Top Of The Atmosphere Radiance Research Articles

3D Monte Carlo surface-atmosphere radiative transfer modelling with DART

Significant errors can arise if the adjacency effect (i.e., the contribution of neighbouring pixels to the radiance of a pixel) is neglected in the interpretation of remote sensing images. For example, adjacency radiances can account for >30% of the signal at the top of the atmosphere (TOA) of a white sand-lined coastline (Bulgarelli and Zibordi, 2018). This paper shows that 3D radiative transfer (RT) modelling can quantify this phenomenon. It presents a new 3D Monte Carlo surface-atmosphere RT modelling in the DART RT model, and a resulting virtual 3D Earth-Atmosphere laboratory for accurate simulation of atmospheric RT, including the adjacency effect. It was first validated with the atmosphere model SMART-G for 2D scenes: relative difference is 0.20% in TOA directional reflectance of infinite black surface for solar zenith equal to 60°, and 0.03% in TOA nadir reflectance of a black disc inside infinite white Lambertian surface, for solar zenith equal to 0° and 30°. Then, the adjacency effect on the TOA radiance and TOA albedo of a 3D scene was studied for a circular city (radius 2 km) surrounded by a forest (dimension 10 km), for four Sentinel-2A bands (blue, green, red, near infrared). Its contribution to TOA nadir radiance reaches ∼20% in the near infrared band, and increases with viewing zenith angle (VZA): ∼60% if VZA = 80°. The 3D scene TOA albedo was calculated after adapting the DART Bidirectional Reflectance Factor (BRF) camera to simulate TOA radiance for all upward directions at any angular resolution. For Sentinel-2A’s four bands, the adjacency effect influences the TOA albedo of the circular city by up to 10% for the green band and up to 27% for the near infrared band, well above the maximum uncertainty 5% usually required in land surface applications. The adjacency effect of the city neighbourhood 3D structure was studied by replacing it by a Lambertian surface with its albedo. The city nadir TOA radiance changed by up to 1.3% with very small change in TOA albedo (<0.5%). This new modelling greatly improves DART potential for accurate simulation of atmospheric effects. It is in the DART version freely available for research and education (https://dart.omp.eu).

Effect of the Vertical Distribution of Absorbing Aerosols on the Atmospheric Correction for Satellite Ocean Color Remote Sensing

The vertical distribution of absorbing aerosols has nonnegligible impact on the atmospheric correction of satellite ocean color remote sensing, especially for the water-leaving radiance retrieval at blue and ultraviolet bands. In this study, we investigated the impact of the vertical distribution of absorbing aerosols on the satellite-measured radiance at the top of the atmosphere (TOA) and the retrieved water-leaving radiance. First, the global occurrence frequency of absorbing aerosols was mapped, and it was found that the annual averaged occurrence frequencies of absorbing aerosols were >30% over the coasts of the Sahara and Arabian Desert, China, South-Central Africa, and the Indian Peninsula. Second, a new aerosol classification algorithm was developed to establish absorbing aerosol optical models based on AERosol RObotic NETwork (AERONET) site observations. Finally, the effects of the vertical distribution of absorbing aerosols on the upward radiance at the TOA at 412 nm and the retrieved water-leaving radiance were evaluated. The results showed that the influence of the vertical distribution on the TOA radiance could be up to 8% for dust and 10% for fine-dominated absorbing aerosols (FDAs), which was comparable with the influence of aerosol optical depth. Not considering absorbing aerosols during atmospheric correction might produce <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 4$ </tex-math></inline-formula> %–10% errors in the water-leaving radiance retrieval at 412 nm over turbid waters under the assumption of a Gaussian distribution. Simplified exponential and two-layer atmospheric vertical distribution models can lead to errors of water-leaving radiance retrieval up to <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 12$ </tex-math></inline-formula> %–15% and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\sim 30$ </tex-math></inline-formula> %–40%, respectively. Overall, the imperfect vertical distribution assumption and aerosol model selection might induce uncertainty in water-leaving radiance retrieval from 10% to 80% under absorbing aerosol conditions.

A New Algorithm for Simultaneous Retrieval of Aerosols and Marine Parameters

We present an algorithm for simultaneous retrieval of aerosol and marine parameters in coastal waters. The algorithm is based on a radiative transfer forward model for a coupled atmosphere-ocean system, which is used to train a radial basis function neural network (RBF-NN) to obtain a fast and accurate method to compute radiances at the top of the atmosphere (TOA) for given aerosol and marine input parameters. The inverse modelling algorithm employs multidimensional unconstrained non-linear optimization to retrieve three marine parameters (concentrations of chlorophyll and mineral particles, as well as absorption by coloured dissolved organic matter (CDOM)), and two aerosol parameters (aerosol fine-mode fraction and aerosol volume fraction). We validated the retrieval algorithm using synthetic data and found it, for both low and high sun, to predict each of the five parameters accurately, both with and without white noise added to the top of the atmosphere (TOA) radiances. When varying the solar zenith angle (SZA) and retraining the RBF-NN without noise added to the TOA radiance, we found the algorithm to predict the CDOM absorption, chlorophyll concentration, mineral concentration, aerosol fine-mode fraction, and aerosol volume fraction with correlation coefficients greater than 0.72, 0.73, 0.93, 0.67, and 0.87, respectively, for 45∘≤ SZA ≤ 75∘. By adding white Gaussian noise to the TOA radiances with varying values of the signal-to-noise-ratio (SNR), we found the retrieval algorithm to predict CDOM absorption, chlorophyll concentration, mineral concentration, aerosol fine-mode fraction, and aerosol volume fraction well with correlation coefficients greater than 0.77, 0.75, 0.91, 0.81, and 0.86, respectively, for high sun and SNR ≥ 95.

System spectral shape factor-based retrieval approach for accuracy improvement of ocean color radiance

Accurate retrieval of top of the atmosphere (TOA) radiance is a sustained research work in ocean color remote sensing. Spectral shapes of sensor input and sensor spectral response (SRF) function may significantly affect the achievable accuracy. A model is presented here to quantify the effects of spectral shapes. We also proposed an innovative method to retrieve the TOA spectral radiance that accounts for spectral shapes. Ten-fold improvement is demonstrated in retrieval accuracy using the proposed method on multiple data sets. Input spectral shape variations are simulated using spectra of TOA radiance, path radiance, exoatmospheric solar irradiance, and integrating sphere. SRF changes are simulated using SRFs of Ocean Color Monitor-2 and Sea-Viewing Wide Field-of-View Sensor. System spectral shape factor, a new parameter, is found to capture the spectral changes and helps quantitative assessment of the inaccuracy. The proposed method will be highly beneficial in deriving accurate geophysical products from the existing and upcoming ocean color remote sensing instruments.

The influence of spectral calibration error of the hyperspectral remote sensor on the TOA radiance

Based on the surface reflectance data of the calibration field and spectral calibration coefficients of typical hyperspectral remote sensor, the quantitative effects of the spectral calibration error of hyperspectral remote sensor on the top-of-the-atmosphere (TOA) radiance was analysed by simulating the TOA radiance with spectral calibration error. The results show that: (1) The TOA radiance errors caused by spectral calibration errors are mainly distributed in the solar and atmospheric absorption channels, and the water vapor absorption band errors near 1380 nm and 1870 nm are the largest. (2) With the increase of spectral calibration error, the maximum TOA radiance error in the whole spectrum range increases significantly. Taking a 10 nm resolution hyperspectral remote sensor as an example, 10% and 30% spectral calibration errors bring maximum errors of 34.28% and 68.67% to the TOA radiance, respectively. The spectral calibration error has a significant linear relationship with the maximum TOA radiance error (R2 > 0.98). (3) The shape of spectral absorption region is sensitive to spectral calibration error, which provides a theoretical basis for the feasibility of spectral calibration based on absorption band. The above conclusions provide a reference for spectral calibration and calibration accuracy requirements of hyperspectral remote sensor.

Adjacency Effects of Layered Clouds by Means of Monte Carlo Ray Tracing

Adjacency effects from layered box shaped clouds are clarified by means of Monte Carlo Simulation: MCS taking into account a phase function of cloud particles and multi-layered plane parallel atmosphere. MCS allows estimation of top of the atmosphere radiance. Influences on adjacency effects of phase function of the clouds in concern and the number of layers of the plane parallel atmosphere are also clarified together with the effects from the top and the bottom clouds. There are 10 of cloud types in meteorological definition. One-layer cloud for cumulus and cumulonimbus clouds are investigated in this study.

Global Sensitivity Analysis of Leaf-Canopy-Atmosphere RTMs: Implications for Biophysical Variables Retrieval from Top-of-Atmosphere Radiance Data.

Knowledge of key variables driving the top of the atmosphere (TOA) radiance over a vegetated surface is an important step to derive biophysical variables from TOA radiance data, e.g., as observed by an optical satellite. Coupled leaf-canopy-atmosphere Radiative Transfer Models (RTMs) allow linking vegetation variables directly to the at-sensor TOA radiance measured. Global Sensitivity Analysis (GSA) of RTMs enables the computation of the total contribution of each input variable to the output variance. We determined the impacts of the leaf-canopy-atmosphere variables into TOA radiance using the GSA to gain insights into retrievable variables. The leaf and canopy RTM PROSAIL was coupled with the atmospheric RTM MODTRAN5. Because of MODTRAN’s computational burden and GSA’s demand for many simulations, we first developed a surrogate statistical learning model, i.e., an emulator, that allows approximating RTM outputs through a machine learning algorithm with low computation time. A Gaussian process regression (GPR) emulator was used to reproduce lookup tables of TOA radiance as a function of 12 input variables with relative errors of 2.4%. GSA total sensitivity results quantified the driving variables of emulated TOA radiance along the 400–2500 nm spectral range at 15 cm–1 (between 0.3–9 nm); overall, the vegetation variables play a more dominant role than atmospheric variables. This suggests the possibility to retrieve biophysical variables directly from at-sensor TOA radiance data. Particularly promising are leaf chlorophyll content, leaf water thickness and leaf area index, as these variables are the most important drivers in governing TOA radiance outside the water absorption regions. A software framework was developed to facilitate the development of retrieval models from at-sensor TOA radiance data. As a proof of concept, maps of these biophysical variables have been generated for both TOA (L1C) and bottom-of-atmosphere (L2A) Sentinel-2 data by means of a hybrid retrieval scheme, i.e., training GPR retrieval algorithms using the RTM simulations. Obtained maps from L1C vs L2A data are consistent, suggesting that vegetation properties can be directly retrieved from TOA radiance data given a cloud-free sky, thus without the need of an atmospheric correction.

A Neural Network Correction to the Scalar Approximation in Radiative Transfer

AbstractThe next generation of advanced high-resolution sensors in geostationary orbit will gather detailed information for studying the Earth system. There is an increasing desire to perform observing system simulation experiments (OSSEs) for new sensors during the development phase of the mission in order to better leverage information content from the new and existing sensors. Forward radiative transfer calculations that simulate the observing characteristics of a new instrument are the first step to an OSSE, and they are computationally intensive. The scalar approximation to the radiative transfer equation, a simplification of the vector representation, can save considerable computational cost, but produces errors in top of the atmosphere (TOA) radiance as large as 10% due to neglecting polarization effects. This article presents an artificial neural network technique to correct scalar TOA radiance over both land and ocean surfaces to within 1% of vector-calculated radiance. A neural network was trained on a database of scalar–vector TOA radiance differences at a large range of solar and viewing angles for several thousand realistic atmospheric vertical profiles that were sampled from a high-resolution (7 km) global atmospheric transport model. The profiles include Rayleigh scattering and aerosol scattering and absorption. Training and validation of the neural network was demonstrated for two wavelengths in the ultraviolet–visible (UV-Vis) spectral range (354 and 670 nm). The significant computational savings accrued from using a scalar approximation plus neural network correction approach to simulating TOA radiance will make feasible hyperspectral forward simulations of high-resolution sensors on geostationary satellites, such as Tropospheric Emissions: Monitoring of Pollution (TEMPO), GOES-R, Geostationary Environmental Monitoring Spectrometer (GEMS), and Sentinel-4.

Method for Uncertainty Evaluation of Vicarious Calibration of Spaceborne Visible to Near Infrared Radiometers

A method for uncertainty evaluation of vicarious calibration for solar reflection channels (visible to near infrared) of spaceborne radiometers is proposed. Reflectance based at sensor radiance estimation method for solar reflection channels of radiometers onboard remote sensing satellites is also proposed. One of examples for vicarious calibration of LISA: Line Imager Space Application onboard LISAT: LAPAN-IPB Satellite is described. Through the preliminary analysis, it is found that the proposed uncertainty evaluation method is appropriate. Also, it is found that percent difference between DN: Digital Number derived radiance and estimated TOA: Top of the Atmosphere radiance (at sensor radiance) ranges from 3.5 to 9.6 %. It is also found that the percent difference at shorter wavelength (Blue) is greater than that of longer wavelength (Near Infrared: NIR). In comparison to those facts to those of Terra/ASTER/VNIR, it is natural and reasonable.

A Geostationary Instrument Simulator for Aerosol Observing System Simulation Experiments

In the near future, there will be several new instruments measuring atmospheric composition from geostationary orbit over North America, East Asia, and Europe. This constellation of satellites will provide high resolution, time resolved measurements of trace gases and aerosols for monitoring air quality and tracking pollution sources. This paper describes a detailed, fast, and accurate (less than 1.0% uncertainty) method for calculating synthetic top of the atmosphere (TOA) radiances from a global simulation with a mesoscale free running model, the GEOS-5 Nature Run, for remote sensing instruments in geostationary orbit that measure in the ultraviolet-visible spectral range (UV-Vis). Generating these synthetic observations is the first step of an Observing System Simulation Experiment (OSSE), a framework for evaluating the impact of a new observation or algorithm. This paper provides details of the model sampling, aerosol and cloud optical properties, surface reflectance modeling, Rayleigh scattering calculations, and a discussion of the uncertainties of the simulated TOA radiance. An application for the simulated TOA radiance observations is demonstrated in the manuscript. Simulated TEMPO (Tropospheric Emissions: Monitoring of Pollution) and GOES-R (Geostationary Operational Environmental Satellites) observations were used to show how observations from the two instruments could be combined to facilitate aerosol type discrimination. The results demonstrate the viability of a detailed instrument simulator for radiance measurements in the UV-Vis that is capable of accurately simulating high resolution, time-resolved measurements with reasonable computational efficiency.

Impact of reflecting land surface on radiation environment over Hornsund, Spitsbergen – a model study for cloudless skies

Abstract This paper addresses the influence of land topography and cover on 3D radiative effects under cloudless skies in the Hornsund area, Spitsbergen, Svalbard. The authors used Monte Carlo simulations of solar radiation transfer over a heterogeneous surface to study the impact of a non-uniform surface on: (1) the spatial distribution of irradiance transmittance at the fjord surface under cloudless skies; (2) the spectral shortwave aerosol radiative forcing at the fjord surface; (3) normalized nadir radiance at the Top Of the Atmosphere (TOA) over the fjord. The modelled transmittances and radiances over the fjord are compared to the transmittances and radiances over the open ocean under the same conditions. The dependence of the 3D radiative effects on aerosol optical thickness, aerosol type, surface albedo distribution, solar azimuth and zenith angle and spectral channel is discussed. The analysis was done for channels 3 (459-479 nm) and 2 (841-876 nm) of the MODIS radiometer. In the simulations a flat water surface was assumed. The study shows that snow-covered land surrounding the fjord strongly modifies the radiation environment over the fjord surface. The enhancement of the mean irradiance transmittance over the fjord with respect to the open ocean is up to 0.06 for channel 3. The enhancement exceeds 0.11 in the vicinity of sunlit cliffs. The influence of the snow-covered land on the TOA radiance over the fjord in channel 3 is comparable to the impact of an increase in aerosol optical thickness of over 100%, and in lateral fjords of up to several hundred percent. The increase in TOA radiance is wavelength dependent. These effects may affect retrievals of aerosol optical thickness.

Radiometric Cross-Calibration of GF-4 in Multispectral Bands

The GaoFen-4 (GF-4), launched at the end of December 2015, is China’s first high-resolution geostationary optical satellite. A panchromatic and multispectral sensor (PMS) is onboard the GF-4 satellite. Unfortunately, the GF-4 has no onboard calibration assembly, so on-orbit radiometric calibration is required. Like the charge-coupled device (CCD) onboard HuanJing-1 (HJ) or the wide field of view sensor (WFV) onboard GaoFen-1 (GF-1), GF-4 also has a wide field of view, which provides challenges for cross-calibration with narrow field of view sensors, like the Landsat series. A new technique has been developed and used to calibrate HJ-1/CCD and GF-1/WFV, which is verified viable. The technique has three key steps: (1) calculate the surface using the bi-directional reflectance distribution function (BRDF) characterization of a site, taking advantage of its uniform surface material and natural topographic variation using Landsat Enhanced Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) imagery and digital elevation model (DEM) products; (2) calculate the radiance at the top-of-the atmosphere (TOA) with the simulated surface reflectance using the atmosphere radiant transfer model; and (3) fit the calibration coefficients with the TOA radiance and corresponding Digital Number (DN) values of the image. This study attempts to demonstrate the technique is also feasible to calibrate GF-4 multispectral bands. After fitting the calibration coefficients using the technique, extensive validation is conducted by cross-validation using the image pairs of GF-4/PMS and Landsat-8/OLI with similar transit times and close view zenith. The validation result indicates a higher accuracy and frequency than that given by the China Centre for Resources Satellite Data and Application (CRESDA) using vicarious calibration. The study shows that the new technique is also quite feasible for GF-4 multispectral bands as a routine long-term procedure.

Absolute Vicarious Calibration of recently launched Indian Meteorological Satellite: INSAT-3D imager

Abstract. Looking towards the advancements and popularity of remote sensing and an ever increasing need for the development of a variety of new and complex satellite sensors, it has become even more essential to continually upgrade the ability to provide absolute calibration of sensors. This article describes a simple procedure to implement post-launch calibration for VIS and SWIR channels of INSAT-3D imager over land site (Little Rann of Kutch (ROK), Gujarat) on three different days to account for characterization errors or undetermined post-launch changes in spectral response of the sensor. The measurements of field reflectance of study site (of extent ~6 km x 6 km) in the wavelength range 325–2500 nm, along with atmospheric parameters (Aerosol Optical Depth, Total Columnar Ozone, Water Vapor) and sensor spectral response functions, were input to the 6S radiative transfer model to simulate radiance at top of the atmosphere (TOA) for VIS and SWIR bands. The uncertainty in vicarious calibration coefficients due to measured spatial variability of field reflectance along with due to aerosol types were also computed for the INSAT-3D imager. The effect of surface anisotropy on TOA radiance was studied using a MODIS Bidirectional Reflectance Distribution Function (BRDF) product covering the experimental site. The results show that there is no indication of change in calibration coefficients in INSAT- 3D imager, for VIS and SWIR band over Little ROK. Comparison made between the INSAT-3D imager measured radiance and 6S simulated radiance. Analysis shows that for clear sky days, the INSAT-3D imager overestimates TOA radiance in the VIS band by 5.1 % and in the SWIR band by 11.7 % with respect to 6S simulated radiance. For these bands, in the inverse mode, the 6S corrected surface reflectance was closer to field surface reflectance. It was found that site spatial variability was a critical factor in estimating change in sensor calibration coefficients and influencing uncertainty in TOA radiance for Little ROK.

Sensitivity Analysis for Aerosol Refractive Index and Size Distribution Estimation Methods Based on Polarized Atmospheric Irradiance Measurements

Aerosol refractive index and size distribution estimations based on polarized atmospheric irradiance measurements are proposed together with its application to reflectance based vicarious calibration. A method for reflectance based vicarious calibration with aerosol refractive index and size distribution estimation using atmospheric polarization irradiance data is proposed. It is possible to estimate aerosol refractive index and size distribution with atmospheric polarization irradiance measured with the different observation angles (scattering angles). The Top of the Atmosphere (TOA) or at-sensor radiance is estimated based on atmospheric codes with estimated refractive index and size distribution then vicarious calibration coefficient can be calculated by comparing to the acquired visible to near infrared instrument data onboard satellites. The estimated TOA radiance based on the proposed method is compared to that with aureole-meter based approach which is based on refractive index and size distribution estimated with solar direct, diffuse and aureole (Conventional AERONET approach). It is obvious that aureole-meter is not portable, heavy and large while polarization irradiance measurement instruments are light and small (portable size and weight).

Experimental Approach of Reflectance Based Vicarious Calibration Method for Solar Reflectance Wavelength Region of Sensor Onboard Remote Sensing Satellites

Experimental approach of reflectance based vicarious calibration of solar reflectance wavelength region of mission instruments onboard remote sensing satellites is conducted. As an example, vicarious calibration of ASTER/VNIR with estimated aerosol refractive index and size distribution that depends on atmospheric conditions is discussed. Strange solution of estimated refractive index and size distribution may occurred due to the fact that solution fell into one of local minima in the inversion process for phase function fitting between measured and estimated with assumed refractive index and size distribution. This paper describes atmospheric conditions that may induce such a situation. Namely, it may occur when the atmospheric optical depth is too thin and or Junge parameter is too small. In such case, refractive index and size distribution estimation accuracy is poor. A relation between refractive index and size distribution estimation accuracy and estimation accuracy of the Top of the Atmosphere (TOA) radiance (vicarious calibration accuracy) is also clarified in particular for ASTER/VNIR vicarious calibration. It is found that 10% of the refractive index and size distribution estimation error causes approximately 1.3% of TOA radiance estimation error.

Inversion of a coupled canopy–atmosphere model using multi-angular top-of-atmosphere radiance data: A forest case study

Since the launch of sensors with angular observation capabilities, such as CHRIS and MISR, the additional potential of multi-angular observations for vegetation structural and biochemical variables has been widely recognised. Various methods have been successfully implemented to estimate forest biochemical and biophysical variables from atmospherically-corrected multi-angular data, but the use of physically based radiative transfer (RT) models is still limited. Because both canopy and atmosphere have an anisotropic behaviour, it is important to understand the multi-angular signal measured by the sensor at the top of the atmosphere (TOA). Coupled canopy–atmosphere RT models allow linking surface variables directly to the TOA radiance measured by the sensor and are therefore very interesting tools to use for estimating forest variables from multi-angular data. We investigated the potential of TOA multi-angular radiance data for estimating forest variables by inverting a coupled canopy–atmosphere physical RT model. The case study focussed on three Norway spruce stands located at the Bily Kriz experimental site (Czech Republic), for which multi-angular CHRIS and field data were acquired in September 2006. The soil–leaf–canopy RT model SLC and the atmospheric model MODTRAN4 were coupled using a method allowing to make full use of the four canopy angular reflectance components provided by SLC. The TOA radiance simulations were in good agreement with the spectral and angular signatures measured by CHRIS. Singular value decompositions of the Jacobian matrices showed that the dimensionality of the variable estimation problem increased from 3 to 6 when increasing the number of observation angles from 1 to 4. The model inversion was conducted for two cases: 4 and 7 variables. The most influential parameters were chosen as free variables in the look-up tables, namely: vertical crown cover (Cv), fraction of bark material (fB), needle chlorophyll content (needleCab), needle dry matter content (needleCdm) for the 4-variable case, and additionally, tree shape factor (Zeta), dissociation factor (D), and needle brown pigments content (needleCs) in the 7-variable case. All angular combinations were tested, and the best estimates were obtained with combinations using two or three angles, depending on the number of variables and on the stand used. Overall, this case study showed that, although making use of its full potential is still a challenge, TOA multi-angular radiance data do have a higher potential for variable estimation than mono-angular data.

Evaluation of CMODIS-measured radiance by a hyperspectral model

A Chinese Moderate Resolution Imaging Spectrometer (CMODIS), an ocean colour sensor onboard the ‘Shenzhou-3’ spaceship, was launched on 25 March 2002. Because CMODIS was not equipped with any onboard calibration systems, there were major concerns about the accuracy of the CMODIS radiance measurements as well as the reliability of the data processing and oceanographic applications. To clarify these concerns, a hyperspectral satellite remote sensing radiance evaluation model (HRSREM) was developed, based on a radiative transfer model with consideration of multiple-scattering effects and atmospheric absorption. The model was used to compute the total radiance at the top of the atmosphere (TOA) to evaluate the CMODIS-derived radiance. The accuracy of the model was validated by Gordon's algorithms [Wang, M. and Gordon, H.R., 1994, A simple, moderately accurate, atmospheric correction algorithm for SeaWiFS. Remote Sensing of Environment, 50, pp. 231–239] and Sea-viewing Wide Field-of-view Sensor (SeaWiFS) data. The results show that the average relative error in the atmospheric scattering radiance computed by the HRSREM is less than 1.5% and the average error in the HRSREM TOA radiance is about 3.0%. Therefore, the HRSREM can be used to evaluate the accuracy of CMODIS-measured radiance. The results show that CMODIS has relatively small errors at the visible bands but large errors at the near-infrared (NIR) bands with an average error of more than 100%. The laboratory calibration coefficients are not reliable and the CMODIS data were recalibrated by the HRSREM to recover the archive of CMODIS data.

Evaluation of the Lakshadweep Sea as a site for vicarious calibration of the Ocean Colour Monitor

The uncertainty in the top-of-the-atmosphere (TOA) radiance is a result of uncertainties in aerosol components, water-leaving radiance (due to seawater constitutions) and whitecap radiance. This paper investigates the variability of these individual terms over the Arabian Sea and particularly in Lakshadweep region, to establish a site for vicarious calibration of the Ocean Colour Monitor (OCM). We found that fractional coverage of whitecap radiance is less than 0.5% for winds lower than 8 m s−1 and its radiance contribution can be assigned to a constant value. For higher winds, the contribution from whitecap radiance to TOA radiance has to be considered along with the atmospheric stability factor. The Lakshadweep Sea, for most of the time, is characterized by a low concentration of chlorophyll-a, an oligotrophic water body and maritime aerosol.

Remote sensing of ocean color: Assessment of the water-leaving radiance bidirectional effects on the atmospheric diffuse transmittance for SeaWiFS and MODIS intercomparisons

The portion of the radiance exiting the ocean and transmitted to the top of the atmosphere (TOA) in a particular direction depends on the angular distribution of the exiting radiance, not just the radiance exiting in the direction of interest. The diffuse transmittance t relates the water component of the TOA radiance to that exiting the water in the same direction. As such t is a property of the ocean–atmosphere system and not just the atmosphere. Its computation requires not only the properties of the atmosphere but the angular distribution of the exiting radiance as well. The latter is not known until a determination of the water properties can be made (which is the point of measuring the radiance in the first place). Because of this, it has been customary to assume an angular distribution (uniform upward radiance beneath the water surface) in the computation of t, which is referred to as t⁎. However, it is known that replacing t with t⁎ can result in an error of several percent in the retrieved water-leaving radiance. Since the error depends on sun-viewing direction, this error could be particularly important when water-leaving radiance from two or more sensors in different orbits are compared. Even given an estimate of the angular distribution of the water-leaving radiance, computation of t using full radiative transfer theory in an image processing environment is not practical. Thus, we developed a first-order correction to t for bidirectional effects in the water-leaving radiance that captures much of the variability of t with viewing direction. The correction computed across a SeaWiFS scan line shows that a t⁎-induced error of as much as 5–6% could occur near the edges of the scan; however, limiting the scan to polar viewing angles (θ) <60° reduces the error to ~1%. Direct application to SeaWIFS and MODIS (AQUA) suggests that the bidirectionally-induced error in the diffuse transmittance will result in an error less than about 1% in the comparison of their normalized water-leaving radiances, as long as θ is less than about 60°. We conclude that, given this constraint, the normalized water-leaving retrievals from these two sensors at a given location can be merged without regard for the bidirectionally-induced error in the diffuse transmittance, as the resulting uncertainty is well below that from other sources. It is important to note that this result is likely to apply to any other polar-orbiting sensor with equatorial crossing times (similar to SeaWiFS and MODIS) between 1030 and 1330 h (local time).

Bidirectional reflectance distribution function of snow: corrections for the Lambertian assumption in remote sensing applications

For remote sensing over snow-covered surfaces, the bidirectional reflectance distribution function (BRDF) of snow plays an important role that should be considered in inverse algorithms for the retrieval of snow properties. However, to simplify retrievals, many researchers assume that snow is a Lambertian reflector. This “forward model” error affects the accuracy of retrieved snow parameters (such as albedo, snow grain size, and impurity concentration). To quantify this error and to compensate for it, we provide a simple yet accurate semi-empirical correction formula. It allows for easy conversion of top-of-the-atmosphere (TOA) reflectance arising from an anisotropically reflecting snow surface to an equivalent TOA reflectance for a Lambertian surface with the same albedo. Conversely, this correction can be used to translate TOA radiance computed with the Lambertian assumption into a more realistic value based on a BRDF treatment. The coefficients in this correction formula are stored in a look-up table (LUT), and a simple LUT interpolation program is provided to allow the user to extract TOA reflectances for any sun-satellite geometry by quick interpolation in the LUTs. For the first 8 channels of the VIIRS spectrometer, the R-square regression coefficient for fitting this correction formula is better than 0.95 for a wide range of sun-satellite geometries.