MODerate Resolution Atmospheric TRANsmission Research Articles

Tracing the physical signatures among the calculated global clear-sky spectral shortwave radiative flux distribution

This study utilized the high-spectral resolution radiative transfer model (MODerate resolution atmospheric TRANsmission, MODTRAN6.0.2.5) to compute global clear-sky shortwave (SW) radiative flux and compared it with NASA’s Clouds and the Earth’s Radiant Energy System (CERES) Synoptic Radiative Fluxes and Clouds (SYN1deg) product. The comparison revealed that the global distributions of clear-sky downwelling SW fluxes at the surface from the M6.0 calculations and SYN1 results are similar, with annual means of 246.51 Wm-2 and 242.42 Wm-2, respectively. Analysis further showed that most of the M6.0 calculations are slightly higher from low to mid-latitudes, particularly in the Northern Hemisphere (NH), but lower in higher latitudes compared to SYN1 results. However, these differences mostly fall within the CERES estimated uncertainty (6 Wm-2) of monthly mean clear-sky downwelling SW flux at the surface. The sensitivity of clear-sky SW/μ0 fluxes to changes in Precipitable Water Vapor (PWV), represented by the clear-sky water vapor radiative kernel, is about -0.7 Wm-2/(kgm-2) over oceans for both M6.0 and CERES SYN1 products, except for SYN1 results over the Southern Hemisphere (SH) ocean. Additionally, the zonal means of land coverage and SW/VIS/NIR albedos from M6.0 calculations indicate that VIS albedos are highest in polar regions (>60°), followed by SW and NIR albedos, while NIR albedos become highest from low to mid-latitudes (<60°). Generally, SW/VIS/NIR albedos and their differences increase monotonically with increased land coverage from 60°S to 60°N. The consistent clear-sky water vapor radiative kernels derived from both products exceeded our expectations, suggesting their potential use to trace physical signatures in climate model calculations. It is recommended that these model-derived radiative kernels should be validated by the long-term global and regional surface observations in order to enhance confidence to implement these radiative kernels in climate models.

Effects of Aluminum/Carbon and Morphology on Optical Characteristics and Radiative Forcing of Alumina Clusters Emitted by Solid Rockets in the Stratosphere

Alumina (Al2O3) particles, the primary combustion products of solid rockets, can accumulate in the stratosphere, changing the global radiative balance. These Al2O3 particles were usually treated as homogeneous spheres. However, they contain impurities and may form clusters during the combustion process. Models representing Al-containing and C-containing Al2O3 clusters were developed, denoted as Al2O3 shell model (ASM) and Al2O3 core model (ACM), respectively. The superposition T-matrix method (STMM) was applied to examine their optical characteristics. Subsequently, a method to obtain the top-of-atmosphere flux was proposed by integrating the models with the moderate resolution atmospheric transmission code (MODTRAN). With the addition of Al/C, the absorption cross-section enhances by several orders of magnitude at 0.55 μm and increases slightly at 10 μm. The equivalent sphere models will weaken their scattering ability. A 4Tg mass burden of Al2O3 produces radiative forcing of −0.439 Wm−2. However, the addition of Al and C reduces the forcing by up to 15% and 12%, respectively. In summary, the optical characteristics and radiative forcing of Al2O3 clusters are sensitive to Al/C and morphology models. While our findings are impacted by various uncertainties, they contribute valuable insights into the radiative forcing of Al2O3 particles, potential climatic changes by space activities.

Land Surface Longwave Radiation Retrieval from ASTER Clear-Sky Observations

Surface longwave radiation (SLR) plays a pivotal role in the Earth’s energy balance, influencing a range of environmental processes and climate dynamics. As the demand for high spatial resolution remote sensing products grows, there is an increasing need for accurate SLR retrieval with enhanced spatial detail. This study focuses on the development and validation of models to estimate SLR using measurements from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. Given the limitations posed by fewer spectral bands and data products in ASTER compared to moderate-resolution sensors, the proposed approach combines an atmospheric radiative transfer model MODerate resolution atmospheric TRANsmission (MODTRAN) with the Light Gradient Boosting Machine algorithm to estimate SLR. The MODTRAN simulations were performed to construct a representative training dataset based on comprehensive global atmospheric profiles and surface emissivity spectra data. Global sensitivity analyses reveal that key inputs influencing the accuracy of SLR retrievals should reflect surface thermal radiative signals and near-surface atmospheric conditions. Validated against ground-based measurements, surface upward longwave radiation (SULR) and surface downward longwave radiation (SDLR) using ASTER thermal infrared bands and surface elevation estimations resulted in root mean square errors of 17.76 W/m2 and 25.36 W/m2, with biases of 3.42 W/m2 and 3.92 W/m2, respectively. Retrievals show systematic biases related to extreme temperature and moisture conditions, e.g., causing overestimation of SULR in hot humid conditions and underestimation of SDLR in arid conditions. While challenges persist, particularly in addressing atmospheric variables and cloud masking, this work lays a foundation for accurate SLR retrieval from high spatial resolution sensors like ASTER. The potential applications extend to upcoming satellite missions, such as the Landsat Next, and contribute to advancing high-resolution remote sensing capabilities for an improved understanding of Earth’s energy dynamics.

Investigation of spectral bands and sensor parameters for methane emission detection imaging spectrometer

Recently, interest has increased in developing remote sensing (RS) sensor systems with a high spatial and spectral resolution for quantifying methane (CH4) emissions from point sources. Evaluating sensor parameters can lead to a better understanding of technological advancements in CH4 emission estimation. This study addresses several important topics, including the robust algorithm for point source CH4 emission detection, selecting optimum CH4 absorption bands and their sensitivity analysis, and evaluating sensor parameters such as spatial and spectral resolution and signal-to-noise ratio (SNR). Since the matched filter algorithm efficiently detects point source emissions with palpable accuracy, it has been used here for the Airborne Visible InfraRed Imaging Spectrometer – Next Generation (AVIRIS-NG) sensor data acquired over the USA. The optimum band selection for CH4 detection was determined using a normalized sensitivity method to investigate the effects of aerosol, water vapor (WV), and surface albedo variations. The impact of these parameters on the CH4 absorption bands in the short-wave infrared (SWIR) range has been analyzed using MODerate resolution atmospheric TRANsmission (MODTRAN) radiative transfer (RT) model simulations. Using AVIRIS-NG data as a testbed, various methods have been used to reconstruct original data and generate data sets with different sensor specifications. The effects of the sensor parameters, such as spatial and spectral resolution and SNR, on the detection accuracy were analyzed. The results show the significance of SNR over the spatial and spectral resolution for better emission detection. It indicates the reasonable selection of sensor parameters that can help to create an optimal sensor system that will ultimately support the development of an imaging spectrometer suitable for CH4 emission estimation.

Next-generation radiance unfiltering process for the Clouds and the Earth's Radiant Energy System instrument

Abstract. The filtered radiances measured by the Clouds and the Earth's Radiant Energy System (CERES) instruments are converted to shortwave (SW), longwave (LW), and window unfiltered radiances based on regressions developed from theoretical radiative transfer simulations to relate filtered and unfiltered radiances. This paper describes an update to the existing Edition 4 CERES unfiltering algorithm (Loeb et al., 2001), incorporating the most recent developments in radiative transfer modeling, ancillary input datasets, and increased computational and storage capabilities during the past 20 years. Simulations are performed with the updated Moderate Resolution Atmospheric Transmission (MODTRAN) 5.4 version. Over land and snow, the surface bidirectional reflectance distribution function (BRDF) is characterized by a kernel-based representation in the simulations, instead of the Lambertian surface used in the Edition 4 unfiltering process. Radiance unfiltering is explicitly separated into four seasonally dependent land surface groups based on the spectral radiation similarities of different surface types (defined by the International Geosphere-Biosphere Programme); over snow, it is separated into fresh snow, permanent snow, and sea ice. This differs from the Edition 4 unfiltering process where only one set of regressions was used for land and snow, respectively. The instantaneous unfiltering errors are estimated with independent test cases generated from radiative transfer simulations in which the “true” unfiltered radiances from radiative transfer simulations are compared with the unfiltered radiances calculated from the regressions. Overall, the relative errors are mostly within ±0.5 % for SW, within ±0.2 % for daytime LW, and within ±0.1 % for nighttime LW for both CERES Terra Flight Model 1 (FM1) and Aqua FM3 instruments. The unfiltered radiances are converted to fluxes and compared to CERES Edition 4 fluxes. The global mean instantaneous fluxes for Aqua FM3 are reduced by 0.34 to 0.45 W m−2 for SW and increased by 0.25 to 0.46 W m−2 for daytime LW; for Terra FM1, they are reduced by 0.24 to 0.34 W m−2 for SW and increased by 0.08 to 0.28 W m−2 for daytime LW. Nighttime LW flux differences are negligible for both instruments.

CO2 concentration retrieval and emission rate estimation over Indian thermal power plants using radiative transfer approach and AVIRIS-NG data

Carbon dioxide (CO2) emissions from thermal power plants (TPPs) are regarded as the primary source of CO2 at the boundary-level atmosphere. The first step toward mitigating these emissions is to improve emission estimation methods at the facility level and establish a reliable emission reporting system. This study aims to establish a transparent and reliable methodology for detecting point source CO2 emissions from TPP and estimating the emission rate released from the stakes of TPP. A remote sensing method continuum interpolated band ratio (CIBR) has been utilized to detect and estimate CO2 emissions over Indian TPPs using Airborne Visible/InfraRed Imaging Spectrometer - Next Generation (AVIRIS-NG) data acquired by the ISRO-NASA joint campaign over India in 2015 and 2018. MODerate resolution atmospheric TRANsmission (MODTRAN) 5.3 radiative transfer (RT) model simulations were carried out for TPP sites to develop a CIBR algorithm for CO2 retrieval. Two AVIRIS-NG short-wave infrared (SWIR) bands were utilized for CO2 concentration retrieval, and the results from each band were compared based on their emission detection capabilities. Comparatively, SWIR-2 (1960–2120 nm) bands show higher sensitivity of CO2 than SWIR-1 (1560–1640 nm). Therefore, we utilize SWIR-2 results for the CO2 retrieval and emission rate estimation. Maximum CO2 emissions of 53.10, 46.36, 96.84, and 53.49 ppmv were detected at Maithon, Dhariwal, Udupi, and Parangipettai TPPs. The study includes one USA-based TPP to compare emission results for validation and test the method in diverse atmospheric conditions. In addition, we compared CO2 estimation results with AVIRIS-NG benchmark data. The simple band ratio method uses only three SWIR bands and operates with minimal computational facility, making it suitable for CO2 retrieval in hyperspectral data. Further, the integrated mass enhancement (IME) method has been used for emission rate estimation using CO2 concentration values from SWIR-2 retrieval results and wind speed at the sites. Yearly 5.96, 6.82, 2.29, and 3.52 Mt emissions were estimated for Maithon, Dhariwal, Udupi, and Parangipettai TPPs, respectively. The emission results have been validated by establishing a relationship between TPP's coal consumption and CO2 emissions. Further, estimated emission rates have been validated with former investigations and the CO2 baseline emission database of India's Central Electricity Authority (CEA) reports. Not having in-situ measurements and uncertainty in wind data may limit the study.

Performance of the Atmospheric Radiative Transfer Simulator (ARTS) in the 600–1650 cm−1 Region

The Atmospheric Radiative Transfer Simulator (ARTS) has been widely used in the radiation transfer simulation from microwave to terahertz. Due to the same physical principles, ARTS can also be used for simulations of thermal infrared (TIR). However, thorough evaluations of ARTS in the TIR region are still lacking. Here, we evaluated the performance of ARTS in 600–1650 cm−1 taking the Line-By-Line Radiative Transfer Model (LBLRTM) as a reference model. Additionally, the moderate resolution atmospheric transmission (MODTRAN) band model (BM) and correlated-k (CK) methods were also used for comparison. The comparison results on the 0.001 cm−1 spectral grid showed a high agreement (sub-0.1 K) between ARTS and LBLRTM, while the mean bias difference (MBD) and root mean square difference (RMSD) were less than 0.05 K and 0.3 K, respectively. After convolving with the spectral response functions of the Atmospheric Infra-Red Sounder (AIRS) and the Moderate Resolution Imaging Spectroradiometer (MODIS), the brightness temperature (BT) differences between ARTS and LBLRTM became smaller with RMSDs of &lt;0.1 K. The comparison results for Jacobians showed that the Jacobians calculated by ARTS and LBLRTM were close for temperature (can be used for Numerical Weather Prediction application) and O3 (excellent Jacobian fit). For the water vapor Jacobian, the Jacobian difference increased with an increasing water vapor content. However, at extremely low water vapor values (0.016 ppmv in this study), LBLRTM exhibited non-physical mutations, while ARTS was smooth. This study aims to help users understand the simulation accuracy of ARTS in the TIR region and the improvement of ARTS via the community.

Vicarious Radiometric Calibration of the Multispectral Imager Onboard SDGSAT-1 over the Dunhuang Calibration Site, China

The multispectral imager (MII), onboard the Sustainable Development Science Satellite 1 (SDGSAT-1), performs detailed terrestrial change detection and coastal monitoring. SDGSAT-1 was launched at 2:19 UTC on 5 November 2021, as the world’s first Earth science satellite to serve the United Nations 2030 Sustainable Development Agenda. A vicarious radiometric calibration experiment was conducted at the Dunhuang calibration site (Gobi Desert, China) on 14 December 2021. In-situ measurements of ground reflectance, aerosol optical depth (AOD), total columnar water vapor, radiosonde data, and diffuse-to-global irradiance (DG) ratio were performed to predict the top-of-atmosphere radiance by the reflectance-, irradiance-, and improved irradiance-based methods using the moderate resolution atmospheric transmission model. The MII calibration coefficients were calculated by dividing the top-of-atmosphere radiance by the average digital number value of the image. The radiometric calibration coefficients calculated by the three calibration methods were reliable (average relative differences: 2.20% (reflectance-based vs. irradiance-based method) and 1.43% (reflectance-based vs. improved irradiance-based method)). The total calibration uncertainties of the reflectance-, irradiance-, and improved irradiance-based methods were 2.77–5.23%, 3.62–5.79%, and 3.50–5.23%, respectively. The extra DG ratio measurements in the latter two methods did not improve the calibration accuracy for AODs ≤ 0.1. The calibrated MII images were verified using Landsat-8 Operational Land Imager (OLI) and Sentinel-2A MultiSpectral Instrument (MSI) images. The retrieved ground reflectances of the MII over different surface types were cross-compared with those of OLI and MSI using the FAST Line-of-sight Atmospheric Analysis of Hypercubes software. The MII retrievals differed by &lt;0.0075 (7.13%) from OLI retrievals and &lt;0.0084 (7.47%) from MSI retrievals for calibration coefficients from the reflectance-based method; &lt;0.0089 (7.57%) from OLI retrievals and &lt;0.0111 (8.65%) from MSI retrievals for the irradiance-based method; and &lt;0.0082 (7.33%) from OLI retrievals and &lt;0.0101 (8.59%) from MSI retrievals for the improved irradiance-based method. Thus, our findings support the application of SDGSAT-1 data.

Empirical model for longwave and midwave radiometric and spatial characteristics of clouds for target detection considerations

The presence of clouds affects the detection of small airborne targets for infrared imaging. Clouds increase the signal of the background and create nonuniformity behind a desired target. This results in low and varying contrast. Clear sky conditions provide a low noise, uniform background that gives a better chance of detection. Understanding key variables of the clouds nonuniform structure allows for better detection and for accurate infrared search and track (IRST) models. Atmospheric modeling software, such as moderate resolution atmospheric transmission (MODTRAN), provides background path radiance in the emissive midwave infrared and longwave infrared bands. These modeled skies have been matched with measured skies in various conditions with low error. MODTRAN clouds, however, assume total cloud cover of uniform thickness and no varying transmission. MODTRAN clouds do not consider the spatially and radiometrically varying structures that make clouds unique. Studied spatial and radiometric characteristics of clouds are used in an empirical approach to predict cloud radiometric temperatures and structures with four simple equations. These cloud properties are measured at night to avoid solar contributions and focus on their emissive characteristics. The empirically modeled clouds are projections from measured or MODTRAN modeled clear skies. This method of modeling clouds allows for easy implementation of a nonclear sky background into IRST models. The range in which a target is first detected from its background can now be compared between clear and cloudy skies.

Towards operational atmospheric correction of airborne hyperspectral imaging spectroscopy: Algorithm evaluation, key parameter analysis, and machine learning emulators

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

Retrieval of Sea Surface Temperature From HY-1B COCTS

The Chinese Ocean Color and Temperature Scanner (COCTS) on board HY-1 series satellites has two thermal infrared channels with the spectrum range of 10.30-11.40 &#x03BC;m and 11.40-12.50 &#x03BC;m for sea surface temperature (SST) observations. To reprocess the Haiyang-1B (HY-1B) COCTS SST, the Bayesian cloud detection and optimal estimation (OE) SST retrieval were applied to COCTS data in this study. The Bayesian cloud detection algorithm that has been developed is based on the Bayes&#x2019; theorem and uses simulation of COCTS observations. The MODerate resolution atmospheric TRANsmission (MODTRAN) model was used for simulation of COCTS brightness temperatures. SSTs were retrieved from COCTS by OE from 2009 to 2011 in the northwest Pacific. Comparison of COCTS OE SST with in situ SST showed that the COCTS SSTs are cooler than buoy measurements by &#x2013;0.23 &#x00B0;C on average, and the standard deviation (SD) of differences was 0.51 &#x00B0;C. A large component of the mean difference is attributable to the cool skin effect at the ocean surface (typically &#x2013;0.15 to &#x2013;0.2 &#x00B0;C), the remainder being attributable to simulation and calibration biases. The mean difference of COCTS OE SST with matched skin temperatures from the Advanced Along Track Scanning Radiometer (AATSR) is closer to zero, being &#x2013;0.09 &#x00B0;C, with a SD of 0.49 &#x00B0;C. These validation results of COCTS OE SST demonstrate that Bayesian cloud detection and OE SST retrieval algorithm work well for improving COCTS SST accuracy, and show the potential of these methods to help develop SST products for operational HY-1 satellites, HY-1C and HY-1D.

Retrieval of Daytime Surface Upward Longwave Radiation Under All-Sky Conditions With Remote Sensing and Meteorological Reanalysis Data

Surface upward longwave radiation (SULR) is a key parameter that regulates surface radiation budget balance and matter-energy exchange. However, the state-of-the-art SULR retrieval methods based on remotely sensed data are only effective under clear skies, which mean that existing methods are unable to generate spatiotemporal continuous SULR product at regional or global scale. Herein, taking the advantage of long-pending abundant ground-based radiation observations, satellite products and meteorological reanalysis data, a data-driven random forest (RF) method is proposed to retrieve the instantaneous SULR under all-sky conditions. Based on spectral samples of different surface types and simulation results from the moderate resolution atmospheric transmission (MODTRAN), spectral transformation is carried out to transform SULR of various measured domains into the defined 4&#x007E;100 &#x03BC;m domain at first. SULR and surface downward shortwave radiation (SDSR) observations from seven stations of the Surface Radiation Budget Network (SURFRAD) and nine stations of the Baseline Surface Radiation Network (BSRN) are used in model&#x2019;s training and testing procedures, and the RF model achieves a high accuracy with the root-mean-square error (RMSE) of 10.45 W/m² on test set. In model evaluation, ground measurements from 14 stations of FLUXNET have been used, and the overall RMSE is 18.40 W/m². In the actual application process, SDSR is estimated by remotely sensed data of Meteosat Second Generation (MSG). The accuracy of RF model has been validated with the observations from five stations of BSRN in 2021, and RMSEs are 17.00, 10.94, 12.17, 27.89 and 12.54 W/m², respectively. Validation result shows that the data-driven method is capable of estimating SULR under all-sky conditions with a high accuracy. Finally, sensitivity analysis has been carried out, and the established RF model keeps robust even though there are great uncertainties among input parameters.

Vicarious Radiometric Calibration of the AHSI Instrument Onboard ZY1E on Dunhuang Radiometric Calibration Site

The Advanced Hyperspectral Imager (AHSI) is the second hyperspectral imager of China, which is also one of the most important payloads onboard the ZY1E satellite. In order to monitor the radiometric calibration status since its launch on 12 Sep. 2019, and thus provide supplementary on-board radiometric calibration, this paper conducts several vicarious radiometric calibration experiments for the ZY-1E AHSI. Five satellite observations over Dunhuang radiometric calibration site, one of the most relevant China Radiometric Calibration Sites (CRCS), were used. Surface reflectance, radiosonde data, aerosol optical depth (AOD) loading, and water vapor content were used to simulate the top of atmosphere (TOA) radiance at the entrance pupil of the satellite via MODerate resolution atmospheric TRANsmission (MODTRAN). Our results show that the vicarious radiometric calibration coefficients are relatively constant with the official coefficients: the mean relative differences are 5.55%, 5.64%, 7.02%, 5.07%, and 5.30% on 11 Jan, 17 Jan, 12 Feb, 07 May, and 23 Nov, 2021, respectively. The vicarious radiometric calibration coefficients were found in good agreement with the official ones according to the validation analysis based on different surface types, i.e., water and vegetation. Uncertainties of vicarious radiometric calibration due to AOD assumptions, AOD measurements, water vapor measurements, radiosonde data measurements, relative spectral response (RSR) shifts and surface reflectance measurements are also discussed in detail. The obtained results show that the ZY-1E AHSI exhibits good on-orbit radiometric status.

Terra and Aqua MODIS Thermal Emissive Bands Calibration and RVS Stability Assessments Using an In Situ Ocean Target

Moderate Resolution Imaging Spectroradiometer (MODIS), whose openly public data have been used for over two decades to monitor and address global issues, has 16 thermal emissive bands (TEBs) with central wavelengths that range from 3.7 to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$14.4~\mu \text{m}$ </tex-math></inline-formula> and are calibrated on-orbit using observations from its on-board blackbody. To maintain MODIS’ rich, well-calibrated archive of multispectral imagery and data, Earth targets are regularly used to track its long-term stability as well as the consistency between the two sensors onboard the Terra and Aqua satellites. Moreover, these scenes can be used to compare MODIS Earth view data over the complete scan-angle range and evaluate the on-orbit performance of the TEBs response-versus-scan-angle (RVS) over the mission lifetime. This article focuses on evaluating the MODIS TEBs Collection (C6.1) radiometric calibration stability for both instruments using an <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> ocean target as reference [hereafter referred to as <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> sea surface temperature (SST)]. Furthermore, it will assess the calibration consistency between the MODIS sensors. Finally, it will analyze the on-orbit RVS stability for Terra and Aqua MODIS. Only cloud-free, nighttime MODIS TEB retrievals were used for the study. A normalization methodology is applied to standardize the MODIS data to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> SST. In addition, spectral corrections were derived between some of the Terra and Aqua MODIS TEBs by using a combination of the MODIS Atmospheric Profile product and MODerate resolution atmospheric TRANsmission (MODTRAN) simulations. Results indicate that most MODIS TEBs exhibit mission-long trends of ±0.50 K—with Terra band 30 presenting the largest downward drift due to residual electronic crosstalk effects. Moreover, the calibration consistency analysis over a warm ocean target demonstrated that the average Terra-to-Aqua MODIS bias for most bands is well within ±0.50 K (bands 27 and 30 show the largest—electronic crosstalk-related—biases). Finally, the MODIS TEBs RVS trends display changes of ±0.50 K (except for bands 25 and 27 at the end-of-scan angles) for both instruments. Overall, the MODIS TEBs remain well-calibrated and their RVSs aptly characterized.

Land Surface Temperature Retrieval Using High-Resolution Vertical Profiles Simulated by WRF Model

This work gives a first insight into the potential of the Weather Research and Forecasting (WRF) model to provide high-resolution vertical profiles for land surface temperature (LST) retrieval from thermal infrared (TIR) remote sensing. WRF numerical simulations were conducted to downscale NCEP Climate Forecast System Version 2 (CFSv2) reanalysis profiles, using two nested grids with horizontal resolutions of 12 km (G12) and 3 km (G03). We investigated the utility of these profiles for the atmospheric correction of TIR data and LST estimation, using the moderate resolution atmospheric transmission (MODTRAN) model and the Landsat 8 TIRS10 band. The accuracy evaluation was performed using 27 clear-sky cases over a radiosonde station in Southern Brazil. We included in the comparative analysis NASA’s Atmospheric Correction Parameter Calculator (ACPC) web-tool and profiles obtained directly from the NCEP CFSv2 reanalysis. The atmospheric parameters from ACPC, followed by those from CFSv2, were in better agreement with parameters calculated using in situ radiosondes. When applied into the radiative transfer equation (RTE) to retrieve LST, the best results (RMSE) were, in descending order: CFSv2 (0.55 K), ACPC (0.56 K), WRF G12 (0.79 K), and WRF G03 (0.82 K). Our findings suggest that there is no special need to increase the horizontal resolution of reanalysis profiles aiming at RTE-based LST retrieval. However, the WRF results were still satisfactory and promising, encouraging further assessments. We endorse the use of the well-known ACPC and recommend the NCEP CFSv2 profiles for TIR atmospheric correction and LST single-channel retrieval.

Vicarious Calibration for the AHSI Instrument of Gaofen-5 With Reference to the CRCS Dunhuang Test Site

The visible-shortwave infrared Advanced Hyperspectral Imager (AHSI) is a payload onboard the Gaofen-5 satellite, which is China’s first hyperspectral satellite and is part of the Chinese High-Resolution Earth Observation System. As a supplement to the onboard radiometric calibration of the AHSI instrument, vicarious calibration is also required, which is independent of the instrument-based calibration. In this article, a reflectance-based vicarious calibration approach is presented, which takes surface reflectance data, aerosol data, and atmospheric water vapor data into account. The Dunhuang test site, which is one of the China Radiometric Calibration Sites (CRCS) for the vicarious calibration of spaceborne sensors, possesses stable, uniform, and measurable surface objects, so it was chosen as the radiation source to replace the laboratory and onboard calibrators. A Spectra Vista Corporation (SVC) spectral radiometer and a CE318 sun photometer were utilized for the measurement of the surface reflectance and the condition of the aerosol, respectively. The radiance at the entrance pupil at the top of atmosphere was then obtained through the MODerate resolution atmospheric TRANsmission (MODTRAN) atmospheric transmission model. The surface reflectance was obtained using the Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) atmospheric model for validation. The results show that, with regard to the calibration coefficients, the calibrated AHSI instrument presents a stable radiometric performance among different land-cover types. The ratios on all the bands are between 0.8 and 1.2 and are consistent with the reflectance data from the Dunhuang test site. The <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">${R} ^{{2}}$ </tex-math></inline-formula> values are all greater than 0.95 and the spectral angle is all less than 2°. The standard deviations of the ratios are less than 3% for each chosen band, which proves that the calibrated data have a high consistency with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">in situ</i> measurements. When compared with Landsat 8 and Sentinel-2, the mean errors of the surface reflectance are all under 0.06, which further demonstrates that the calibrated reflectance has a high accuracy.

Estimation of Land Surface Temperature in an Agricultural Region of Bangladesh from Landsat 8: Intercomparison of Four Algorithms.

The presence of two thermal bands in Landsat 8 brings the opportunity to use either one or both of these bands to retrieve Land Surface Temperature (LST). In order to compare the performances of existing algorithms, we used four methods to retrieve LST from Landsat 8 and made an intercomparison among them. Apart from the direct use of the Radiative Transfer Equation (RTE), Single-Channel Algorithm and two Split-Window Algorithms were used taking an agricultural region in Bangladesh as the study area. The LSTs retrieved in the four methods were validated in two ways: first, an indirect validation against reference LST, which was obtained in the Atmospheric and Topographic CORection (ATCOR) software module; second, cross-validation with Terra MODerate Resolution Imaging Spectroradiometer (MODIS) daily LSTs that were obtained from the Application for Extracting and Exploring Analysis Ready Samples (AEEARS) online tool. Due to the absence of LST-monitoring radiosounding instruments surrounding the study area, in situ LSTs were not available; hence, validation of satellite retrieved LSTs against in situ LSTs was not performed. The atmospheric parameters necessary for the RTE-based method, as well as for other methods, were calculated from the National Centers for Environmental Prediction (NCEP) database using an online atmospheric correction calculator with MODerate resolution atmospheric TRANsmission (MODTRAN) codes. Root-mean-squared-error (RMSE) against reference LST, as well as mean bias error against both reference and MODIS daily LSTs, was used to interpret the relative accuracy of LST results. All four methods were found to result in acceptable LST products, leaving atmospheric water vapor content (w) as the important determinant for the precision result. Considering a set of several Landsat 8 images of different dates, Jiménez-Muñoz et al.’s (2014) Split-Window algorithm was found to result in the lowest mean RMSE of . Du et al.’s (2015) Split-Window algorithm resulted in mean RMSE of . The RTE-based direct method and the Single-Channel algorithm provided the mean RMSE of and , respectively. For Du et al.’s algorithm, the w range of to g cm−2 was considered, whereas for the other three methods, w values as retrieved from the NCEP database were considered for corresponding images. Land surface emissivity was retrieved through the Normalized Difference Vegetation Index (NDVI)-threshold method. This intercomparison study provides an LST retrieval methodology for Landsat 8 that involves four algorithms. It proves that (i) better LST results can be obtained using both thermal bands of Landsat 8; (ii) the NCEP database can be used to determine atmospheric parameters using the online calculator; (iii) MODIS daily LSTs from AEEARS can be used efficiently in cross-validation and intercomparison of Landsat 8 LST algorithms; and (iv) when in situ LST data are not available, the ATCOR-derived LSTs can be used for indirect verification and intercomparison of Landsat 8 LST algorithms.

An Atmospheric Correction Using High Resolution Numerical Weather Prediction Models for Satellite-Borne Single-Channel Mid-Wavelength and Thermal Infrared Imaging Sensors

This paper presents a single-channel atmospheric correction method for remotely sensed infrared (wavelength of 3–15 μm) images with various observation angles. The method is based on basic radiative transfer equations with a simple absorption-focused regression model to calculate the optical thickness of each atmospheric layer. By employing a simple regression model and re-organization of atmospheric profiles by considering viewing geometry, the proposed method conducts atmospheric correction at every pixel of a numerical weather prediction model in a single step calculation. The Visible Infrared Imaging Radiometer Suite (VIIRS) imaging channel (375 m) I4 (3.55~3.93 μm) and I5 (10.50~12.40 μm) bands were used as mid-wavelength and thermal infrared images to demonstrate the effectiveness of the proposed single-channel atmospheric correction method. The estimated sea surface temperatures (SSTs) obtained by the proposed method with high resolution numerical weather prediction models were compared with sea-truth temperature data from ocean buoys, multichannel-based SST products from VIIRS/MODIS, and results from MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5), for validation. High resolution (1.5 km and 12 km) numerical weather prediction (NWP) models distributed by the Korea Meteorological Administration (KMA) were employed as input atmospheric data. Nighttime SST estimations with the I4 band showed a root mean squared error (RMSE) of 0.95 °C, similar to that of the VIIRS product (RMSE: 0.92 °C) and lower than that of the MODIS product (RMSE: 1.74 °C), while estimations with the I5 band showed an RMSE of 1.81 °C. RMSEs from MODTRAN simulations were similar (within 0.2 °C) to those of the proposed method (I4: 0.81 °C, I5: 1.67 °C). These results demonstrated the competitive performance of a regression-based method using high-resolution numerical weather prediction (NWP) models for atmospheric correction of single-channel infrared imaging sensors.

The Solar-Induced Chlorophyll Fluorescence Imaging Spectrometer (SIFIS) Onboard the First Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1): Specifications and Prospects.

The global monitoring of solar-induced chlorophyll fluorescence (SIF) using satellite-based observations provides a new way of monitoring the status of terrestrial vegetation photosynthesis on a global scale. Several global SIF products that make use of atmospheric satellite data have been successfully developed in recent decades. The Terrestrial Ecosystem Carbon Inventory Satellite (TECIS-1), the first Chinese terrestrial ecosystem carbon inventory satellite, which is due to be launched in 2021, will carry an imaging spectrometer specifically designed for SIF monitoring. Here, we use an extensive set of simulated data derived from the MODerate resolution atmospheric TRANsmission 5 (MODTRAN 5) and Soil Canopy Observation Photosynthesis and Energy (SCOPE) models to evaluate and optimize the specifications of the SIF Imaging Spectrometer (SIFIS) onboard TECIS for accurate SIF retrievals. The wide spectral range of 670−780 nm was recommended to obtain the SIF at both the red and far-red bands. The results illustrate that the combination of a spectral resolution (SR) of 0.1 nm and a signal-to-noise ratio (SNR) of 127 performs better than an SR of 0.3 nm and SNR of 322 or an SR of 0.5 nm and SNR of 472 nm. The resulting SIF retrievals have a root-mean-squared (RMS) diff* value of 0.15 mW m−2 sr−1 nm−1 at the far-red band and 0.43 mW m−2 sr−1 nm−1 at the red band. This compares with 0.20 and 0.26 mW m−2 sr−1 nm−1 at the far-red band and 0.62 and 1.30 mW m−2 sr−1 nm−1 at the red band for the other two configurations described above. Given an SR of 0.3 nm, the increase in the SNR can also improve the SIF retrieval at both bands. If the SNR is improved to 450, the RMS diff* will be 0.17 mW m−2 sr−1 nm−1 at the far-red band and 0.47 mW m−2 sr−1 nm−1 at the red band. Therefore, the SIFIS onboard TECIS-1 will provide another set of observations dedicated to monitoring SIF at the global scale, which will benefit investigations of terrestrial vegetation photosynthesis from space.

A split-window method to retrieving sea surface temperature from landsat 8 thermal infrared remote sensing data in offshore waters

Sea surface temperature (SST) is an important parameter used to describe the air-sea interaction and the state of marine structures. Atmospheric water vapor has a significant attenuation effect on thermal infrared information, which will reduce the accuracy of SST inversion. However, the effect of atmospheric water vapor on the inversion accuracy is not considered carefully in the existing SST inversion algorithm. In this study, a new method is proposed for retrieving SST from Landsat 8 Thermal Infrared Remote Sensing (TIRS) data based on the variation of atmospheric water vapor content (wvc). First, simulating atmospheric conditions by using moderate resolution atmospheric transmission (MODTRAN) based on atmospheric profiles data (air temperature and pressure). Second, calculating the bright temperature based on the radiation transfer equation and Planck's law. Then, constructing the SST retrieval model of thermal infrared remote sensing based on wvc. Finally, evaluating the accuracy of the proposed model by using simulation data. The root mean square errors (RMSEs) are within 0.5 K, indicating that the accuracy of the model is good in theory. In addition, taking the Zhoushan sea area as the research area, SST is retrieved by Landsat 8 TIRS data. The accuracy of inversion results is evaluated by advanced very high-resolution radiometer (AVHRR) SST products. The bias and RMSE based on the AVHRR SST products are within 1 K and 2 K, respectively. The results show that the accurate SST with high spatial resolution was successfully obtained by using the method. The study is of great significance to the acquisition of marine structural parameters, the exploitation of marine resources, and the monitoring of marine disasters.