Atmospheric Radiative Transfer Model Research Articles

Retrieval of hourly aerosol single scattering albedo over land using geostationary satellite data

The single scattering albedo (SSA) of aerosol particles is one of the key variables that determine aerosol radiative forcing. Herein, an Algorithm for the retrieval of Single scattering albedo over Land (ASL) is proposed for application to full-disk data from the advanced Himawari imager (AHI) sensor flying on board the Himawari-8 satellite. In this algorithm, an atmospheric radiative transfer model known as the USM (the top of the atmosphere reflectance as the sum of Un-scattered, Single-scattered, and Multiple-scattered components) is used to calculate the SSA instead of predetermining the aerosol model; the USM is constrained by the surface bidirectional reflectance distribution function shape and aerosol optical depth (AOD) in the retrieval process. Combining two consecutive observations and a 2 * 2 pixel window, the optimal estimation algorithm is adopted to obtain the optimal solution for the aerosol SSA. These SSA results are evaluated by comparing with aerosol robotic network (AERONET) data. Linear regression shows that SSAASL = 0.60*SSSAERONET + 0.38, with a correlation coefficient (0.7284), mean absolute error (0.0319), mean bias error (0.00324), root mean square error (0.0427), and ~80.11% of the ASL SSA data within an uncertainty of ±0.05 of the AERONET data. A comparison of the ASL SSA products with collocated Himawari-8 SSA products (Version 03, officially released by the Japan Meteorological Agency (JMA), referred to herein as JMA SSA) shows that the accuracy of the ASL SSA is better than that of the JMA SSA products. For the SSA retrieval in large AODs (>0.4), the validation metrics vs. AERONET data are better.

Land Surface Temperature Retrieval From GF5-01A Based on Split- Window Algorithm

Abstract. Land Surface temperature (LST) is a core parameter in the energy exchange between the surface and the atmosphere, and the use of thermal infrared remote sensing can realize the wide-range, fast, and accurate acquisition of surface temperature. GF5-01A is an important part and the final satellite of the major special project on high-resolution Earth observation system which is equipped with a wide-area thermal infrared imager with a resolution of 100 meters and a width of 1,500 kilometers. In this paper, based on the GF5-01A WTI spectral response function, combined with the TIGR2000 atmospheric profiles data and the ASTER spectral library, the data simulation was carried out by using the atmospheric radiative transfer model MODTRAN 5.2, and then constructed the split-window algorithm. Then, the method proposed in this paper was validated and evaluated using Landsat 8/9 temperature products and measured surface temperature data from SURFRAD sites acquired on the same day. The results show that the RMSE between the GF5-01A retrieved LST and the Landsat8/9 retrieved LST is between 1.27–2.24K, and the Bias is between −2.08–1.12K. The RMSE is between 0.68–2.64K and the bias is between −0.68–1.49K compared to the measured surface temperature. The split-window algorithm of GF5-01A proposed in this paper can meet the requirements of thermal infrared remote sensing monitoring and has enormous potential and value.

The investigation of retrieval method for aerosol and water vapor profiles based on MAX-DOAS technology

Simultaneously obtaining the vertical distribution and profile of aerosol and water vapor is crucial for in-depth understanding of the Earth's radiation balance, regional water cycle, and the causes of frequent haze weather in China. This study developed a system and inversion method based on multi-axis differential optical absorption spectroscopy (MAX-DOAS) to simultaneously retrieve tropospheric aerosol and water vapor profiles. The absorption of dioxy (O4) was obtained by multi-elevation measurement using the developed ground-based MAX-DOAS system. By minimizing a cost function combining an atmospheric radiative transfer model, aerosol extinction profiles were first inverted, followed by water vapor profiles. Optimized parameter selection in the look-up table method reduced reliance on prior profile information, enhancing pollutant distribution retrieval accuracy. In the monitoring period in Huaibei region, aerosols were mainly below 1.5 km, while water vapor concentrations decreased with altitude. Comparisons of water vapor vertical column densities (VCDs) retrieved through the look-up table method with the European Centre for Medium-Range Weather Forecasts(ECMWF) ERA5 model and geometric approximation showed good agreement, with correlation coefficients of 0.93 and 0.98, respectively. To comprehend water vapor sources at various vertical layers, a 24-h backward trajectory clustering analysis was conducted using the HYSPLIT model based on observed wind fields. Findings revealed that at 500 m altitude, water vapor primarily emanated from the southeast, whereas at 1 km and 2 km altitudes, it predominantly originated from the southwest. The study advances simultaneous tropospheric aerosol and water vapor profile retrieval, providing reliable technical support for the investigation of regional pollution.

Surface and Atmospheric Heating Responses to Spectrally Resolved Albedo of Frozen and Liquid Water Surfaces

AbstractMultiple Earth system models (ESMs) discretize surface albedo into two semi‐broadbands comprising the UV/visible and near‐infrared (NIR) wavelengths. Here, we use an offline single‐column radiative transfer model to investigate the radiative effects of spectrally resolving the surface albedo. We use the Snow, Ice, and Aerosol Radiative model, extended to simulate liquid water, to calculate snow, ice, and liquid water albedo. We flux‐weight the hyperspectral albedo into the coarser spectral bands used by the atmospheric shortwave radiative transfer model. We establish representative atmospheric profiles for the three surface types and compare their shortwave fluxes and atmospheric warming rates with the spectrally resolved albedo to those calculated with the semi‐broadband approximation. Spectrally resolved surface albedo over snow and ice reduces atmospheric warming by darkening the albedo of NIR bands, correcting the too‐strong surface absorption in visible bands, and too‐weak surface absorption in shortwave infrared bands caused by the semi‐broadband approximation. We explore the effects on surface and atmospheric warming rates of varying solar zenith angle, cloud cover, relative humidity, and snow grain/air bubble radii. The semi‐broadband albedo biases can exceed 10% and 2% for the surface and atmospheric net flux respectively, being particularly strong under conditions which alter the distributions of surface insolation (i.e., cloud cover or increased atmospheric water vapor). These results show that transmitting a higher resolution spectral radiation field between the atmosphere and surface reduces biases in surface absorption and atmospheric heating present in ESMs that currently use the semi‐broadband approximation.

An improved BRDF hotspot model and its use in VLIDORT for studying the impact of atmospheric scattering on hotspot directional signatures in the atmosphere

Abstract. The term “hotspot” refers to the sharp increase in the reflectance occurring when incident (solar) and reflected (viewing) directions almost coincide in the backscatter direction. The accurate simulation of hotspot directional signatures is important for many remote sensing applications. The RossThick–LiSparse–Reciprocal (RTLSR) bidirectional reflectance distribution function (BRDF) model is widely used in radiative transfer simulations, and the hotspot model mostly used is from Maignan–Bréon, but it typically requires large values of numerical quadrature and Fourier expansion terms in order to represent the hotspot accurately for its use coupled with atmospheric radiative transfer modeling (RTM). In this paper, we have developed a modified version based on the Maignan–Bréon's hotspot BRDF model that converges much faster numerically, making it more practical for use in the RTMs that require Fourier expansion of BRDF to simulate the top-of-atmosphere (TOA) hotspot signatures, such as in the RTM models using the Doubling–Adding or discrete ordinate method. Using the vector linearized discrete ordinate radiative transfer model (VLIDORT), we found that reasonable TOA–hotspot accuracy can be obtained with just 23 Fourier terms for clear atmosphere and 63 Fourier terms for atmosphere with aerosol scattering. In order to study the impact of molecular and aerosol scattering on hotspot signatures, we carried out a number of hotspot signature simulations with VLIDORT. We confirmed that (1) atmospheric molecule scattering and the existence of aerosol tend to smooth out the hotspot signature at the TOA and that (2) the hotspot signature at the TOA in the near-infrared is larger than in the visible, and its impact by surface reflectance is more significant. As the hotspot amplitude at the TOA with aerosol scattering included is smaller than that with molecular scattering only, the amplitude of hotspot signature at the surface is likely underestimated in the previous analysis based on the POLDER measurements, where the atmospheric correction was based on a single-scatter Rayleigh-only calculation. This modified model can calculate the amplitude of the hotspot accurately, and, as it agrees very well with the original RossThick model away from the hotspot region, this model can be simply used in conditions with and without hotspots. However, there are some differences in this modified model compared to the original Maignan–Bréon model for the scattering angles close to the hotspot point; thus, it may not be appropriate for those who need an exact representation of the hotspot angular signature.

Interactions between aerosols and surface ozone in arid and semi-arid regions of China.

Atmospheric aerosols affect surface ozone concentrations by influencing radiation, but the mechanism and dominant factors are unclear. Therefore, this paper analyses the changes in aerosol-radiative-surface ozone in China's arid and semi-arid regions with the help of the Atmospheric Radiative Transfer (SBDART) model. The results suggest that Aerosol Optical Depth (AOD) and coarse Particulate Matter (PM10) have the same trend, with high values in spring and winter and low values in summer and autumn. Surface ozone is high in spring and summer and low in autumn and winter. Surface ozone is higher in spring and summer and lower in autumn and winter. In winter, mainly secondary pollutants are dominated by high pollution levels. In the rest of the seasons, a mixture of dust, motor vehicle exhaust, and soot is dominated by low pollution levels. Surface ozone is positively correlated with fine particles and negatively correlated with coarse particles. Temperature is positively correlated with surface ozone in all seasons and negatively correlated with PM10 in summer, autumn, and winter. Precipitation negatively correlates with PM10 each season and surface ozone in winter and spring. Analysis of surface ozone and PM10 sources in the more polluted city of Hohhot based on the back-line trajectory model showed that airflow trajectories mainly transported surface ozone and PM10 pollution from northwestern Inner Mongolia and western Mongolia. During dusty solid weather, the decrease in radiation reaching the Earth's surface and the cooling effect of aerosols lead to lower temperatures, which slows down the rate of chemical reactions of precursors of surface ozone, resulting in lower ozone concentrations at the surface. This study can provide a theoretical reference for aerosol and surface ozone control in arid and semi-arid areas of China.

Radiative effect of atmospheric Brown clouds over the Indo-Gangetic plain

Atmospheric brown clouds (ABCs) are widespread hazy accumulation of particles and gases dominant with absorbing nature impacting the air quality, hydrology and climate. The Indo-Gangetic plain (IGP) being an ABCs hotspots is prone to extreme pollution episodes due to anthropogenic activities. However, there is a scarcity of studies that specifically compare episodes of absorbing aerosols (ABCs) and comparatively non-absorbing aerosols (non-ABCs) along with their respective radiative impacts over the IGP. Here, we have used ground-based remote-sensors’ retrieved datasets from AErosol RObotic NETwork (AERONET) to study spatio-temporal variation of ABCs’ frequency and associated radiative properties across the IGP region during 2001–2019. The shortwave aerosol radiative effect (ARE) during ABC and non-ABC episodes is estimated using Santa Barbara DISORT Atmospheric Radiative Transfer (SBDART) model. Our results show significant spatial variability in the frequency of ABC episodes across the IGP stations with ∼17 ± 5%, ∼19 ± 11% and ∼18 ± 13% average occurrence during winter, pre-monsoon and post-monsoon seasons, respectively. ARE estimations show enhanced surface dimming (ARESRF) and atmospheric warming (AREATM) during ABC episodes as compared to non-ABC episodes. A general increasing trend for ARE is observed as we move from west to east across the IGP due to the meteorological and geographical variations in the region. Our results show day-time averaged ARESRF varying between −142.46 ± 25.13 and −56.66 ± 6.88 W/m2 while AREATM ranging from 58.07 ± 2.58 to 132.69 ± 7.43 W/m2 for ABC episodes. Though ABC episodes show lower aerosol loading as compared to non-ABC episodes in our study, we found higher atmospheric heating during ABC episodes mainly due to distinct chemical composition (lower single scattering albedo) of absorbing aerosols. The upper bound of the difference between atmospheric heating rate during ABC and non-ABC episodes is found to be 0.48 ± 0.27 K/day.

Applicability Analysis of Three Atmospheric Radiative Transfer Models in Nighttime

The relatively stable lunar illumination may be used to realize radiometric calibration under low light. However, there is still an insufficient understanding of the accuracy of models and the influence of parameters when conducting research on low-light radiometric calibration. Therefore, this study explores the applicability of three atmospheric radiative transfer models under different nighttime conditions. The simulation accuracies of three nighttime atmospheric radiative transfer models (Night-SCIATRAN, Night-MODTRAN, and Night-6SV) were evaluated using the visible-infrared imaging radiometer suite day/night band (VIIRS/DNB) data. The results indicate that Night-MODTRAN has the highest simulation accuracy under DNB. The consistency between simulated top-of-atmosphere (TOA) radiance and DNB radiance is approximately 3.1%, and uncertainty is 2.5%. This study used Night-MODTRAN for parameter sensitivity analysis. The results indicate that for the lunar phase angle, aerosol optical depth, surface reflectance, lunar zenith angle, satellite zenith angle, and relative azimuth angle, the average change rates are 68%, 100%, 2561%, 75%, 20%, and 0%. This paper can help better understand the performance of models under different atmospheric and geographical conditions, as well as whether existing models can simulate the complex processes of atmospheric radiation.

Prediction of Atmospheric Carbon Dioxide Radiative Transfer Model based on Machine Learning

Global warming is caused by the increasing amount of greenhouse gases in the atmosphere. The Kyoto Protocol to the United Nations Framework Convention on Climate Change defines carbon dioxide (CO), methane (CH:), nitrous oxide (N0) fluorocarbons (HFC), holocarbon (PFC), and sulfur hexafluoride (SF.) Six gases are divided into the main greenhouse gases, of which CO, the largest proportion of emissions, is an important man-made greenhouse gas." As the main gas affecting the global greenhouse effect (75%), the amount of CO in the atmosphere has increased from 315 ppm in 1958 to 417 ppm in 2022, and global annual CO emissions have increased from 27 Pg to 49 Pg% over the past 40 years. Therefore, under the background of deep reinforcement learning, it is a problem that all countries pay more attention to predict the concentration emission of carbon dioxide to cope with the severe situation of climate warming. In order to obtain the radiative forcing value under the influence of carbon dioxide concentration, a simplified net radiative flux model is established. Based on the radiative transfer equation, this model can calculate the net radiative flux and radiative forcing of each layer of the atmosphere due to changes in carbon dioxide concentration. The results are compared with the RRTMG-LW long-wave radiative transfer model of American Center for Atmospheric and Environmental Research (AER) under the same factor, and the error is less than 1%.

Non-Lambertian snow surface reflection models for simulated top-of-the-atmosphere radiances in the NIR and SWIR wavelengths

Accurately modeling snow reflectance is one way to improve satellite observations in the high-latitude regions. Snow surfaces are known to be challenging for atmospheric retrievals in the short-wave infrared (SWIR) wavelength regime due to their low reflectance. For example, current algorithms for satellite-based remote sensing of atmospheric carbon dioxide (CO2) do not take into account the unique reflective properties of snow surfaces. In this paper, we present a measurement-based snow surface reflectance model in the near-infrared (NIR; 755–775 nm) and SWIR (1590–1620 nm, 2040–2080 nm) bands used in remote sensing of atmospheric CO2. We study snow reflectance in detail using a novel atmospheric radiative transfer model (RTM) software and a measurement-based model for snow bi-directional reflectance distribution function (BRDF) to identify how the observations could be optimized in regards of observation geometry and wavelengths. The novel simulation software for NIR-SWIR atmospheric radiative transfer, Raysca, is presented and validated. Top-of-the-atmosphere radiance simulations show that forward-viewing geometries over snow-covered surfaces yield higher radiances than the traditional nadir-viewing geometries, which could indicate the preferability of forward-viewing observation modes in the retrieval of atmospheric CO2. Similarly, atmospheric observations in the 1.6 μm CO2 absorption band might be preferable to the 2.0 μm CO2 absorption band due to higher radiances in the 1.6 μm band.

Evaluation of MAX-DOAS Profile Retrievals under Different Vertical Resolutions of Aerosol and NO2 Profiles and Elevation Angles

In the Multi-Axis Differential Absorption Spectroscopy (MAX-DOAS) trace gas and aerosol profile inversion algorithm, the vertical resolution and the observation information obtained through a series of continuous observations with multiple elevation angles (EAs) can affect the accuracy of an aerosol profile, thus further affecting the results of the gas profile. Therefore, this study examined the effect of the vertical resolution of an aerosol profile and EAs on the NO2 profile retrieval by combining simulations and measurements. Aerosol profiles were retrieved from MAX-DOAS observations and co-observed using light detection and ranging (Lidar). Three aerosol profile shapes (Boltzmann, Gaussian, and exponential) with vertical resolutions of 100 and 200 m were used in the atmospheric radiative transfer model. Firstly, the effect of the vertical resolution of the input aerosol profile on the retrieved aerosol profile with a resolution of 200 m was studied. The retrieved aerosol profiles from the two vertical resolution aerosol profiles as input were similar. The aerosol profile retrieved from a 100 m resolution profile as input was slightly overestimated compared to the input value, whereas that from a 200 m resolution input was slightly underestimated. The relative deviation of the aerosol profile retrieved from the 100 m resolution as input was higher than that of the 200 m. MAX-DOAS observations in Hefei city on 4 September 2020 were selected to verify the simulation results. The aerosol profiles retrieved from the oxygen collision complex (O4) differential slant column density derived from MAX-DOAS observations and Lidar simulation were compared with the input Lidar aerosol profiles. The correlation between the retrieved and input aerosol profiles was high, with a correlation coefficient R > 0.99. The aerosol profiles retrieved from the Lidar profile at 100 and 200 m resolutions as input closely matched the Lidar aerosol profiles, consistent with the simulation result. However, aerosol profiles retrieved from MAX-DOAS measurements differed from the Lidar profiles due to the influence of the averaging kernel matrix smoothing, the different location and viewing geometry, and uncertainties associated with the Lidar profiles. Next, NO2 profiles of different vertical resolutions were used as input profiles to retrieve the NO2 profiles under a single aerosol profile scenario. The effect of the vertical resolution on the retrieval of NO2 profiles was found to be less significant compared to aerosol retrievals. Using the Lidar aerosol profile as the a priori aerosol information had little effect on NO2 profile retrieval. Additionally, the retrieved aerosol profiles and aerosol optical depths varied under different EAs. Ten EAs (i.e., 1, 2, 3, 4, 5, 6, 8, 15, 30, and 90°) were found to obtain more information from observations.

Daily monitoring of Effective Green Area Index and Vegetation Chlorophyll Content from continuous acquisitions of a multi-band spectrometer over winter wheat

Green area index (GAI), leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) are key variables that are closely related to crop growth. Concurrent and continuous monitoring of GAI, LCC and CCC is critical to keep consistency among variables and make decisions for field precision managements. Previous studies have developed several instruments and algorithms to monitor continuous GAI, while the autonomous monitoring of three variables simultaneously has been lacking. This study presents a novel algorithm to retrieve daily GAI, LCC and CCC from continuous directional observations acquired by a fixed and economic affordable multi-band spectrometer (6 bands covering red, red-edge and near infrared domains) and a photosynthetically active radiation (PAR) sensor in the field. It is composed of three main steps, corresponding to three crucial questions when retrieving variables under natural environments using multi-band spectrometer installed on a near-surface platform: diffuse fraction in each spectral band, radiometric calibration and diurnal sun variation of daily acquisitions. First, we estimated diffuse fraction in each spectral band from the relationship with PAR diffuse fraction based on simulations of the 6S atmospheric radiative transfer model. Second, we computed the relative value of each band to the reference of mean of measurements on all six bands from near-surface measurements, in place of absolute radiometric calibration to limit the influence of changing illumination conditions. In the third step, we combined PROSAIL canopy radiative transfer model and kernel-driven models to retrieved GAI, LCC and CCC from artificial neural network using above spectral diffuse fraction and diurnal multi-angle relative observations. The algorithm was evaluated over 43 IoTA (Internet of things for Agriculture) systems that were installed in 29 wheat fields in France from March to May 2019. Results showed that our method provides good estimates of GAI with root mean square error (RMSE) of 0.54, relative RMSE (RRMSE) of 26.95%, R2 of 0.86, LCC (RMSE = 12.06 μg/cm2, RRMSE = 33.34%, R2 = 0.52) and CCC (RMSE = 0.23 g/m2, RRMSE = 24.58%, R2 = 0.93). This study shows great potentials for concurrent estimates of GAI, LCC and CCC from continuous ground measurements. It will be useful over other vegetations or other near-surface platforms for simultaneous estimations of biophysical variables.

Optical Properties of Individual Tar Balls in the Free Troposphere.

Tar balls are brown carbonaceous particles that are highly viscous, spherical, amorphous, and light absorbing. They are believed to form in biomass burning smoke plumes during transport in the troposphere. Tar balls are also believed to have a significant impact on the Earth's radiative balance, but due to poorly characterized optical properties, this impact is highly uncertain. Here, we used two nighttime samples to investigate the chemical composition and optical properties of individual tar balls transported in the free troposphere to the Climate Observatory "Ottavio Vittori" on Mt. Cimone, Italy, using multimodal microspectroscopy. In our two samples, tar balls contributed 50% of carbonaceous particles by number. Of those tar balls, 16% were inhomogeneously mixed with other constituents. Using electron energy loss spectroscopy, we retrieved the complex refractive index (RI) for a wavelength range from 200 to 1200 nm for both inhomogeneously and homogeneously mixed tar balls. We found no significant difference in the average RI of inhomogeneously and homogeneously mixed tar balls (1.40-0.03i and 1.36-0.03i at 550 nm, respectively). Furthermore, we estimated the top of the atmosphere radiative forcing using the Santa Barbara DISORT Atmospheric Radiative Transfer model and found that a layer of only tar balls with an optical depth of 0.1 above vegetation would exert a positive radiative forcing ranging from 2.8 W m-2 (on a clear sky day) to 9.5 W m-2 (when clouds are below the aerosol layer). Understanding the optical properties of tar balls can help reduce uncertainties associated with the contribution of biomass-burning aerosol in current climate models.

A dataset of JLS-5-20M black soil image in Northeast China

This is a dataset of the surface reflectance data covering some black soil areas in the three northeastern provinces of China, based on the L1 data of the JL1GP satellites’ multispectral imager with a spatial resolution of 5-20 meters. Specifically, we adopted the on-orbit absolute calibration technology based on multiple stable sites to ensure that the on-orbit absolute radiometric calibration accuracy of the JL1GP satellites’ multispectral imagers is no less than 7%. Then, we selected the L1 data of JL1GP satellite multispectral imager that imaged in the three provinces of Northeast China with clear weather and no clouds are as the input image of atmospheric correction process. Finally, the L1-level image data are atmospherically corrected and orthorectified by the atmospheric correction algorithm based on the atmospheric radiative transfer model, to generate the reflectance product dataset covering some black soil areas in the three northeastern provinces of China with an absolute accuracy of no less than 7%. This dataset takes advantage of the domestic JL1GP satellites’ advantages of high spatial resolution, a large number of bands, and stable imaging quality, hence it can provide reliable support for quantitative remote sensing applications in the black soil areas of the three northeastern provinces in China.

Assessment of aerosol optical and physical properties and implications for radiative effects over the semiarid region of Indo‐Gangetic Basin

This study assesses the physical and optical properties and estimated the radiative forcing of aerosol at Agra over the Indo‐Gangetic Basin (IGB) during July 2016–December 2019 using black carbon (BC) mass concentration (AE‐33 aethalometer), data sets from satellite and model simulations. The optical properties of aerosol and radiative forcing have been measured by the Optical and Physical Properties of Aerosols and Clouds (OPAC) and Santa Barbara Discrete Ordinate Radiative Transfer Atmospheric Radiative Transfer (SBDART) model. The high BC mass concentration has been observed in November and lowest in August. An adverse meteorological condition due to a combination of temperature and low wind speed results in poor dispersion in the wintertime is a common factor for high concentration level pollutants over Agra. The diurnal and temporal cycle of BC mass concentration exhibits a high concentration at nighttime due to the lower atmospheric boundary layer. The seasonal variation of absorption coefficient (βabs) and Absorption Angstrom Exponent (AAE) is found to be higher during post‐monsoon and lowest in monsoon season. This suggests that black carbon concentration over Agra is mainly generated from crop burning, waste burning, automobile exhaust and long‐range transport from Punjab and Haryana as the present site is downwind. OPAC‐derived aerosol optical depth (AOD), single‐scattering albedo (SSA), Angstrom Exponent (AE) and asymmetry parameter (AsyP) were estimated to be 0.57 ± 0.07, 0.78 ± 0.16, 0.99 ± 0.21 and 0.81 ± 0.15, respectively. AOD and AE from the OPAC and the moderate resolution imaging spectroradiometer (MODIS) have shown the consistent relationship. The mean radiative forcing is 18.3 ± 2.1 W m−2 at the top of the atmosphere while, at the surface, net radiative forcing is −42.4 ± 7.2 and 59.1 ± 6.5 W m−2 at the atmosphere during the study period. Vertical profiles were estimated using the observations from Cloud‐Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) satellite and the change in heating rate from the SBDART model over Agra.

Retrieval of Sea Surface Skin Temperature from the High Resolution Picture Transmission Data of the National Oceanic and Atmospheric Administration Series Satellites

The High Resolution Picture Transmission (HRPT) data of the National Oceanic and Atmospheric Administration (NOAA) series meteorological satellites had been received by the SeaSpace ground station located at the Ocean University of China (OUC). Based on the atmospheric radiative transfer model, we obtained the NOAA-15/16/17/18/19 Advanced Very High Resolution Radiometer (AVHRR) sea surface skin temperature (SSTskin) data using the Bayesian cloud detection method and the optimal estimation (OE) sea surface temperature (SST) retrieval algorithm. Compared with the NOAA/AVHRR multi-channel SST data, the AVHRR SSTskin data have higher data accuracy. We also compared the AVHRR SSTskin with the buoy SST with spatial and temporal windows of 0.01° and 30 min. The daytime biases ranged from −0.32 °C (NOAA-16) to 0.08 °C (NOAA-17) with standard deviations (SDs) ranging from 0.36 °C (NOAA-18/ NOAA-19) to 0.60 °C (NOAA-16), and the nighttime biases ranged from −0.26 °C (NOAA-16) to −0.02 °C (NOAA-17) with SDs ranging from 0.33 °C (NOAA-19) to 0.60 °C (NOAA-16). The accuracy of all five satellite data during daytime and nighttime was significantly improved. These results show that the AVHRR SSTskin of NOAA series satellites is good and consistent in different periods, and the SSTskin data products with high spatial resolution and accuracy can be used for mesoscale and submesoscale marine applications.

Construction of the Global Reference Atmospheric Profile Database

Atmospheric profiles are important input parameters for atmospheric radiative transfer models and atmospheric parameter inversions. The construction of regionally representative reference atmospheric profiles can provide basic data for global atmospheric and environmental research. Most reference atmospheric profile databases commonly used lag behind in updating frequency. These databases usually have limited spatial and temporal resolution and differ greatly from the real atmospheric state. To present the real atmospheric state, this article constructs the Global Reference Atmospheric Profile Database (GRAP) based on ACE-FTS satellite products of 2021 and 2022, AIRS satellite products and ERA5 reanalysis data of 2022 u6sing a random forest regression model and a hierarchical mean algorithm. The radiance spectrum of FY-3E HIRAS-II using different profile databases was simulated and compared with the measured spectrum. The results show that GRAP spectral simulations fit better with the measured HIRAS-II spectrum. Comparing the CO2, CH4, O3 and N2O profiles of GRAP, AFGL, MIPAS, RTTOV and NDACC ground station profiles in equatorial, mid-latitude summer and polar winter, the results show that GRAP has high spatial and temporal resolution and better fits the current real atmospheric state. Comparing the temperature profiles of eight regions in China, the results illustrate that GRAP is a better representation of the state of the atmosphere in the Chinese region. GRAP can provide fundamental atmospheric data for radiative transfer studies and atmospheric parameter inversions.

A new method to estimate aerosol radiative forcing on photosynthetically active radiation

A new method to estimate aerosol radiative forcing (ARF) on photosynthetically active radiation (PAR) is proposed using as input only the solar position and global irradiance measurements, available in many radiometric stations worldwide. The main contribution of this work is the proposal of a new and simple tool (max-kt method) based in the parameterization of the envelope of the relationship between clearness index (kt) and the solar position. To this aim, a 1-year database (2020) of cloud-free data acquired in a Southwest Mediterranean site was used for the proposal and two more years (2017 and 2018) were used to extend the results. The ARF values retrieved using the new method were compared with estimates calculated by two physical models widely employed in the literature, such as the SBDART (Santa Barbara DISORT Atmospheric Radiative Transfer model) and an improved version of the CPCR2 (Code for Physical Computation of Radiation, 2 bands) model. The new method provided ARF values of the order of those provided by these physical models, especially with the SBDART model, confirming the validity of this new method. An ARF seasonal pattern was found with higher values in summer, (−30,7 ± 9,0) Wm−2 in August during 2017, (−40,1 ± 11,8) Wm−2 in June during 2018 and (−28,8 ± 7,7) Wm−2 in July during 2020 and minimum values in winter, (−8,1 ± 5,1) Wm−2 in January during 2017, (−5,7 ± 5,8) Wm−2 in December during 2018 and (−7,8 ± 5,4) Wm−2 in December during 2020. Moreover, a dependence on solar zenith angle (θz) was detected excepting during the year 2018, increasing ARF absolute values at θz from 0° to 45°-60° and decreasing to zero for the Sun near the horizon. This technique is very useful due to the difficulty of knowing all the inputs requested by the physical models.

A Unified Physically Based Method for Monitoring Grassland Nitrogen Concentration with Landsat 7, Landsat 8, and Sentinel-2 Satellite Data

The increasing number of satellite missions provides vast opportunities for continuous vegetation monitoring, crucial for precision agriculture and environmental sustainability. However, accurately estimating vegetation traits, such as nitrogen concentration (N%), from Landsat 7 (L7), Landsat 8 (L8), and Sentinel-2 (S2) satellite data is challenging due to the diverse sensor configurations and complex atmospheric interactions. To address these limitations, we developed a unified and physically based method that combines a soil–plant–atmosphere radiative transfer (SPART) model with the bottom-of-atmosphere (BOA) spectral bidirectional reflectance distribution function. This approach enables us to assess the effect of rugged terrain, viewing angles, and illumination geometry on the spectral reflectance of multiple sensors. Our methodology involves inverting radiative transfer model variables using numerical optimization to estimate N% and creating a hybrid model. We used Gaussian process regression (GPR) to incorporate the inverted variables into the hybrid model for N% prediction, resulting in a unified approach for N% estimation across different sensors. Our model shows a validation accuracy of 0.35 (RMSE %N), a mean prediction interval width (MPIW) of 0.35, and an R2 of 0.50, using independent data from multiple sensors collected between 2016 and 2019. Our unified method provides a promising solution for estimating N% in vegetation from L7, L8, and S2 satellite data, overcoming the limitations posed by diverse sensor configurations and complex atmospheric interactions.

A fast and accurate parametric model of estimating thermal infrared atmospheric parameters: a case study for LANDSAT 8 TIRS sensor

ABSTRACT The traditional methods used in atmospheric correction depend on the empirical relationships or the atmospheric radiative transfer model (RTM). However, both of them have some deficiencies. The empirical method often depends highly on the training data; it would be applicable under certain conditions. In contrast, the RTM-based method is time consuming and has to run the code each time, which is also not an appropriate choice for operational estimation of the atmospheric parameters. In this study, a fast and accurate parametric model of estimating atmospheric parameters for Landsat 8 thermal infrared sensor was proposed to balance the accuracy and efficiency. The layer optical thickness for water vapour, water vapour continuum and other gases are well parameterized in the proposed parametric model, where the band coefficients can be easily determined by a least square fitting over MODTRAN simulations. The results show that the root mean squared error (RMSE) of the total transmission is 0.004 and the RMSE of for both the atmospheric upward and downward radiances are about 0.0004. Therefore, the proposed model could be used without the help of any atmospheric RTM to achieve the same accuracy as RTM does, but with less computational demand; that is, this model would have a better application market.