gap of atmospheric information. The uncertainties of atmospheric products, negligence of the bidirectional effects, and two aerosol models were also identified as a limitation. Finally, this study presents a framework basis for atmospheric correction of CBERS-4 MUX images. The utility of CBERS data comes from its use, and this new product enables the quantitative remote sensing for land monitoring and environmental assessment at 20 m spatial resolution. Highlights • The practical approach was developed for atmospheric correction of CBERS-4 MUX data. • First assessment of CBERS-4 MUX surface reflectance across South America • Moderate atmospheric products are used as ancillary products in the approach. • MUX surface reflectance (SR) was compared to Landsat-8 SR e AERONET-derived SR. • Combining MUX and OLI surface reflectance is possible using this product. [ABSTRACT FROM AUTHOR]

Abstract Atmospheric correction methods that are designed for either ocean color or land applications often result in low-quality or even no surface reflectance data for coastal and inland waters. In contrast, Rayleigh-corrected reflectance (R rc) has been used in water applications, although without removing aerosol scattering. However, a systematic effort has not been made to investigate the uncertainties and applicability of R rc in ocean color studies. This knowledge gap was filled in this study by using Moderate Resolution Imaging Spectroradiometer (MODIS) data covering a large area (almost the entire middle and lower reaches of the Yangtze River Basin and the Yangtze River Estuary) between 2002 and 2016. We first examined the correlation between a quality-controlled reflectance product that was generated using a shortwave-infrared (SWIR)-based atmospheric correction method (R rs_swir) and R rc. Improved relationships between these two products were found for all MODIS bands if a subtraction of the Rayleigh-corrected reflectance at 1240-nm was utilized as the aerosol correction for R rc (R rc-1240). The robust correlations between the two products allow for R rs_swir to be replaced with R rc-1240 -converted reflectance (denoted as R rs_rrc-1240) in water applications. In situ validations further demonstrated that the accuracy levels between usable R rs_rrc-1240 and R rs_swir data are comparable for most MODIS wavelengths. The most striking superiority of R rs_rrc-1240 over R rs_swir is the pronounced increase in data coverage (especially for small water bodies), where the percentages of usable observations (PUOs) of the former are several times to more than one order of magnitude higher than those of the latter. The differences in PUOs were mostly due to perturbations related to land adjacency effects (LAEs) in the SWIR-based atmospheric correction. Such effects could also explain the reduced PUOs for smaller water bodies. Ocean color applications in the examined regions could benefit from such a dramatic increase in PUOs, which could enhance the capability of tracking short- to long-term dynamics and could create new possibilities for inland water system monitoring at the basin scale. [ABSTRACT FROM AUTHOR]

The management of suspended solids and associated contaminants in rivers requires knowledge of sediment sources. In-situ sampling can only describe the integrated impact of the upstream sources. Empirical models that use surface reflectance from satellite images to estimate total suspended solid (TSS) concentrations can be used to supplement measurements and provide spatially continuous maps. However, there are few examples, especially in narrow, shallow and hydrologically dynamic rivers found in mountainous areas. A case study of the Didipio catchment in Philippines was used to address these issues. Four 5-m resolution RapidEye images, from between the years 2014 and 2016, and near-simultaneous ground measurements of TSS concentrations were used to develop a power law model that approximates the relationship between TSS and reflectance for each of four spectral bands. A second dataset using two 2-m resolution Pleiades-1A and a third using a 6-m resolution SPOT-6 image along with ground-based measurements, were consistent with the model when using the red band data. Using that model, encompassing data from all three datasets, gave an R 2 value of 65% and a root mean square error of 519 mg L −1 . A linear relationship between reflectance and TSS exists from 1 mg L −1 to approximately 500 mg L −1 . In contrast, for TSS measurements between 500 mg L −1 and 3580 mg L −1 reflectance increases at a generally lower and more variable rate. The results were not sensitive to changing the pixel location within the vicinity of the ground sampling location. The model was used to generate a continuous map of TSS concentration within the catchment. Further ground-based measurements including TSS concentrations that are higher than 3580 mg L −1 would allow the model to be developed and applied more confidently over the full relevant range of TSS. [ABSTRACT FROM AUTHOR]

Recently, corneal illuminance attracts much attention because it is closely related to important functions of indoor lighting. Especially, applying circadian light in the built environment places a challenging requirement on indirect corneal illuminance. In this work, rule-of-thumb equations are proposed to guide circadian lighting design: (i) for artificial lighting, E cor,avg (i) = ( Φ / C 1 ) · ρ /(1− ρ ′), where E cor,avg (i) is the average indirect corneal illuminance at standing or sitting positions, Φ is the initial flux from luminaires, C 1 is a constant comparable to the total room surface area, ρ is the reflectance of the surface where the first reflection occurs, and ρ ′ is the area-weighted average of surface reflectance; and (ii) for daylighting, E cor,avg (i)  =  C 2 · WWR · ρ /(1− ρ ′), where C 2 is a constant, and WWR represents the window-to-wall ratio. The equations above are validated by comparing against numerical simulation data obtained with the Radiance software. For artificial lighting simulation, various combinations of room surface reflectance, initial light distribution, and WWR are investigated; and for daylighting simulation, different combinations of surface reflectance, WWR, and geographic location are analyzed. The good fits to simulation data indicate that the proposed simple equations can provide reasonably accurate results for quick feedback at the field. It is also demonstrated that room surface reflectance has a dominant impact on indirect corneal illuminance. The approach of improving surface reflectance is more favorable than increasing luminaire flux or expanding window area, and therefore should be the recommended approach to achieve quality and efficient circadian lighting. [ABSTRACT FROM AUTHOR]

Air quality in developed areas is being increasingly compromised by the effect of urbanization, which is favouring the presence of atmospheric pollutants derived from human-induced activities. Land cover change is one of the consequences most closely associated with urbanization, leading to a growing presence of dark built-up surfaces. The target of this investigation was to model the Catalonian Air Quality Index (CAQI) from the combined effect of the surface reflectance capacity of urban surfaces with solar radiation and elevation. Geoprocessing tools were used to produce the information required to characterise these variables in the buffer areas surrounding 75 different air quality monitoring stations located across the region. Cluster analysis and Multiple Linear Regression (MLR) were applied to group these stations according to their similarity and replicate the annual mean values of CAQI recorded in Catalonia in 2011, respectively. Finally, discriminant analysis enabled assigning ungauged areas to the cluster and MLR model that best fitted their solar radiation, surface reflectance and elevation features. The implementation of this approach resulted in highly accurate predictions of CAQI, providing a mechanism of identification of areas having a number of days with poor air quality during the year. Since these areas were related to the presence of land cover types with high sunlight absorption, the proposed methodology was suggested to support the adoption of measures aimed at controlling urban air pollution based on replacing built-up surfaces by green infrastructure. [ABSTRACT FROM AUTHOR]

Temporal variation in cloud cover and surface conditions greatly affects estimation errors associated with reference surface reflectance and atmospheric composition retrievals. In this study, to determine an optimal temporal window for clear-sky composition methods that are used to identify surface reflectance for the retrieval of atmospheric properties, we analyzed temporal variation in surface reflectance and cloud fractions using long-term daily observations from the Moderate Resolution Imaing Spectroradiometer (MODIS) satellite. For the temporal variation in surface reflectance, gridded pixels with a standard deviation less than 0.025 represented 87.0%, 84.5%, 80.5%, and 77.3% of the total pixels for periods of 15, 20, 30, and 40 days, respectively. The temporal variability of surface reflectance was lowest in summer and highest in winter due to vegetation and snow cover changes over land surface in East Asia. For the temporal variation in cloud fractions, pixels with a cloud fraction <10% ranged from 91.2% (15-day) to 98.1% (40-day). Only temporal windows of 30 and 40 days satisfied the criterion of 95% cumulative distribution in the 10% cloud fraction range. Thus, and appropriate temporal window for clear-sky composition methods must be selected in consideration of the seasonal dependency of surface types and cloud cover variation. The temporal window for the clear-sky composition must be longer than 30 days considering the temporal variability of cloud cover, and shorter than 30 days considering that of surface reflectance. However, seasonal dependencies of surface reflectance and cloud fraction are also additionally considered to select the appropriated temporal window for the clear-sky composition. • Analysis of temporal variation in surface reflectance and cloud fraction using daily satellite observation. • Seasonal and regional dependence for temporal variability of surface reflectance due to vegetation and snow cover change. • Identify an optimized temporal window for the clear-sky composition by considering surface reflectance and cloud fraction. [ABSTRACT FROM AUTHOR]

Accurate long-term sensor calibration and periodic re-processing to ensure consistency and continuity of atmospheric, land and ocean geophysical retrievals from space within the mission period and across different missions is a major requirement of climate data records. In this work, we applied the Multi-Angle Implementation of Atmospheric Correction (MAIAC)-based vicarious calibration technique over Libya-4 desert site to perform calibration analysis of Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (SNPP) and NOAA-20 satellites. For both VIIRS sensors we characterized residual linear calibration trends and cross-calibrated both sensors to MODerate resolution Imaging Spectroradiometer (MODIS) Aqua regarded as a calibration standard. The relative spectral response (RSR) differences were accounted for using the German Aerospace Center (DLR) Earth Sensing Imaging Spectrometer (DESIS) hyperspectral surface reflectance data. Our results agree with independent vicarious calibration results of both the MODIS/VIIRS Characterization Support Team as well as the CERES Imager and Geostationary Calibration Group within estimated uncertainty of 1–2%. Analysis of MAIAC geophysical products with the new calibration shows a high level of agreement of MAIAC aerosol, surface reflectance and NDVI records between MODIS and VIIRS. Excluding high aerosol optical depth (AOD), all three sensors agree in AOD with mean difference (MD) less than 0.01 and residual mean squared difference rmsd ∼ 0.04. Spectral geometrically normalized surface reflectance agrees within rmsd of 0.003–0.005 in the visible and 0.01–0.012 at longer wavelengths. The residual surface reflectance differences are fully explained by differences in spectral filter functions. Finally, difference in NDVI is characterized by rmsd ∼ 0.02 and MD less than 0.003 for NDVI based on VIIRS imagery bands I1/I2 and less than 0.01 for NDVI based on VIIRS radiometric bands M5/M7. In practical sense, these numbers indicate consistency and continuity in MAIAC records ensuring the smooth transition from MODIS to VIIRS. • VIIRS on SNPP and NOAA 20 was cross-calibrated to MODIS Aqua over Libya-4 site. • Our results agree with analyses of NASA MCST/VCST and CERES-IGCG calibration teams. • MAIAC VIIRS aerosol, surface reflectance and NDVI records show continuity with MODIS. [ABSTRACT FROM AUTHOR]

After the end of the ‘Satellite Pour l'Observation de la Terre’ (SPOT) VEGETATION (SPOT/VGT) mission in May/2014, the SPOT/VGT data archive, consisting of raw data coming from both the VEGETATION 1 (VGT1) and VEGETATION 2 (VGT2) instruments, was reprocessed, aiming at improved cloud screening and correcting for known artefacts such as the smile pattern in the VGT2 Blue band and the Sun-Earth distance bug in Top-of-Atmosphere reflectance calculation, with the objective of improving temporal consistency. The aim of this paper is to inform the user community of the changes in and the evaluation of the new SPOT/VGT Collection 3 (VGT-C3). The evaluation of the reprocessing is based on (i) the relative comparison between SPOT/VGT Collection 2 (VGT-C2) and VGT-C3 surface reflectances and Normalized Difference Vegetation Index (NDVI), (ii) consistency analysis between VGT1-C3 and VGT2-C3, and (iii) the comparison of the archive with external datasets from METOP/Advanced Very High Resolution Radiometer (AVHRR) and TERRA/Moderate Resolution Imaging Spectroradiometer (MODIS). Surface reflectances are slightly higher after the reprocessing, with larger differences in July compared to January, caused by the corrected Sun-Earth distance modelling. For NDVI, the overall impact of the reprocessing is relatively small and differences show no seasonality. Trends in the differences over the years are related to changes in calibration coefficients. Systematic differences between VGT1-C3 and VGT2-C3 surface reflectance are well below 1%, with largest bias between VGT1 and VGT2 for the NIR band and the NDVI (VGT2 > VGT1, especially for larger NDVI values). Both the comparison with METOP/AVHRR (surface reflectance and NDVI) and TERRA/MODIS (NDVI) reveal trends over time: systematic bias between VGT2 and METOP/AVHRR tends to decrease over time, while comparison with TERRA/MODIS indicates an increasing bias between VGT2 and MODIS. VGT2 NDVI seems to be gradually evolving to slightly larger values, which is consistent with the change in overpass time of VGT2 and the different illumination conditions caused by the orbital drift of the sensor. Results demonstrate however the SPOT/VGT-C3 archive is more stable over time compared to the previous archive, although bidirectional reflectance distribution function (BRDF) normalization is recommended in order to correct for bidirectional effects. [ABSTRACT FROM AUTHOR]

Land change was assessed in the Albertine rift region (ARR) using its central section of north-western Rwanda as case study. This region is one of Africa's most ecologically sensitive environments under severe pressure from human activities. The study maps and quantifies the spatial extent of land use-land cover (LULC) changes between 1987 and 2016 from Landsat images. Transitions between five major land classes were identified in order to understand the trajectory of observed changes. In terms of gains, the forest class, the urban built-up and bare land class increased by 9% and 4% respectively over the study period. The gains of forest were mainly derived from the afforestation of some agricultural lands in the southern part, whereas the gains of built-up and bare lands were mostly from cultivated land which was a net losing class. Forest increase is in line with government's policy to increase the national forest cover to 30% by 2020. Forest losses occurred mostly outside protected areas due to land conversion for settlement and agricultural purposes. Much needed information about changes in LULC over the last three decades is provided. This study demonstrates in a timely manner how to analyse and monitor LULC change and the drivers in an environment where field based research is a challenge due to the mountainous terrain. The ecological richness of the region, which coincides with heightened human population pressure, necessitates the monitoring of land change as input for improving land use planning with focus on conserving biodiversity. [ABSTRACT FROM AUTHOR]

The “trapezoid” or “triangle” model constitutes the most popular approach to remote sensing (RS) of surface soil moisture based on coupled thermal (i.e., land surface temperature) and optical RS observations. The model, hereinafter referred to as Thermal-Optical TRAapezoid Model (TOTRAM), is based on interpretation of the pixel distribution within the land surface temperature - vegetation index ( LST - VI ) space. TOTRAM suffers from two inherent limitations. It is not applicable to satellites that do not provide thermal data (e.g., Sentinel-2) and it requires parameterization for each individual observation date. To overcome these restrictions we propose a novel OPtical TRApezoid Model (OPTRAM), which is based on the linear physical relationship between soil moisture and shortwave infrared transformed reflectance ( STR ) and is parameterized based on the pixel distribution within the STR - VI space. The OPTRAM-based surface soil moisture estimates derived from Sentinel-2 and Landsat-8 observations for the Walnut Gulch and Little Washita watersheds were compared with ground truth soil moisture data. Results indicate that the prediction accuracies of OPTRAM and TOTRAM are comparable, with OPTRAM only requiring observations in the optical electromagnetic frequency domain. The volumetric moisture content estimation errors of both models were below 0.04 cm 3 cm − 3 with local calibration and about 0.04–0.05 cm 3 cm − 3 without calibration. We also demonstrate that OPTRAM only requires a single universal parameterization for a given location, which is a significant advancement that opens a new avenue for remote sensing of soil moisture. [ABSTRACT FROM AUTHOR]

Biodiversity mapping in extensive tropical forest areas poses a major challenge for the interpretation of Landsat images, because floristically clearly distinct forest types may show little difference in reflectance. In such cases, the effects of the bidirectional reflection distribution function (BRDF) can be sufficiently strong to cause erroneous image interpretation and classification. Since the opening of the Landsat archive in 2008, several BRDF normalization methods for Landsat have been developed. The simplest of these consist of an empirical view angle normalization, whereas more complex approaches apply the semi-empirical Ross–Li BRDF model and the MODIS MCD43-series of products to normalize directional Landsat reflectance to standard view and solar angles. Here we quantify the effect of surface anisotropy on Landsat TM/ETM+ images over old-growth Amazonian forests, and evaluate five angular normalization approaches. Even for the narrow swath of the Landsat sensors, we observed directional effects in all spectral bands. Those normalization methods that are based on removing the surface reflectance gradient as observed in each image were adequate to normalize TM/ETM+ imagery to nadir viewing, but were less suitable for multitemporal analysis when the solar vector varied strongly among images. Approaches based on the MODIS BRDF model parameters successfully reduced directional effects in the visible bands, but removed only half of the systematic errors in the infrared bands. The best results were obtained when the semi-empirical BRDF model was calibrated using pairs of Landsat observation. This method produces a single set of BRDF parameters, which can then be used to operationally normalize Landsat TM/ETM+ imagery over Amazonian forests to nadir viewing and a standard solar configuration. [ABSTRACT FROM AUTHOR]

The atmospheric correction of satellite data is challenging over desert agricultural systems, due to the relatively high aerosol optical thicknesses (τ 550 ), bright soils, and a heterogeneous surface reflectance field. Indeed, the contribution of reflected radiation from adjacent pixels scattered into the field of view of a target pixel is considerable and can significantly affect the fidelity of retrieved reflectances. In this study, uncertainties and quantitative errors associated with the atmospheric correction of multi-spectral Landsat 8 and RapidEye data were characterized over a desert agricultural landscape in Saudi Arabia. Surface reflectances were retrieved using an implementation of the 6SV atmospheric correction code, and validated against field collected spectroradiometer measurements over desert, cultivated soil, and vegetated surface targets. A combination of satellite and Aerosol Robotic Network (AERONET) data were used to parameterize aerosol properties and atmospheric state parameters. With optimal specification of τ 550 and aerosol optical properties and correction for adjacency effects, the relative Mean Absolute Deviation (MAD) for all bands combined was 5.4% for RapidEye and 6.8% for Landsat 8. However uncertainties associated with satellite-based τ 550 retrievals were shown to introduce significant error into the reflectance estimates. With respect to deriving common vegetation indices from corrected reflectance data, the Normalized Difference Vegetation Index (NDVI) was associated with the smallest errors (3–8% MAD). Surface reflectance errors were highest for bands in the visible part of the spectrum, particularly the blue band (5–16%), while there was more consistency within the red-edge (~ 5%) and near-infrared (5–7%). Results were generally better constrained when a τ 550 -dependent aerosol model for desert dust particles, parameterized on the basis of nearby AERONET site data, was used in place of a generic rural or background desert model. This adaptation was particularly pertinent for the Landsat bands in the shortwave infrared. Failure to correct for adjacency effects increased the overall surface reflectance MAD to around 18%. The impact was especially significant for the visible bands over vegetated surface targets, as evidenced by relative MADs increasing to > 200%. In comparison, a relatively minor adjacency effect was observed within the near-infrared and over the more reflective bare soil and desert sites. Overall, the impact of adjacency effects was substantial (20–25% MAD) for all of the studied vegetation indices. This investigation fills a research gap in the quantification of uncertainties and evaluation of achievable accuracy levels in high spatial resolution surface reflectance imagery over desert agriculture and highlights the need for accurate and regionally representative atmospheric correction techniques. [ABSTRACT FROM AUTHOR]

The correction of the atmospheric effects on optical satellite images is essential for quantitative and multi-temporal remote sensing applications. In order to study the performance of the state-of-the-art methods in an integrated way, a voluntary and open-access benchmark Atmospheric Correction Inter-comparison eXercise (ACIX) was initiated in 2016 in the frame of Committee on Earth Observation Satellites (CEOS) Working Group on Calibration & Validation (WGCV). The first exercise was extended in a second edition wherein twelve atmospheric correction (AC) processors, a substantially larger testing dataset and additional validation metrics were involved. The sites for the inter-comparison analysis were defined by investigating the full catalogue of the Aerosol Robotic Network (AERONET) sites for coincident measurements with satellites' overpass. Although there were more than one hundred sites for Copernicus Sentinel-2 and Landsat 8 acquisitions, the analysis presented in this paper concerns only the common matchups amongst all processors, reducing the number to 79 and 62 sites respectively. Aerosol Optical Depth (AOD) and Water Vapour (WV) retrievals were consequently validated based on the available AERONET observations. The processors mostly succeeded in retrieving AOD for relatively light to medium aerosol loading (AOD < 0.2) with uncertainties <0.08, while the overall uncertainty values were typically 0.23 ± 0.15. Better performances were observed for WV retrievals with >90% of the results falling within the suggested empirical specifications and with the Root Mean Square Error (RMSE) being mostly <0.25 g/cm2. Regarding Surface Reflectance (SR) validation two main approaches were followed. For the first one, a simulated SR reference dataset was computed over all of the test sites by using the 6SV (Second Simulation of the Satellite Signal in the Solar Spectrum vector code) full radiative transfer modelling (RTM) and AERONET measurements for the required aerosol variables and water vapour content. The performance assessment demonstrated that the retrievals were not biased for most of the bands. The uncertainties ranged from approximately 0.003 to 0.01 (excluding B01) for the best performing processors in both sensors' analyses. For the second one, measurements from the radiometric calibration network RadCalNet over La Crau (France) and Gobabeb (Namibia) were involved in the validation. The performance of the processors was in general consistent across all bands for both sensors and with low standard deviations (<0.04) between on-site and estimated surface reflectance. Overall, our study provides a good insight of AC algorithms' performance to developers and users, pointing out similarities and differences for AOD, WV and SR retrievals. Such validation though still lacks of ground-based measurements of known uncertainty to better assess and characterize the uncertainties in SR retrievals. • The performance of 12 processors is investigated for Sentinel-2 and Landsat 8 data. • Aerosol Optical Depth retrievals' errors for Sentinel-2 and Landsat 8 were 0.1–0.2. • Water vapour retrievals' errors for Sentinel-2 were 0.2–0.4 g/cm2. • Low standard deviations (<0.04) between on-site and estimated surface reflectance. • Lower surface reflectance uncertainties for SWIR and NIR than visible bands. • Surface reflectance uncertainties < 0.01 for the best performing processors. [ABSTRACT FROM AUTHOR]

We present a high spectral resolution climatology of visible surface reflectance as a function of wavelength for use in satellite measurements of ozone and other atmospheric species. The Tropospheric Emissions: Monitoring of Pollution (TEMPO) instrument is planned to measure backscattered solar radiation in the 290–740 nm range, including the ultraviolet and visible Chappuis ozone bands. Observation in the weak Chappuis band takes advantage of the relative transparency of the atmosphere in the visible to achieve sensitivity to near-surface ozone. However, due to the weakness of the ozone absorption features this measurement is more sensitive to errors in visible surface reflectance, which is highly variable. We utilize reflectance measurements of individual plant, man-made, and other surface types to calculate the primary modes of variability of visible surface reflectance at a high spectral resolution, comparable to that of TEMPO (0.6 nm). Using the Moderate-resolution Imaging Spectroradiometer (MODIS) Bidirection Reflectance Distribution Function (BRDF)/albedo product and our derived primary modes we construct a high spatial resolution climatology of wavelength-dependent surface reflectance over all viewing scenes and geometries. The Global Ozone Monitoring Experiment–2 (GOME-2) Lambertian Equivalent Reflectance (LER) product provides complementary information over water and snow scenes. Preliminary results using this approach in multispectral ultraviolet+visible ozone retrievals from the GOME-2 instrument show significant improvement to the fitting residuals over vegetated scenes. [ABSTRACT FROM AUTHOR]

After alkaline chemical texturing process, SF 6 /O 2 reactive ion etching (RIE) was used for maskless textured Si surface independently from crystal orientation. Additional RIE texturing on alkaline textured Si surface increased the light receiving areas and decreased the surface reflectance by random pyramidal structures with a width of hundreds of nanometers. The weighted average reflectance (WAR) of the textured Si surface by two-step texturing processes was achieved as low as 9.6% in the 300–1200 nm wavelength range. Compared to the alkaline texturing, the alkaline and RIE texturing of front Si surfaces decreased the WAR by 6%. By means of two-step texturing processes for quasi-single crystalline silicon (QSC-Si) surfaces, performance parameters of the solar cell such as conversion efficiency, short circuit current density (J sc ), open circuit voltage (V oc ) and fill factor (FF) were examined in 18.2%, 36.6 mA/cm 2 , 624 mV and 79.7%, respectively. [ABSTRACT FROM AUTHOR]

Summary Despite recent advances in estimation of water quality parameters using satellite remote sensing, the estimation of Chlorophyll-a (Chl-a) has remained problematic due to optical complexity of coastal waters and imprecise atmospheric correction of imagery. Local environmental agencies require frequent measurement and monitoring of Chl-a over coastal regions at detailed level, for water quality assessment and control. To monitor Chl-a around the complex coastal waters of Hong Kong using remote sensing, 27 Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+) images over a 13-year period from January 2000 to December 2012, were used, along with 120 in situ Chl-a samples. Atmospherically corrected Landsat TM/ETM+ bands 1–4 along with in situ Chl-a data were used to develop and validate regression models for a Chl-a concentration range of 0.3–13.0 μg/l. Validation results indicated that the ratio of band 3 (red, 0.63–0.69 μm) and the square of band 1 (blue, 0.45–0.52 μm), with correlation coefficient ( R ) of 0.89, Root Mean Square Error (RMSE) of 2.53 μg/l and Mean Absolute Error (MAE) of 1.02 μg/l was most capable of representing actual Chl-a concentrations. This is attributed to the differential response of the red and blue wavebands to the Chl-a signal. The study is considered more robust than previous studies of Chl-a retrieval, due to the much larger number of images and in situ samples used for model development and validation, as well as the different times of year, water quality zones, and wide range of Chl-a concentrations which were investigated. The robustness of the developed model was also tested by its application to monitoring an extensive red tide event. The results indicate that the developed model is capable of routine monitoring of such algal blooms which frequently occur from late summer to early autumn in Hong Kong and its adjacent coastal waters. [ABSTRACT FROM AUTHOR]

Multi-temporal remote sensing information and spectral indices have been extensively used in studies to monitor ecosystem functioning and surface-energy budgets. However, most of these indices did not show good results in areas covered by sparse vegetation, like most of the Drylands. In these ecosystems, open spaces between plants are often covered by biological soil crusts (biocrusts), i.e. communities of cyanobacteria, algae, microfungi, lichens, mosses and other microorganisms growing in the uppermost millimeters of the soil. Due to their mostly dark color, biocrusts influence the spectral response of dryland surfaces, making it necessary to assess the sensibility of widely used spectral indices to variations in biocrusts cover. In this study we used spectra of biocrusts, bare soil and vegetation to analyze the effect of biocrust cover on the spectral response of heterogeneous areas. In a second approach we investigated the impact of biocrust water status on spectral characteristics. Based on spectral mixture analysis, we calculated the response of a wide range of vegetation/biocrust/bare soil landscape compositions, obtaining a total of 702 spectra. These were used to calculate the Normalized Difference Vegetation Index (NDVI), the Enhanced Vegetation Index (EVI), the Water Index (WI) and surface albedo, and the effect of biocrust cover and water status on these indices was analyzed. Biocrusts exerted a considerable effect on vegetation indices and surface albedo, whereas WI was mostly affected by vegetation type and cover. As biocrust cover increased, the value of NDVI and EVI also increased, whereas albedo decreased, and these effects were more important under low vegetation cover. Moreover, as biocrusts almost immediately turned dark after water pulses, the effect of biocrust cover on spectral indices increased already 30 min after wetting. Although these results varied depending on vegetation type, they demonstrate, that biocrusts largely affect the spectral response of dryland surfaces, and they illustrate how this effect is reinforced by water. Thus, biocrusts need to be considered in studies analyzing dryland phenology, productivity and water status. Moreover, in order to increase the accuracy of hydrological and climate forecast predictions, biocrust effects on surface albedo, both in a dry and wet stage, need to be included. [ABSTRACT FROM AUTHOR]

The assessment of uncertainties in satellite-derived global surface albedo products is a critical aspect for studying the climate, ecosystem change, hydrology or the Earth's radiant energy budget. However, it is challenged by the spatial scaling errors between satellite and field measurements. This study aims at evaluating the forthcoming MODerate Resolution Imaging Spectroradiometer (MODIS) (Collection V006) Bidirectional Reflectance Distribution Function (BRDF)/albedo product MCD43D over a Mediterranean agricultural area. Here, we present the results from the accuracy assessment of the MODIS blue-sky albedo. The analysis is based on collocated comparisons with higher spatial resolution estimates from Formosat-2 that were first evaluated against local in situ measurements. The inter-sensor comparison is achieved by taking into account the effective point spread function (PSF) for MODIS albedo, modeled as Gaussian functions in the North–South and East–West directions. The equivalent PSF is estimated by correlation analysis between MODIS albedo and Formosat-2 convolved albedo. Results show that it is 1.2 to 2.0 times larger in the East–West direction as compared to the North–South direction. We characterized the equivalent PSF by a full width at half maximum size of 1920 m in East–West, 1200 m in North–South. This provided a very good correlation between the products, showing absolute (relative) Root Mean Square Errors from 0.004 to 0.013 (2% to 7%), and almost no bias. By inspecting 1-km plots homogeneous in land cover type, we found poorer performances over rice and marshes (i.e., relative Root Mean Square Error of about 11% and 7%, and accuracy of 0.011 and − 0.008, respectively), and higher accuracy over dry and irrigated pastures, as well as orchards (i.e., relative uncertainty < 3.8% and accuracy < 0.003). The study demonstrates that neglecting the MODIS PSF when comparing the Formosat-2 albedo against the MODIS one induces an additional uncertainty up to 0.02 (10%) in albedo. The consistency between fine and coarse spatial resolution albedo estimates indicates the ability of the daily MCD43D product to reproduce reasonably well the dynamics of albedo. [ABSTRACT FROM AUTHOR]

Maintaining consistent datasets of Surface Reflectance (SR) is an important challenge to ensure long-term quality of Climate Data Records. The Landsat 5 and 7 archives offer a unique data source to monitor globally the land surface at high spatial resolution. The Landsat-5 TM and Landsat-7 ETM + SR products, derived from the on-demand processing Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS), require periodic evaluation to check the data consistency. Two evaluation approaches are presented in this paper. The first approach used the Aerosol Robotic Network (AERONET) data set for the period 2000 to 2013 over 489 sites with 3600 Landsat-5 TM and Landsat-7 ETM + scenes selected. For each scene, 10 × 10 km subsets of LEDAPS-derived Landsat SR and AERONET-derived SR are compared. The latter are computed using Landsat top of atmosphere reflectance, AERONET measurements of atmospheric parameters, and the 6S radiative transfer model. Second, we introduce a methodology to cross-compare Landsat data and MODIS data acquired on the same day. The analysis is based on 4000 random Landsat scenes globally distributed from 2000 to 2013. This method includes: (i) a surface anisotropy adjustment, based on the VJB Bidirectional Reflectance Distribution Function (BRDF) method, to adjust Terra and Aqua MODIS data to Landsat 5 and 7 sun-view geometry, (ii) a spectral adjustment based on an artificial neural network trained with the PROSAIL vegetation radiative transfer model, to adjust MODIS data to TM and ETM + spectral responses. The overall results of both approaches show a good match in over 80% of the scenes, i.e. the TM and ETM + SR uncertainty remained within the SR specification, defined as 0.05 × SR + 0.005. The worst results are found in the blue band used in LEDAPS to adjust the Aerosol Optical Thickness (AOT). The MODIS-Landsat SR cross-comparison confirms the utility of a BRDF adjustment method to decrease the scattering between Landsat sensors and MODIS sensors (Terra and Aqua). The spectral adjustment removes part of the biases related to spectral response differences. Global analysis is used to identify AOT retrieval issues over specific scenes, mostly over bright surfaces. From 2000 to 2013, no significant temporal variation of the performance is detected, which enhanced the consistency of LEDAPS-derived surface reflectance data set. [ABSTRACT FROM AUTHOR]

This study contributes to the quality assessment of atmospherically corrected Landsat surface reflectance data that are routinely generated by the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS). This dataset, named Landsat Surface Reflectance Climate Data Record (Landsat CDR), is available at global scale and offers unprecedented opportunities to land monitoring and management services that require atmospherically corrected Earth observation (EO) data. Our assessment is based on the comparison of the Landsat CDR data against a set of Landsat and DEIMOS-1 images processed to a high degree of accuracy using an industry-standard atmospheric correction algorithm (ATCOR-2). The software package has been used for many years and its correction procedures can be considered consolidated and well-established. The dataset of Landsat and DEIMOS-1 images was acquired over a semi-arid agricultural area located in Lower Austria and was independently corrected by using a manual fine-tuning of ATCOR-2 parameters to reach the highest possible accuracy. Results show a very good correspondence of the surface reflectance in each of the six reflective spectral channels as well as for the NDVI (Normalized Difference Vegetation Index). An additional comparison against a NDVI time series from MODIS revealed also a good correspondence. Coefficients of determination ( R 2 ) between the two multi-year and multi-seasonal Landsat/DEIMOS datasets range between 0.91 (blue band) and 0.98 (nIR, SWIR-1 and SWIR-2). The results obtained for our semi-arid test site in Austria confirm previous findings and suggest that automatic atmospheric procedures, such as the one implemented by LEDAPS are accurate enough to be used in land monitoring services that require consistent multi-temporal surface reflectance data. [ABSTRACT FROM AUTHOR]

This paper presents a method called SACRS2, a scheme for atmospheric correction of RS2 - AWiFS (Resourcesat2-Advanced Wide Field Sensor) data. The SACRS2 is a computationally fast scheme developed from a physics-based detailed radiative transfer model 6SV for correcting large amount of data from the high-repetivity AWiFS sensor. The method is based on deriving a set of equations with coefficients which depend on the spectral bands of the RS2-AWiFS sensor through forward signal simulations by 6SV. Semi-empirical formulations provided in the SMAC method with a few improvements have been used to describe various atmospheric interactions. A total of 112 coefficients for different equations are determined using the best fit equations against the computations of the 6SV. After the specific coefficients for the RS2-AWiFS spectral bands are determined, the major inputs of the scheme are raw digital numbers recorded by RS2-AWiFS sensor, atmospheric columnar water vapour content, ozone content, aerosol optical thickness at 550 nm and viewing-illumination conditions. Results showed a good performance of the SACRS2 with a maximum relative error in the SACRS2 simulations ranged between 1% for a reflectance of 0.5 and 8.6% for reflectance of 0.05 with respect to 6SV computations. Validation of retrieved surface reflectance using the SACRS2 scheme with respect to in-situ measurements at two sites indicated a capability of this scheme to determine the surface reflectance within 10%. This is a first of its kind scheme developed for the atmospheric correction of any Indian Remote Sensing satellite data. A package containing the SACRS2 software is available on the MOSDAC website for the researchers. [ABSTRACT FROM AUTHOR]

The purpose of this study was to evaluate the effect of instrument noise, atmospheric conditions, and reflectance retrieval method on multispectral reflectance and fractional cover estimates derived from spectral mixture analysis. Top-of-atmosphere (TOA) radiance was simulated for pixels with various ground (fractional cover of green vegetation (GV), nonphotosynthetic vegetation (NPV), and soil) and atmospheric/geometric (solar zenith angle, visibility, and water vapor) conditions for four operational spaceborne multispectral images, Landsat 5 Thematic Mapper (TM), Landsat 7 Enhance Thematic Mapper Plus (ETM +), Landsat 8 Operational Land Imager (OLI), and the Moderate Resolution Imaging Spectroradiometer (MODIS). Instrument noise was added to the TOA radiance. Three reflectance retrieval methods were used to estimate surface reflectance, and fractional cover estimates of GV, NPV, and soil were made using spectral mixture analysis (SMA) and multiple endmember spectral mixture analysis (MESMA). Among atmospheric characteristics, we found that visibility and solar zenith angle made the greatest contributions to spectral and fractional cover error. In all cases, GV fraction was estimated with lower error than NPV or soil fraction. The reflectance retrieval method that provided the best reflectance estimates did not produce the fractional cover estimates with the lowest error due to variable bias across bands. Fractional cover estimates for all ground components were strongly impacted by instrument noise, but MESMA significantly improved fractional cover estimates for NPV and soil, regardless of noise level. This study suggests that there are practical constraints on the level of accuracy that can be attained in multispectral estimates of NPV and soil. A set of recommendations for the use of spectral unmixing of multispectral reflectance data is developed. [ABSTRACT FROM AUTHOR]

A new algorithm for generating synthetic Landsat images is developed based on all available Landsat data. This algorithm is capable of predicting Landsat surface reflectance for any desired date. It first excludes cloud, cloud shadow, and snow observations, and then uses the remaining clear observations to estimate time series models for each Landsat pixel. Three time series models (a simple model, advanced model, and full model) are used for estimating surface reflectance for each pixel, and the selection of a time series model is dependent on the number of clear observations available: the more clear observations, the more complex the model will be that is used. For each time series model there are three components (seasonality, trend, and breaks), that are used for modeling intra-annual and inter-annual differences and abrupt surface change. Abrupt surface changes are detected by differencing predicted and observed Landsat observations, and if the difference is larger than twice the Root Mean Square Error (RMSE) for six consecutive observations, it will be detected as a “break” in the time series model. The RMSE values are temporally adjusted to provide better threshold range. For each “synthetic” image, a Quality Assessment (QA) Band is provided that contains information on how the time series model was estimated and used for generating the synthetic data. We have applied this approach to six Landsat scenes within the United States. We visually compared the synthetic images with real Landsat images for different kinds of environments and they are similar for all image pairs. We also quantitatively assessed the accuracy of the synthetic data by calculating the RMSE value for all clear Landsat observations. The RMSE values for the three visible bands are the lowest (approximately 0.01), and the Short-wave Infrared (SWIR) bands are slightly higher in magnitude (between 0.01 and 0.02). The Near Infrared (NIR) band has the highest RMSE values (between 0.02 and 0.03). The goal of this paper is to provide Landsat images that are free of cloud, cloud shadow, snow, and Scan Line Corrector (SLC)-off gaps that can be used to derive land cover and bio-physical products. [ABSTRACT FROM AUTHOR]

Total and fine mode aerosol optical depth (AODT and AODF), as well as the fine mode fraction (FMF = AODF/AODT), are critical variables for climate change and atmospheric environment studies. The retrievals with high accuracy from satellite observations, particularly FMF and AODF over land, remain challenging. This study aims to improve the Moderate-resolution Imaging Spectro-radiometer (MODIS) land dark target (DT) algorithm for retrieving AODT, AODF, and FMF on a global scale. Based on the fact that the underestimated surface reflectance (SR) could overestimate the AODT and underestimate the aerosol size parameter in the DT algorithm, two robust schemes were developed to improve SR determination: the first (NEW1 DT) used the top of the atmosphere reflectance instead of SR at 2.12 µm; the second (NEW2 DT) used eleven-year MODIS data to establish a monthly spectral SR relationship model (2.12–0.47 and 2.12–0.65 µm) database at pixel-by-pixel scale. Then a novel lookup table approach based on the physical process was proposed to retrieve the AODF and FMF. The new MODIS AODT, FMF, and AODF were compared to AERosol RObotic NETwork (AERONET) retrievals. Results showed that the root mean square error (RMSE) was 0.096–0.103, 0.098–0.099, and 0.167–0.180 for the new AODTs, AODFs, and FMFs, respectively, which were better than that of the Collection 6.1 (C6.1) DT (0.117, 0.235, and 0.426) in the validation by global AERONET sites. From the validation results, NEW2 DT provided better AODT and coarse mode AOD retrievals, while NEW1 DT had better AODF and FMF performances. The spatial patterns of AODF, FMF, and AODC of the new DT algorithms were comparable to those of the Polarization and Directionality of the Earth's Reflectances aerosol product. Hence, the new algorithms have the potential to provide global AODT, FMF, and AODF products over land to the scientific community with high accuracy using long-term MODIS data. [ABSTRACT FROM AUTHOR]

Highlights: [•] Electrochemical polishing of copper foils at elevated temperature in H3PO4 electrolytes. [•] Copper foils electropolished at 65°C in 2.17M H3PO4 exhibited a lower surface roughness. [•] New prospects for the preparation of copper foils before the growth of high quality graphene layers. [ABSTRACT FROM AUTHOR]

Abstract: Surface reflectance is one of the key products from VIIRS and as with MODIS, is used in developing several higher-order land products. The VIIRS Surface Reflectance (SR) Intermediate Product (IP) is based on the heritage MODIS Collection 5 product (Vermote, El Saleous, & Justice, 2002). The quality and character of surface reflectance depend on the accuracy of the VIIRS Cloud Mask (VCM), the aerosol algorithms and the adequate calibration of the sensor. The focus of this paper is the early evaluation of the VIIRS SR product in the context of the maturity of the operational processing system, the Interface Data Processing System (IDPS). After a brief introduction, the paper presents the calibration performance and the role of the surface reflectance in calibration monitoring. The analysis of the performance of the cloud mask with a focus on vegetation monitoring (no snow conditions) shows typical problems over bright surfaces and high elevation sites. Also discussed is the performance of the aerosol input used in the atmospheric correction and in particular the artifacts generated by the use of the Navy Aerosol Analysis and Prediction System. Early quantitative results of the performance of the SR product over the AERONET sites show that with the few adjustments recommended, the accuracy is within the threshold specifications. The analysis of the adequacy of the SR product (Land PEATE adjusted version) in applications of societal benefits is then presented. We conclude with a set of recommendations to ensure consistency and continuity of the JPSS mission with the MODIS Land Climate Data Record. [Copyright &y& Elsevier]

Abstract: Many remote sensing applications rely on the knowledge of light scattering by particulate surface. We present results of photometric and polarimetric laboratory measurements of natural particulate surface at a scattering angle range from 140° to 172° in the principal plane. Fifteen samples with different particle size, which is much larger than the wavelength of incident radiation, have been measured in visible and near infrared (350–2500nm). Each sample displays the prominent phenomena of both opposition surge and negative polarized branch. We can find that the particulate surfaces with small radius show brighter reflectance than those of large radius. For overall samples, the absolute values of negative polarized branch increase with an increase in particle size. Simultaneously, there is a particular case that the particle-size dependence of the negative polarization becomes more obvious for low reflectance particulate surface than for high reflectance particulate surface. The differences in the spectral behavior of degree of polarization of particle surface are found. These results are useful to better understand the scattering property of surface layer with large particles. [Copyright &y& Elsevier]

Abstract: The scattered light from particulate surfaces is useful for the verification of light scattering theory and the remote sensing applications. We present the photometric and polarimetric measurement results of natural snow surfaces with different grain size in the backward direction in field and in the laboratory. The snow grains are assumed that they exhibit both nonsphericity of shape and small-scale surface irregularities. All the samples display the prominent phenomena of brightness opposition surge and negative polarization; both of them are in dependence on the phase angle. There are clear effects of snow grain size on the photopolarimetric measurement results: the snow surfaces with small diameter show brighter reflectance than those of larger diameter, because of the amount of contaminants there is an exception of snow samples consist of small grain in the visible wavelengths; the absolute values of negative polarization increase with a decrease in grain size (corresponding to packing density), and the effect of snow grain size on negative polarization is quite significant in this study. The relation between snow grain size and negative polarization at near-backscattering geometries can be explained by the coherent backscattering mechanism. These results are useful to better understand the scattering property of high-albedo particulate surface with large particles. [Copyright &y& Elsevier]

Abstract: Realizing the full benefits of MODIS' temporal resolution requires, among others, the correction of the directional effect (i.e. the combined impact of the variation of the measurement geometry and of the observed land surface upon the registered radiant flux). While different BRDF methods have been proposed to address this effect, its performance has been evaluated at coarse spatial resolutions making it difficult to assess its applicability to, for example, crop monitoring. Here we test 2 approaches based on two different assumptions: the Classic approach that relies on the hypothesis of stable target and a recent Alternative that is based on the idea that despite reflectance magnitude may change rapidly, the BRDF shape varies slowly in time. Additionally, we segmented the growing season into different numbers of periods for the BRDF correction (a single period along the growing season, 3 periods based in phenology and 9–12 periods of fixed 16-days). The resulting 6 methods were compared over annual crops (wheat, maize and soybean) at 250m spatial resolution from a site located in the Argentine Pampas. We used MOD and MYD 09 GQ and GA as inputs and compared the corrected daily red and infrared reflectances and the NDVI time series against the filtered benchmark (input time series with quality filters applied) by means of the high frequency variability (i.e. noise). We also tested whether corrected time series were better correlated with soybean PAR interception and biomass. Our results showed that methods' performance was more explained by the number of periods than by the approach (Classic or Alternative). Single period methods decreased noise by 52%, 55% and 4% for red, infrared and NDVI time series. The use of 3 periods improved the correction performance to 63, 64 and 24% for red, infrared and NVDI time series respectively, while the highest reductions (65, 68 and 32% for red, infrared and NVDI) were found with 16-day intervals (9–12 periods) considering a magnitude inversion process. Wheat displayed the lowest noise reduction compared to the other crops. BRDF parameters obtained from different methods were associated to crop structure, suggesting that they have biophysical meaning. The decrease in noise obtained with correction methods was translated into a better assessment of the fraction of intercepted PAR and biomass. These promising results suggest the possibility of extensive field crop monitoring at an unprecedented temporal resolution. [Copyright &y& Elsevier]

Abstract: This study aims at validating Leaf Area Index (LAI) and Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) products derived from MODIS surface reflectance (MOD09CMG) at coarse resolution (0.05°) over crops. These Essential Climate Variables (ECVs) are estimated by using the inversion of the PROSAIL radiative transfer (BV-NNET tool) applied on MODIS BRDF (Bidirectional Reflectance Distribution Function) corrected surface reflectances and non-corrected. ECV estimates and the corresponding MCD15A3 Collection 5 and GEOLAND-2 (GEOv1) products are compared with ECV reference maps derived from BV-NNET applied on 105 high spatial resolution images (Formosat-2, 8m) which were acquired from 2006 to 2010 in Southwest France. These latter are compared with local scale in situ measurements. The validation shows an uncertainty of 0.35 and 0.07 for LAI and FAPAR, respectively. The comparison shows that the ECV estimates from the three products properly capture the crops phenology in agreement with reference maps. Results indicate that MCD15A3 uncertainties (0.23 and 0.07 for LAI and FAPAR, respectively) are similar to previous intercomparison studies. GEOv1 shows a systemic positive bias for both LAI and FAPAR. The best agreement with the reference maps is found for MODIS BV-NNET products with r2 higher than 0.9 and relative uncertainties lower than 17%. The use of BRDF-corrected surface reflectances as input of BV-NNET tool improves the uncertainty of LAI estimates (0.11, compared to 0.17 when directional surface reflectances are used as input) but not the uncertainty of FAPAR estimates. The deviation between FAPAR products which mostly affects low winter FAPAR, is related to the discrepancy of the soil directional assumption in PROSAIL model and BRDF correction method. The temporal stability of the daily MODIS BV-NNET products is better than the 4-day composite MCD15A3 products. Finally, BV-NNET tool applied at finer resolutions demonstrates that the increase of the resolution results in a decrease of the LAI and FAPAR uncertainties and a conservation of the biases. [Copyright &y& Elsevier]

Abstract: Global, long-term monitoring of changes in Earth's land surface requires quantitative comparisons of satellite images acquired under widely varying atmospheric conditions. Although physically based estimates of surface reflectance (SR) ultimately provide the most accurate representation of Earth's surface properties, there has never been a globally consistent SR dataset at the spatial resolution (<1ha) or temporal extent (~40years) of the Landsat mission. To increase the consistency and robustness of Landsat-based land cover monitoring, we atmospherically corrected the Global Land Survey (GLS) Landsat dataset using the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) implementation of the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model. The GLS provides synoptic, orthorectified, cloud-free Landsat coverage of Earth's land area in four nominal epochs (1975, 1990, 2000, and 2005). This paper presents the resulting GLS surface reflectance dataset and a global assessment of the 2000- and 2005-epoch data against coincident Moderate Resolution Imaging Spectroradiometer (MODIS) daily SR and Normalized Bidirectional Distribution Function-Adjusted Reflectance (NBAR) measurements. Agreement with respect to MODIS SR and NBAR data is very high, with overall discrepancies (Root-Mean-Squared Deviation (RMSD)) between 1.3 and 2.8percent reflectance for Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and between 2.2 and 3.5percent reflectance for Landsat-5 Thematic Mapper (TM). The resulting Landsat surface reflectance dataset and the associated quality metrics for each image are hosted on the Global Land Cover Facility web site for free download (http://www.landcover.org/data/gls_SR). This new repository will provide consistent, calibrated, multi-decadal image data for robust land cover change detection and monitoring across the Earth sciences. [Copyright &y& Elsevier]

Abstract: MODIS land surface reflectance product (MOD09) is one of the most popular data sources for characterizing land surface environments. Because those cloudy observations should be excluded from further analysis, the reliable cloud screening is important for downstream applications. In this paper, an approach is proposed to generate cloud masks from time series of MOD09 products. It is found that an inflexion point exists between the clear-sky and cloudy observations if time series of reflectances assembled from the same location are sorted. The maximum surface reflectance can be composited from these inflexions and those observations with reflectance values larger than the inflexion are identified as cloudy. To the best of our knowledge, this is the first method to composite the maximum snow-free surface reflectance. And a new method is proposed to objectively compare cloud detection results derived from different approaches. Comparisons show that this inflexion-based cloud detection algorithm performs generally better than the cloud masks accompanying with the MOD09 products. The new cloud masks are valuable for those applications relying on the MOD09 products as input and for analysis of the uncertainty of the MODIS cloud mask products. [Copyright &y& Elsevier]

Abstract: This paper describes the atmospheric correction (AC) component of the Multi-Angle Implementation of Atmospheric Correction algorithm (MAIAC) which introduces a new way to compute parameters of the Ross-Thick Li-Sparse (RTLS) Bi-directional reflectance distribution function (BRDF), spectral surface albedo and bidirectional reflectance factors (BRF) from satellite measurements obtained by the Moderate Resolution Imaging Spectroradiometer (MODIS). MAIAC uses a time series and spatial analysis for cloud detection, aerosol retrievals and atmospheric correction. It implements a moving window of up to 16days of MODIS data gridded to 1km resolution in a selected projection. The RTLS parameters are computed directly by fitting the cloud-free MODIS top of atmosphere (TOA) reflectance data stored in the processing queue. The RTLS retrieval is applied when the land surface is stable or changes slowly. In case of rapid or large magnitude change (as for instance caused by disturbance), MAIAC follows the MODIS operational BRDF/albedo algorithm and uses a scaling approach where the BRDF shape is assumed stable but its magnitude is adjusted based on the latest single measurement. To assess the stability of the surface, MAIAC features a change detection algorithm which analyzes relative change of reflectance in the Red and NIR bands during the accumulation period. To adjust for the reflectance variability with the sun-observer geometry and allow comparison among different days (view geometries), the BRFs are normalized to the fixed view geometry using the RTLS model. An empirical analysis of MODIS data suggests that the RTLS inversion remains robust when the relative change of geometry-normalized reflectance stays below 15%. This first of two papers introduces the algorithm, a second, companion paper illustrates its potential by analyzing MODIS data over a tropical rainforest and assessing errors and uncertainties of MAIAC compared to conventional MODIS products. [Copyright &y& Elsevier]

Abstract: Surface reflectance time series measured from space borne instruments, such as the MODIS sensor, show an apparent high-frequency noise that limits their information content. A major contributor to this noise is the directional effect as the target reflectance varies with the observation geometry. The operational MODIS processing inverts the parameters of a BRDF model which are provided in the so-called MCD43C2 product with a frequency of (8days)−1. Recently, Vermote et al. (2009) suggested an alternative BRDF inversion method. A major assumption is that the BRDF model shape (i.e. the BRDF normalized by its overall amplitude) varies little throughout the year so that the two model parameters are linear functions of the NDVI. Consequently, a given target BRDF shape is described by four parameters (slope and intercept for the two NDVI-dependent parameters) rather than 2 parameters that change for each 8days period. This method imposes additional constrain for the surface BRDF inversion. In this paper, we evaluate the performance of these two approaches for the correction of surface reflectance time series. We work at the 0.05° (≈5km) resolution of the CMG grid and analyze a representative set of +100 targets selected on the basis of the location of AERONET sites. The performance is quantified by the high-frequency noise in the corrected time series. We demonstrate that the performances of the two approaches are very similar. This result demonstrates that a simple four-parameter NDVI-scaled model performs as well as a more complex model with many more degrees of freedom. Besides, the four-parameter model, which is inverted on a given year, can be applied to the measurements of other years with a similar level of performance. Finally, a single “averaged” model can be applied to any target with a performance that is only slightly reduced compared to what is achieved with a model derived through a full inversion of the multi-temporal data. The proposed four-parameter BRDF model permits the reduction of noise in the reflectance time series by a factor of the order of three in the red and four in the near infrared. After correction, the reflectance time series are very clean, with an apparent noise that is ≈0.005 in the red band and 0.01 in the near infrared. The quality of the BRDF correction makes it possible to use the individual reflectance time-series at high temporal resolution, rather than indices based on their ratio, and thus retain more information about the vegetation dynamics. [Copyright &y& Elsevier]

Abstract: Surface reflectance adjusted for atmospheric effects is a primary input for land cover change detection and for developing many higher level surface geophysical parameters. With the development of automated atmospheric correction algorithms, it is now feasible to produce large quantities of surface reflectance products using Landsat images. Validation of these products requires in situ measurements, which either do not exist or are difficult to obtain for most Landsat images. The surface reflectance products derived using data acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS), however, have been validated more comprehensively. Because the MODIS on the Terra platform and the Landsat 7 are only half an hour apart following the same orbit, and each of the 6 Landsat spectral bands overlaps with a MODIS band, good agreements between MODIS and Landsat surface reflectance values can be considered indicators of the reliability of the Landsat products, while disagreements may suggest potential quality problems that need to be further investigated. Here we develop a system called Landsat–MODIS Consistency Checking System (LMCCS). This system automatically matches Landsat data with MODIS observations acquired on the same date over the same locations and uses them to calculate a set of agreement metrics. To maximize its portability, Java and open-source libraries were used in developing this system, and object-oriented programming (OOP) principles were followed to make it more flexible for future expansion. As a highly automated system designed to run as a stand-alone package or as a component of other Landsat data processing systems, this system can be used to assess the quality of essentially every Landsat surface reflectance image where spatially and temporally matching MODIS data are available. The effectiveness of this system was demonstrated using it to assess preliminary surface reflectance products derived using the Global Land Survey (GLS) Landsat images for the 2000 epoch. As surface reflectance likely will be a standard product for future Landsat missions, the approach developed in this study can be adapted as an operational quality assessment system for those missions. [Copyright &y& Elsevier]

Abstract: The AERONET-based Surface Reflectance Validation Network (ASRVN) is an operational processing system developed for validation of satellite derived surface reflectance products at regional and global scales. The ASRVN receives 50×50km2 subsets of MODIS data centered at AERONET sites along with AERONET aerosol and water vapor data, and performs an atmospheric correction. The ASRVN produces surface bidirectional reflectance factor (BRF), albedo, parameters of the Ross-Thick Li-Sparse (RTLS) BRF model, as well as Hemispherical-Directional Reflectance Factor (HDRF), which is required for comparison with the ground-based measurements. This paper presents a comparison of ASRVN HDRF with the ground-based HDRF measurements collected during 2001–2008 over a bright calibration Railroad Valley, Nevada site as part of the MODIS land validation program. The ground measurements were conducted by the Remote Sensing Group (RSG) at the University of Arizona using an ASD spectrometer. The study reveals a good agreement between ASRVN and RSG HDRF for both MODIS Terra and Aqua with rmse ~0.01–0.025 in the 500m MODIS land bands B1–B7. Obtained rmse is below uncertainties due to the spatial and seasonal variability of the bright calibration 1km2 area. While two MODIS instruments have a similar rmse in the visible bands, MODIS Aqua has a better agreement (lower rmse) with the ground data than MODIS Terra at wavelengths 0.87–2.1μm. An independent overall good agreement of two MODIS instruments with the ground data indicates that the relative calibration of MODIS Terra and Aqua at medium-to-bright reflectance levels for the stated time period is significantly better than uncertainties of the ASRVN and ground data. [Copyright &y& Elsevier]