MODerate Resolution Atmospheric TRANsmission Research Articles

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 <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 <0.0075 (7.13%) from OLI retrievals and <0.0084 (7.47%) from MSI retrievals for calibration coefficients from the reflectance-based method; <0.0089 (7.57%) from OLI retrievals and <0.0111 (8.65%) from MSI retrievals for the irradiance-based method; and <0.0082 (7.33%) from OLI retrievals and <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 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.

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.

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.

First Comparisons of Surface Temperature Estimations between ECOSTRESS, ASTER and Landsat 8 over Italian Volcanic and Geothermal Areas

The ECO System Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) is a new space mission developed by NASA-JPL which launched on July 2018. It includes a multispectral thermal infrared radiometer that measures the radiances in five spectral channels between 8 and 12 μm. The primary goal of the mission is to study how plants use water by measuring their temperature from the vantage point of the International Space Station. However, as ECOSTRESS retrieves the surface temperature, the data can be used to measure other heat-related phenomena, such as heat waves, volcanic eruptions, and fires. We have cross-compared the temperatures obtained by ECOSTRESS, the Advanced Spaceborne Thermal Emission and Reflectance radiometer (ASTER) and the Landsat 8 Thermal InfraRed Sensor (TIRS) in areas where thermal anomalies are present. The use of ECOSTRESS for temperature analysis as well as ASTER and Landsat 8 offers the possibility of expanding the availability of satellite thermal data with very high spatial and temporal resolutions. The Temperature and Emissivity Separation (TES) algorithm was used to retrieve surface temperatures from the ECOSTRESS and ASTER data, while the single-channel algorithm was used to retrieve surface temperatures from the Landsat 8 data. Atmospheric effects in the data were removed using the moderate resolution atmospheric transmission (MODTRAN) radiative transfer model driven with vertical atmospheric profiles collected by the University of Wyoming. The test sites used in this study are the active Italian volcanoes and the Parco delle Biancane geothermal area (Italy). In order to test and quantify the difference between the temperatures retrieved by the three spaceborne sensors, a set of coincident imagery was acquired and used for cross comparison. Preliminary statistical analyses show a very good agreement in terms of correlation and mean values among sensors over the test areas.

Improvement of Split-Window Algorithm for Land Surface Temperature Retrieval from Sentinel-3A SLSTR Data Over Barren Surfaces Using ASTER GED Product

Land surface temperature (LST) is a key variable influencing the energy balance between the land surface and the atmosphere. In this work, a split-window algorithm was used to calculate LST from Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) thermal infrared data. The National Centers for Environmental Prediction (NCEP) reanalysis atmospheric profiles combined with the radiation transport model MODerate resolution atmospheric TRANsmission version 5.2 (MODTRAN 5.2) were utilized to obtain atmospheric water vapor content (WVC). The ASTER Global Emissivity Database Version 3 (ASTER GED v3) product was utilized to estimate surface emissivity in order to improve the accuracy of LST estimation over barren surfaces. Using a simulation database, the coefficients of the algorithm were fitted and the performance of the algorithm was evaluated. The root-mean-square error (RMSE) values of the differences between the estimated LST and the actual LST of the MODTRAN radiative transfer simulation at each WVC subrange of 0–6.5 g/cm2 were less than 1.0 K. To validate the retrieval accuracy, ground-based LST measurements were collected at two relatively homogeneous desert study sites in Dalad Banner and Wuhai, Inner Mongolia, China. The bias between the retrieved LST and the in situ LST was about 0.2 K and the RMSE was about 1.3 K at the Dalad Banner site, whereas they were approximately -0.4 and 1.0 K at the Wuhai site. As a reference, the retrieved LST was compared with the operational SLSTR LST product in this study. The bias between the SLSTR LST product and the in situ LST was approximately 1 K and the RMSE was approximately 2 K at the Dalad Banner site, whereas they were approximately 1.1 and 1.4 K at the Wuhai site. The results demonstrate that the split-window algorithm combined with improved emissivity estimation based on the ASTER GED product can distinctly obtain better accuracy of LST over barren surfaces.

Quantification of the Adjacency Effect on Measurements in the Thermal Infrared Region

Sensor-observed energy from adjacent pixels, known as the adjacency effect, influences land surface reflectivity retrieval accuracy in optical remote sensing. As the spatial resolution of thermal infrared (TIR) images increases, the adjacency effect may influence land surface temperature (LST) retrieval accuracy in TIR remote sensing. However, to our knowledge, few studies have focused on quantifying this adjacency effect on TIR measurements. In this study, a forward adjacency effect radiative transfer model (FAERTM) was developed to quantify the adjacency effect on high-spatial-resolution TIR measurements. The model was verified to be in good agreement with moderate resolution atmospheric transmission (MODTRAN) code, with a discrepancy 3 K in some cases. These findings indicate that the adjacency effect should be considered when retrieving LSTs from TIR measurements, at least in some specific conditions. The proposed FAERTM provides a useful model for quantifying and addressing the adjacency effect on TIR measurements.

Net Surface Shortwave Radiation Retrieval Using Random Forest Method With MODIS/AQUA Data

The net surface shortwave radiation (NSSR) at the Earth's surface drives evapotranspiration, photosynthesis, and other physical and biological processes. The primary objective of this study is to estimate NSSR in all sky conditions by using narrowband data of the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument onboard the AQUA satellite. The random forest (RF) machine learning method for retrieving NSSR was developed with MODerate resolution atmospheric TRANsmission model (MODTRAN 5) simulated data. The bias, root mean square error (RMSE), and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> for the training dataset of the model are 0.04 W m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> , 2.03 W m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> , and 1.00, respectively; for testing data, these values are 0.53 W m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> , 5.50 W m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> , and 1.00, respectively. Note that the proposed method is better than the traditional method (RMSE 7.29 W m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> ) with MODTRAN data, and the sky conditions (clear and cloudy) do not need to be distinguished in the RF method. Seven in situ measurements of the Surface Radiation (SURFRAD) observation network were used to validate the estimated NSSR with MODIS/AQUA data using the proposed RF method, and the bias, RMSE, and R <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> of the comparison are -8.4 W m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> , 76.8 W m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> , and 0.91, respectively. Approximately 70% of the absolute difference of all the samples is below 50 W m <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">-2</sup> . Considering its concise process and relatively improved accuracy, both in regard to model development and validation, it can be concluded that the retrieval of NSSR with RF will be an efficient and feasible method in the future.

Flexible atmospheric compensation technique (FACT): a 6S based atmospheric correction scheme for remote sensing data

Atmospheric correction is an important pre-processing step in various spatio-temporal and multi-sensor remote sensing data analyzes based applications. Absolute atmospheric corrections are carried out using physical based models, generally known as radiative transfer (RT) codes such as MODerate resolution atmospheric TRANsmission (MODTRAN), Second Simulation of the Satellite Signal in the Solar Spectrum (6S) etc. Most of the available atmospheric correction schemes are commercially off-the-shelf and use patented RT codes. The objective of the present work is to develop an open-end atmospheric correction scheme, named as Flexible Atmospheric Compensation Technique (FACT), based on open source 6S RT code. The proposed FACT scheme utilizes look-up architecture for simulating the outputs of 6S RT code for various input parameters’ combination. Input parameters such as initial visibility, columnar water vapour are estimated using the dark object and the Continuum Interpolated Band Ratio (CIBR) methods respectively. The proposed FACT scheme has been evaluated exhaustively using spatio-spectral statistical error measures such as Spatial-Root Mean Square Error (S-RMSE), Spatial-Mean Absolute Error (S-MAE) and spectral-RMSE by comparing the performance with widely used Fast Line-of-sight Atmospheric Analysis of Hypercubes (FLAASH) and the customized NASA JPL’s atmospheric correction scheme. Datasets from hyperspectral (AVIRIS-NG and Hyperion) and multispectral (LANDSAT-8 OLI and WorldView-3) remote sensors were chosen for comparative analysis of the developed atmospheric correction scheme against other atmospheric correction schemes. Results confirm that the proposed FACT scheme offers accuracy of about 95% for hyperspectral imaging sensors and close to 98% for multispectral imaging sensors when compared with FLAASH. Despite marginal disagreements for certain land cover features at the water vapour absorbing spectral regions, we find the proposed FACT scheme a plausible option for carrying out absolute atmospheric correction of various operational remote imaging sensors.