Correction Of Atmospheric Effects Research Articles

Calibration and testing strategies to correct atmospheric effects on star tracking algorithms

Star trackers are usually considered to be the most accurate sensors, able to achieve a sub-arcminute precision. Star tracker algorithms are often tested and validated with simulated space views. Testing the algorithms with real space images is expensive as it requires implementing them on existing in-space star trackers, or to launch new satellites. This study shows that those algorithms are usually performing poorly with ground-based sky pictures and that some adaptations are necessary to take into account the atmospheric effects. The adaptation of star tracking algorithms to ground pictures could ease the prototyping phase for new star trackers, or for ground-based and air-borne star trackers, without the need to buy specific testing simulators. In order to tackle this issue, this study will start by implementing and testing two published Lost-In-Space algorithms with a simulated sensor to compare their performance against various noise sources. After comparing the space-based generated views with ground-based images, an adaptation for the aforementioned algorithms is proposed. In order to counter the effect of atmospheric extinction, the number of stars visible in the image is increased by modifying the field-of-view of the camera, the exposure time and estimating the experimental inter-star angular distance error. The idea is to match the star density used in the state-of-the-art algorithms in the experimental pictures. The modified algorithms are tested with the experimental images, and the adaptation process is validated with a good success rate.

Assessment of the Performance of the Atmospheric Correction Algorithm MAJA for Sentinel-2 Surface Reflectance Estimates

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

Evaluation of seven atmospheric correction algorithms for OLCI images over the coastal waters of Qinhuangdao in Bohai Sea

Atmospheric correction (AC) over coastal waters is always critical in ocean color remote sensing. The performance of the AC algorithms determines the reliability of the retrieval of the water-leaving radiance and subsequent bio-optical parameters. In this study, the seven AC algorithms for the Ocean and Land Color Instrument (OLCI) were evaluated with an in situ dataset over the coastal waters of Qinhuangdao in the Bohai Sea, China: the Baseline Atmospheric Correction (BAC), the Case 2 Regional Coast Color Atmospheric Correction (C2RCC) and its alternative version (C2RCC-AltNets), the Management Unit Mathematical Models (MUMM), the polynomial-based algorithm applied to MERIS (POLYMER), the image correction for atmospheric effects (iCOR), and ACOLITE for Dark Spectrum Fitting (ACOLITE-DSF). For OLCI-derived remote sensing reflectance, Rrs(λ), where λ is light wavelength, on the respective match-ups, all algorithms except ACOLITE-DSF had higher accuracy in the wavelength range from 490 nm to 560 nm, with the absolute percentage difference (APD) < 43%. On the common match-ups of all algorithms, the performance of seven AC algorithms was better than that of the respective match-ups. POLYMER obtained the best performance in visible bands, with APD less than 24.04% and 17.13% for respective and common match-ups, respectively. In terms of blue/green band ratios, Rrs(490)/Rrs(560) obtained by all algorithms except C2RCC performed better than Rrs(442)/Rrs(560). POLYMER performed best in blue/green band ratios, with the APD of 10.38% and 5.51% for Rrs(442)/Rrs(560) and Rrs(490)/Rrs(560), respectively. Using a scoring scheme based on all statistical parameters, POLYMER scored the highest. ACOLITE-DSF represented the worst accuracy for all evaluations over our in situ dataset.

Evaluation of Sentinel-2/MSI Atmospheric Correction Algorithms over Two Contrasted French Coastal Waters

The Sentinel-2A and Sentinel-2B satellites, with on-board Multi-Spectral Instrument (MSI), and launched on 23 June 2015 and 7 March 2017, respectively, are very useful tools for studying ocean color, even if they were designed for land and vegetation applications. However, the use of these satellites requires a process called “atmospheric correction”. This process aims to remove the contribution of the atmosphere from the total top of atmosphere reflectance measured by the remote sensors. For the purpose of assessing this processing, seven atmospheric correction algorithms have been compared over two French coastal regions (English Channel and French Guiana): Image correction for atmospheric effects (iCOR), Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour (C2RCC), Sentinel 2 Correction (Sen2Cor), Polynomial-based algorithm applied to MERIS (Polymer), the standard NASA atmospheric correction (NASA-AC) and the Ocean Color Simultaneous Marine and Aerosol Retrieval Tool (OC-SMART). The satellite-estimated remote-sensing reflectances were spatially and temporally matched with in situ measurements collected by an ASD FieldSpec4 spectrophotometer. Results, based on 28 potential individual match-ups, showed that the best performance processor is OC-SMART with the highest values for the total score Stot (16.89) and for the coefficient of correlation R2 (ranging from 0.69 at 443 nm to 0.92 at 665 nm). iCOR and Sen2Cor show the less accurate performances with total score Stot values of 2.01 and 7.70, respectively. Since the size of the in situ observation platform can be significant compared to the pixel resolution of MSI onboard Sentinel-2, it can create bias in the pixel extraction process. Thus, to study this impact, we used different methods of pixel extraction. However, there are no significant changes in results; some future research may be necessary.

Retrieval of Chlorophyll-a concentration and associated product uncertainty in optically diverse lakes and reservoirs

Satellite product uncertainty estimates are critical for the further development and evaluation of remote sensing algorithms, as well as for the user community (e.g., modelers, climate scientists, and decision-makers). Optical remote sensing of water quality is affected by significant uncertainties stemming from correction for atmospheric effects as well as a lack of algorithms that can be universally applied to waterbodies spanning several orders of magnitude in non-covarying substance concentrations. We developed a method to produce estimates of Chlorophyll-a (Chla) satellite product uncertainty on a pixel-by-pixel basis within an Optical Water Type (OWT) classification scheme. This scheme helps to dynamically select the most appropriate algorithms for each satellite pixel, whereas the associated uncertainty informs downstream use of the data (e.g., for trend detection or modeling) as well as the future direction of algorithm research. Observations of Chla were related to 13 previously established OWT classes based on their corresponding water-leaving reflectance (Rw), each class corresponding to specific bio-optical characteristics. Uncertainty models corresponding to specific algorithm - OWT combinations for Chla were then expressed as a function of OWT class membership score. Embedding these uncertainty models into a fuzzy OWT classification approach for satellite imagery allows Chla and associated product uncertainty to be estimated without a priori knowledge of the biogeochemical characteristics of a water body. Following blending of Chla algorithm results according to per-pixel fuzzy OWT membership, Chla retrieval shows a generally robust response over a wide range of class memberships, indicating a wide application range (ranging from 0.01 to 362.5 mg/m3). Low OWT membership scores and high product uncertainty identify conditions where optical water types need further exploration, and where biogeochemical satellite retrieval algorithms require further improvement. The procedure is demonstrated here for the Medium Resolution Imaging Spectrometer (MERIS) but could be repeated for other sensors, atmospheric correction methods and optical water quality variables.

Offline Correction of Atmospheric Effects on Single-dish Radio Spectroscopy

We present a method to mitigate the atmospheric effects (residual atmospheric lines) in single-dish radio spectroscopy caused by the elevation difference between the target and reference positions. The method is developed as a script using the Atmospheric Transmission at Microwaves (ATM) library built into the Common Astronomy Software Applications (CASA) package. We apply the method to the data taken with the Total Power Array of the Atacama Large Millimeter/submillimeter Array. The intensities of the residual atmospheric (mostly O3) lines are suppressed by, typically, an order of magnitude for the tested cases. The parameters for the ATM model can be optimized to minimize the residual line and, for a specific O3 line at 231.28 GHz, a seasonal dependence of a best-fitting model parameter is demonstrated. The method will be provided as a task within the CASA package in the near future. The atmospheric removal method we developed can be used by any radio/millimeter/submillimeter observatory to improve the quality of its spectroscopic measurements.

Impact of Various Atmospheric Corrections on Sentinel-2 Land Cover Classification Accuracy Using Machine Learning Classifiers

Atmospheric correction is one of the key parts of remote sensing preprocessing because it can influence and change the final classification result. This research examines the impact of five different atmospheric correction processing on land cover classification accuracy using Sentinel-2 satellite imagery. Those are surface reflectance (SREF), standardized surface reflectance (STDSREF), Sentinel-2 atmospheric correction (S2AC), image correction for atmospheric effects (iCOR), dark object subtraction (DOS) and top of the atmosphere (TOA) reflectance without any atmospheric correction. Sentinel-2 images corrected with stated atmospheric corrections were classified using four different machine learning classification techniques namely extreme gradient boosting (XGB), random forests (RF), support vector machine (SVM) and catboost (CB). For classification, five different classes were used: bare land, low vegetation, high vegetation, water and built-up area. SVM classification provided the best overall result for twelve dates, for all atmospheric corrections. It was the best method for both cases: when using Sentinel-2 bands and radiometric indices and when using just spectral bands. The best atmospheric correction for classification with SVM using radiometric indices is S2AC with the median value of 96.54% and the best correction without radiometric indices is STDSREF with the median value of 96.83%.

Evaluation of Atmospheric Correction Algorithms for Sentinel-2-MSI and Sentinel-3-OLCI in Highly Turbid Estuarine Waters

The present study assesses the performance of state-of-the-art atmospheric correction (AC) algorithms applied to Sentinel-2-MultiSpectral Instrument (S2-MSI) and Sentinel-3-Ocean and Land Color Instrument (S3-OLCI) data recorded over moderately to highly turbid estuarine waters, considering the Gironde Estuary (SW France) as a test site. Three spectral bands of water-leaving reflectance ( R h o w ) are considered: green (560 nm), red (655 or 665 nm) and near infrared (NIR) (865 nm), required to retrieve the suspended particulate matter (SPM) concentrations in clear to highly turbid waters (SPM ranging from 1 to 2000 mg/L). A previous study satisfactorily validated Acolite short wave infrared (SWIR) AC algorithm for Landsat-8-Operational Land Imager (L8-OLI) in turbid estuarine waters. The latest version of Acolite Dark Spectrum Fitting (DSF) is tested here and shows very good agreement with Acolite SWIR for OLI data. L8-OLI satellite data corrected for atmospheric effects using Acolite DSF are then used as a reference to assess the validity of atmospheric corrections applied to other satellite data recorded over the same test site with a minimum time difference. Acolite DSF and iCOR (image correction for atmospheric effects) are identified as the best performing AC algorithms among the tested AC algorithms (Acolite DSF, iCOR, Polymer and C2RCC (case 2 regional coast color)) for S2-MSI. Then, the validity of six different AC algorithms (OLCI Baseline Atmospheric Correction (BAC), iCOR, Polymer, Baseline residual (BLR), C2RCC-V1 and C2RCC-V2) applied to OLCI satellite data is assessed based on comparisons with OLI and/or MSI Acolite DSF products recorded on a same day with a minimum time lag. Results show that all the AC algorithms tend to underestimate R h o w in green, red and NIR bands except iCOR in green and red bands. The iCOR provides minimum differences in green (slope = 1.0 ± 0.15, BIAS = 1.9 ± 4.5% and mean absolute percentage error (MAPE) = 12 ± 5%) and red (slope = 1.0 ± 0.17, BIAS = −9.8 ± 9% and MAPE = 28 ± 20%) bands with Acolite DSF products from OLI and MSI data. For the NIR band, BAC provides minimum differences (slope = 0.7 ± 0.13, BIAS = −33 ± 17% and MAPE = 55 ± 20%) with Acolite DSF products from OLI and MSI data. These results based on comparisons between almost simultaneous satellite products are supported by match-ups between satellite-derived and field-measured SPM concentrations provided by automated turbidity stations. Further validation of satellite products based on rigorous match-ups with in-situ R h o w measurements is still required in highly turbid waters.

Pre-Processing of Landsat-8 Data for Population Estimation: A Study With Respect to Dimoria Development Block of Assam

This paper reviews various works on population estimation and concentrates on revealing features of the study area’s satellite data. The work emphasizes data pre-processing which is a crucial stage in population estimation. The study region is a rural area of Kamrup Metro district of Assam. Our work is deprived of gridded data, so the satellite data is cropped into grids of images. We have collected landsat8 level-1 products and performed the atmospheric effect correction. Satellite brightness temperature for the bands 10 and 11 are observed. The vegetation coverage is also visualized for the bands 3, 4 and 5. In the end we have proposed a modular based approach for analyzing population of the selected site.

Application of Sentinel 3 OLCI for chl-a retrieval over small inland water targets: Successes and challenges

Eutrophication and increasing prevalence of potentially toxic cyanobacterial blooms among global inland water bodies is becoming a major concern and requires direct attention. The European Space Agency recently launched the Ocean and Land Color Instrument (OLCI) aboard the Sentinel 3 satellite. The success of the mission will depend on extensive validation efforts for the development of accurate and robust in-water algorithms. In this study, four full atmospheric correction methods are assessed over four inland water reservoirs in South Africa, along with a suite of red/NIR based semi-analytic and band difference models for chl-a estimation which were applied to both full and partial atmospherically corrected data. In addition, we tested a novel duplicate pixel correction method to account for duplicate pixels induced by high observation zenith angles. Radiometric errors associated with OLCI Top of Atmosphere (TOA) radiances over small water targets were also investigated by modeling in situ reflectance measurements to at-sensor radiances using MODTRAN. Of the four atmospheric corrections, the 6SV1 radiative transfer code showed the most promise for producing reasonable reflectances when compared to in-situ measurements. Empirically derived band difference models outperformed all other chl-a retrieval methods on both partially and fully corrected reflectances. The Maximum Peak Height (MPH) algorithm applied to Bottom of Rayleigh Reflectance (BRR) performed best overall (R2 = 0.55, RMSE(%) = 99), while the Maximum Chlorophyll Index (MCI) performed best on atmospherically corrected data using 6SV1 (R2 = 0.35, RMSE(%) = 107). Semi-analytic chl-a retrieval methods proved very successful when applied to in situ Rrs, however, are not reliable when applied to low quality reflectance data. The SIMilarity Environment Correction (SIMEC), an adjacency correction applied in conjunction with the image correction for atmospheric effects (iCOR) processor, did not improve retrieval results for these small water targets.

Assessment of atmospheric correction results by iCOR for MSI and OLI data on TSS concentration

Atmospheric correction is very important process to determine of land and ocean surface properties measured from satellite data, especially optical remote sensing satellite system, because passive satellite instruments will always be contaminated by the influence of the atmosphere. The result of this processing is the surface reflectance (sr) product, and it is a necessary process when quantitatively monitoring environmental quality parameters from space. The goal of this study is to assessing of the spectral remote sensing reflectance satellite (Rrs(λ) by the image correction for atmospheric effects (iCOR) tools on total suspended solid (TSS) concentration from the MultiSpectral Instrument (MSI) sensor on-board Sentinel-2 and the Operational Land Imager (OLI) sensor on-board Landsat-8. Involvement of 25 in-situ TSS stations in Kendari bay waters is to assess the results of iCOR-S2 and iCOR-L8. An assessment of the sr results reduced to Rrs(λ) on the MSI and OLI data respectively, affected the value of R2 where the highest value R2 = 0.665 is shown on red band OLI data. Meanwhile, the assessment of three TSS algorithms models is built on Rrs(λ), all of them showed mean relative error (MRE) < 30% and were considered capable of defining TSS concentrations in the study area.

Evaluation of Atmospheric Correction Algorithms over Spanish Inland Waters for Sentinel-2 Multi Spectral Imagery Data

The atmospheric contribution constitutes about 90 percent of the signal measured by satellite sensors over oceanic and inland waters. Over open ocean waters, the atmospheric contribution is relatively easy to correct as it can be assumed that water-leaving radiance in the near-infrared (NIR) is equal to zero and it can be performed by applying a relatively simple dark-pixel-correction-based type of algorithm. Over inland and coastal waters, this assumption cannot be made since the water-leaving radiance in the NIR is greater than zero due to the presence of water components like sediments and dissolved organic particles. The aim of this study is to determine the most appropriate atmospheric correction processor to be applied on Sentinel-2 MultiSpectral Imagery over several types of inland waters. Retrievals obtained from different atmospheric correction processors (i.e., Atmospheric correction for OLI ‘lite’ (ACOLITE), Case 2 Regional Coast Colour (here called C2RCC), Case 2 Regional Coast Colour for Complex waters (here called C2RCCCX), Image correction for atmospheric effects (iCOR), Polynomial-based algorithm applied to MERIS (Polymer) and Sen2Cor or Sentinel 2 Correction) are compared against in situ reflectance measured in lakes and reservoirs in the Valencia region (Spain). Polymer and C2RCC are the processors that give back the best statistics, with coefficients of determination higher than 0.83 and mean average errors less than 0.01. An evaluation of the performance based on water types and single bands–classification based on ranges of in situ chlorophyll-a concentration and Secchi disk depth values- showed that performance of these set of processors is better for relatively complex waters. ACOLITE, iCOR and Sen2Cor had a better performance when applied to meso- and hyper-eutrophic waters, compare with oligotrophic. However, other considerations should also be taken into account, like the elevation of the lakes above sea level, their distance from the sea and their morphology.

Remote Sensing of Sea Surface Salinity: Comparison of Satellite and In Situ Observations and Impact of Retrieval Parameters

Since 2009, three low frequency microwave sensors have been launched into space with the capability of global monitoring of sea surface salinity (SSS). The European Space Agency’s (ESA’s) Microwave Imaging Radiometer using Aperture Synthesis (MIRAS), onboard the Soil Moisture and Ocean Salinity mission (SMOS), and National Aeronautics and Space Administration’s (NASA’s) Aquarius and Soil Moisture Active Passive mission (SMAP) use L-band radiometry to measure SSS. There are notable differences in the instrumental approaches, as well as in the retrieval algorithms. We compare the salinity retrieved from these three spaceborne sensors to in situ observations from the Argo network of drifting floats, and we analyze some possible causes for the differences. We present comparisons of the long-term global spatial distribution, the temporal variability for a set of regions of interest and statistical distributions. We analyze some of the possible causes for the differences between the various satellite SSS products by reprocessing the retrievals from Aquarius brightness temperatures changing the model for the sea water dielectric constant and the ancillary product for the sea surface temperature. We quantify the impact of these changes on the differences in SSS between Aquarius and SMOS. We also identify the impact of the corrections for atmospheric effects recently modified in the Aquarius SSS retrievals. All three satellites exhibit SSS errors with a strong dependence on sea surface temperature, but this dependence varies significantly with the sensor. We show that these differences are first and foremost due to the dielectric constant model, then to atmospheric corrections and to a lesser extent to the ancillary product of the sea surface temperature.

Atmospheric Effect Correction for InSAR With Wavelet Decomposition-Based Correlation Analysis Between Multipolarization Interferograms

This paper presents a wavelet decomposition-based correlation analysis (WDCA) method to correct atmospheric effects for interferometric synthetic aperture radar interferometry. The main idea is based on the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">a priori</i> knowledge that the atmospheric effects are independent of the polarizations. This provides the possibility to find the identical atmospheric phases (ATPs) from the two different polarimetric interferograms. To achieve this goal, differential interferometry is performed with different topographic data so that the obtained differential interferograms (D-Infs) have different topographic errors. A polynomial incorporating topographic information is then used to remove the orbit error phase. Thus, the ATPs are the only identical components in the obtained D-Infs. A forward wavelet transform is then utilized to perform multiresolution analysis for the two obtained D-Infs. After this, we apply correlation analysis to identify the wavelet coefficients attributed to the atmospheric effects. The corrected D-Infs are then obtained by down-weighting the wavelet coefficients during inverse wavelet transform. The performance of the WDCA method was tested with L-band ALOS-1 PALSAR dual-polarization SAR images acquired over Southern California and Qilian mountain test sites characterized by different topographic conditions. For the Southern California test site, two interferometric pairs with long and short baselines (750 and 50 m) were formulated. The results show that the WDCA method can work well for both of the interferometric pairs, and the root-mean-square errors (RMSEs) of the obtained DEMs with respect to the Shuttle Radar Topography Mission digital elevation model (DEM) are 7.86 and 13.78 m, and show a decrease of 34.7% and 80.4% for the long- and short-baseline cases, respectively. For the Qilian mountain test site, the corrected interferogram can provide a DEM with an RMSE of 19.73 m, which is an improvement of 22.3% with respect to the DEM containing the atmospheric signals. In addition, the above two experiments show that compared with the existing topographic information-based wavelet method, this approach can remove not only the topography-dependent ATP but also the turbulent ATP.

Compensation of Oxygen Transmittance Effects for Proximal Sensing Retrieval of Canopy-Leaving Sun-Induced Chlorophyll Fluorescence.

Estimates of Sun–Induced vegetation chlorophyll Fluorescence (SIF) using remote sensing techniques are commonly determined by exploiting solar and/or telluric absorption features. When SIF is retrieved in the strong oxygen (O2) absorption features, atmospheric effects must always be compensated. Whereas correction of atmospheric effects is a standard airborne or satellite data processing step, there is no consensus regarding whether it is required for SIF proximal–sensing measurements nor what is the best strategy to be followed. Thus, by using simulated data, this work provides a comprehensive analysis about how atmospheric effects impact SIF estimations on proximal sensing, regarding: (1) the sensor height above the vegetated canopy; (2) the SIF retrieval technique used, e.g., Fraunhofer Line Discriminator (FLD) family or Spectral Fitting Methods (SFM); and (3) the instrument’s spectral resolution. We demonstrate that for proximal–sensing scenarios compensating for atmospheric effects by simply introducing the O2 transmittance function into the FLD or SFM formulations improves SIF estimations. However, these simplistic corrections still lead to inaccurate SIF estimations due to the multiplication of spectrally convolved atmospheric transfer functions with absorption features. Consequently, a more rigorous oxygen compensation strategy is proposed and assessed by following a classic airborne atmospheric correction scheme adapted to proximal sensing. This approach allows compensating for the O2 absorption effects and, at the same time, convolving the high spectral resolution data according to the corresponding Instrumental Spectral Response Function (ISRF) through the use of an atmospheric radiative transfer model. Finally, due to the key role of O2 absorption on the evaluated proximal–sensing SIF retrieval strategies, its dependency on surface pressure (p) and air temperature (T) was also assessed. As an example, we combined simulated spectral data with p and T measurements obtained for a one–year period in the Hyytiälä Forestry Field Station in Finland. Of importance hereby is that seasonal dynamics in terms of T and p, if not appropriately considered as part of the retrieval strategy, can result in erroneous SIF seasonal trends that mimic those of known dynamics for temperature–dependent physiological responses of vegetation.

Using Dual-Polarization Interferograms to Correct Atmospheric Effects for InSAR Topographic Mapping

Atmospheric effect represents one of the major error sources for interferometric synthetic aperture radar (InSAR), particularly for the repeat-pass InSAR data. In order to further improve the practicability of InSAR technology, it is essential to study how to estimate and eliminate the undesired impact of atmospheric effects. In this paper, we propose the multi-resolution weighted correlation analysis (MRWCA) method between the dual-polarization InSAR data to estimate and correct atmospheric effects for InSAR topographic mapping. The study is based on the a priori knowledge that atmospheric effects is independent of the polarization. To find the identical atmospheric phase (ATP) signals of interferograms in different polarizations, we need to remove the other same or similar phase components. Using two different topographic data, differential interferometry was firstly performed so that the obtained differential interferograms (D-Infs) have different topographic error phases. A polynomial fitting method is then used to remove the orbit error phases. Thus, the ATP signals are the only identical components in the final obtained D-Infs. By using a forward wavelet transform, we break down the obtained D-Infs into building blocks based on their frequency properties. We then applied weighted correlation analysis to estimate the wavelet coefficients attributed to the atmospheric effects. Thus, the ATP signals can be obtained by the refined wavelet coefficients during inverse wavelet transform (IWT). Lastly, we tested the proposed method by the L-band Advanced Land Observing Satellite (ALOS)-1 PALSAR dual-polarization SAR data pairs covering the San Francisco (USA) and Moron (Mongolia) regions. By using Ice, Cloud, and land Elevation Satellite (ICESat) data as the reference data, we evaluated the vertical accuracy of the InSAR digital elevation models (DEMs) with and without atmospheric effects correction, which shows that, for the San Francisco test site, the corrected interferogram could provide a DEM with a root-mean-square error (RMSE) of 7.79 m, which is an improvement of 40.5% with respect to the DEM without atmospheric effects correction. For the Moron test site, the corrected interferogram could provide a DEM with an RMSE of 10.74 m, which is an improvement of 30.2% with respect to the DEM without atmospheric effects correction.

Land-surface temperature retrieval from Landsat 8 single-channel thermal infrared data in combination with NCEP reanalysis data and ASTER GED product

ABSTRACTLand-surface temperature (LST) is an important parameter in the climatological, hydrological, ecological, and meteorological studies. The Thermal Infrared Sensor (TIRS) on board the Landsat 8 is a key instrument to collect thermal infrared (TIR) data. The Landsat series sensors provide continuously acquired collection of space-based TIR data. In this study, we proposed a method for retrieving LST from Landsat 8 TIRS single-channel data. The National Centers for Environmental Prediction reanalysis data in conjunction with the Moderate Resolution Transmittance Code 5 were used to correct atmospheric effects. The ASTER Global Emissivity Database product was used to correct the effects of surface emissivity. In situ LST measurements were collected by eight and four SI-111 infrared radiometers in the study areas A and B, respectively. The in situ LST was used to validate the retrieved LST. For the study area A (sands), the bias varies from approximately −1.3 to 1.7 K, and the root mean square error (RMSE) from approximately 1.2 to 2.1 K. For the study area B (grasslands/snow), the bias ranges from approximately −1.0 to 0.4 K, and the RMSE from approximately 1.1 to 1.5 K. To further compare the retrieved LST and the in situ LST at coarser pixel scale, all of the retrieved LST and the in situ LST were, respectively, averaged as the corresponding LST at 1 km pixel scale (e.g. Moderate Resolution Imaging Spectroradiometer). The biases of the differences between the two averaged LST at 1 km pixel scale for all TIRS scenes are approximately −0.2 and −0.5 K for the study areas A and B, respectively, and the RMSE values are approximately 1.2 and 1.0 K for the study area A and B, respectively. These results indicate that the proposed method can be used to retrieve LST from single-channel TIR data with a reasonable accuracy.

A modified version of the kernel-driven model for correcting the diffuse light of ground multi-angular measurements

When using the kernel-driven bidirectional reflectance distribution function (BRDF) model to process multi-angular measurements, the input multi-angular measurements must be corrected for atmospheric effects. However, in current databases, a significant number of ground-based multi-angular measurements contain either no corrections or only approximate corrections for atmospheric effects. Thus, the blended diffuse light in the total incident irradiance will result in considerable smoothing of the reflectance anisotropy retrieved by the kernel-driven model unless an atmospheric correction process is conducted. In this study, we propose a diffuse-light correction (DLC) form of the kernel-driven model that improves its ability to process multi-angular measurements blended with hemispherical diffuse light. The DLC form of the kernel-driven model can be used to retrieve the intrinsic reflectance anisotropy of the observed target from atmospheric-uncorrected multi-angular measurements. This study used multi-angular data simulated by the PROSAIL and Radiosity Applicable to Porous IndiviDual objects (RAPID) BRDF model, atmospheric-corrected Polarization and Directionality of the Earth's Reflectances (POLDER), Cloud Absorption Radiometer (CAR) multi-angular measurements and their simulated data based on the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) tools to validate the effectiveness of the DLC form of the kernel-driven model. The results indicated that the reflectance factors directly retrieved by the kernel-driven model are considerably smoothed by the blended diffuse light, especially in hotspot regions. Even under clear and cloudless sky conditions, the retrieved hotspot reflectance in the red band is still underestimated by an average of 9.25%, 7.72%, 11.0% and 13.8% for the PROSAIL, RAPID, POLDER and CAR data, respectively. In contrast, the hotspot reflectance retrieved by the DLC form of the kernel-driven model is very close to the intrinsic reflectance anisotropy of the targets; the average relative error of the DLC form of the kernel-driven model is only 1.99%, 1.50%, 4.57% and 3.42%, respectively. Although the reflectance reconstructed by the DLC form of the kernel-driven model in the hotspot region represents a considerable improvement compared with the reflectance retrieved by the original kernel-driven model, its improvement on the root mean square error (RMSE) and the bias of the entire datasets is not very apparent. Using the DLC form of the kernel-driven model can significantly improve the ability of the kernel-driven model to process multi-angular measurements blended with hemispherical diffuse irradiance.

Atmospheric correction of Landsat-8/OLI and Sentinel-2/MSI data using iCOR algorithm: validation for coastal and inland waters

ABSTRACTImage correction for atmospheric effects (iCOR) is an atmospheric correction tool that can process satellite data collected over coastal, inland or transitional waters and land. The tool is adaptable with minimal effort to hyper- or multi-spectral radiometric sensors. By using a single atmospheric correction implementation for land and water, discontinuities in reflectance within one scene are reduced. iCOR derives aerosol optical thickness from the image and allows for adjacency correction, which is SIMilarity Environmental Correction (SIMEC) over water. This paper illustrates the performance of iCOR for Landsat-8 OLI and Sentinel-2 MSI data acquired over water. An intercomparison of water leaving reflectance between iCOR and Aerosol Robotic Network – Ocean Color provided a quantitative assessment of performance and produced coefficient of determination (R2) higher than 0.88 in all wavebands except the 865 nm band. For inland waters, the SIMEC adjacency correction improved results in the red-edge and near-infrared region in relation to optical in situ measurements collected during field campaigns.

A Comparative Study of NDVI Values Between North- and South-Facing Slopes in a Semiarid Mountainous Region

This paper used Landsat-5 Thematic Mapper (TM) data from 2007 to 2011 to examine seasonal changes in the normalized difference vegetation index (NDVI) in north-facing and south-facing slopes of a small (approximately 1900 ha.) west-draining hydrographic basin in the Guadalupe Valley in northwest Mexico. The analysis was done for the wet and dry seasons of this mid-latitude (32°N) basin with a rugged geomorphology covered by mostly native vegetation (chaparral) with minor anthropogenic disturbance. We used ten Landsat-5 TM satellite images that were preprocessed to correct atmospheric effects, to further estimate radiance and reflectance before the NDVI computation was performed. A LiDAR derived digital terrain model was used to calculate the terrain slope and aspect to further select and classify north and south facing slopes. With the slopes classified, zonal statistics of the NDVI were computed estimating mean and standard deviation for both slope orientations. We found higher NDVI values in the north-facing than in the south-facing slopes, regardless of the season (wet and dry), and also higher vegetation index values during the wet season compared to the dry season. For the period analyzed, we distinguished the signal of extreme dry year during 2007.