Sun Zenith Angle Research Articles

Integrating physical model and image simulations to correct topographic effects on surface reflectance

Topography complicates the illumination distribution over rugged terrains and hinders the applications of surface reflectance data over mountainous areas. Topographic correction is an essential process to remove the topographic effects in surface reflectance data. This study proposed a Physical model and image Simulation-based topographic Correction method (PSC) for atmospherically corrected surface reflectance images. In contrast with traditional methods, the proposed approach explicitly estimates the illumination distribution over rugged terrains based on image information and then corrects the topographic effects following physical laws. The proposed method was validated and compared with existing well-performed topographic correction methods, including path length correction (PLC), sun-canopy-sensor correction with c factor (SCSC), C correction (CC), and statistical empirical correction (SE). Simulated and satellite data obtained at different times and geolocations are used for evaluation and comparison. The results demonstrated that most existing methods face challenges in removing the biases induced by topography in surface reflectance images. The PLC method failed to obtain reasonable results for faint illumination conditions when the sun zenith angle is high. SE showed relatively poor performance in terms of physical consistency and outlier percentage. Besides, all the traditional methods failed in the correction of cast shadow regions. Comparably, our method consistently demonstrated superior performance in physical consistency, cast shadow correction, and outlier percentage across various regions, encompassing different sun zenith angles and illumination conditions. The proposed method offers an effective and physically consistent approach for the topographic correction of surface reflectance images, facilitating multi-temporal applications over mountainous areas.

Reconstructing hyper-spectral downwelling irradiance from multi-spectral measurements

Checking the radiometric calibration of satellite hyper-spectral sensors such as the PACE Ocean Color Instrument (OCI) while they operate in orbit and evaluating remote sensing reflectance, the basic variable from which a variety of optical and biogeochemical ocean properties can be derived, requires measuring upwelling radiance just above the surface (Lw) and downwelling planar irradiance reaching the surface (Es). For this, the current HyperNav systems measure Lw at about 2 nm spectral resolution in the ultraviolet to near infrared, but Es in only four 10 nm wide spectral bands centered on 412, 489, 555, and 705 nm. In this study, the Es data acquired in these spectral bands in clear sky conditions are used to reconstruct via a multi-linear regression model the hyper-spectral Es signal at 0.5 nm resolution from 315 to 900 nm, the OCI spectral range, allowing an estimate of Es at the HyperNav, OCI, and other sensors’ resolutions. After correction of gaseous absorption and normalization by the top-of-atmosphere incident solar flux, the atmospheric diffuse transmittance is expressed as a linear combination of Es measured in those 4 spectral bands. Based on simulations for Sun zenith angles from 0 to 75° and a wide range of (i.e., expected) atmospheric, surface, and water conditions, the Es spectrum is reconstructed with a bias of less than 0.4% in magnitude and an RMS error (RMSE) ranging from 0% to 2.5%, depending on wavelength. The largest errors occur in spectral regions with strong gaseous absorption. In the presence of typical noise on Es measurements and uncertainties on the ancillary variables, the bias and RMSE become −2.5% and 7.0%, respectively. Using a General Additive Model with coefficients depending on Sun zenith angle and aerosol optical thickness at 550 nm improves statistical performance in the absence of noise, especially in the ultraviolet, but provides similar performance on noisy data, indicating more sensitivity to noise. Adding spectral bands in the ultraviolet, e.g., centered on 325, 340, and 380 nm, yields marginally more accurate results in the ultraviolet, due to uncertainties in the gaseous transmittance. Comparisons between the measured and reconstructed Es spectra acquired by the MOBY spectroradiometer show agreement within predicted uncertainties, i.e., biases less than 2% in magnitude and RMS differences less than 5%. Reconstruction can also be achieved accurately with other sets of spectral bands and extended to cloudy conditions since cloud optical properties, like aerosol properties, tend to vary regularly with wavelength. These results indicate that it is sufficient, for many scientific applications involving hyper-spectral Es, to measure Es in a few coarse spectral bands in the ultraviolet to near infrared and reconstruct the hyperspectral signal using the proposed multivariate linear modeling.

A synthetic optical database generated by radiative transfer simulations in support of studies in ocean optics and optical remote sensing of the global ocean

Abstract. Radiative transfer (RT) simulations have long been used to study the relationships between the inherent optical properties (IOPs) of seawater and light fields within and leaving the ocean, from which ocean apparent optical properties (AOPs) can be calculated. For example, inverse models used to estimate IOPs from ocean color radiometric measurements have been developed and validated using the results of RT simulations. Here we describe the development of a new synthetic optical database based on hyperspectral RT simulations across the spectral range of near-ultraviolet to near-infrared performed with the HydroLight radiative transfer code. The key component of this development is the generation of a synthetic dataset of seawater IOPs that serves as input to RT simulations. Compared to similar developments of optical databases in the past, the present dataset of IOPs is characterized by the probability distributions of IOPs that are consistent with global distributions representative of vast areas of open-ocean pelagic environments and coastal regions, covering a broad range of optical water types. The generation of synthetic data of IOPs associated with particulate and dissolved constituents of seawater was driven largely by an extensive set of field measurements of the phytoplankton absorption coefficient collected in diverse oceanic environments. Overall, the synthetic IOP dataset consists of 3320 combinations of IOPs. Additionally, the pure seawater IOPs were assumed following recent recommendations. The RT simulations were performed using 3320 combinations of input IOPs, assuming vertical homogeneity within an infinitely deep ocean. These input IOPs were used in three simulation scenarios associated with assumptions about inelastic radiative processes in the water column (not considered in previous synthetically generated optical databases) and three simulation scenarios associated with the sun zenith angle. Specifically, the simulations were made assuming no inelastic processes, the presence of Raman scattering by water molecules, and the presence of both Raman scattering and fluorescence of chlorophyll a pigment. Fluorescence of colored dissolved organic matter was omitted from all simulations. For each of these three simulation scenarios, the simulations were made for three sun zenith angles of 0, 30, and 60∘ assuming clear skies, standard atmosphere, and a wind speed of 5 m s−1. Thus, overall 29 880 RT simulations were performed. The output results of these simulations include radiance distributions, plane and scalar irradiances, and a whole set of AOPs, including remote-sensing reflectance, vertical diffuse attenuation coefficients, and mean cosines, where all optical variables are reported in the spectral range of 350 to 750 nm at 5 nm intervals for different depths between the sea surface and 50 m. The consistency of this new synthetic database has been assessed through comparisons with in situ data and previously developed empirical relationships involving IOPs and AOPs. The database is available at the Dryad open-access repository of research data (https://doi.org/10.6076/D1630T, Loisel et al., 2023).

Influence of DEM resolution on topographic correction models using spot satellite image

Topographic correction models (TCMs) are valid on satellite image data preprocessing steps. Theillumination angle may be sensitive to different terrain slope and aspect conditions base on sun-terrain-sensorgeometry. Although the topographic correction is influenced by the sun azimuth and zenith angle, the correction resultcan be equally in the same image status. By contrast, the terrain factors change with different digital elevation model(DEM) resolution in the topographic correction equations and cause a significant effect. Slope is sensitive in ruggedterrain, and aspect is impressionable at flat surface at a coarse DEM resolution data. As the DEM resolution lead adistinct result on TCMs, this research is aimed to examine the impact of DEM resolution on the accuracy of terrainrepresentation and of the gradient determined. In this study, five TCMs, including cosine correction, C correction, SCS correction, SCS+C correction and Minnaert correction models are compared by different resolutions using SPOTimage data. The 5 meter DEM obtained from Ministry of the interior will be resampled to 10 to 500 meters to testthose topographic models sustainability on Lienhuachih Research Center. The accuracy of five topographic correctionmodels base on different DEM resolution will be evaluated by root-mean-square error (RMSE).

Improvement of GOCI-II Water Vapor Absorption Correction through Fusion with GK-2A/AMI Data

In remote sensing of the ocean color, in particular, in coarse-resolution global model simulations, atmospheric trace gases including water vapor are generally treated as auxiliary data, which create uncertainties in atmospheric correction. The second Korean geostationary satellite mission, Geo-Kompsat 2 (GK-2), is unique in combining visible and infrared observations from the second geostationary ocean color imager (GOCI-II) and the advanced meteorological imager (AMI) over Asia and the Pacific Ocean. In this study, we demonstrate that AMI total precipitable water (TPW) data to allow realistic water vapor absorption correction of GOCI-II color retrievals for the ocean. We assessed the uncertainties of two candidate TPW products for GOCI-II atmospheric correction using atmospheric sounding data, and then analyzed the sensitivity of four ocean-color products (remote sensing reflectance [Rrs], chlorophyll-a concentration [CHL], colored dissolved organic matter [CDOM], and total suspended sediment [TSS]) for GOCI-II water vapor transmittance correction using AMI and global model data. Differences between the TPW sources increased the mean absolute percentage error (MAPE) of Rrs from 2.97% to 6.43% in the blue to green bands, higher than the global climate observing system requirements (<5%) at 412 nm. By contrast, MAPE values of 3.53%, 6.18%, and 7.71% were increased to 6.63%, 13.53%, and 16.14% at high sun and sensor zenith angles for CHL, CDOM, and TSS, respectively. Uncertainty analysis provided similar results, indicating that AMI TPW produced approximately 3-fold lower error rates in ocean-color products than obtained using TPW values from the National Centers for Environmental Prediction. These results imply that AMI TPW can improve the accuracy and ability of GOCI-II ocean-color products to capture diurnal variability.

Improving the standard protocol for above-water reflectance measurements: 1. Estimating effective wind speed from angular variation of sunglint.

The standard above-water protocol for measurement of water reflectance uses a measurement of wind speed to estimate the air-water interface reflectance factor and, thus, remove reflected skylight from upwelling radiance. This aerodynamic wind speed measurement may be a poor proxy for the local wave slope distribution in cases such as fetch-limited coastal and inland waters and/or where there are spatial or temporal differences between the wind speed measurement and the location of reflectance measurements. Here, an improved method is proposed, with a focus on sensors mounted on autonomous pan-tilt units and deployed on fixed platforms, replacing the aerodynamic wind speed measurement by optical measurements of angular variation of upwelling radiance. Using radiative transfer simulations, it is shown that the difference between two upwelling (i.e.,water plus air-water interface) reflectances acquired at least 10° apart from each other in the solar principal plane is strongly and monotonically related to effective wind speed. The approach shows good performance in twin experiments using radiative transfer simulations. Limitations of the approach are identified, including difficulties for a very high Sun zenith angle (>60∘), very low wind speed (<2m s -1), and, potentially, cases in which nadir-pointing angles are limited by optical perturbations from the viewing platform.

A data-driven approach to flag land-affected signals in satellite derived water quality from small lakes

The land-affected signal in remotely sensed radiance from nearshore waters is a common problem for remote sensing, introducing uncertainty in atmospheric correction and subsequent water quality constituent concentration estimates. This study proposes a new method for identifying effects of land on satellite remote sensing of water quality. The new optical water types (OWT) containing the land-affected signal were derived from POLYMER-corrected imagery of the Medium Resolution Imaging Spectrometer in reduced resolution (MERIS RR) and Sentinel-3 Ocean and Land Colour Instrument (OLCI). These were then applied, as part of a larger set of existing OWTs corresponding to the variability observed in natural waters, to satellite images. The ability to identify pixels containing both water and land, and those contaminated with radiance from adjacent land, was evaluated. Our test sites include dark lakes of varying size in Sweden (Lakes Rusken, Bolmen, Ringsjön, and Ivösjön) where the classification showed high sensitivity to land near the lake shore. The land-affected signal is shown to lead to underestimations of chlorophyll-a concentration and Forel-Ule colour indices, and overestimations of turbidity in these lakes, which can be corrected after masking out the land-affected pixels. The land-affected signal is strongest in summer, both NDVI and sun zenith angle covaried with the seasonal variation of land-affected signal. Further, the results confirmed that satellite images with coarser spatial resolution are more prone to land-affected signal compared to images with finer spatial resolution, for small inland water bodies. We propose a data-driven approach for water quality processing with ‘land-affected water types’ as an effective way to improve the lake optical water quality monitoring from water colour sensors.

Evaluating the contribution of Sentinel-2 view and illumination geometry to the accuracy of retrieving essential crop parameters

ABSTRACT Wide field-of-view (FOV) sensors such as Sentinel-2 exhibit per-pixel view and illumination geometry variation that may influence the retrieval accuracy of essential crop biophysical and biochemical variables (BVs) for precision agriculture. However, this aspect is rarely studied in the existing literature. Hence, the current study aimed to evaluate the contribution of view and illumination geometries to the accuracy of retrieving Leaf Chlorophyll a and b (LC ab ), Canopy Chlorophyll Content (CCC), and Leaf Area Index (LAI) using the Random Forest (RF). The experiments were performed on various input variable scenarios where per-pixel geometric covariates, i.e. View and Sun Zenith Angles (VZA and SZA, respectively), and Relative Azimuth Angle (RAA), are excluded and included in spectral bands (SB) and spectral vegetation indices (SVIs), respectively, in two semi-arid areas. The results showed that spectral bands or vegetation indices combined with geometric covariates improved the R 2 by 10–15% for LAI and 3–5% for CCC. In contrast, negligible improvements of 1–2% were achieved for LC ab with cross-validation test data and independent held-out dataset, respectively. In agreement with previous studies, VZA and SZA were among the topmost influential variables in the RF models for estimating LAI, LC ab , and CCC. Collectively, per-pixel geometric variables explained more than 30% of the variability in surface reflectance for all Sentinel-2 spectral bands (p < 2.2e-16). Overall, the results showed that incorporating geometric covariates improved the accuracy of retrieving BVs; thus, it provided additional information that improves the predictive power of SB and SVIs. The significant benefits of the geometric variables were mainly realized for canopy-level BVs (i.e. LAI and CCC) than for LC ab . Therefore, it is recommended to incorporate per-pixel view and illumination geometry in estimating LAI and CCC, especially when using wide-view sensors such as Sentinel-2. However, further testing in different phenology, site and acquisition conditions is needed to confirm the contribution of the geometric covariates to facilitate reliable retrieval of BVs from remotely sensed data and aid better agronomic decisions.

Modeling Shadow with Voxel-Based Trees for Sentinel-2 Reflectance Simulation in Tropical Rainforest

Satellite-based gross primary production (GPP) estimation has uncertainties due to shadow fraction caused by the geometric relationship between the complex forest structure and the Sun. The virtual forests allow shadow fraction estimation without 3D measurements, but require optimal structural parameters. In this study, we developed the reflectance simulator (Canopy-level Shadow and Reflectance Simulator, CSRS) that considers tree shadows and the method to determine the optimal canopy shape for shadow fraction estimation. The target forest is any tropical evergreen forest which accounts for 58% of tropical forests. Firstly, we analyzed the effects of canopy shape on the reflectance simulation based on virtual forests created with different canopy shapes. This result was checked by Tukey’s honestly significant difference (HSD) test. Secondly, the optimal canopy shape was determined by comparing the reflectance from Sentinel-2 Band 4 (red) bottom of atmosphere reflectance with those simulated from virtual forests. Finally, the shadow fraction estimated from the virtual forest was evaluated. Since the focus of this study was to derive the optimal canopy shape, unmanned aerial vehicle (UAV) structure from motion (SfM) was used to obtain the parameters other than canopy shape and to validate the estimated shadow fraction. The results showed that when the Sun zenith angle (SZA) was more than 20°, significant differences were observed among canopy shapes. The least root mean square error (RMSE) for reflectance simulation was 0.385 from the canopy shape of a half ellipsoid. Moreover, the half ellipsoid also showed the smallest RMSE in estimating shadow fraction (0.032), which indicated the reliability and applicability of CSRS. This study is the first attempt to determine the optimal canopy shape for estimating shadow fraction and is expected to improve the accuracy of GPP estimation in the future.

The impact of the large-scale circulation anomalies in the northern hemisphere on air quality in Wuhan of China during the Spring Festival in 2020

During the 2020 Chinese Spring Festival extended holidays and the COVID-19 period in Wuhan, the “quasi-zero emissions increase” occurred due to the human activities have a considerably lower impact on air quality. A new scientific question of concern: Why has high of O3 and PM2.5 still been observed? The comparative analysis of the influences of anomalous atmospheric circulation and weather conditions during this special period in 2019–2020 with those in the historical years can be useful. The results in the study were as follows:(1) Even during this “quasi-zero emissions increase” period, the concentrations of O3 and PM2.5 are higher than normal, with O3 being significantly higher in Wuhan. Which is associated with a anomaly large-scale humid/warm water vapor transport in the Northern Hemisphere which is significantly different from that of the same period with historical more-haze years. (2) In this scenario, the large-scale humid/warm water vapor transport brings sustained high-humidity water vapor lifting conditions to north China. The daily cycling change of the sun's zenith angle to support the high-humidity and high-condensation condition are conducive to the elevation of high condensation (fc) and degree of super-saturation (S) going up to the peak of S >10%. Under the condition driven by high fc, it causes the power exponent law to be followed converting NO2 to O3, thereby resulting in an increase in O3 concentration. This additional contribution to O3 concentration is as high as 37% than those from pure chemical process. (3) An objective quantitative reversal method for calculating the monthly threshold in fc is given based on the power exponential conversion law by the observed data. It can be prospectively applied to assess the feedback on the impact of pollution on urban climate.

Simulation system of lighting environment for optical imaging test

In order to overcome the shortcomings of the solar simulator in the aspect of the performance testing for optical imaging system, a lighting environment simulation system is designed. We select dysprosium lamp, xenon lamp and multiple LEDs as light sources of the solar simulator, and the change of the sun zenith angle is simulated by using the symmetrical method of the light source. Also, the light intensity is controlled by adjusting the output current of the power supply. Lighting environment simulation system not only can simulate spectral irradiance and intensity change of real solar, but also can simulate the real sun depending on the season and the angle of illumination period. This system provided a technical support for the performance testing of optical imaging systems.

The NASA EPIC/DSCOVR Ocean PAR Product

The EPIC/DSCOVR observations of the Earth’s surface lit by the Sun made from the first Lagrange point several times during the day in spectral bands centered on 443, 551, and 680 nm are used to estimate daily mean photosynthetically available radiation (PAR) at the ice-free ocean surface. The PAR algorithm uses a budget approach, in which the solar irradiance reaching the surface is obtained by subtracting from the irradiance arriving at the top of the atmosphere (known), the irradiance reflected to space (estimated from the EPIC Level 1b radiance data), taking account of atmospheric transmission and surface albedo (modeled). Clear and cloudy regions within a pixel do not need to be distinguished, which dismisses the need for often-arbitrary assumptions about cloudiness distribution within a pixel and is therefore adapted to the relatively large EPIC pixels. A daily mean PAR is estimated on the source grid for each EPIC instantaneous daytime observation, assuming no cloudiness changes during the day, and the individual estimates are remapped and weight-averaged using the cosine of the Sun zenith angle. In the computations, wind speed, surface pressure, and water vapor amount are extracted from NCEP Reanalysis 2 data, aerosol optical thickness and Angström coefficient from MERRA-2 data, and ozone amount from EPIC Level 2 data. Areas contaminated by Sun glint are excluded using a threshold on Sun glint reflectance calculated using wind data. Ice masking is based on NSIDC near-real-time ice fraction data. The product is evaluated against in situ measurements at various locations and compared with estimates from sensors in polar and geostationary orbits (MODIS, AHI). Unlike with MODIS, the EPIC PAR product does not exhibit gaps at low and middle latitudes. Accuracy is satisfactory for long-term studies of aquatic photosynthesis, especially given the much larger uncertainties on the fraction of PAR absorbed by live algae and the quantum yield of carbon fixation. The EPIC daily mean PAR product is generated operationally on a Plate Carrée (equal-angle) grid with 18.4 km resolution at the equator and on an 18.4 km equal-area grid, i.e., it is fully compatible with the NASA Greenbelt OBPG ocean-color products. Data are available since the beginning of the DSCOVR mission (i.e., June 2015) from the NASA Langley ASDC website.

Observational study of the PM2.5 and O3 superposition-composite pollution event during spring 2020 in Beijing associated with the water vapor conveyor belt in the northern hemisphere

Unlike the heavy haze situations in previous years, the emergence of a water vapor conveyor belt flowing through North China is an important factor in the foggy-haze event during the 48-h snowfall in Beijing and its surroundings, which disrupted the customary pollution snow removal atmospheric purification.①In the spring of 2020, during a period of negative emissions increment and weak human activity impact, a special interaction between the high humidity content of large-scale continuous water vapor transmission and the weather-scale daily cycling radiation cycle change (due to daily change of sun's zenith angle), as well as the atmospheric microphysical process, provided rare meteorological conditions for the superposition-compound growth of PM2.5 and O3 pollution. The water vapor conveyor belt, through enhanced transmission of easterly wind momentum, induced a high value super-saturation signal (S) and condensation rate (fc), resulting in 48 consecutive hours of snow in Beijing, but was also conducive to fine particulates, including precursor emissions, through a microphysical humidification process, which became synchronized. ②The weather-scale process impact refers to the cloud height rise in the daytime and decrease at night. Due to the daily fluctuations in cloud height, the concentration of O3 followed a coordinated daily cycle. During the day, when the sun's zenith angle going on the top, the PM2.5 and O3 superposition composite effects occur. Therefore, even during a negative emission period, PM2.5 and O3 superposition-composite serious pollution events occur as usual, rather than the expected possible atmospheric cleaning through snow.

An improved downscaled sun-induced chlorophyll fluorescence (DSIF) product of GOME-2 dataset

ABSTRACT Sun-induced chlorophyll fluorescence (SIF) is a proxy for plant photosynthesis. However, available SIF products either have a low spatial resolution or are spatially discontinuous. This paper proposes an improved downscaling method to generate a continuous 0.05-degree SIF dataset from GOME-2 retrievals, covering the period from February 2007 to March 2019. First, a random forest model was developed to predict SIF using training samples from GOME-2 SIF and explanatory variables with a resolution of 0.5 degrees (including reflectance at visible and near-infrared bands, vegetation index, temperature, and cosine values of the sun-zenith angle). Then, a 0.05-degree SIF dataset was predicted using the trained model and the corresponding explanatory variables at a resolution of 0.05 degrees. Subsequently, the predicted 0.05-degree SIF dataset was used as the weighting coefficient to redistribute the original 0.5-degree GOME-2 SIF to a downscaled 0.05-degree SIF dataset (DSIF) based on the energy conservation principle. The results showed that DSIF was more consistent with the original 0.5-degree retrievals than the similar GOME-2 derived spatially extended SIF dataset, which produced spatial details with a resolution of 0.05 degrees. The structure and physiology of SIF information were well represented in the DSIF, which is imperative for assessing global photosynthetic activity.

Remote Retrieval of Suspended Particulate Matter in Inland Waters: Image-Based or Physical Atmospheric Correction Models?

The objective of this paper was to compare the limits of three image-based atmospheric correction models (top of the atmosphere (ToA), dark object subtraction (DOS), and cosine of the sun zenith angle (COST)), and three physical models (atmospheric correction for flat terrain (ATCOR), fast line-of-sight atmospheric analysis of spectral hypercubes (FLAASH)), and ACOLITE) for retrieving suspended particulate matter (SPM) concentrations in inland water bodies using Landsat imagery. For SPM concentration estimates, all possible combinations of 2-band normalized ratios (2bNR) were computed, and a stepwise regression was applied. The correlation analysis allowed highlighting that the red/blue 2bNR was the best spectral index to retrieve SPM concentrations in the case of image-based models, while the red/green 2bNR was the best in the case of physical models. Contrary to expectations, image-based atmospheric models outperformed the accuracy of physical models. The cross-validation results underlined the good performance of the DOS and COST models, with R2 &gt; 0.83, NASH-criterion (Nash) &gt; 0.83, bias = −0.01 mg/L, and RMSE &lt; 0.27 mg/L. This outperformance was confirmed using blind test validation data, with an R2 &gt; 0.86 and Nash &gt; 0.58 for the DOS and COST models. The challenges and limitations involved in the remote monitoring of SPM spatial distribution in turbid productive waters using satellite data are discussed at the end of the paper.

Sea-surface reflectance factor: replicability of computed values.

The sea-surface reflectance factor ρ required for the determination of the water- leaving radiance from above-water radiometric measurements is derived from radiative transfer simulations relying on models of the sky-radiance distribution and sea-surface statistics. This work primarily investigates the impact on ρ of various sky-radiance and sea-surface models. A specific replicability analysis, restricted to the 550 nm wavelength, has been performed with the Monte Carlo code for Ocean Color Simulations (so-called MOX) accounting for the measurement geometry recommended in protocols for the validation of satellite ocean color data and commonly applied for operational measurements. Results indicate that the variability of ρ increases with wind speed and reaches the largest values for sun elevations close to the zenith or approaching the horizon. In particular, a variability up to about 2% is observed for wind speeds below 4 ms-1 and sun zenith angles larger than 20°. Finally, the benchmark of the ρ values from this study with those formally determined with the Hydrolight code and widely utilized by the ocean color community, exhibits systematic differences. The source of these differences is discussed and the implications for field measurements are addressed.

Polarization of Light Reflected by Grass: Modeling Using Visible-Sunlit Areas

The Bidirectional polarization distribution function (BPDF) of land surfaces is important for studies of land surfaces and aerosol. With the availability of a huge number of polarization measurements, several semi-empirical BPDF models have been proposed. However, these models do not pay much attention to canopy structure, which is fundamental for generation of polarization. In this article, we propose a new BPDF model using canopy structure information, which is parameterized by visible-sunlit areas. It is evaluated over grassland using POLDER BPDF and MODIS leaf area index data sets. Experiments suggest that compared to Nadal–Bréon and Litvinov models, the new BPDF model reduces root-mean-square error by 7% and 10%, respectively. The new BPDF model also provides better performance when it is fitted using observations clustered by sun zenith angle. The new BPDF model thus provides an effective tool for the study of land surface polarization.

Revised and extended benchmark results for Rayleigh scattering of sunlight in spherical atmospheres

While most of traditional Earth-atmosphere satellite remote sensing relies on radiative transfer (RT) in the plane parallel geometry, effects of sphericity are important at high sun and view zenith angles. Broad understanding of these effects is limited and, contrary to the plane-parallel case, finding accurate numerical results to test spherical RT codes is not easy. This paper aims to partially fill in this gap. Using the full-spherical RT code MYSTIC (Monte Carlo), and the plane-parallel RT code VLIDORT (discrete ordinates) corrected for atmospheric sphericity in the single and multiple scattering, we reproduced with better accuracy and extended the benchmark results by Adams & Kattawar[1].

Influence of sun zenith angle on canopy clumping and the resulting impacts on photosynthesis

Addressing the impact of vegetation architecture on shortwave radiation transfer in land surface models is important for accurate weather forecasting, carbon budget estimates, and climate predictions. This paper investigates to what extent it is possible to retrieve structural parameters of two different parameterization schemes from direct transmittance derived from digital hemispherical photography and 3D radiative transfer modeling for two study sites with different vegetation canopy architectures. Neglecting the representation of 3D canopy structure in radiative transfer schemes leads to significant errors in shortwave radiation partitioning (up to 3.5 times more direct transmittance in the 3D model). Structural parameters, referred to as whole canopy ‘clumping indices’, were obtained in order to evaluate the impact of angular variation in clumping on shortwave radiation transfer. Impacts on photosynthesis were evaluated at site level with the UKESM land surface model, JULES. A comparison between flux tower derived and modeled photosynthesis indicates that considering zenith angular variations of structural parameters in the radiative transfer scheme of the UKESM land surface model significantly improves photosynthesis prediction in light limited ecosystems (from RMSE = 2.91 μmol CO2.m− 2.s − 1 to RMSE = 1.51 μmol CO2.m − 2.s − 1, 48% smaller), typically with enhanced photosynthesis from bottom layers.

Upscaling seasonal phenological course of leaf dorsiventral reflectance in radiative transfer model

Forest top-of-canopy (TOC) reflectance observed by remote sensing instruments is driven by a wide range of parameters, including optical properties of leaves being the major driver, understory and bark together with structural properties of the forest - spatial distribution of leaves and branches in the canopy contributing also to variable extent. In addition, the instantaneous observation geometry (e.g. the sun and sensor zenith and azimuth angles) largely influences the observed TOC reflectance due to the changes in shadow fraction in the canopies. As a result, forest TOC reflectance is highly dynamic, following the seasonal patterns of optical and structural properties of vegetation.From the above-mentioned factors, the leaf optical properties are the most important. They hold an information on leaf biochemical (e.g. chlorophyll and carotenoid contents), water content and structural (e.g., leaf thickness, distribution of pigments within a leaf) properties at the leaf-level. Whereas biochemical effects on leaf reflectance are well understood, the role of dorsiventral asymmetry leaf internal structure on two-sided leaf optical properties has not been paid much attention, assuming equal optical properties on both leaf sides. However, the internal structure of a dorsiventral leaf is highly asymmetric often having different surface structure, tissue density and pigment distribution. Moreover, leaf optical properties are rarely measured during phenological development with high time resolution. Typically, studies focus on peak growing season neglecting the dynamic seasonal change of optical properties for broadleaved vegetation of temperate forests.In this study, we measured the seasonal course of leaf-level two-sided optical properties, biochemical and structural traits of five common broadleaved tree species of Central Europe (Betula pendula, Fagus sylvatica, Acer pseudoplatanus, Acer platanoides, and Sorbus aucuparia) with a typical dorsiventral, leaf anatomy without heliotropism. We link their leaf optical properties (e.g. reflectance difference of adaxial and abaxial sides, reflectance to transmittance ratio, fraction of specular component of leaf reflectance) with the laboratory analyses of inner leaf structure for contrasting leaf phenological stages from spring to fall. This allowed us to quantify both, species and seasonal differences of leaf dorsiventral optical properties and the role of inner leaf structure on leaf dorsiventral optical properties. Next, we up-scaled measured leaf-level dorsiventral optical properties to canopy level using Discrete Anisotropic Radiative Transfer model (DART) for three contrasting seasonal phenological stages - early spring (day of the year; DOY 115), summer (DOY 212) and fall (DOY 285). The aim was to quantify the impact of leaf optical properties parametrization (i.e. assuming equal optical properties on both sides, or dorsiventral leaf optical properties asymmetry) for forest of different species composition, vegetation phenological stage and observation geometries. The biggest differences between simulations were observed in VIS and NIR regions, where the neglecting differences in abaxial side leaf reflectance may introduce relative difference up to 20%, causing the underestimation of “one-sided” scenario compared to “two-sided” one, especially for off-nadir backscattering viewing directions. This may lead to increase in uncertainty when interpreting remote sensing observations, namely the chlorophyll content retrievals from back-scattering observation geometries and canopy structural retrievals (e.g. leaf area index) for all viewing geometries. We, thus, suggest to account for leaf reflectance dorsiventral differences in the next generation of radiative transfer models.