6S Radiative Transfer Model Research Articles

Quantifying the effects of the microphysical hygroscopic restructuring of soot on ensemble optical properties and satellite aerosol optical depth retrievals

Black carbon, or soot, significantly contributes to atmospheric light absorption due to its low single scattering albedo (SSA). This study investigates the impact of soot's hygroscopic restructuring on satellite remote sensing, focusing on radiative forcing, top-of-atmosphere (TOA) reflectance, and aerosol optical depth (AOD) retrievals. We characterized soot aging using relative humidity (RH) growth factor functions and modeled fresh and aging soot aggregates using a cluster-cluster aggregation algorithm. Bulk optical properties for each RH level were simulated using core-mantle generalized multi-sphere Mie-solution, weighted by the probability density function of soot monomer numbers. We incorporated soot aging models into the 6S radiative transfer model by adjusting the geometric mean radius of the soot component to match bulk SSA values at each aging degree. Extensive radiative transfer simulations were conducted, complemented by an analysis of 16 actual soot-containing cases in China from 2016 to 2019. Results showed that identical soot loads correspond to lower reflectance in dry environments and higher reflectance in humid environments. MODIS AOD retrieval errors approached zero when RH was close to 70 %, likely due to the similarity between the 6S default soot and the aging model at this humidity. Neglecting RH led to overestimations in AOD for cases with RH < 65 % and underestimations in high humidity cases (RH > 75 %). The average MODIS AOD retrieval errors for RH < 65 %, RH between 65 % and 75 %, and RH > 75 % were −33.15 %, −1.80 %, and +42.42 %, respectively. This study underscores the necessity of incorporating RH into satellite AOD retrieval algorithms to enhance accuracy and reduce uncertainties.

Integration of Landsat 8 (OLI) and MODIS images to monitor suspended particles and evaluate the spatial pattern of air pollution

Air pollution has become one of the disturbances of daily life in urban areas due to its negative and repulsive effect in current conditions. On the other hand, it has a long-term negative impact on the behavior and quantity of climate change scenarios at the local, national and global levels. In this research, the integration of Landsat 8 (OLI) satellite images and Moderate Resolution Imaging Spectroradiometer (MODIS) sensor (to estimate the ozone permeability and the water vapor permeability coefficients) using the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiation transfer model was used to determine Aerosol Optical Depth (AOD). For this purpose, instead of using the look-up table and approximating the AOD values, the components of the 6S radiative transfer model were estimated with a combined approach and the AOD values were retrieved without using the look-up table approach. One of the main problems of past research is to identify a suitable conversion model to connect the refractive index to the composition of aerosols. Therefore, the main innovation of this research is focused on a simplified, high resolution AOD retrieval method for Landsat 8 (OLI) images over mixed surfaces which does not require a LUT. Considering the relationship between AOD values and various indicators of air quality measurement, in this work, AOD estimation has been done with the aim of helping to predict these indicators. In this research, 24,569 square kilometers from a part of two states of Maryland and Virginia in the United States of America were investigated in order to estimate AOD. In order to evaluate the results of AOD retrieval, 5 Aerosol Robotic Network (AERONET) ground stations were used for comparison and their accuracy was evaluated. The results show that the proposed method has been able to extract the amount of aerosol with acceptable accuracy. According to the results obtained in this research, the root mean square errors (RMSE) is equal to 0.1036, the adjusted R2 coefficient (AdjR2) is equal to 0.8032 and the coefficient of determination (R2) is equal to 0.8079. It can be concluded that the integration of Landsat 8 (OLI) images with MODIS images has been able to provide more suitable results.

Polar Cloud Detection of FengYun-3D Medium Resolution Spectral Imager II Imagery Based on the Radiative Transfer Model

The extensive existence of high-brightness ice and snow underlying surfaces in polar regions presents notable complexities for cloud detection in remote sensing imagery. To elevate the accuracy of cloud detection in polar regions, a novel polar cloud detection algorithm is proposed in this paper. Employing the MOD09 surface reflectance product, we compiled a database of monthly composite surface reflectance in the shortwave infrared bands specific to polar regions. Through the forward simulation of the correlation between the apparent reflectance and surface reflectance across diverse conditions using the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) radiative transfer model, we established a dynamic cloud detection model for the shortwave infrared channels. In contrast to a machine learning algorithm and the widely used MOD35 cloud product, the algorithm introduced in this study demonstrates enhanced congruence with the authentic cloud distribution within cloud products. It precisely distinguishes between the cloudy and clear-sky pixels, achieving rates surpassing 90% for both, while maintaining an error rate and a missing rate each under 10%. The algorithm yields positive results for cloud detection in polar regions, effectively distinguishing between ice, snow, and clouds. It provides robust support for comprehensive and long-term cloud detection efforts in polar regions.

High-Frequency Observations of Cyanobacterial Blooms in Lake Taihu (China) from FY-4B/AGRI

China’s FY-4B satellite, launched on 3 June 2021, is a new-generation geostationary meteorological satellite. The Advanced Geosynchronous Radiation Imager (AGRI) onboard FY-4B has 15 spectral channels, including 2 visible (470 and 650 nm), 1 near infrared (825 nm), and 3 shortwave infrared (1379, 1610, and 2225 nm) bands, which can be used to observe the Earth system with the highest spatial resolution of 500 m and 15 min temporal resolution. In this study, FY-4B/AGRI observations were applied for the first time to monitor cyanobacterial blooms in Lake Taihu, China. The AGRI reflectance at visible and near-infrared bands was first corrected to surface reflectance using the 6S radiative transfer model. Due to the similar spectral reflectance characteristics to those of land-based vegetation, the normalized difference vegetation index (NDVI) and some other remote sensing vegetation indices are usually used for the retrieval of cyanobacterial blooms. The fractional vegetation cover (FVC) of algae, defined as the fraction of green vegetation in the nadir view, was adopted to depict the status and trend of cyanobacterial blooms. NDVI and FVC, the two remote sensing indices developed for the retrieval of land vegetation, were used for the detection of cyanobacteria blooms in Lake Taihu. Finally, the FVC derived from AGRI measurements was compared with that obtained from the Advanced Himawari Imager (AHI) onboard the Himawari-8 satellite to validate the effectiveness of our method. It was found that atmospheric correction can substantially improve the determination of the normalized difference vegetation index (NDVI) values of cyanobacterial blooms in the lake. As a proof of the robustness of the algorithm, the NDVIs are both derived from both AGRI and AHI and their magnitudes are similar. In addition, the distribution of cyanobacterial blooms derived from AGRI FVC is highly consistent with that derived from FY-3D/MERSI and EOS/MODIS. While a lower spatial resolution of FY-4B/AGRI might restrict its capability in capturing some spatial details of cyanobacterial blooms, the high-frequency measurements can provide information for the timely and effective management of aquatic ecosystems and help researchers better quantify and understand the dynamics of cyanobacterial blooms. In particular, AGRI can provide greater details on the diurnal variation in the distribution of cyanobacterial blooms owing to the high temporal resolution.

Atmospheric Correction Model for Water–Land Boundary Adjacency Effects in Landsat-8 Multispectral Images and Its Impact on Bathymetric Remote Sensing

Atmospheric correction (AC) is the basis for quantitative water remote sensing, and adjacency effects form an important part of AC for medium- and high-spatial-resolution optical satellite images. The 6S radiative transfer model is widely used, but its background reflectance function does not take the radiance changes at water–land boundaries into account. If the observed land possesses bright features, the radiance of the adjacent water will be affected, leading to deviations in the AC results and increasing the uncertainty of water depth-based optical quantitative remote sensing. In this paper, we propose a model named WL-AE (a correction model for water–land boundary adjacency effects), which is based on the obvious radiance differences at water–land boundaries. This model overcomes the problem by which the background reflectance calculation is not terminated due to the highlighting pixel. We consider the influences of different Rns (neighborhood space) on the target pixel. The effective calculation of the equivalent background reflectance of the target pixel is realized, and the influence of the land area anomaly highlighting the pixel on the adjacent water is avoided. The results show that WL-AE can effectively improve the entropy and contrast of the input image and that the water–land boundary is greatly affected by adjacency effects, especially in the green and near-infrared bands, where the Mrc (mean rate of change) are as high as 14.2% and 20.1%, respectively. In the visible wavelength, the Sd of Rrc (the relative rate of change) is positively correlated with Rns, and the Sd reaches 16.9%. Although the adjacency effect is affected by ground object types, its influence area remains within 3 km offshore. Based on the WL-AE and 6S results, the comparative test regarding bathymetric inversion shows that the influence is significant in the 0–5 m depth section. In Penang, the MRE of the 0–4 m inversion results is 31.4%, which is 10.5% lower than that of the 6S model.

On-Orbit Radiometric Calibration of Hyperspectral Sensors on Board Micro-Nano Satellite Constellation Based on RadCalNet Data

The stability and accuracy of the on-orbit radiometric calibration of hyperspectral sensors are prerequisites for the quantitative application of satellite hyperspectral data. The Zhuhai-1 micro-nano satellite constellation is composed of eight hyperspectral satellite missions. The Orbita Hyperspectral Sensor (OHS) on board each satellite has a gradient filter spectroscopic design. When observing the Earth, eight integration stages can be set for each band according to different lighting conditions. Due to high manufacturing costs, OHSs are not equipped with on-board calibration devices. Therefore, it is very difficult to accurately calibrate OHSs for all of the integration stages. On the other hand, it is extremely important to ensure radiometric consistency between different OHSs within the Zhuhai-1 micro-nano satellite constellation. To carry out the rapid radiometric calibration of the Zhuhai-1 constellation, an on-orbit radiometric calibration model considering all of the integration stages related to hyperspectral sensors was built based on the BOA reflectance and atmosphere parameters published by the Committee on Earth Observation Satellites (CEOS) radiometric calibration network (RadCalNet). The RadCalNet product was used to derive the TOA radiance base in the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer (RT) model. In this paper, we analyzed the radiometric stability of the same sensor and the consistency of different calibration results regarding four RadCalNet sites, and the on-orbit radiometric performance evaluation of OHSs was also carried out. The data retrieved from OHSs regarding hyperspectral surface reflectance were preliminarily validated using site-synchronous surface reflectance measurements.

Monitoring Land Vegetation from Geostationary Satellite Advanced Himawari Imager (AHI)

For many years, the Advanced Very High-Resolution Radiometer (AVHRR) and Moderate Resolution Imaging Spectroradiometer (MODIS) instruments have been widely used to monitor the condition of surface vegetation. Since the polar-orbiting satellite provides limited daily samples on surface, a completed spatial coverage of land vegetation is often relied on over multiple days of observations. In this study, observations from the Japanese geostationary satellite imager Advanced Himawari Imagers (AHI) are used to derive the surface vegetation index. The AHI reflectance at visible and near-infrared bands are first corrected to the surface reflectance by using the 6S radiative transfer model. The AHI surface reflectance from various viewing angles and solar geometry is further normalized to form an angular-independent reflectance by using a BRDF model. Finally, the surface vegetation index is calculated and synthesized from the daytime AHI data. It is found that the high-frequency AHI observations can significantly reduce the impact of clouds on compositing land NDVI and require a shorter time for a complete coverage of surface conditions. Also, a single NDVI image from AHI exhibits spatial distribution similar to that from 16 days of MODIS data.

Land Surface Albedo Estimation With Chinese GF-1 WFV Data in Northwest China

Land surface albedo (LSA) is one of the driving factors in the energy balance of surface radiation and the interaction between the earth and atmosphere. LSA is an important parameter that is widely used in surface energy balance, medium- and long-term weather forecasting, and global change studies. GF-1 wide field view (WFV) data provide a spatial resolution of 16 m and temporally intensive land surface observations, but efficient algorithm was still lacking for quantitatively land surface parameters estimation. It is essential to improve the data use ability by generating efficient land surface parameter retrieval algorithms. This study proposed an LSA retrieval algorithm by using GF-1 WFV data. Land surface bidirectional reflectance distribution function characteristic parameters were used to represent the non-Lambertian characteristic of land surface. The top of atmosphere (TOA) reflectance is simulated by the 6S radiative transfer model by considering non-Lambertian land surfaces. Linear regression is applied in the TOA reflectance, and LSA is simulated with the surface bidirectional reflectance characteristic parameters to build a lookup table. The proposed algorithm can estimate LSA with high accuracy according to the TOA reflectance without the complex multistep inversion process. The validation results of ground measurements in Northwest China for different land cover types show that the algorithm is effective, and the overall root mean square error was 0.036 when compared with field observation. The algorithm also shows great consistency with Landsat albedo data. The proposed algorithm is of great significance for improving GF data utilization.

Influence of atmospheric modeling on spectral target detection through forward modeling approach in multi-platform remote sensing data

Identifying objects or pixels of interest that are few in numbers and sparsely populated in imagery is referred to as target detection. Traditionally, the inverse modeling (IM) approach, usually a slow and computationally intensive process, is used for detecting targets using surface reflectance spectra. For the emerging online methods in remote sensing, modeling the at-sensor radiance of target material, i.e., a forward modeling (FM) approach, can be used. Compared to the IM approach, FM is better suited to online methods due to its potential for adaptation to regional atmospheric modeling. Spectral knowledge transfer of a target from a known to an unknown atmospheric condition is the primary outcome of an efficient target detection framework. However, such an endeavor requires an exhaustive assessment of the target detection process under different atmospheric models and associated uncertainties. The objective of this work is to assess the quantitative impact of atmospheric parameters on the detectability of engineered targets. Specifically, the impact of critical atmospheric parameters such as aerosol optical thickness (AOT), standard atmospheric profiles, and aerosol models are considered. For this effect, we designed a multi-platform image acquisition setup that acquired targets concurrently using a ground-based terrestrial hyperspectral imager (THI), an airborne hyperspectral imager (AVIRIS-NG), and a space-borne multispectral imager (Sentinel-2). We used a point-based spectroradiometer and pixel-based THI to collect the in-situ reference target reflectance spectra and generated a radiance spectral library by simulating TOA radiance spectra using the Second Simulation of the Satellite Signal in the Solar Spectrum (6S) radiative transfer model. We have considered two cases of target radiance simulations, i.e., (i) corresponding to a grid of different AOT values for a predefined atmospheric and aerosol profile, and (ii) corresponding to varying combinations of atmospheric and aerosol profiles at a given AOT. The detection has been carried out using multiple target detection algorithms. Results indicate that the spectral knowledge-based targets can be detected in remote sensing data under different atmospheric model scenarios using the FM approach. A detection rate of about 75% and 50% have been consistently obtained for remote sensing data from airborne and space-borne platforms with a false alarm (FA) rate of 10-2 to 10-3 respectively. Change in the AOT across atmospheric models has resulted in decision-changing implications in the target detection modeling. The selection of the wrong atmospheric profile can potentially aggravate the number of FAs produced by a particular detection algorithm.

Retrieving High-Resolution Aerosol Optical Depth from GF-4 PMS Imagery in Eastern China

Gaofen 4 (GF-4) is a geostationary satellite, with a panchromatic and multispectral sensor (PMS) onboard, and has great potential in observing atmospheric aerosols. In this study, we developed an aerosol optical depth (AOD) retrieval algorithm for the GF-4 satellite. AOD retrieval was realized based on the pre-calculated surface reflectance database and 6S radiative transfer model. We customized the unique aerosol type according to the long time series aerosol parameters provided by the Aerosol Robotic Network (AERONET) site. The solar zenith angle, relative azimuth angle, and satellite zenith angle of the GF-4 panchromatic multispectral sensor image were calculated pixel-by-pixel. Our 1 km AOD retrievals were validated against AERONET Version 3 measurements and compared with MOD04 C6 AOD products at different resolutions. The results showed that our GF-4 AOD algorithm had a good robustness in both bright urban areas and dark rural areas. A total of 71.33% of the AOD retrievals fell within the expected errors of ±(0.05% + 20%); root-mean-square error (RMSE) and mean absolute error (MAE) were 0.922 and 0.122, respectively. The accuracy of GF-4 AOD in rural areas was slightly higher than that in urban areas. In comparison with MOD04 products, the accuracy of GF-4 AOD was much higher than that of MOD04 3 km and 10 km dark target AOD, but slightly worse than that of MOD04 10 km deep blue AOD. For different values of land surface reflectance (LSR), the accuracy of GF-4 AOD gradually deteriorated with an increase in the LSR. These results have theoretical and practical significance for aerosol research and can improve retrieval algorithms using the GF-4 satellite.

A novel way to calculate shortwave black carbon direct radiative effect

Black carbon (BC) aerosol has a strong radiative forcing effect and significantly affects human beings and the environment. Therefore, it is important to quantitatively calculate its direct radiative effect (BC DRE) at the surface (SFC) and the top of the atmosphere (TOA). Current studies mainly use empirical formula methods or broadband methods to calculate BC DRE. However, these two methods do not consider the differences of sky diffuse light ratios in different wavelength bands. To overcome this problem, a new scheme named the multiband synthetic method is proposed to calculate blue sky albedo at MODIS narrow bands, and then, the blue sky albedo at the whole shortwave band is synthesized with these separate narrowband blue sky albedos. Based on BC concentration measured in Xuzhou over two years (from May 2014 to July 2016), aerosol optical depth (AOD) and microphysical parameters provided by AERONET, and the black sky albedo (BSA) and white sky albedo (WSA) provided by Google Earth Engine (GEE) products, shortwave BC DRE was calculated numerically with the use of the 6S radiative transfer model. The range of BC DRE computed by the multiband synthetic method at the TOA and SFC are 0.84 ± 0.08 to 3.27 ± 1.01W/m2 and −14.57 ± 4.53 to −4.31 ± 0.36W/m2. The shortwave BC DRE calculated by the multiband synthetic method was higher than that calculated with the broadband method and empirical formula method by 0.11% to 0.36% (at the SFC), 0.14% to 1.4% (at the SFC) and 3.4% to 10.1% (at the TOA), 5.5% to 15.8% (at the TOA), respectively. The BC DREs calculated by these three methods have small differences at the SFC. However, the difference was large at the TOA. The results of this study suggest that it is important to consider the differences between different narrow bands when calculating the broadband shortwave blue sky albedo.

Evaluation of NDVI Estimation Considering Atmospheric and BRDF Correction through Himawari-8/AHI

Satellite-based vegetation indices are an essential element in understanding the Earth’s surface. In this study, we estimated the normalized difference vegetation index (NDVI) using Himawari-8/Advanced Himawari Imager (AHI) data and analyzed the sensitivity of products to atmospheric and surface correction. We used the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer model for atmospheric correction, and kernel-based semi-empirical bidirectional reflectance distribution function (BRDF) model to remove surface anisotropic effects. From this, top-of-atmosphere, top-of-canopy, and normalized NDVIs were produced. A sensitivity analysis showed that the normalized NDVI had the lowest number of missing values compared with the others and almost no low peaks during the study period. These results were validated by Terra and Aqua/Moderate Resolution Imaging Spectroradiometer (MODIS) and Project for On-Board Autonomy/Vegetation (PROBA) NDVI product, showing the root mean square error (RMSE) and bias of 0.09 and + 0.04 (MODIS) and 0.09 and − 0.04 (PROBA), respectively. These results also satisfied the FP7 Geoland2/BioPar project-defined user requirements (threshold: 0.15; target: 0.10).

Radiometric Variations of On-Orbit FORMOSAT-5 RSI from Vicarious and Cross-Calibration Measurements

A new Taiwanese satellite, FORMOSAT-5 (FS-5), with a payload remote sensing instrument (RSI) was launched in August 2017 to continue the mission of its predecessor FORMOSAT-2 (FS-2). Similar to FS-2, the RSI provides 2-m resolution panchromatic and 4-m resolution multi-spectral images as the primary payload on FS-5. However, the radiometric properties of the optical sensor may vary, based on the environment and time after being launched into the space. Thus, maintaining the radiometric quality of FS-5 RSI imagery is essential and significant to scientific research and further applications. Therefore, the objective of this study aimed at the on-orbit absolute radiometric assessment and calibration of on-orbit FS-5 RSI observations. Two renowned approaches, vicarious calibrations and cross-calibrations, were conducted at two calibration sites that employ a stable atmosphere and high surface reflectance, namely, Alkali Lake and Railroad Valley Playa in North America. For cross-calibrations, the Landsat-8 Operational Land Imager (LS-8 OLI) was selected as the reference. A second simulation of the satellite signal in the solar spectrum (6S) radiative transfer model was performed to compute the surface reflectance, atmospheric effects, and path radiance for the radiometric intensity at the top of the atmosphere. Results of vicarious calibrations from 11 field experiments demonstrated high consistency with those of seven case examinations of cross-calibration in terms of physical gain in spectra, implying that the practicality of the proposed approaches is high. Moreover, the multi-temporal results illustrated that RSI decay in optical sensitivity was evident after launch. The variation in the calibration coefficient of each band showed no obvious consistency (6%–24%) in 2017, but it tended to be stable at the order of 3%–5% of variation in most spectral bands during 2018. The results strongly suggest that periodical calibration is required and essential for further scientific applications.

Updating Absolute Radiometric Characteristics for KOMPSAT-3 and KOMPSAT-3A Multispectral Imaging Sensors Using Well-Characterized Pseudo-Invariant Tarps and Microtops II

Radiometric calibration of satellite imaging sensors should be performed periodically to account for the effect of sensor degradation in the space environment on image accuracy. In this study, we performed vicarious radiometric calibrations (relying on in situ data) of multispectral imaging sensors on the Korea multi-purpose satellite-3 and -3A (KOMPSAT-3 and -3A) to adjust the existing radiometric conversion coefficients according to time delay integration (TDI) adjustments and sensor degradation over time. The Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer model was used to obtain theoretical top of atmosphere radiances for both satellites. As input parameters for the 6S model, surface reflectance values of well-characterized pseudo-invariant tarps were measured using dual ASD FieldSpec® 3 hyperspectral radiometers, and atmospheric conditions were measured using Microtops II® Sunphotometer and Ozonometer. We updated the digital number (DN) of the radiance coefficients of the satellites; these had been used to calibrate the sensors during in-orbit test periods in 2013 and 2015. The coefficients of determination, R2, values between observed DNs of the sensors, and simulated radiances for the tarps were more than 0.999. The calibration errors were approximately 5.7% based on manifested error sources. We expect that the updated coefficients will be an important reference for KOMPSAT-3 and -3A users.

Assessments of preprocessing methods for Landsat time series images of mountainous forests in the tropics

ABSTRACTMonitoring forest changes based on numerous satellite images has been recently conducted in the tropics. Preparation of a time series of satellite images, sometimes referred to as preprocessing, is essential for conducting robust detection of forest change. To create consistent and stable conditions in satellite images, the best methods have to be used in each step to correct various sources of noise. This study assessed three atmospheric correction methods, six topographic correction methods, and eight gap-filling methods to produce the best possible time series of Landsat images of tropical seasonal forests. The results showed that the best methods for atmospheric and topographic correction were relative corrections using a Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS), which is based on a 6S radiative transfer model, and the C-correction, respectively. Weighted linear regression and multiple linear regression models were selected as the best models for the filling of data gaps associated with Scan Line Corrector-off and clouds, respectively. This study provided the best possible image preprocessing for trajectory-based change detection using annual Landsat images. Although the best possible preprocessing methods might vary depending on the change detection methods used in different study areas, the results highlight the preferable preprocessing methods, even for different types of time series analysis.

Radiometric calibration evaluation for RSBs of Suomi-NPP/VIIRS and Aqua/MODIS based on the 2015 Dunhuang Chinese Radiometric Calibration Site in situ measurements

ABSTRACTThe Dunhuang Chinese Radiometric Calibration Site (CRCS), used for the vicarious calibration (VC) of reflective solar bands (RSBs), was determined as the primary radiometric calibration site for Chinese space-borne optical sensors and was also selected in 2008 by the Working Group on Calibration and Validation of the Committee on Earth Observation Satellites as one of the instrumented reference sites. In August 2015, an in situ measurement was carried out at the Dunhuang site to evaluate the RSB radiometric calibration of the Visible Infrared Imaging Radiometer Suite (VIIRS) on Suomi National Polar-orbiting Partnership (NPP) and Moderate Resolution Imaging Spectroradiometer (MODIS) on Aqua based on the reflectance-based method. A portable spectroradiometer was used in the experiment to obtain the surface reflectance, and the atmospheric parameters were obtained by sun photometers and radiosonde. A Dunhuang surface bidirectional reflectance distribution function model obtained during the field missions in 2008 and 2013 was implemented. Two days of in situ measurement data including 2 days of VIIRS data and 1 day of MODIS data were used for this evaluation. The results show that the radiometric calibration accuracy is within ±2% for most NPP/VIIRS and Aqua/MODIS RSBs based on the Dunhuang site. It should be noted that there is a relatively large difference in the NPP/VIIRS day–night band (DNB) and Aqua/MODIS band 7 results at the central wavelength of 2.1 μm, with biases of – 4.78% and – 5.71%, respectively. One factor contributing to the difference is the atmospheric transmittance calculation in these bands using the 6S radiative transfer model. If Moderate Resolution Atmospheric Transmission model is used for atmospheric transmittance correction, part of the bias of the MODIS band 7 and VIIRS DNB can be eliminated. However, the consistency of the VIIRS M11 and MODIS B7 is 3.47%, which is larger than that of the other bands.

Initial Radiometric Characteristics of KOMPSAT-3A Multispectral Imagery Using the 6S Radiative Transfer Model, Well-Known Radiometric Tarps, and MFRSR Measurements

On-orbit radiometric characterization of the multispectral (MS) imagery of the Korea Aerospace Research Institute (KARI)’s Korea Multi-Purpose Satellite-3A (KOMPSAT-3A), which was launched on 25 March 2015, was conducted to provide quantitative radiometric information about KOMPSAT-3A. During the in-orbit test (IOT), vicarious radiometric calibration of KOMPSAT-3A was performed using the Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer model. The characteristics of radiometric tarps, the atmospheric optical depth from multi-filter rotating shadowband radiometer (MFRSR) measurements, and sun–sensor–geometry were carefully considered, in order to calculate the exact top of atmosphere (TOA) radiance received by KOMPSAT-3A MS bands. In addition, the bidirectional reflectance distribution function (BRDF) behaviors of the radiometric tarps were measured in the laboratory with a two-dimensional hyperspectral gonioradiometer, to compensate for the geometry discrepancy between the satellite and the ASD FieldSpec® 3 spectroradiometer. The match-up datasets between the TOA radiance and the digital number (DN) from KOMPSAT-3A were used to determine DN-to-radiance conversion factors, based on linear least squares fitting for two field campaigns. The final results showed that the R2 values between the observed and simulated radiances for the blue, green, red, and near-infrared (NIR) bands, are greater than 0.998. An approximate error budget analysis for the vicarious calibration of KOMPSAT-3A showed an error of less than 6.8%. When applying the laboratory-based BRDF correction to the case of higher viewing zenith angle geometry, the gain ratio was improved, particularly for the blue (1.3%) and green (1.2%) bands, which exhibit high sensitivity to the BRDF of radiometric tarps during the backward-scattering phase. The calculated gain ratio between the first and second campaigns showed a less than 5% discrepancy, indicating that the determined radiometric characteristics of KOMPSAT-3A are reliable and useful to the user group for quantitative applications.

Radiometric Characteristics of KOMPSAT-3 Multispectral Images Using the Spectra of Well-Known Surface Tarps

A vicarious calibration with reference to characterized surface tarps was conducted to determine the first radiometric characteristics of KOMPSAT-3. The 6S radiative transfer model was also used by inputting various initial parameters, such as the spectral response function of KOMPSAT-3, and atmospheric and geometric conditions. Moderate-Resolution Imaging Spectroradiometer atmospheric products, such as aerosol optical depth, precipitable water, and total ozone, were used as input parameters to interpret solar radiation reflection, scattering, and absorption effects. In the first field campaign, the radiometric coefficients from each of the spectral bands were estimated by calculating the predicted radiance at sensor level and the digital number (DN) of KOMPSAT-3 based on a linear least squares fit over a range of target reflectance levels. The second field campaign measurements were also used to upgrade the KOMPSAT-3 DNs to radiance coefficients. The root-mean-square error differences between simulated radiance and measured radiance during the second field campaign for “sensor-to-itself” calibration were 2.072 W/m 2 sr (blue), 6.80 W/m 2 sr (green), 7.512 W/m 2 sr (NIR), and 5.712 W/m 2 sr (red), respectively. This highlights that radiometric calibration with tarps is a reliable method. Furthermore, the gain ratio between the first and the second one was <; 5%, indicating reasonable radiometric calibration results. Additionally, cross-validation of KOMPSAT-3 with radiometrically well-calibrated Landsat-8 was performed over bright desert. Although the difference between the vicarious calibration with surface tarps and cross-validation with Landsat-8 was significant, reasonable results were obtained under close geometrical conditions, despite inherent vicarious calibration error.

FOREST COVER MAPPING IN ISKANDAR MALAYSIA USING SATELLITE DATA

Abstract. Malaysia is the third largest country in the world that had lost forest cover. Therefore, timely information on forest cover is required to help the government to ensure that the remaining forest resources are managed in a sustainable manner. This study aims to map and detect changes of forest cover (deforestation and disturbance) in Iskandar Malaysia region in the south of Peninsular Malaysia between years 1990 and 2010 using Landsat satellite images. The Carnegie Landsat Analysis System-Lite (CLASlite) programme was used to classify forest cover using Landsat images. This software is able to mask out clouds, cloud shadows, terrain shadows, and water bodies and atmospherically correct the images using 6S radiative transfer model. An Automated Monte Carlo Unmixing technique embedded in CLASlite was used to unmix each Landsat pixel into fractions of photosynthetic vegetation (PV), non photosynthetic vegetation (NPV) and soil surface (S). Forest and non-forest areas were produced from the fractional cover images using appropriate threshold values of PV, NPV and S. CLASlite software was found to be able to classify forest cover in Iskandar Malaysia with only a difference between 14% (1990) and 5% (2010) compared to the forest land use map produced by the Department of Agriculture, Malaysia. Nevertheless, the CLASlite automated software used in this study was found not to exclude other vegetation types especially rubber and oil palm that has similar reflectance to forest. Currently rubber and oil palm were discriminated from forest manually using land use maps. Therefore, CLASlite algorithm needs further adjustment to exclude these vegetation and classify only forest cover.

Evaluation of the Landsat-5 TM and Landsat-7 ETM+ surface reflectance products

Abstract 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.