Radiative Transfer Model Simulations Research Articles

The effect of spatial resolution on radiometric and geometric performances of a UAV-mounted hyperspectral 2D imager

The effect of spatial resolution on the radiometric and geometric performances of hyperspectral sensors is an essential issue in remote sensing that urgently needs to be investigated, especially for low-altitude remote sensing principles and applications. Using an unmanned aerial vehicle (UAV)-mounted miniature hyperspectral 2D imager (Cubert UHD 185) system, a series of hyperspectral images of several reflectance targets (5%, 20%, 30%, 40%, 60% and 65%) were imaged in hovering flight at various spatial resolutions (ground sampling distances (GSDs)) from 1.2 cm to 4.8 cm, with intervals of 0.4 cm, which correspond to flight altitudes from 30 m to 120 m in increments of 10 m. Subsequently, the effect of spatial resolution on radiometric and geometric performances was evaluated in terms of the change in reflectance and geometric recognition ability of the shape of targets at visible to near-infrared wavelengths. This paper provides a set of methods for assessing the effect of spatial resolution on radiometric and geometric performance, including a radiative transfer model simulation for imaging quality performance, the geometric recognition loss degree (GRLD) for measuring image geometry recognition ability, and a trend projection analysis for developing continuous distribution images of radiometric and geometric performances. The results show that when the size of the target is not less than 50 (row) × 50 (column) pixels in a Cubert hyperspectral image, the absolute error (AE) and the root mean square error (RMSE) of the reflectances of its central pixel are both less than 0.05. Additionally, as the spatial resolution decreased, the AEs of the target reflectances in visible bands increased and then stabilized, and those in the red-edge band and near-infrared bands first increased slowly and then decreased rapidly because an increasing number of pixels were influenced by the surrounding area; thus, the shapes of the spectral curves of the sample area became increasingly similar to those of the surrounding area. This study provides a guide for selecting an appropriate spatial resolution for UAV remote sensing to improve operational efficiency. The reflectance and geometric quantitative losses at different spatial resolutions are conducive to parameter inversion in quantitative remote sensing and spatial resolution transformation and enrich the knowledge of low-altitude UAV hyperspectral remote sensing.

Radiative impact of an extreme Arctic biomass-burning event

Abstract. The aim of the presented study was to investigate the impact on the radiation budget of a biomass-burning plume, transported from Alaska to the High Arctic region of Ny-Ålesund, Svalbard, in early July 2015. Since the mean aerosol optical depth increased by the factor of 10 above the average summer background values, this large aerosol load event is considered particularly exceptional in the last 25 years. In situ data with hygroscopic growth equations, as well as remote sensing measurements as inputs to radiative transfer models, were used, in order to estimate biases associated with (i) hygroscopicity, (ii) variability of single-scattering albedo profiles, and (iii) plane-parallel closure of the modelled atmosphere. A chemical weather model with satellite-derived biomass-burning emissions was applied to interpret the transport and transformation pathways. The provided MODTRAN radiative transfer model (RTM) simulations for the smoke event (14:00 9 July–11:30 11 July) resulted in a mean aerosol direct radiative forcing at the levels of −78.9 and −47.0 W m−2 at the surface and at the top of the atmosphere, respectively, for the mean value of aerosol optical depth equal to 0.64 at 550 nm. This corresponded to the average clear-sky direct radiative forcing of −43.3 W m−2, estimated by radiometer and model simulations at the surface. Ultimately, uncertainty associated with the plane-parallel atmosphere approximation altered results by about 2 W m−2. Furthermore, model-derived aerosol direct radiative forcing efficiency reached on average −126 W m-2/τ550 and −71 W m-2/τ550 at the surface and at the top of the atmosphere, respectively. The heating rate, estimated at up to 1.8 K day−1 inside the biomass-burning plume, implied vertical mixing with turbulent kinetic energy of 0.3 m2 s−2.

Downscaling of solar-induced chlorophyll fluorescence from canopy level to photosystem level using a random forest model

Solar-induced chlorophyll fluorescence (SIF), an electromagnetic signal that can potentially indicate vegetation photosynthetic activity, can be retrieved from ground-based, airborne and satellite measurements. However, due to the scattering and re-absorption effects inside the leaves and canopy, SIF measured at the canopy level is only a small part of the total SIF emission at the photosystem level. Therefore, a downscaling mechanism of SIF from the canopy level to the photosystem level is important for better understanding the relationship between SIF and the vegetation gross primary production (GPP). In this study, firstly, we analyzed the canopy scattering effects using a simple parameterization model based on the spectral invariant theory. The probability for SIF photons to escape from the canopy was found to be related to the anisotropic spectral reflectance, canopy interception of the upward solar radiation, and leaf absorption. An empirical approach based on a Random Forest (RF) regression algorithm was applied to downscale SIF constrained by the red, red-edge and far-red anisotropic reflectance. The RF was trained using simulations conducted with the Soil Canopy Observation, Photochemistry and Energy fluxes (SCOPE) model. The performance of the SIF downscaling method was evaluated with SCOPE and Discrete Anisotropic Radiative Transfer (DART) model simulations, ground measurements and airborne data. Results show that estimated SIF at the photosystem level matches well with simulated reference data, and the relationship between SIF and photosynthetically active radiation absorbed by chlorophyll is improved by SIF downscaling. This finding in combination with other evaluation criteria suggests the downscaling of canopy SIF as an efficient strategy to normalize species dependent effects of canopy structure and varying solar-view geometries. Based on our results for the SIF-APAR relationship, we expect that such normalization approaches can be helpful to improve estimates of photosynthesis using remote sensing measurements of SIF.

Realistic Forest Stand Reconstruction from Terrestrial LiDAR for Radiative Transfer Modelling

Forest biophysical variables derived from remote sensing observations are vital for climate research. The combination of structurally and radiometrically accurate 3D “virtual” forests with radiative transfer (RT) models creates a powerful tool to facilitate the calibration and validation of remote sensing data and derived biophysical products by helping us understand the assumptions made in data processing algorithms. We present a workflow that uses highly detailed 3D terrestrial laser scanning (TLS) data to generate virtual forests for RT model simulations. Our approach to forest stand reconstruction from a co-registered point cloud is unique as it models each tree individually. Our approach follows three steps: (1) tree segmentation; (2) tree structure modelling and (3) leaf addition. To demonstrate this approach, we present the measurement and construction of a one hectare model of the deciduous forest in Wytham Woods (Oxford, UK). The model contains 559 individual trees. We matched the TLS data with traditional census data to determine the species of each individual tree and allocate species-specific radiometric properties. Our modelling framework is generic, highly transferable and adjustable to data collected with other TLS instruments and different ecosystems. The Wytham Woods virtual forest is made publicly available through an online repository.

Preflight Evaluation of the Performance of the Chinese Environmental Trace Gas Monitoring Instrument (EMI) by Spectral Analyses of Nitrogen Dioxide

The Environmental trace gas Monitoring Instrument (EMI) onboard the Chinese high-resolution remote sensing satellite GaoFen-5 is an ultraviolet–visible imaging spectrometer, aiming to quantify the global distribution of tropospheric and stratospheric trace gases and planned to be launched in spring 2018. The preflight calibration phase is essential to characterize the properties and performance of the EMI in order to provide information for data processing and trace gas retrievals. In this paper, we present the first EMI measurement of nitrogen dioxide (NO2) from a gas absorption cell using scattered sunlight as the light source by the differential optical absorption spectroscopy technique. The retrieved NO2 column densities in the UV and Vis wavelength ranges are consistent with the column density in the gas cell calculated from the NO2 mixing ratio and the length of the gas cell. Furthermore, the differences of the retrieved NO2 column densities among the adjoining spatial rows of the detector are less than 3%. This variation is similar to the well-known “stripes-pattern” of the Ozone Monitoring Instrument and is probably caused by remaining systematic effects like a nonperfect description of the individual instrument functions. Finally, the signal-to-noise ratios of EMI in-orbit measurements of NO2 are estimated on the basis of on-ground scattered sunlight measurements and radiative transfer model simulations. Based on our results, we conclude that the EMI is capable of measuring the global distribution of the NO2 column with the retrieval precision and accuracy better than 3% for the tested wavelength ranges and viewing angles.

Daytime variation of aerosol optical depth in North China and its impact on aerosol direct radiative effects

Daytime cycles of aerosol optical depth (AOD) and Angstrom exponent (AE) climatology were analyzed based on long-term (5–15 years) measurements at 18 stations of the China Aerosol Remote Sensing Network (CARSNET) in North China. AOD in Northwest China (NWC) exhibits a daytime trend with negative departures in the early morning and later afternoon while positive departures at midday. Daytime AOD relative departure in different sites and seasons varies from −30.26% to 30.28%. AE in NWC shows an opposite pattern to daytime variation of AOD. Daytime variation of AE is negligible in North China Plain (NCP), AOD increases steadily throughout the day. This trend is consistent and repeatable in four seasons. Such pronounced variability in AOD and AE should be taken accounted for in the estimation of diurnal aerosol direct radiative effects (ADRE), as suggested by the radiative transfer model simulations. The replacement of the observed daytime variation of AOD and AE by daytime mean values in NWC results in ADRE differences of 0.31 Wm−2 at the surface and 0.05 Wm−2 at the top of the atmosphere (TOA). ADRE in NWC will be underestimated by −0.47 Wm-2 and -0.25 Wm-2 at the surface and TOA, respectively, if instantaneous AOD and AE during the overpass time of Terra and Aqua are taken as the daytime mean values. The annual mean ADRE at the surface and TOA will be underestimated by −0.17 Wm-2 and -0.03 Wm-2 if daytime variations of AOD and AE are replaced by daytime mean values in NCP. ADRE will be underestimated by −0.07 Wm-2 at the surface and −0.06 Wm-2 at the TOA if instantaneous AOD and AE during the overpass time of Terra and Aqua other than daytime variation of AOD and AE are used in the calculations.

A Methodology to Determine Recipe Adjustments for Multispectral Composites Derived from Next-Generation Advanced Satellite Imagers

AbstractThe European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) began creating multispectral [i.e., red–green–blue (RGB)] composites in the early 2000s with the advent of the Meteosat-8 Spinning Enhanced Visible and Infrared Imager (SEVIRI). As new satellite sensors—for example, the Himawari-8 Advanced Himawari Imager (AHI) and the Geostationary Operational Environmental Satellite Advanced Baseline Imager (ABI)—become available, there is a need to adjust the EUMETSAT RGB standard thresholds (i.e., recipes) to account for differences in spectral characteristics, spectral response, and atmospheric absorption in order to maintain an interpretation consistent with legacy composites. For the purpose of comparing RGB composites derived from nonoverlapping geostationary sensors, an adjustment technique was applied to the Suomi National Polar-Orbiting Partnership (Suomi-NPP) Visible Infrared Imaging Radiometer Suite (VIIRS) to create an intermediate reference sensor (i.e., SEVIRI proxy). Brightness temperature offset values between each AHI and SEVIRI proxy band centered near 3.9, 8.6, 11.0, and 12.0 µm were determined with this technique and through line-by-line radiative transfer model simulations. The relationship between measured brightness temperature of AHI and the SEVIRI proxy was determined though linear regression similar to research by the Japan Meteorological Agency. The linear regression coefficients were utilized to determine the RGB recipe adjustments. Adjusting the RGB recipes to account for the differences in spectral characteristics results in RGB composites consistent with legacy EUMETSAT composites. The methodology was applied to an example of the Nighttime Microphysics RGB, confirming the Japan Meteorological Agency adjustments and demonstrating a simple methodology to determine recipe adjustments for RGB composites derived with next-generation sensors.

Relationships of S-Band Radar Backscatter and Forest Aboveground Biomass in Different Forest Types

Synthetic Aperture Radar (SAR) signals respond to the interactions of microwaves with vegetation canopy scatterers that collectively characterise forest structure. The sensitivity of S-band (7.5–15 cm) backscatter to the different forest types (broadleaved, needleleaved) with varying aboveground biomass (AGB) across temperate (mixed, needleleaved) and tropical (broadleaved, woody savanna, secondary) forests is less well understood. In this study, Michigan Microwave Canopy Scattering (MIMICS-I) radiative transfer model simulations showed strong volume scattering returns from S-band SAR for broadleaved canopies caused by ground/trunk interactions. A general relationship between AirSAR S-band measurements and MIMICS-I simulated radar backscatter with forest AGB up to nearly 100 t/ha in broadleaved forest in the UK was found. Simulated S-band backscatter-biomass relationships suggest increasing backscatter sensitivity to forest biomass with a saturation level close to 100 t/ha and errors between 37 t/ha and 44 t/ha for HV and VV polarisations for tropical ecosystems. In the near future, satellite SAR-derived forest biomass from P-band BIOMASS mission and L-band ALOS-2 PALSAR-2 in combination with S-band UK NovaSAR-S and the joint NASA-ISRO NISAR sensors will provide better quantification of large-scale forest AGB at varying sensitivity levels across primary and secondary forests and woody savannas.

Soil Moisture Estimation by SAR in Alpine Fields Using Gaussian Process Regressor Trained by Model Simulations

In this paper, we address the problem of retrieving soil moisture over a grassland alpine area from Synthetic Aperture Radar (SAR) data using a statistical algorithm trained by simulations of a physical model. A time series of C-band VV-polarized Wide Swath images acquired by Envisat Advanced SAR (ASAR) in the snow-free periods of 2010 and 2011 was simulated using a discrete radiative transfer model (RTM). The test area was located in the Mazia valley, South Tyrol (Italy), where the main land types are meadows and pastures. Soil moisture was collected from five meteorological stations, two of which situated in meadows and the rest in pastures. The smallest and the highest RMSEs of the RTM simulations were 0.78 dB and 1.91 dB, respectively. After backscattering simulation, the top soil moisture was estimated using Gaussian Process Regression (GPR). GPR was trained with the backscatter model simulations (including terrain features) for 2010, and then used to predict moisture from radar observations acquired in 2011. The relative importance of different input features was also assessed. The RMSE of the predicted soil moisture for the largest training data set (including aspect as a terrain feature) was 5.6% Vol. and the corresponding correlation coefficient was 0.84.

Influence of aerosols, clouds, and sunglint on polarization spectra of Earthshine

Ground-based observations of the Earthshine, i.e., the light scattered by Earth to the Moon, and then reflected back to Earth, simulate space observations of our planet and represent a powerful benchmark for the studies of Earth-like planets. Earthshine spectra are strongly linearly polarized, owing to scattering by molecules and small particles in the atmosphere of the Earth and surface reflection, and may allow us to measure global atmospheric and surface properties of planet Earth. Aims. We aim to interpret already published spectropolarimetric observations of the Earthshine by comparing them with new radiative transfer model simulations including a fully realistic three-dimensional (3D) surface-atmosphere model for planet Earth. We used the highly advanced Monte Carlo radiative transfer model MYSTIC to simulate polarized radiative transfer in the atmosphere of the Earth without approximations regarding the geometry, taking into account the polarization from surface reflection and multiple scattering by molecules, aerosol particles, cloud droplets, and ice crystals. We have shown that Earth spectropolarimetry is highly sensitive to all these input parameters, and we have presented simulations of a fully realistic Earth atmosphere-surface model including 3D cloud fields and two-dimensional (2D) surface property maps. Our modeling results show that scattering in high ice water clouds and reflection from the ocean surface are crucial to explain the continuum polarization at longer wavelengths as has been reported in Earthshine observations taken at the Very Large Telescope in 2011 (3.8 % and 6.6 % at 800 nm, depending on which part of Earth was visible from the Moon at the time of the observations). We found that the relatively high degree of polarization of 6.6 % can be attributed to light reflected by the ocean surface in the sunglint region.

Dust impact on surface solar irradiance assessed with model simulations, satellite observations and ground-based measurements

Abstract. This study assesses the impact of dust on surface solar radiation focussing on an extreme dust event. For this purpose, we exploited the synergy of AERONET measurements and passive and active satellite remote sensing (MODIS and CALIPSO) observations, in conjunction with radiative transfer model (RTM) and chemical transport model (CTM) simulations and the 1-day forecasts from the Copernicus Atmosphere Monitoring Service (CAMS). The area of interest is the eastern Mediterranean where anomalously high aerosol loads were recorded between 30 January and 3 February 2015. The intensity of the event was extremely high, with aerosol optical depth (AOD) reaching 3.5, and optical/microphysical properties suggesting aged dust. RTM and CTM simulations were able to quantify the extent of dust impact on surface irradiances and reveal substantial reduction in solar energy exploitation capacity of PV and CSP installations under this high aerosol load. We found that such an extreme dust event can result in Global Horizontal Irradiance (GHI) attenuation by as much as 40–50 % and a much stronger Direct Normal Irradiance (DNI) decrease (80–90 %), while spectrally this attenuation is distributed to 37 % in the UV region, 33 % in the visible and around 30 % in the infrared. CAMS forecasts provided a reliable available energy assessment (accuracy within 10 % of that obtained from MODIS). Spatially, the dust plume resulted in a zonally averaged reduction of GHI and DNI of the order of 150 W m−2 in southern Greece, and a mean increase of 20 W m−2 in the northern Greece as a result of lower AOD values combined with local atmospheric processes. This analysis of a real-world scenario contributes to the understanding and quantification of the impact range of high aerosol loads on solar energy and the potential for forecasting power generation failures at sunshine-privileged locations where solar power plants exist, are under construction or are being planned.

Estimating Cloud and Aerosol UV Modification Factors Based on Spectral Measurement from the Brewer Spectrophotometer

Cloud and aerosol modification factors are investigated in the spectral range of ultraviolet (UV) to correct for cloud and aerosol extinction effects from clear sky irradiance. The cloud modification factor (CMF) and aerosol modification factor (AMF) are estimated using radiative transfer model (RTM) simulations and ground-based observations in Seoul, Korea. The modification factors show a spectral dependence within the spectral range of 300 to 360 nm, which is the range used to estimate erythemal UV. The CMF and AMF values are estimated with high spectral resolution with considerations of solar zenith angle (SZA), cloud/aerosol amount, and total ozone variation. From the simulation studies, the variation in the CMFs within the spectral range of 300–360 nm is estimated to be 0.031–0.055, which is twice as large as the decrease in CMFs resulting from a SZA increase of 10°. In addition, the CMFs estimated from observational data show significant spectral dependence, varying from 2.5% to 10.0%. Because of the small aerosol optical depth (AOD) value, however, the variation in the AMF calculated from simulations is estimated to be between 0.007 and 0.045, indicating lower spectral dependence than the CMF. Furthermore, the spectral difference in the AMF calculated from observational data is insignificant relative to the daily-averaged total ozone error and uncertainties related to the reference irradiance spectrum under aerosol-free conditions.

Daytime Top-of-the-Atmosphere Cirrus Cloud Radiative Forcing Properties at Singapore

AbstractDaytime top-of-the-atmosphere (TOA) cirrus cloud radiative forcing (CRF) is estimated for cirrus clouds observed in ground-based lidar observations at Singapore in 2010 and 2011. Estimates are derived both over land and water to simulate conditions over the broader Maritime Continent archipelago of Southeast Asia. Based on bookend constraints of the lidar extinction-to-backscatter ratio (20 and 30 sr), used to solve extinction and initialize corresponding radiative transfer model simulations, relative daytime TOA CRF is estimated at 2.858–3.370 W m−2 in 2010 (both 20 and 30 sr, respectively) and 3.078–3.329 W m−2 in 2011 and over water between −0.094 and 0.541 W m−2 in 2010 and −0.598 and 0.433 W m−2 in 2011 (both 30 and 20 sr, respectively). After normalizing these estimates for an approximately 80% local satellite-estimated cirrus cloud occurrence rate, they reduce in absolute daytime terms to 2.198–2.592 W m−2 in 2010 and 2.368–2.561 W m−2 in 2011 over land and −0.072–0.416 W m−2 in 2010 and −0.460–0.333 W m−2 in 2011 over water. These annual estimates are mostly consistent despite a tendency toward lower relative cloud-top heights in 2011. Uncertainties are described. Estimates support the open hypothesis of a meridional hemispheric gradient in cirrus cloud daytime TOA CRF globally, varying from positive near the equator to presumably negative approaching the non-ice-covered poles. They help expand upon the paradigm, however, by conceptualizing differences zonally between overland and overwater forcing that differ significantly. More global oceans are likely subject to negative daytime TOA CRF than previously implied.

Estimating Infrared Radiometric Satellite Sea Surface Temperature Retrieval Cold Biases in the Tropics due to Unscreened Optically Thin Cirrus Clouds

AbstractPassive longwave infrared radiometric satellite–based retrievals of sea surface temperature (SST) at instrument nadir are investigated for cold bias caused by unscreened optically thin cirrus (OTC) clouds [cloud optical depth (COD) ≤ 0.3]. Level 2 nonlinear SST (NLSST) retrievals over tropical oceans (30°S–30°N) from Moderate Resolution Imaging Spectroradiometer (MODIS) radiances collected aboard the NASA Aqua satellite (Aqua-MODIS) are collocated with cloud profiles from the Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) instrument. OTC clouds are present in approximately 25% of tropical quality-assured (QA) Aqua-MODIS Level 2 data, representing over 99% of all contaminating cirrus found. Cold-biased NLSST (MODIS, AVHRR, and VIIRS) and triple-window (AVHRR and VIIRS only) SST retrievals are modeled based on operational algorithms using radiative transfer model simulations conducted with a hypothetical 1.5-km-thick OTC cloud placed incrementally from 10.0 to 18.0 km above mean sea level for cloud optical depths between 0.0 and 0.3. Corresponding cold bias estimates for each sensor are estimated using relative Aqua-MODIS cloud contamination frequencies as a function of cloud-top height and COD (assuming they are consistent across each platform) integrated within each corresponding modeled cold bias matrix. NLSST relative OTC cold biases, for any single observation, range from 0.33° to 0.55°C for the three sensors, with an absolute (bulk mean) bias between 0.09° and 0.14°C. Triple-window retrievals are more resilient, ranging from 0.08° to 0.14°C relative and from 0.02° to 0.04°C absolute. Cold biases are constant across the Pacific and Indian Oceans. Absolute bias is lower over the Atlantic but relative bias is higher, indicating that this issue persists globally.

The CU 2-D-MAX-DOAS instrument – Part 2: Raman scattering probability measurements and retrieval of aerosol optical properties

Abstract. The multiannual global mean of aerosol optical depth at 550 nm (AOD550) over land is ∼ 0.19, and that over oceans is ∼ 0.13. About 45 % of the Earth surface shows AOD550 smaller than 0.1. There is a need for measurement techniques that are optimized to measure aerosol optical properties under low AOD conditions. We present an inherently calibrated retrieval (i.e., no need for radiance calibration) to simultaneously measure AOD and the aerosol phase function parameter, g, based on measurements of azimuth distributions of the Raman scattering probability (RSP), the near-absolute rotational Raman scattering (RRS) intensity. We employ radiative transfer model simulations to show that for solar azimuth RSP measurements at solar elevation and solar zenith angle (SZA) smaller than 80°, RSP is insensitive to the vertical distribution of aerosols and maximally sensitive to changes in AOD and g under near-molecular scattering conditions. The University of Colorado two-dimensional Multi-AXis Differential Optical Absorption Spectroscopy (CU 2-D-MAX-DOAS) instrument was deployed as part of the Two Column Aerosol Project (TCAP) at Cape Cod, MA, during the summer of 2012 to measure direct sun spectra and RSP from scattered light spectra at solar relative azimuth angles (SRAAs) between 5 and 170°. During two case study days with (1) high aerosol load (17 July, 0.3 < AOD430 < 0.6) and (2) near-molecular scattering conditions (22 July, AOD430 < 0.13) we compare RSP-based retrievals of AOD430 and g with data from a co-located CIMEL sun photometer, Multi-Filter Rotating Shadowband Radiometer (MFRSR), and an airborne High Spectral Resolution Lidar (HSRL-2). The average difference (relative to DOAS) for AOD430 is +0.012 ± 0.023 (CIMEL), −0.012 ± 0.024 (MFRSR), −0.011 ± 0.014 (HSRL-2), and +0.023 ± 0.013 (CIMELAOD − MFRSRAOD) and yields the following expressions for correlations between different instruments: DOASAOD = −(0.019 ± 0.006) + (1.03 ± 0.02) × CIMELAOD (R2 = 0.98), DOASAOD = −(0.006 ± 0.005) + (1.08 ± 0.02) × MFRSRAOD (R2 = 0.98), and CIMELAOD = (0.013 ± 0.004) + (1.05 ± 0.01) × MFRSRAOD (R2 = 0.99). The average g measured by DOAS on both days was 0.66 ± 0.03, with a difference of 0.014 ± 0.05 compared to CIMEL. Active steps to minimize the error in the RSP help to reduce the uncertainty in retrievals of AOD and g. As AOD decreases and SZA increases, the RSP signal-to-noise ratio increases. At AOD430 ∼ 0.4 and 0.10 the absolute AOD errors are ∼ 0.014 and 0.003 at 70° SZA and 0.02 and 0.004 at 35° SZA. Inherently calibrated, precise AOD and g measurements are useful to better characterize the aerosol direct effect in urban polluted and remote pristine environments.

Spectral dependence on the correction factor of erythemal UV for cloud, aerosol, total ozone, and surface properties: A modeling study

Radiative transfer model simulations were used to investigate the erythemal ultraviolet (EUV) correction factors by separating the UV-A and UV-B spectral ranges. The correction factor was defined as the ratio of EUV caused by changing the amounts and characteristics of the extinction and scattering materials. The EUV correction factors (CFEUV) for UV-A [CFEUV(A)] and UV-B [CFEUV(B)] were affected by changes in the total ozone, optical depths of aerosol and cloud, and the solar zenith angle. The differences between CFEUV(A) and CFEUV(B) were also estimated as a function of solar zenith angle, the optical depths of aerosol and cloud, and total ozone. The differences between CFEUV(A) and CFEUV(B) ranged from −5.0% to 25.0% for aerosols, and from −9.5% to 2.0% for clouds in all simulations for different solar zenith angles and optical depths of aerosol and cloud. The rate of decline of CFEUV per unit optical depth between UV-A and UV-B differed by up to 20% for the same aerosol and cloud conditions. For total ozone, the variation in CFEUV(A) was negligible compared with that in CFEUV(B) because of the effective spectral range of the ozone absorption band. In addition, the sensitivity of the CFEUVs due to changes in surface conditions (i.e., surface albedo and surface altitude) was also estimated by using the model in this study. For changes in surface albedo, the sensitivity of the CFEUVs was 2.9%–4.1% per 0.1 albedo change, depending on the amount of aerosols or clouds. For changes in surface altitude, the sensitivity of CFEUV(B) was twice that of CFEUV(A), because the Rayleigh optical depth increased significantly at shorter wavelengths.

Analysis of Red and Far-Red Sun-Induced Chlorophyll Fluorescence and Their Ratio in Different Canopies Based on Observed and Modeled Data

Sun-induced canopy chlorophyll fluorescence in both the red (FR) and far-red (FFR) regions was estimated across a range of temporal scales and a range of species from different plant functional types using high resolution radiance spectra collected on the ground. Field measurements were collected with a state-of-the-art spectrometer setup and standardized methodology. Results showed that different plant species were characterized by different fluorescence magnitude. In general, the highest fluorescence emissions were measured in crops followed by broadleaf and then needleleaf species. Red fluorescence values were generally lower than those measured in the far-red region due to the reabsorption of FR by photosynthetic pigments within the canopy layers. Canopy chlorophyll fluorescence was related to plant photosynthetic capacity, but also varied according to leaf and canopy characteristics, such as leaf chlorophyll concentration and Leaf Area Index (LAI). Results gathered from field measurements were compared to radiative transfer model simulations with the Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model. Overall, simulation results confirmed a major contribution of leaf chlorophyll concentration and LAI to the fluorescence signal. However, some discrepancies between simulated and experimental data were found in broadleaf species. These discrepancies may be explained by uncertainties in individual species LAI estimation in mixed forests or by the effect of other model parameters and/or model representation errors. This is the first study showing sun-induced fluorescence experimental data on the variations in the two emission regions and providing quantitative information about the absolute magnitude of fluorescence emission from a range of vegetation types.

AVHRR GAC SST Reanalysis Version 1 (RAN1)

In response to its users’ needs, the National Oceanic and Atmospheric Administration (NOAA) initiated reanalysis (RAN) of the Advanced Very High Resolution Radiometer (AVHRR) Global Area Coverage (GAC; 4 km) sea surface temperature (SST) data employing its Advanced Clear Sky Processor for Oceans (ACSPO) retrieval system. Initially, AVHRR/3 data from five NOAA and two Metop satellites from 2002 to 2015 have been reprocessed. The derived SSTs have been matched up with two reference SSTs—the quality controlled in situ SSTs from the NOAA in situ Quality Monitor (iQuam) and the Canadian Meteorological Centre (CMC) L4 SST analysis—and analyzed in the NOAA SST Quality Monitor (SQUAM) online system. The corresponding clear-sky ocean brightness temperatures (BT) in AVHRR bands 3b, 4 and 5 (centered at 3.7, 11, and 12 µm, respectively) have been compared with the Community Radiative Transfer Model simulations in another NOAA online system, Monitoring of Infrared Clear-sky Radiances over Ocean for SST (MICROS). For some AVHRRs, the time series of “AVHRR minus reference” SSTs and “observed minus model” BTs are unstable and inconsistent, with artifacts in the SSTs and BTs strongly correlated. In the official “Reanalysis version 1” (RAN1), data from only five platforms—two midmorning (NOAA-17 and Metop-A) and three afternoon (NOAA-16, -18 and -19)—were included during the most stable periods of their operations. The stability of the SST time series was further improved using variable regression SST coefficients, similarly to how it was done in the NOAA/NASA Pathfinder version 5.2 (PFV5.2) dataset. For data assimilation applications, especially those blending satellite and in situ SSTs, we recommend bias-correcting the RAN1 SSTs using the newly developed sensor-specific error statistics (SSES), which are reported in the product files. Relative performance of RAN1 and PFV5.2 SSTs is discussed. Work is underway to improve the calibration of AVHRR/3s and extend RAN time series, initially back to the mid-1990s and later to the early 1980s.

Statistical Retrieval of Ozone and Meteorological Parameters Using SHADOZ Observations and Radiative Transfer Model

A statistical retrieval algorithm has been developed using SHADOZ ozonesonde observations and radiative transfer model simulations for the retrieval of vertical profiles of temperature, water vapor and ozone. Retrieved profiles of ozone and meteorological parameters are compared with corresponding in-situ ozonesonde observations and IASI observations to test accuracy of the retrieval algorithm. The standard deviation error in the temperature profile, estimated using retrieved and ozonesonde observed profiles, is found to be in the range of 0.7–2.7° K. The percentage root mean square error (RMSE) estimated using retrieved and actual profiles of ozone and water vapor are found to be in the range of 5–30 % and 10–30 %, respectively. The standard deviation error in temperature (2 to 3.5° K) and RMSE in water vapor (~35–55 %) estimated using actual IASI observations (retrieved and observed) is relatively higher than the retrieval errors from the simulated radiances. The RMSE in the retrieval of total column ozone is also estimated using simulated and actual IASI radiances. The RMSE for simulated data is found to be 1.9 ± 1.4 %. The RMSE in the total column ozone estimated for actual IASI observations is found to be 3.8 ± 3.2 %. Inclusion of the zenith angle as a predictor in the regression coefficients has improved retrieval error of atmospheric parameters. The standard deviation error for temperature is improved by 0.2–0.3° K and RMSE for ozone and water vapor is improved by 2–4 % and 4–7 % respectively in different atmospheric regions.

Fractional vegetation cover estimation algorithm for Chinese GF-1 wide field view data

Abstract Wide field view (WFV) sensor on board the Chinese GF-1, the first satellite of the China High-resolution Earth Observation System, is acquiring multi-spectral data with decametric spatial resolution, high temporal resolution and wide coverage, which are valuable data sources for environment monitoring. The objective of this study is to develop a general and reliable fractional vegetation cover (FVC) estimation algorithm for GF-1 WFV data under various land surface conditions. The algorithm is expected to estimate FVC from GF-1 WFV reflectance data with spatial resolution of 16 m and temporal resolution of four dates. The proposed algorithm is based on training back propagation neural networks (NNs) using PROSPECT + SAIL radiative transfer model simulations for GF-1 WFV canopy reflectance and corresponding FVC values. Green, red and near-infrared bands' reflectances of GF-1 WFV data are the input variables of the NNs, as well as the corresponding FVC is the output variable, and finally 842,400 simulated samples covering various land surface conditions are used for training the NNs. A case study in Weichang County of China, having abundant land cover types, was conducted to validate the performance of the proposed FVC estimation algorithm for GF-1 WFV data. The validation results showed that the proposed algorithm worked effectively and generated reasonable FVC estimates with R 2 = 0.790 and root mean square error of 0.073 based on the field survey data. The proposed algorithm can be operated without prior knowledge on the land cover and has the potential for routine production of high quality FVC products using GF-1 WFV surface reflectance data.