Sun Zenith Angle Research Articles

Field Intercomparison of Radiometer Measurements for Ocean Colour Validation

A field intercomparison was conducted at the Acqua Alta Oceanographic Tower (AAOT) in the northern Adriatic Sea, from 9 to 19 July 2018 to assess differences in the accuracy of in- and above-water radiometer measurements used for the validation of ocean colour products. Ten measurement systems were compared. Prior to the intercomparison, the absolute radiometric calibration of all sensors was carried out using the same standards and methods at the same reference laboratory. Measurements were performed under clear sky conditions, relatively low sun zenith angles, moderately low sea state and on the same deployment platform and frame (except in-water systems). The weighted average of five above-water measurements was used as baseline reference for comparisons. For downwelling irradiance ( E d ), there was generally good agreement between sensors with differences of <6% for most of the sensors over the spectral range 400 nm–665 nm. One sensor exhibited a systematic bias, of up to 11%, due to poor cosine response. For sky radiance ( L s k y ) the spectrally averaged difference between optical systems was <2.5% with a root mean square error (RMS) <0.01 mWm−2 nm−1 sr−1. For total above-water upwelling radiance ( L t ), the difference was <3.5% with an RMS <0.009 mWm−2 nm−1 sr−1. For remote-sensing reflectance ( R r s ), the differences between above-water TriOS RAMSES were <3.5% and <2.5% at 443 and 560 nm, respectively, and were <7.5% for some systems at 665 nm. Seabird-Hyperspectral Surface Acquisition System (HyperSAS) sensors were on average within 3.5% at 443 nm, 1% at 560 nm, and 3% at 665 nm. The differences between the weighted mean of the above-water and in-water systems was <15.8% across visible bands. A sensitivity analysis showed that E d accounted for the largest fraction of the variance in R r s , which suggests that minimizing the errors arising from this measurement is the most important variable in reducing the inter-group differences in R r s . The differences may also be due, in part, to using five of the above-water systems as a reference. To avoid this, in situ normalized water-leaving radiance ( L w n ) was therefore compared to AERONET-OC SeaPRiSM L w n as an alternative reference measurement. For the TriOS-RAMSES and Seabird-HyperSAS sensors the differences were similar across the visible spectra with 4.7% and 4.9%, respectively. The difference between SeaPRiSM L w n and two in-water systems at blue, green and red bands was 11.8%. This was partly due to temporal and spatial differences in sampling between the in-water and above-water systems and possibly due to uncertainties in instrument self-shading for one of the in-water measurements.

Assessment of Workflow Feature Selection on Forest LAI Prediction with Sentinel-2A MSI, Landsat 7 ETM+ and Landsat 8 OLI.

The European Space Agency (ESA)’s Sentinel-2A (S2A) mission is providing time series that allow the characterisation of dynamic vegetation, especially when combined with the National Aeronautics and Space Administration (NASA)/United States Geological Survey (USGS) Landsat 7 (L7) and Landsat 8 (L8) missions. Hybrid retrieval workflows combining non-parametric Machine Learning Regression Algorithms (MLRAs) and vegetation Radiative Transfer Models (RTMs) were proposed as fast and accurate methods to infer biophysical parameters such as Leaf Area Index (LAI) from these data streams. However, the exact design of optimal retrieval workflows is rarely discussed. In this study, the impact of five retrieval workflow features on LAI prediction performance of MultiSpectral Instrument (MSI), Enhanced Thematic Mapper Plus (ETM+) and Operational Land Imager (OLI) observations was analysed over a Dutch beech forest site for a one-year period. The retrieval workflow features were the (1) addition of prior knowledge of leaf chemistry (two alternatives), (2) the choice of RTM (two alternatives), (3) the addition of Gaussian noise to RTM produced training data (four and five alternatives), (4) possibility of using Sun Zenith Angle (SZA) as an additional MLRA training feature (two alternatives), and (5) the choice of MLRA (six alternatives). The features were varied in a full grid resulting in 960 inversion models in order to find the overall impact on performance as well as possible interactions among the features. A combination of a Terrestrial Laser Scanning (TLS) time series with litter-trap derived LAI served as independent validation. The addition of absolute noise had the most significant impact on prediction performance. It improved the median prediction Root Mean Square Error (RMSE) by 1.08 m2 m−2 when 5 % noise was added compared to inversions with 0 % absolute noise. The choice of the MLRA was second most important in terms of median prediction performance, which differed by 0.52 m2 m−2 between the best and worst model. The best inversion model achieved an RMSE of 0.91 m2 m−2 and explained 84.9% of the variance of the reference time series. The results underline the need to explicitly describe the used noise model in future studies. Similar studies should be conducted in other study areas, both forest and crop systems, in order to test the noise model as an integral part of hybrid retrieval workflows.

The global long-term microwave Vegetation Optical Depth Climate Archive (VODCA)

Abstract. Since the late 1970s, space-borne microwave radiometers have been providing measurements of radiation emitted by the Earth’s surface. From these measurements it is possible to derive vegetation optical depth (VOD), a model-based indicator related to the density, biomass, and water content of vegetation. Because of its high temporal resolution and long availability, VOD can be used to monitor short- to long-term changes in vegetation. However, studying long-term VOD dynamics is generally hampered by the relatively short time span covered by the individual microwave sensors. This can potentially be overcome by merging multiple VOD products into a single climate data record. However, combining multiple sensors into a single product is challenging as systematic differences between input products like biases, different temporal and spatial resolutions, and coverage need to be overcome. Here, we present a new series of long-term VOD products, the VOD Climate Archive (VODCA). VODCA combines VOD retrievals that have been derived from multiple sensors (SSM/I, TMI, AMSR-E, WindSat, and AMSR2) using the Land Parameter Retrieval Model. We produce separate VOD products for microwave observations in different spectral bands, namely the Ku-band (period 1987–2017), X-band (1997–2018), and C-band (2002–2018). In this way, our multi-band VOD products preserve the unique characteristics of each frequency with respect to the structural elements of the canopy. Our merging approach builds on an existing approach that is used to merge satellite products of surface soil moisture: first, the data sets are co-calibrated via cumulative distribution function matching using AMSR-E as the scaling reference. To do so, we apply a new matching technique that scales outliers more robustly than ordinary piecewise linear interpolation. Second, we aggregate the data sets by taking the arithmetic mean between temporally overlapping observations of the scaled data. The characteristics of VODCA are assessed for self-consistency and against other products. Using an autocorrelation analysis, we show that the merging of the multiple data sets successfully reduces the random error compared to the input data sets. Spatio-temporal patterns and anomalies of the merged products show consistency between frequencies and with leaf area index observations from the MODIS instrument as well as with Vegetation Continuous Fields from the AVHRR instruments. Long-term trends in Ku-band VODCA show that since 1987 there has been a decline in VOD in the tropics and in large parts of east-central and north Asia, while a substantial increase is observed in India, large parts of Australia, southern Africa, southeastern China, and central North America. In summary, VODCA shows vast potential for monitoring spatial–temporal ecosystem changes as it is sensitive to vegetation water content and unaffected by cloud cover or high sun zenith angles. As such, it complements existing long-term optical indices of greenness and leaf area. The VODCA products (Moesinger et al., 2019) are open access and available under Attribution 4.0 International at https://doi.org/10.5281/zenodo.2575599.

Developing a New Machine-Learning Algorithm for Estimating Chlorophyll-a Concentration in Optically Complex Waters: A Case Study for High Northern Latitude Waters by Using Sentinel 3 OLCI

The monitoring of Chlorophyll-a (Chl-a) concentration in high northern latitude waters has been receiving increased focus due to the rapid environmental changes in the sub-Arctic, Arctic. Spaceborne optical instruments allow the continuous monitoring of the occurrence, distribution, and amount of Chl-a. In recent years, the Ocean and Land Color Instruments (OLCI) onboard the Sentinel 3 (S3) A and B satellites were launched, which provide data about various aquatic environments on advantageous spatial, spectral, and temporal resolutions with high SNR. Although S3 OLCI could be favorable to monitor high northern latitude waters, there have been several challenges related to Chl-a concentration retrieval in these waters due to their unique optical properties coupled with challenging environments including high sun zenith angle, presence of sea ice, and frequent cloud covers. In this work, we aim to overcome these difficulties by developing a machine-learning (ML) approach designed to estimate Chl-a concentration from S3 OLCI data in high northern latitude optically complex waters. The ML model is optimized and requires only three S3 OLCI bands, reflecting the physical characteristic of Chl-a as input in the regression process to estimate Chl-a concentration with improved accuracy in terms of the bias (five times improvements.) The ML model was optimized on data from Arctic, coastal, and open waters, and showed promising performance. Finally, we present the performance of the optimized ML approach by computing Chl-a maps and corresponding certainty maps in highly complex sub-Arctic and Arctic waters. We show how these certainty maps can be used as a support to understand possible radiometric calibration issues in the retrieval of Level 2 reflectance over these waters. This can be a useful tool in identifying erroneous Level 2 Remote sensing reflectance due to possible failure of the atmospheric correction algorithm.

Drifting Effects of NOAA Satellites on Long-Term Active Fire Records of Europe

Explicit knowledge of different error sources in long-term climate records from space is required to understand and mitigate their impacts on resulting time series. Imagery of the heritage Advanced Very High Resolution Radiometer (AVHRR) provides unique potential for climate research dating back to the 1980s, flying onboard a series of successive National Oceanic and Atmospheric Administration (NOAA) and Meteorological Operational (MetOp) satellites. However, the NOAA satellites are affected by severe orbital drift that results in spurious trends in time series. We identified the impact and extent of the orbital drift in 1 km AVHRR long-term active fire data. This record contains data of European fire activity from 1985–2016 and was analyzed on a regional scale and extended across Europe. Inconsistent sampling of the diurnal active fire cycle due to orbital drift with a maximum delay of ∼5 h over NOAA-14 lifetime revealed a ∼90% decline in the number of observed fires. However, interregional results were less conclusive and other error sources as well as interannual variability were more pronounced. Solar illumination, measured by the sun zenith angle (SZA), related changes in background temperatures were significant for all regions and afternoon satellites with major changes in −0.03 to −0.09 K deg − 1 for ▵ B T 34 (p ≤ 0 . 001). Based on example scenes, we simulated the influence of changing temperatures related to changes in the SZA on the detection of active fires. These simulations showed a profound influence of the active fire detection capabilities dependent on biome and land cover characteristics. The strong decrease in the relative changes in the apparent number of active fires calculated over the satellites lifetime highlights that a correction of the orbital drift effect is essential even over short time periods.

Different representations of canopy structure—A large source of uncertainty in global land surface modeling

Land surface models generally use a “mosaic” approach to represent subgrid variation in canopy structure within a given land cell in two ways: one is based on vegetation cover types (VCT) (e.g., biomes or land cover classes); the other uses plant functional types (PFT)—a further breakdown of VCTs. The two representations largely differ in their representations of vegetation composition and canopy geometries, but both are one-dimensional (1D) treatments. To isolate the effects arising from the canopy structures, idealized experiments in a simple three-dimensional (3D) canopy scenario are conducted to analyze the effects of different representations (VCT, PFT and a newly developed 3D representation) on the simulated surface fluxes. The results show that the conventional VCT and PFT representations may have large discrepancies in terms of radiation transfer, resistance and surface flux calculations, whereas the 3D representation usually has values in between or closer to one of the other representations. The discrepancies in surface flux can be attributed to differences in the radiation absorptions and resistance values of different canopy representations. The resistance values of the 3D representation are more similar to those of the VCT case. For shortwave radiation absorption at small sun zenith angles, the 3D representation shows values closer to those of the PFT case but approaches the values of the VCT case for large sun zenith angles. Sensitivity analysis identified such a large source of uncertainty resulting from different representations of canopy structure in land surface modeling, especially for the VCT and PFT representations currently being used. We recommend that proper canopy structures (at least at the crown level), as well as the corresponding physical processes, need to be incorporated in the future development of land surface models.

Simulation of Reflected Solar Radiation for Atmosphere Gas Composition Evaluation for Optical Remote Sensing from Space

At present, in optical remote sensing of atmosphere from space, a new problem class appears: to determine little gas components (carbon dioxide, methane, etc.), which cause greenhouse effect. Concentration of these gases in atmosphere is less than one percent, which rigidly limits accuracy of satellite measurements and of simulating spatial concentration of the radiation (signal) flow reflected by Earth. In the article, a description of simulating signals received by satellite spectrometer in nearinfrared spectrum region is given. The signals are solar radiation passed through an atmosphere layer and reflected from Earth surface. It is calculated based on parametrical radiation atmosphere scattering and absorption model, which takes into consideration both multidimensional atmosphere parameter structure and Earth surface relief. Accounting such information allows you to go from measurement of spatial radiation flux to calculations of gas concentration for an arbitrary geographical Earth surface point and for any time point. As an example, calculations for Fourier spectrometer for spectrum measurement near IR region with an average spectral resolution are presented. The spectrometer was installed on the GOSAT satellite of the Space Agency of Japan. A comparison of computed and of really measured values of the signal received by the satellite shows that deviation for the Sun zenith angle equal to 30o does not exceed 3 %.

Performance Assessment of the COMET Cloud Fractional Cover Climatology across Meteosat Generations

The CM SAF Cloud Fractional Cover dataset from Meteosat First and Second Generation (COMET, https://doi.org/10.5676/EUM_SAF_CM/CFC_METEOSAT/V001) covering 1991–2015 has been recently released by the EUMETSAT Satellite Application Facility for Climate Monitoring (CM SAF). COMET is derived from the MVIRI and SEVIRI imagers aboard geostationary Meteosat satellites and features a Cloud Fractional Cover (CFC) climatology in high temporal (1 h) and spatial (0.05° × 0.05°) resolution. The CM SAF long-term cloud fraction climatology is a unique long-term dataset that resolves the diurnal cycle of cloudiness. The cloud detection algorithm optimally exploits the limited information from only two channels (broad band visible and thermal infrared) acquired by older geostationary sensors. The underlying algorithm employs a cyclic generation of clear sky background fields, uses continuous cloud scores and runs a naïve Bayesian cloud fraction estimation using concurrent information on cloud state and variability. The algorithm depends on well-characterized infrared radiances (IR) and visible reflectances (VIS) from the Meteosat Fundamental Climate Data Record (FCDR) provided by EUMETSAT. The evaluation of both Level-2 (instantaneous) and Level-3 (daily and monthly means) cloud fractional cover (CFC) has been performed using two reference datasets: ground-based cloud observations (SYNOP) and retrievals from an active satellite instrument (CALIPSO/CALIOP). Intercomparisons have employed concurrent state-of-the-art satellite-based datasets derived from geostationary and polar orbiting passive visible and infrared imaging sensors (MODIS, CLARA-A2, CLAAS-2, PATMOS-x and CC4CL-AVHRR). Averaged over all reference SYNOP sites on the monthly time scale, COMET CFC reveals (for 0–100% CFC) a mean bias of −0.14%, a root mean square error of 7.04% and a trend in bias of −0.94% per decade. The COMET shortcomings include larger negative bias during the Northern Hemispheric winter, lower precision for high sun zenith angles and high viewing angles, as well as an inhomogeneity around 1995/1996. Yet, we conclude that the COMET CFC corresponds well to the corresponding SYNOP measurements, and it is thus useful to extend in both space and time century-long ground-based climate observations.

BUILDING SHADOW DETECTION FROM GHOST IMAGERY

Abstract. Shadow is one of the basic features of remote sensing image, it expresses a lot of information of the object which is loss or interference, and the removal of shadow is always a difficult problem to remote sensing image processing. In this paper, it is mainly analyzes the characteristics and properties of shadows from the ghost image (traditional orthorectification). The DBM and the interior and exterior orientation elements of the image are used to calculate the zenith angle of sun. Then this paper combines the scope of the architectural shadows which has be determined by the zenith angle of sun with the region growing method to make the detection of architectural shadow areas. This method lays a solid foundation for the shadow of the repair from the ghost image later. It will greatly improve the accuracy of shadow detection from buildings and make it more conducive to solve the problem of urban large-scale aerial imagines.

Unstructured-Mesh Terrain Analysis and Incident Solar Radiation for Continuous Hydrologic Modeling in Mountain Watersheds

This article presents a methodology for estimating total incoming solar radiation from Triangular Irregular Network (TIN) topographic meshes. The algorithm also computes terrain slope degree and aspect (slope orientation) and accounts for self shading and cast shadows, sky view fractions for diffuse radiation, remote albedo and atmospheric backscattering, by using a vectorial approach within a topocentric coordinate system establishing geometric relations between groups of TIN elements and the sun position. A normal vector to the surface of each TIN element describes its slope and aspect while spherical trigonometry allows computing a unit vector defining the position of the sun at each hour and day of the year. Sky view fraction, useful to determine diffuse and backscattered radiation, is computed for each TIN element at prescribed azimuth intervals targeting the steepest elevation gradient. A comparison between the sun zenith angle and the steepest gradient allows deciding whether or not the pivot element is shaded. Finally, remote albedo is computed from the sky view fraction complementary functions for observed albedo values of the surrounding terrain. The sensitivity of the different radiative components to seasonal changes in atmospheric transmissivitties and surrounding albedo is tested in a mountainous watershed in Wyoming. This methodology represents an improvement on the current algorithms to compute terrain and radiation values on unstructured-mesh terrain models. All terrain-related features (e.g., slope, aspect, sky view fraction) can be pre-computed and stored for easy access into a subsequent, progressive-in-time, numerical simulation.

Comparison of two sensor technologies for solar irradiance measurement in a desert environment

Using 24 months of 1-min radiometric measurements conducted at two remote arid sites in Kuwait, the impact of sensor technology (thermopile vs. photodiode with rotating shadowband) on the magnitude of the three components of solar irradiance (global, direct and diffuse) is analyzed. The deviations (photodiode minus thermopile) are typically affected by both sun zenith angle and irradiance magnitude. For the global and direct components, most deviations (91% in the case of GHI, 87–91% in the case of DNI, depending on site) are within ±5%, and can thus be considered satisfactory. Larger deviations in direct and global irradiance are typically found under low zenith angles (summer conditions). The main source of concern is the negative bias and intricate pattern found in the diffuse deviations, most of the time. Only 46–61% of the deviations (depending on site) are within ±5%. The diffuse issue seems to be caused by an insufficient spectral correction of the diffuse reading. The so-called “cat ear” angular issue (a sudden spike in sensitivity for an angle of incidence ≈80°, combined with a sudden drop in sensitivity beyond ≈85°) is also still present in the direct and global irradiance measurements under clear conditions. The present results underline the imperfect nature of the empirical corrections typically applied to photodiode instruments to improve their irradiance estimates. Nonetheless, the deviations observed here are sufficiently low in general to guarantee good resource assessments, even under harsh and variable desert conditions, to the condition that the photodiode instruments are properly calibrated on site during periods whose atmospheric conditions are representative of the whole year, and their readings are duly corrected with the best possible algorithms.

Re-examining the effect of particle phase functions on the remote-sensing reflectance.

Even though it is well known that both the magnitude and detailed angular shape of scattering (phase function, PF), particularly in the backward angles, affect the color of the ocean, the current remote-sensing reflectance (Rrs) models typically account for the effect of its magnitude only through the backscattering coefficient (bb). Using 116 volume scattering function (VSF) measurements previously collected in three coastal waters around the U.S. and in the water of the North Atlantic Ocean, we re-examined the effect of particle PF on Rrs in four scenarios. In each scenario, the magnitude of particle backscattering (i.e., bbp) is known, but the knowledge on the angular shape of particle backscattering is assumed to increase from knowing nothing about the shape of particle PFs to partially knowing the particle backscattering ratio (Bp), the exact backscattering shape as defined by β˜p(γ≥90°) (particle VSF normalized by the particle total scattering coefficient), and the exact backscattering shape as defined by the χp factor (particle VSF normalized by the particle backscattering coefficient). At sun zenith angle=30°, the nadir-viewed Rrs would vary up to 65%, 35%, 20%, and 10%, respectively, as the constraints on the shape of particle backscattering become increasingly stringent from scenarios 1 to 4. In all four scenarios, the Rrs variations increase with both viewing and sun angles and are most prominent in the direction opposite the sun. Our results show a greater impact of the measured particle PFs on Rrs than previously found, mainly because our VSF data show a much greater variability in Bp, β˜p(γ≥90°), and χp than previously known. Among the uncertainties in Rrs due to the particle PFs, about 97% can be explained by χp, 90% by β˜p(γ≥90°), and 27% by Bp. The results indicate that the uncertainty in ocean color remote sensing can be significantly constrained by accounting for χp of the VSFs.

Assessment of Radiometric Data from a Buoy in the St. Lawrence Estuary

AbstractFisheries and Oceans Canada maintains a network of scientific buoys in the St. Lawrence estuary and gulf. Among a suite of environmental parameters documented, the in-water upwelling radiance is measured using an ocean color radiometer located underneath the center of the buoy. The shadow effect from the 1.05-m-radius buoy on the measured upwelling radiance is estimated and empirical models to correct for it are proposed. On average, the shading error (i.e., the percent of missing radiance) was and for the 555- and 412-nm channels, respectively. Two analytical models were tested to predict the shading error using measured inherent optical properties, the sun zenith angle, and the fraction of diffuse sky irradiance. Neglecting light scattering led to overestimates of the shading error. In contrast, the bias was removed when the scattering coefficient was accounted for, but the overall error was only barely improved (root-mean-square error ). Empirical relationships based on the uncorrected reflectance ratio measured by the buoy were used to predict both the shading error and the diffuse attenuation of the upwelling radiance, two quantities needed to calculate remote sensing reflectance . Overall, was retrieved with an averaged absolute percent difference ranging from 12% to 20%, which appears adequate for the validation of ocean color data such as the Moderate Resolution Imaging Spectroradiometer (MODIS-Aqua) and Visible Infrared Imaging Radiometer Suite (VIIRS) products in the optically complex waters of the St. Lawrence estuary.

Polarized reflectances of urban areas: Analysis and models

Accurate BPDF (Bidirectional Polarization Distribution Functions) of urban will help to improve the accuracy of inverted aerosol parameters that is very important for the research of urban atmospheric pollution and climate change. With Fresnel formula of polarization as the foundation, this paper studies three important factors that influence the BPDF: shadow, slope distribution and NDVI, and proposes a new BPDF model (named as Xie-Cheng model for convenience in comparison) for urban areas. Because the influence of slope distribution is trivial, only two parameters are needed in the new model, one controlling the shadow and the other for overall scale. An experimental factor is introduced into the model to compensate the influence of NDVI for polarized reflectance more accurately. Experiments prove that new model performs best in both correlation and RMSE for measurements not only with different urban places around the world but also clustered urban data by different NDVIs. Compared with the best current BPDF model, new model can reduce the average RMSE error by about 4.5% for different urban areas. Error distribution in polar coordinates also shows that new model can achieve smallest errors in almost all directions under fixed sun zenith angle.

Retrieval of macro- and micro-physical properties of oceanic hydrosols from polarimetric observations

Abstract Remote sensing has mainly relied on measurements of scalar radiance and its spectral and angular features to retrieve micro- and macro-physical properties of aerosols/hydrosols. However, it is recognized that measurements that include the polarimetric characteristics of light provide more intrinsic information about particulate scattering. To take advantage of this, we used vector radiative transfer (VRT) simulations and developed an analytical relationship to retrieve the macro and micro-physical properties of the oceanic hydrosols. Specifically, we investigated the relationship between the observed degree of linear polarization (DoLP) and the ratio of attenuation-to-absorption coefficients (c/a) in water, from which the scattering coefficient can be readily computed (b = c − a), after retrieving a. This relationship was parameterized for various scattering geometries, including sensor zenith/azimuth angles relative to the Sun's principal plane, and for varying Sun zenith angles. An inversion method was also developed for the retrieval of the microphysical properties of hydrosols, such as the bulk refractive index and the particle size distribution. The DoLP vs c/a relationship was tested and validated against in-situ measurements of underwater light polarization obtained by a custom-built polarimeter and measurements of the coefficients a and c, obtained using an in-water WET Labs ac-s instrument package. These measurements confirmed the validity of the approach, with retrievals of attenuation coefficients showing a high coefficient of determination depending on the wavelength. We also performed a sensitivity analysis of the DoLP at the Top of Atmosphere (TOA) over coastal waters showing the possibility of polarimetric remote sensing application for ocean color.

CPBAC: A quick atmospheric correction method using the topographic information

Abstract Topography is one of the most important factors that can affect the radiative transfer process in remote sensing earth observation by making the ground objects of the same type but in different topographical locations present different radiative characteristics which increases the complexity of spectral radiation information and violates the radiometric consistency among those type-identical objects. Utilizing the topographic information we proposed a coupling parameter based atmospheric correction method or CPBAC for short. This method firstly establishes the radiative transfer equations using the data of objects with good Lambertian properties under various topographical positions, and then calculates the coupling parameters of atmosphere and topography and finally accomplishes both the atmospheric and topographic correction simultaneously using those parameters. To validate it, the Landsat 7 multispectral data about one region in Cody Town of Wyoming, USA which was acquired in different seasons was used to do a comparison with the commonly used FLAASH and QUAC methods. The comparison revealed that the CPBAC method can effectively achieve atmospheric correction with its result similar to that produced by either FLAASH or QUAC, and the topography caused spectral radiation difference among the objects of the same type was eliminated by showing an increased spectral consistency and a decreased relevance between radiometric information and topography. Although the assumptions which the CPBAC method is based upon impose restrictions on its widespread use, it can achieve the best effect when it is applied to handle the data containing objects of good Lambertian properties located in different terrains, and the selected pixels for the parameters calculation are various combinations of those with small slope and small sun zenith angle.

Canopy Light Interception Conversion in Upright Fruiting Offshoot (UFO) Sweet Cherry Orchard

<abstract> The sun‘s position varies throughout the day. The changes in time and/or location cause different angles between the sunbeam and tree canopy (sunbeam-canopy angle, θ). Canopy light interception, the fractional interception of photosynthetically active radiation (PAR), varies as θ changes, resulting in a different measurement basis of light interception if light interception is measured at different θ. This situation is more noticeable in planar tree architecture, such as upright fruiting offshoot (UFO) architecture. This study aimed to develop a conversion method to provide a common basis for determining canopy light interception at different sun positions for such architectures. In addition to sun azimuth and zenith angles (α and β, respectively), conversion inputs included canopy dimensions, orchard configuration, canopy light interception, and canopy ground occupation porosity (the fraction of non-shaded area). Field data collection was conducted at 18 different times within a time window of ±2.50 h from solar noon (no mutual shadows were cast between adjacent rows) with 0.25 h intervals for eight sample blocks. The conversion method was able to convert canopy light interception with an overall root mean square error (RMSE) of 0.03 and mean absolute percentage error (MAPE) of 7.85%. Based on the overall accuracies when using a different basis and using different measured values in this study, conversion is recommended when θ is greater than 8°. In addition, we recommend conducting conversion with canopy projection from the same side to avoid the influence of asymmetrical canopies.

Evaluation of Radiometric and Atmospheric Correction Algorithms for Aboveground Forest Biomass Estimation Using Landsat 5 TM Data

Solar radiation is affected by absorption and emission phenomena during its downward trajectory from the Sun to the Earth’s surface and during the upward trajectory detected by satellite sensors. This leads to distortion of the ground radiometric properties (reflectance) recorded by satellite images, used in this study to estimate aboveground forest biomass (AGB). Atmospherically-corrected remote sensing data can be used to estimate AGB on a global scale and with moderate effort. The objective of this study was to evaluate four atmospheric correction algorithms (for surface reflectance), ATCOR2 (Atmospheric Correction for Flat Terrain), COST (Cosine of the Sun Zenith Angle), FLAASH (Fast Line-of-sight Atmospheric Analysis of Spectral Hypercubes) and 6S (Second Simulation of Satellite Signal in the Solar), and one radiometric correction algorithm (for reflectance at the sensor) ToA (Apparent Reflectance at the Top of Atmosphere) to estimate AGB in temperate forest in the northeast of the state of Durango, Mexico. The AGB was estimated from Landsat 5 TM imagery and ancillary information from a digital elevation model (DEM) using the non-parametric multivariate adaptive regression splines (MARS) technique. Field reference data for the model training were collected by systematic sampling of 99 permanent forest growth and soil research sites (SPIFyS) established during the winter of 2011. The following predictor variables were identified in the MARS model: Band 7, Band 5, slope (β), Wetness Index (WI), NDVI and MSAVI2. After cross-validation, 6S was found to be the optimal model for estimating AGB (R2 = 0.71 and RMSE = 33.5 Mg·ha−1; 37.61% of the average stand biomass). We conclude that atmospheric and radiometric correction of satellite images can be used along with non-parametric techniques to estimate AGB with acceptable accuracy.

Investigation of Atmospheric Effects on Retrieval of Sun-Induced Fluorescence Using Hyperspectral Imagery.

Significant research progress has recently been made in estimating fluorescence in the oxygen absorption bands, however, quantitative retrieval of fluorescence data is still affected by factors such as atmospheric effects. In this paper, top-of-atmosphere (TOA) radiance is generated by the MODTRAN 4 and SCOPE models. Based on simulated data, sensitivity analysis is conducted to assess the sensitivities of four indicators—depth_absorption_band, depth_nofs-depth_withfs, radiance and Fs/radiance—to atmospheric parameters (sun zenith angle (SZA), sensor height, elevation, visibility (VIS) and water content) in the oxygen absorption bands. The results indicate that the SZA and sensor height are the most sensitive parameters and that variations in these two parameters result in large variations calculated as the variation value/the base value in the oxygen absorption depth in the O2-A and O2-B bands (111.4% and 77.1% in the O2-A band; and 27.5% and 32.6% in the O2-B band, respectively). A comparison of fluorescence retrieval using three methods (Damm method, Braun method and DOAS) and SCOPE Fs indicates that the Damm method yields good results and that atmospheric correction can improve the accuracy of fluorescence retrieval. Damm method is the improved 3FLD method but considering atmospheric effects. Finally, hyperspectral airborne images combined with other parameters (SZA, VIS and water content) are exploited to estimate fluorescence using the Damm method and 3FLD method. The retrieval fluorescence is compared with the field measured fluorescence, yielding good results (R2 = 0.91 for Damm vs. SCOPE SIF; R2 = 0.65 for 3FLD vs. SCOPE SIF). Five types of vegetation, including ailanthus, elm, mountain peach, willow and Chinese ash, exhibit consistent associations between the retrieved fluorescence and field measured fluorescence.

A fast radiative transfer method for the simulation of visible satellite imagery

A computationally efficient radiative transfer method for the simulation of visible satellite images is presented. The top of atmosphere reflectance is approximated by a function depending on vertically integrated optical depths and effective particle sizes for water and ice clouds, the surface albedo, the sun and satellite zenith angles and the scattering angle. A look-up table (LUT) for this reflectance function is generated by means of the discrete ordinate method (DISORT). For a constant scattering angle the reflectance is a relatively smooth and symmetric function of the two zenith angles, which can be well approximated by the lowest-order terms of a 2D Fourier series. By storing only the lowest Fourier coefficients and adopting a non-equidistant grid for the scattering angle, the LUT is reduced to a size of 21MB per satellite channel.The computation of the top of atmosphere reflectance requires only the calculation of the cloud parameters from the model state and the evaluation and interpolation of the reflectance function using the compressed LUT and is thus orders of magnitude faster than DISORT. The accuracy of the method is tested by generating synthetic satellite images for the 0.6μm and 0.8μm channels of the SEVIRI instrument for operational COSMO-DE model forecasts from the German Weather Service (DWD) and comparing them to DISORT results. For a test period in June the root mean squared absolute reflectance error is about 10−2 and the mean relative reflectance error is less than 2% for both channels. For scattering angles larger than 170° the rapid variation of reflectance with the particle size related to the backscatter glory reduces the accuracy and the errors increase by a factor of 3–4. Speed and accuracy of the new method are sufficient for operational data assimilation and high-resolution model verification applications.