Top Of Atmosphere Reflectance Research Articles

Evaluation of Remote Sensing Reflectance Derived From the Sentinel-2 Multispectral Instrument Observations Using POLYMER Atmospheric Correction

With a five-day revisit frequency over coastal regions and a spatial resolution of 10–60 m, the Sentinel-2 multispectral instrument (MSI) has shown its capacity to provide a reasonably accurate remote sensing reflectance ( $R_{\mathrm {rs}}$ ) data product over water when the standard “black pixel” (BP) atmospheric correction algorithm was applied to the top-of-atmospheric (TOA) reflectance data. Alternative atmospheric correction approaches, such as the POLYnomial-based algorithm applied to Medium Resolution Imaging Spectrometer (MERIS) (POLYMER), may show advantages under nonoptimal observation conditions (e.g., in the presence of strong sun glint). Here, POLYMER is implemented to process the data collected by both MSI and the Moderate Resolution Imaging Spectroradiometer (MODIS) with the resulting $R_{\mathrm {rs}}$ evaluated with concurrent and colocated in situ $R_{\mathrm {rs}}$ data collected from the AERONET-OC platforms. The results indicate less uncertainties in the MSI $R_{\mathrm {rs}}$ than those in the MODIS $R_{\mathrm {rs}}$ , and also less uncertainties in the MSI $R_{\mathrm {rs}}$ than those reported earlier. This is possibly attributed to the spatial heterogeneity of coastal waters where MODIS coarse-resolution data may suffer, and to the high-quality AERONET-OC data. In addition, for the evaluation data set, MSI $R_{\mathrm {rs}}$ does not appear to suffer from adjacency effects from the AERONET-OC platform and clouds, leading to more coverage than MODIS in nearshore waters. However, MSI $R_{\mathrm {rs}}$ is noisy in relatively clear waters, possibly due to the noisy TOA reflectance in the atmospheric correction bands over clear waters.

Narrowband-to-Broadband Conversions for Top-of-Atmosphere Reflectance from the Advanced Very High Resolution Radiometer (AVHRR)

The current lack of a long, 30+ year, global climate data record of reflected shortwave top-of-atmosphere (TOA) radiation could be tackled by relying on existing narrowband records from the Advanced Very High Resolution Radiometer (AVHRR) instruments, and transform these measurements into broadband quantities like provided by the Clouds and the Earth’s Radiant Energy System (CERES). This paper presents the methodology of an AVHRR-to-CERES narrowband-to-broadband conversion for shortwave TOA reflectance, including the ready-to-use results in the form of scene-type dependent regression coefficients, allowing a calculation of CERES-like shortwave broadband reflectance from AVHRR channels 1 and 2. The coefficients are obtained using empirical relations in a large data set of collocated, coangular and simultaneous AVHRR-CERES observations, requiring specific orbital conditions for the AVHRR- and CERES-carrying satellites, from which our data analysis uses all available data for an unprecedented observation matching between both instruments. The multivariate linear regressions were found to be robust and well-fitting, as demonstrated by the regression statistics on the calibration subset (80% of data): adjusted R 2 higher than 0.9 and relative RMS residual mostly below 3%, which is a significant improvement compared to previous regressions. Regression models are validated by applying them on a validation subset (20% of data), indicating a good performance overall, roughly similar to the calibration subset, and a negligible mean bias. A second validation approach uses an expanded data set with global coverage, allowing regional analyses. In the error analysis, instantaneous accuracy is quantified at regional scale between 1.8 Wm − 2 and 2.3 Wm − 2 (resp. clear-sky and overcast conditions) at 1 standard deviation (RMS bias). On daily and monthly time scales, these errors correspond to 0.7 and 0.9 Wm − 2 , which is compliant with the GCOS requirement of 1 Wm − 2 .

Application of nanosatellites PlanetScope data to monitor crop growth

In this research, an approach to monitor crop growth and development is presented using time series satellite data of high spatial resolution. Monitoring of winter wheat phenology based on images of PlanetScope constellations is considered. By applying various PlanetScope data processing types and ground based GreenSeeker data, differences of NDVI values at two variants of crop fertilization are determined. In particular, the following approaches were used in the research: obtaining the Top of Atmosphere Reflectance (TOA), the Planet Surface Reflectance (SR), and receiving NDVI image in Python using a Rasterio module. It was estimated that NDVI values derived from the surface reflectance imagery were significantly correlated to the ground data of a manual active GreenSeeker optical sensor (p < 0.05). The proposed simplified technique, based on PlanetScope NDVI time series, demonstrates the possibilities to monitor temporal changes in crop growth.

GOES-16 ABI solar reflective channel validation for earth science application

This paper presents the validation results of GOES-16 Satellite's Advanced Baseline Imager (ABI) obtained from a reflectance-based field campaign undertaken at the Salar de Uyuni in Bolivia in June 2017. In situ ground measurements are used to characterize the surface reflectance and the atmosphere in order to constrain the radiative transfer code and predict at-sensor reflectance (also referred to as top-of-atmosphere (TOA) reflectance) to compare with concurrent GOES-16 ABI measurements. The five-day field campaign provided repeated TOA reflectance estimates, allowing assessment not only of the calibration accuracy of the ABI reflective channels 1, 2, 3, 5 and 6, but also of its stability over the duration of the campaign. The results show that the accuracy of the ABI reflective channels calibration is within specification for channels 1, 3, 5, and 6 - average biases within 2%; for channel 2 the bias is 5%. The estimated uncertainty on the derived biases is 2–2.4%. Some calibration stability issues were present in the ABI calibration at the time of the campaign: (i) a jump on the order of 2% in channels 1 and 6, coincident with an ABI solar calibration event, reflects an instability of the ABI gains in these channels, and (ii) short-term variability in channels 1 and 2 is due to striping (ABI detector-to-detector calibration differences). Continued validation and subsequent reprocessing of ABI reflectance imagery would allow Earth scientists to fully benefit from the high spatial and spectral fidelity of the GOES-16 ABI diurnal measurements at the continental scale.

Development of the Direct-Estimation Albedo Algorithm for Snow-Free Landsat TM Albedo Retrievals Using Field Flux Measurements

Anisotropy information from moderate-to-coarse-resolution sensors [e.g., 500-m Moderate Resolution Imaging Spectroradiometer (MODIS)] is widely applied to estimate high-resolution surface albedo. Simulated albedos using MODIS bidirectional reflectance distribution function (BRDF) parameters as prior knowledge based on the kernel-driven model are employed to build and assess the lookup table (LUT) of the direct-estimation method, which is then used to estimate high-resolution albedos directly from top-of-atmosphere (TOA) reflectance data (e.g., Landsat albedo). Previously, the errors in the simulated albedos were not considered in building and assessing the LUT. In this article, daytime time-series (30 min) of snow-free albedo measurements with sufficient solar zenith angles (SZAs) were introduced to build the LUT for snow-free Landsat TM surface shortwave broadband albedo (TM albedo) retrievals, together with TOA-simulated reflectance by concurrent daily MODIS BRDF parameters. The assessment utilizes an independent data set and shows larger discrepancies between the estimated and measured albedos [root-mean-square errors (RMSEs) of >0.03 at SZAs ≥ 60°] than those in previous articles. To reduce inconsistencies between the MODIS BRDF parameters and the observed albedos, as well as possible spatial resolution differences between the MODIS and Landsat data, we adopted a correction strategy that first linearly adjusts the MODIS BRDF parameters to match the albedo measurements by a magnitude method, and second, the TOA reflectance simulations were further corrected by concurrent TM reflectances. The developed algorithm shows a significant improvement after using such corrections as a priori (RMSE < 0.02 at SZA ≤ 75°). The validation indicates improved accuracies in the TM albedo estimation. These improvements may provide potential albedo estimations for nadir-viewing high-resolution sensors using coarse-resolution anisotropy information.

Harmonizing Landsat 8 and Sentinel-2: A time-series-based reflectance adjustment approach

We developed a Time-series-based Reflectance Adjustment (TRA) approach for reducing the reflectance differences between Landsat 8 and Sentinel-2 observations. This TRA approach used the time series of matched Landsat 8 and Sentinel-2 observations to build linear regression models to adjust reflectance differences between the two sensors for each individual pixel and each spectral band. We evaluated this approach for the NASA harmonized Landsat and Sentinel-2 (HLS) surface reflectance product (V1.4; https://hls.gsfc.nasa.gov/data/v1.4/) and top-of-atmosphere (TOA) reflectance with approximately 4 years of temporal coverage at five Military Grid Reference System (MGRS) tiles. Using this approach, the surface reflectance difference between Landsat 8 and Sentinel-2 in the HLS product reduced 45% for the blue band, 42% for the green band, 38% for the red band, 30% for the Near Infrared (NIR) band, 37% for the Shortwave Infrared (SWIR) 1 band, and 32% for the SWIR2 band. The TRA approach also reduced TOA reflectance difference between Landsat 8 and Sentinel-2 substantially, in which the blue band reduced 46%, the green and NIR bands reduced 42%, the red band reduced 48%, and the SWIR1 and SWIR2 bands reduced 44%. If the high aerosol observations were screened, the reflectance differences in the HLS product could be further reduced by 2–4% and the TOA reflectance differences could be further reduced by 3–6% for the six spectral bands. The TRA approach has also shown good results in reserving the spatial patterns and the heterogeneity of land surface. The transformation parameters estimated from the TRA approach can be directly used for future Landsat 8 and Sentinel-2 reflectance adjustment, with slightly lower (5%) reduction of reflectance difference.

Estimating the suspended sediment concentration from TM/Landsat-5 images for the Araguaia River – Brazil

ABSTRACTIn this study, 68 images from TM/Landsat-5 sensor were used to estimate Suspended Sediment Concentration (SSC) along of the Araguaia River, Brazil. These were combined with in-situ SSC, hydrosedimentometric station (categorical variable), and remote sensing reflectance. Top-of-Atmosphere (ToA) and surface reflectance data were evaluated. Multiple regression models with ToA reflectance using VNIR bands, band ratios, SWIR band 5 as input and station as categorical variable were more accurate with adjusted coefficient of determination (adjusted R2) = 0.87 and normalized root mean square error (NRMSE) = 10.09% compared to the models with surface reflectance with adjusted R2 = 0.60 and NRMSE = 15.43%. Results confirm the potential for estimation of SSC from TM/Landsat-5 historical series data between 1984 and 2012, for which in-situ database is rare. Based on this empirical model, future studies may provide better analysis of spatiotemporal variations of sediment transport along the Araguaia River with the SSC temporal series reconstitution.

Benchmarking Machine Learning Algorithms for Instantaneous Net Surface Shortwave Radiation Retrieval Using Remote Sensing Data

Net surface shortwave radiation (NSSR) is one of the most important fundamental parameters in various land processes. Benefiting from its efficient nonlinear fitting ability, machine learning algorithms have a great potential in the retrieval of NSSR. However, few studies have explored the level of accuracy that machine learning algorithms can reach for different land covers on the worldwide scale and what the optimal independent variables are in the machine learning-based NSSR model. To guide the use of machine learning algorithms correctly in the retrieval of NSSR, it is necessary to give a comprehensive analysis from algorithm complexity, accuracy, and other aspects. In this study, three classic machine learning algorithms, including Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Regression (SVR), were built well to estimate instantaneous NSSR with optimal hyperparameters by elaborately selecting different independent variables, including top of atmosphere (TOA) channel spectral reflectance, geographic parameters, surface information, and atmosphere conditions. Global FLUXNET in situ measurements throughout 2014 were used to validate the accuracies of retrieved NSSR over various land cover types. The root mean square error (RMSE) is below 55 W/m2, and the distributions of error histogram are also similar. Approximately 50% of absolute error were within 25 W/m2. There was a performance difference of NSSR estimations in various surface types, and the performance of three machine learning methods in a specific surface type was also different. However, the RF method may be considered as the optimal methodology to retrieve NSSR from MODIS data, owing to its relatively better precision and concise hyperparameter-tuned process. The importance analysis of the proposed independent variables of NSSR retrieval shows that the introduction of geographic information can effectively reduce the error of NSSR retrieval, and surface information and atmosphere information are not necessary. It was also found that a combination of geographic information and blue band TOA reflectance already have a pretty good accuracy in NSSR retrieval, which implies there is a possibility to transfer our NSSR model to other satellite sensors, especially with insufficient channels. In a word, the NSSR model with machine learning algorithms would be an efficient, concise, and general method in the future.

An Operational Approach for Generating the Global Land Surface Downward Shortwave Radiation Product From MODIS Data

Surface shortwave net radiation (SSNR) and surface downward shortwave radiation (DSR) are the two surface shortwave radiation components in earth's radiation budget and the fundamental quantities of energy available at the earth's surface. Although several global radiation products from global circulation models, global reanalyses, and satellite observations have been released, their coarse spatial resolutions and low accuracies limit their application. In this paper, the Global LAnd Surface Satellite (GLASS) DSR product was generated from the Moderate Resolution Imaging Spectroradiometer top-of-atmosphere (TOA) spectral reflectance based on a direct-estimation method. First, the TOA reflectances were derived based on the atmospheric radiative transfer simulations under different solar/view geometries; second, a linear regression relationship between the TOA reflectance and SSNR was developed under various atmospheric conditions and surface properties for different solar/view geometries; third, the coefficients derived from the linear regression were used to compute the SSNR; and finally, the DSR was estimated using the SSNR estimates and broadband albedo at the surface. A 13-year (2003-2015) GLASS DSR product was generated at a 5-km spatial resolution and 1-day temporal resolution. Compared with the ground measurements collected from 525 stations from 2003 to 2005 around the world, the model-computed SSNR (DSR) had an overall bias of 8.82 (3.72) W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> and a root mean square error of 28.83 (32.84) W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> at the daily time scale. Moreover, the global land annual mean of the DSR was determined to be 184.8 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> with a standard deviation of 0.8 W/m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> over a 13-year (2003-2015) period.

Evaluation of RadCalNet Output Data Using Landsat 7, Landsat 8, Sentinel 2A, and Sentinel 2B Sensors

In 2013, the Committee on Earth Observation Satellites (CEOS) Working Group on Calibration and Validation (WGCV) Infrared and Visible Optical Sensors Subgroup (IVOS) established the Radiometric Calibration Network (RadCalNet), consisting of four international test sites providing automated in situ measurements and estimates of propagated top-of-atmosphere (TOA) reflectance. This work evaluates the ‘reliability’ of RadCalNet TOA reflectance data at three of these sites—RVUS, LCFR, and GONA—using Landsat 7 ETM+, Landsat 8 operational land imager (OLI), and Sentinel 2A/2B (S2A/S2B) MSI TOA reflectance data. This work identified a viewing angle effect in the MSI data at the RVUS and LCFR sites; when corrected, the overall standard deviation in relative reflectance differences decreased by approximately 2% and 0.5% at the RVUS and LCFR sites, respectively. Overall, the relative mean differences between the RadCalNet surface data and sensor data for the RVUS and GONA sites are within 5% for ETM+, OLI, and S2A MSI, with an approximately 2% higher difference in the S2B MSI data at the RVUS site. The LCFR site is different from the other two sites, with relative mean differences ranging from approximately -10% to 1%, even after performing the viewing angle effect correction on the MSI data. The data from RadCalNet are easy to acquire and use. More effort is needed to better understand the behavior at LCFR. One significant improvement on the accuracy of the RadCalNet data might be the development of a site-specific BRDF characterization and correction.

Detection and Validation of Tropical Peatland Flaming and Smouldering Using Landsat-8 SWIR and TIRS Bands

A Tropical Peatland Combustion Algorithm (ToPeCAl) was first established from Landsat-8 images acquired in 2015, which were used to detect peatland combustion in flaming and smouldering stages. Detection of smouldering combustion from space remains a challenge due to its low temperature and generally small spatial extent. The ToPeCAl consists of the Shortwave Infrared Combustion Index based on reflectance (SICIρ), and Top of Atmosphere (TOA) reflectance in Shortwave Infrared band-7 (SWIR-2), TOA brightness temperature of Thermal Infrared band-10 (TIR-1), and TOA reflectance of band-1, the Landsat-8 aerosol band. The implementation of ToPeCAl was then validated using terrestrial and aerial images (helicopter and drone) collected during fieldwork in Central Kalimantan, Indonesia in the 2018 fire season, on the same day as Landsat-8 overpasses. The overall accuracy of ToPeCAl was found to be 82% with omission errors in a small area (less than 30 m × 30 m) from mixtures of smouldering and vegetation pixels, and commission errors (with minimum area of 30 m x 30 m) on high reflective building rooftops in urban areas. These errors were further reduced by masking and removing urban areas prior to analysis using landuse Geographic Information System (GIS) data; improving the overall mapping accuracy to 93%. For comparison, the day and night-time VIIRS (375 m) active fire product (VNP14IMG) was utilised, obtaining a lower probability of fire detection of 71% compared to ground truth, and 57–72% agreement in a buffer distance of 375 m to 1500 m when compared to the Landsat-8 ToPeCAl results. The night-time data of VNP14IMG was found to have a better correspondence with ToPeCAl results from Landsat 8 than day-time data. This finding could lead to a potential merger of ToPeCAl with VNP14IMG to fill the temporal gaps of peatland fire information when using Landsat. However, the VNP14IMG product exhibited overestimation compared with the results of ToPeCAl applied to Landsat-8.

Coastal and inland water pixels extraction algorithm (WiPE) from spectral shape analysis and HSV transformation applied to Landsat 8 OLI and Sentinel-2 MSI

Identification of water pixels over natural water bodies is a prerequisite step prior to applying algorithms dedicated to the estimation of bio-optical properties of surface waters from remote sensing observations. For visible remote sensing sensors, clouds affect the quantity and quality of the observations, directly by hiding part of the scene and indirectly by their shadows. A certain level of confusion could occur for detection of clouds over turbid (i.e. bright) waters and for detection of their shadows over any kind of surface water. Some algorithms exist but their performance is not satisfactory, especially over turbid waters where cloud-free pixels are sometimes classified as cloud or land, leading to a loss of data. This is particularly important for medium spatial resolution observations such as those performed by the Operational Land Imager (OLI) sensor on Landsat-8 or the Multispectral Instrument (MSI) on Sentinel-2 (a and b). In the frame of this study, we developed a two-step algorithm for the extraction of water pixels (referred to as WiPE) for these medium spatial resolution sensors. In contrast to other approaches based on the top of atmosphere (TOA) reflectance, this algorithm uses the Rayleigh-corrected TOA reflectance (ρrc(λ)) as input parameter allowing the spectral signature of each object to be better characterized. The first step, based on the ρrc(λ) spectral shape analysis of each object, allows water pixels to be discriminated from cloud, vegetation, barren land, and constructions pixels. The second step, in which the ρrc(λ) spectra are transferred into the Hue-Saturation-Value space, greatly improves the detection of cloud shadow over waters. This second step, based on the processing of the whole image, does not require any knowledge on the location and altitude of clouds. Thin clouds are identified during the two steps of the algorithm. This algorithm has been successfully tested over a broad range of environments. WiPE, specifically designed for the extraction of water pixels, generally shows better performance over turbid waters than the standard algorithm developed for Landsat imagery (Fmask). This is explained by the fact that Fmask does not specifically focus on the detection of water pixels, but on the masking of cloud, cloud shadow, and snow over land and water.

Fractional Woody Cover Mapping of Texas Savanna at Landsat Scale

Texas savanna experienced substantial woody plant encroachment during the past several decades, resulting in habitat fragmentation and species loss. A detailed map of woody plant abundance and distribution in this area is critically needed for management purpose. This study endeavors to map the fractional woody cover of Texas savanna at Landsat scale (30 m) in an affordable way. The top of atmosphere reflectance, thermal bands, and NDVI layer of Web-Enabled Landsat Data (WELD) of 2012 were used as predictors, together with mean annual precipitation. Classification and Regression Trees (CART) were calibrated against training data of a whole range of fractional woody cover, which were derived from 1-m resolution digital orthophotos of 2012. Validation indicates a reasonable pixel level accuracy of the result fractional woody cover map, with a R-squared value of 0.45. Moreover, the result map clearly depicts the distribution of woody plants across the study area, as reflected by the orthophotos. Furthermore, this new map proves an improvement over the existing Landsat Vegetation Continuous Fields (VCF) tree cover product. The method developed here, combining remote sensing and statistical techniques, can contribute to savanna management through revealing the abundance and distribution of woody plants.

Optimal estimation retrieval for directional polarimetric camera onboard Chinese Gaofen-5 satellite: an analysis on multi-angle dependence and a posteriori error

Data from the directional polarimetric camera (DPC) instrument onboard Chinese Gaofen-5 satellite dedicated to aerosol monitoring have been available recently. By measuring the spectral, angular and polarization properties of the radiance at the top of atmosphere (TOA), a DPC provides the aerosol optical depths (AODs) as well as partial microphysical aerosol properties. In order to evaluate the capability and the retrieval uncertainty of DPC sensor systematically, the information content and a posteriori error analysis are applied to the synthetic data of DPC multi-angle observation in this paper, which inherits from the optimal estimate theoretical framework. The forward simulation is conducted by the unified linearized vector radiative transfer model (UNL-VRTM), and the Jacobians of four Stokes elements with respect to aerosol and surface model parameters can be obtained simultaneously. Firstly, the error influences of surface parameter on the TOA measurements are simulated. The results indicate that a 10% relative error of parameter &lt;i&gt;k&lt;/i&gt;&lt;sub&gt;1&lt;/sub&gt; in the improved BRDF model results in about 4.65% error of the TOA reflectance, while the error of TOA polarized reflectance caused by the same error of parameter &lt;i&gt;C&lt;/i&gt; in BPDF model is negligibly small. Secondly, the multi-angle dependence of total information content in DPC measurements is investigated. It is shown that the information content increases significantly with the number of viewing angles, especially for the measurements of the first 9 angles. The DPC multi-angle observation can provide extra 5 degrees of freedom for signal (DFS) for the retrieval of aerosol and surface parameters, in which the retrieval of aerosol parameters is more sensitive to observation geometries than the retrieval of surface parameters in most cases. In addition, the total aerosol DFS increases with the range extension of scattering angle under the same number of viewing angles. After that, the DFS of each retrieved aerosol and surface parameter are given. For the aerosols, the volume concentration, real-part refractive index and effective radius show a high DFS (greater than 0.8). For the surfaces, the mean DFS of each parameter is greater than 0.5, which indicates the well capability of DPC in the surface retrieval. Finally, the a posteriori error of each aerosol, surface parameter and corresponding vary with the number of viewing angles, and the observation error and aerosol model error are discussed. The a posteriori error decrease significantly with the number of viewing angles, and the influence of the aerosol model error on the a posteriori error is not remarkable. In general, the observation error is the main influence factor on the uncertainty of the inversion results.

Estimating Regional Ground‐Level PM2.5 Directly From Satellite Top‐Of‐Atmosphere Reflectance Using Deep Belief Networks

AbstractAlmost all remote sensing atmospheric PM2.5 estimation methods need satellite aerosol optical depth (AOD) products, which are often retrieved from top‐of‐atmosphere (TOA) reflectance via an atmospheric radiative transfer model. Then, is it possible to estimate ground‐level PM2.5 directly from satellite TOA reflectance without a physical model? In this study, this challenging work was achieved based on a machine learning model. Specifically, we established the relationship between PM2.5, satellite TOA reflectance, observation angles, and meteorological factors in a deep learning architecture (denoted as Ref‐PM modeling). This relationship was trained with station PM2.5 measurements, and then the PM2.5 values of those locations without stations could be retrieved. Taking the Wuhan Urban Agglomeration as a case study, the results demonstrate that, compared with AOD‐PM modeling, the Ref‐PM modeling obtains a competitive performance, with sample‐based cross‐validated R2 and root‐mean‐square error values of 0.87 and 9.89 μg/m3, respectively. Also, the TOA‐reflectance‐derived PM2.5 has a finer resolution and a larger spatial coverage than the AOD‐derived PM2.5. This work provides an alternative technique to estimate ground‐level PM2.5, and may have the potential to promote the application in atmospheric environmental monitoring.

&lt;italic&gt;A Priori&lt;/italic&gt; Surface Reflectance-Based Cloud Shadow Detection Algorithm for Landsat 8 OLI

Prior knowledge of the background land surface reflectance (LSR) constitutes one of the most important factors affecting the precision of cloud shadow detection. To resolve this problem, a surface reflectance-based cloud shadow detection (SRCSD) algorithm is proposed for multitemporal Landsat images. Monthly surface reflectance data sets constructed from MODIS surface reflectance products (MOD09A1) were used to provide the background LSR for cloud shadow detection. Based on the background LSR, the possible variation in the top of atmosphere (TOA) reflectance for each clear pixel can be estimated using the radiative transfer equation under different atmospheric conditions. If a pixel has a smaller TOA reflectance than the minimum value of the possible range under clear conditions, it is identified as being shadow covered. One hundred and twenty-five Landsat 8 Operational Land Imager scenes covered by various surface types were selected to evaluate the feasibility of the algorithm. A validation using manual cloud shadow masks showed that the average producer’s accuracy and user’s accuracy were approximately 0.805 and 0.893, respectively. A comparison of the results of the SRCSD algorithm with those of an object-based cloud shadow detection algorithm (Fmask) recently developed for Landsat images revealed that SRCSD generally detects cloud shadows better than Fmask. The most significant improvement of the SRCSD algorithm is the better detection capability for thin and broken cloud shadows, and this algorithm can be extended to multiple types of satellite data after proper modification.

The importance of surface reflectance anisotropy for cloud and NO&amp;lt;sub&amp;gt;2&amp;lt;/sub&amp;gt; retrievals from GOME-2 and OMI

Abstract. The angular distribution of the light reflected by the Earth's surface influences top-of-atmosphere (TOA) reflectance values. This surface reflectance anisotropy has implications for UV/Vis satellite retrievals of albedo, clouds, and trace gases such as nitrogen dioxide (NO2). These retrievals routinely assume the surface to reflect light isotropically. Here we show that cloud fractions retrieved from GOME-2A and OMI with the FRESCO and OMCLDO2 algorithms have an east–west bias of 10 % to 50 %, which are highest over vegetation and forested areas, and that this bias originates from the assumption of isotropic surface reflection. To interpret the across-track bias with the DAK radiative transfer model, we implement the bidirectional reflectance distribution function (BRDF) from the Ross–Li semi-empirical model. Testing our implementation against state-of-the-art RTMs LIDORT and SCIATRAN, we find that simulated TOA reflectance generally agrees to within 1 %. We replace the assumption of isotropic surface reflection in the equations used to retrieve cloud fractions over forested scenes with scattering kernels and corresponding BRDF parameters from a daily high-resolution database derived from 16 years' worth of MODIS measurements. By doing this, the east–west bias in the simulated cloud fractions largely vanishes. We conclude that across-track biases in cloud fractions can be explained by cloud algorithms that do not adequately account for the effects of surface reflectance anisotropy. The implications for NO2 air mass factor (AMF) calculations are substantial. Under moderately polluted NO2 and backward-scattering conditions, clear-sky AMFs are up to 20 % higher and cloud radiance fractions up to 40 % lower if surface anisotropic reflection is accounted for. The combined effect of these changes is that NO2 total AMFs increase by up to 30 % for backward-scattering geometries (and decrease by up to 35 % for forward-scattering geometries), which is stronger than the effect of either contribution alone. In an unpolluted troposphere, surface BRDF effects on cloud fraction counteract (and largely cancel) the effect on the clear-sky AMF. Our results emphasise that surface reflectance anisotropy needs to be taken into account in a coherent manner for more realistic and accurate retrievals of clouds and NO2 from UV/Vis satellite sensors. These improvements will be beneficial for current sensors, in particular for the recently launched TROPOMI instrument with a high spatial resolution.

A comparative evaluation of arid inflow-dependent vegetation maps derived from LANDSAT top-of-atmosphere and surface reflectances

ABSTRACTIn remote sensing, it is commonly accepted that land remote-sensing satellite (LANDSAT) top-of-atmosphere (TOA) reflectance is less accurate than atmospheric correction (AC) reflectance, as the former is not calibrated for possible modifications in the electromagnetic radiation signals due to atmospheric scattering and absorption. This article investigates whether LANDSAT data calibrated for TOA reflectance are an appropriate information source for delineating inflow-dependent vegetation (IDV) in regions with an arid and desert climate, such as the Pilbara region in Western Australia. Knowledge of where IDVs are in the landscape underpins planning their protection and define the baseline for their monitoring when water resource management options are considered. The appropriateness of TOA calibration for the delineation of IDV in the Pilbara was assessed through its comparison with IDV maps derived from AC reflectance. Both radiometric calibration methods (TOA and AC) were applied to a multi-date LANDSAT 5 TM (Thematic Mapper) dataset of 10 images acquired in 2009 and 2010. Two methods based on the application of remote-sensing techniques to identify the extent of temporally invariant vegetation were applied for IDV delineation in the study area. The first method, groundwater-dependent ecosystems mapping (GEM), employs a two-date normalized difference vegetation index (NDVI) dataset for identifying ‘no-change’ clusters of land cover and detecting those related to IDV. The second method applies principal component analysis (PCA) to a multi-date NDVI dataset. The first principal component (PC1) typically contains features that remain unchanged over time. This includes vegetation with continuous or frequent access to surface and/or groundwater, such as IDV. To delineate the extent of IDV, a thresholding technique was further employed. Spatial similarity between IDV maps produced from TOA and AC reflectance was quantitatively evaluated by the Kappa coefficient. The results showed that TOA and AC IDV maps are in ‘almost perfect’ agreement with the Kappa values above 0.83. This suggests that TOA reflectance is equally appropriate to AC reflectance for mapping in arid and desert climate such as in Pilbara. When the GEM- and PCA-based methods are applied in other study areas with arid or desert climate, the accuracy of the delineated IDV extent may vary. Therefore, the results need to be validated using ground-truth information about known IDV occurrences in the area of interest.

A General Approach to Enhance Short Wave Satellite Imagery by Removing Background Atmospheric Effects

Atmospheric interaction distorts the surface signal received by a space-borne instrument. Images derived from visible channels appear often too bright and with reduced contrast. This hampers the use of RGB imagery otherwise useful in ocean color applications and in forecasting or operational disaster monitoring, for example forest fires. In order to correct for the dominant source of atmospheric noise, a simple, fast and flexible algorithm has been developed. The algorithm is implemented in Python and freely available in PySpectral which is part of the PyTroll family of open source packages, allowing easy access to powerful real-time image-processing tools. Pre-calculated look-up tables of top of atmosphere reflectance are derived by off-line calculations with RTM DISORT as part of the LibRadtran package. The approach is independent of platform and sensor bands, and allows it to be applied to any band in the visible spectral range. Due to the use of standard atmospheric profiles and standard aerosol loads, it is possible just to reduce the background disturbance. Thus signals from excess aerosols become more discernible. Examples of uncorrected and corrected satellite images demonstrate that this flexible real-time algorithm is a useful tool for atmospheric correction.

Stability Monitoring of the VIIRS Day/Night Band over Dome C with a Lunar Irradiance Model and BRDF Correction

The unique feature of the Visible Infrared Imager Radiometer Suite (VIIRS) day/night band (DNB) is its ability to take quantitative measurements of low-light scenes at night. In order to monitor the stability of the high gain stage (HGS) of the DNB, nighttime observations over the Dome C site under moonlight are analyzed in this study. The Miller and Turner 2009 (MT2009) lunar irradiance model has been used to simulate lunar illumination over Dome C. However, the MT2009 model does not differentiate the waxing and waning lunar phases. In this paper, the MT-SWC (SeaWiFS Corrected) lunar irradiance model differentiating the waxing and waning lunar phases is derived by correcting the MT2009 model using lunar observations made by the Sea-Viewing Wide Field-of-View Sensor (SeaWiFS). In addition, a top of atmosphere (TOA) bi-directional reflectance distribution function (BRDF) model during nighttime over Dome C is developed to remove the angular dependence from the nighttime TOA reflectance. The long-term stability monitoring of the DNB high-gain stage (HGS) reveals a lower reflectance factor in 2012 in comparison to the following years, which can be traced back to the change in relative spectral response (RSR) of National Oceanic &amp; Atmospheric Administration’s (NOAA’s) Interface Data Processing Segment (IDPS) VIIRS DNB in April 2013. It also shows the radiometric stability of DNB data, with long-term stability of less than 1.58% over the periods from 2013 to 2016. This method can be used to monitor the radiometric stability of other low-light observing sensors using vicarious calibration sites under moonlight illumination.