View Zenith Angle Research Articles

Adjusting ISCCP Cloud Detection to Increase Consistency of Cloud Amount and Reduce Artifacts

AbstractThe International Satellite Cloud Climatology Project (ISCCP) began collecting data in the 1980s to help understand the distribution of clouds. Since then, it has provided important information on clouds in time and space and their radiative characteristics. However, it was apparent from some long-term time series of the data that there are some latent artifacts related to the changing satellite coverage over the more than 30 years of the record. Changes in satellite coverage effectively create secular changes in the time series of view zenith angle (VZA) for a given location. There is an inconsistency in the current ISCCP cloud detection algorithm related to VZA: two satellites viewing the same location from different VZAs can produce vastly different estimates of cloud amount. Research is presented that shows that a simple change to the cloud detection algorithm can vastly increase the consistency. This is accomplished by making the cloud–no cloud threshold VZA dependent. The resulting cloud amounts are more consistent between different satellites and the distributions are shown to be more spatially homogenous. Likewise, the more consistent spatial data lead to more consistent temporal statistics.

Simulated reflectance above snow constrained by airborne measurements of solar radiation: implications for the snow grain morphology in the Arctic

Abstract. Accurate knowledge of the reflectance from snow/ice-covered surfaces is of fundamental importance for the retrieval of snow parameters and atmospheric constituents from space-based and airborne observations. In this paper, we simulate the reflectance in a snow–atmosphere system, using the phenomenological radiative transfer model SCIATRAN, and compare the results with that of airborne measurements. To minimize the differences between measurements and simulation, we determine and employ the key atmospheric and surface parameters, such as snow grain morphologies (or habits). First, we report on a sensitivity study. This addresses the requirement for adequate a priori knowledge about snow models and ancillary information about the atmosphere. For this aim, we use the well-validated phenomenological radiative transfer model, SCIATRAN. Second, we present and apply a two-stage snow grain morphology (i.e., size and shape of ice crystals in the snow) retrieval algorithm. We then describe the use of this new retrieval for estimating the most representative snow model, using different types of snow morphologies, for the airborne observation conditions performed by NASA's Cloud Absorption Radiometer (CAR). Third, we present a comprehensive comparison of the simulated reflectance (using retrieved snow grain size and shape and independent atmospheric data) with that from airborne CAR measurements in the visible (0.670 µm) and near infrared (NIR; 0.870 and 1.6 µm) wavelength range. The results of this comparison are used to assess the quality and accuracy of the radiative transfer model in the simulation of the reflectance in a coupled snow–atmosphere system. Assuming that the snow layer consists of ice crystals with aggregates of eight column ice habit and having an effective radius of ∼99 µm, we find that, for a surface covered by old snow, the Pearson correlation coefficient, R, between measurements and simulations is 0.98 (R2∼0.96). For freshly fallen snow, assuming that the snow layer consists of the aggregate of five plates ice habit with an effective radius of ∼83 µm and having surface inhomogeneity, the correlation is ∼0.97 (R2∼0.94) in the infrared and 0.88 (R2∼0.77) in the visible wavelengths. The largest differences between simulated and measured values are observed in the glint area (i.e., in the angular regions of specular and near-specular reflection), with relative azimuth angles <±40∘ in the forward-scattering direction. The absolute difference between the modeled results and measurements in off-glint regions, with a viewing zenith angle of less than 50∘, is generally small ∼±0.025 and does not exceed ±0.05. These results will help to improve the calculation of snow surface reflectance and relevant assumptions in the snow–atmosphere system algorithms (e.g., aerosol optical thickness retrieval algorithms in the polar regions).

Assessment of BRDF Impact on VIIRS DNB from Observed Top-of-Atmosphere Reflectance over Dome C in Nighttime

The Day–Night Band (DNB) imaging sensor of the Visible Infrared Imaging Radiometer Suite (VIIRS) adds nighttime monitoring capability to the Suomi National Polar-Orbiting Partnership and National Oceanic and Atmospheric Administration 20 weather satellite launched in 2011 and 2017, respectively. Nighttime visible imagery has already found diverse applications, but image quality is often unsatisfactory. In this study, variations in observed top-of-atmosphere (TOA) reflectance were examined in terms of nighttime bidirectional effects. The Antarctica Dome C ground site was selected due to high uniformity. First, variation of reflectance was characterized in terms of viewing zenith angle, lunar zenith angle, and relative lunar azimuth angle, using DNB data from 2012 to 2020 and Miller–Turner 2009 simulations. Variations in reflectance were observed to be strongly anisotropic, suggesting the presence of bidirectional effects. Then, based on this finding, three popular bidirectional reflectance distribution function (BRDF) models were evaluated for effectiveness in correcting for these effects on the nighttime images. The observed radiance of VIIRS DNB was compared with the simulated radiance respectively based on the three BRDF models under the same geometry. Compared with the RossThick-LiSparseReciprocal (RossLi) BRDF model and Hudson model, the Warren model has a higher correlation coefficient (0.9899–0.9945) and a lower root-mean-square-error (0.0383–0.0487). Moreover, the RossLi BRDF model and Hudson model may have similar effects in the description of the nighttime TOA over Dome C. These findings are potentially useful to evaluate the radiometric calibration stability and consistency of nighttime satellite sensors.

A Thermal Infrared Land Surface Temperature Retrieval Algorithm for Thin Cirrus Skies Using Cirrus Optical Properties

To acquire daytime land surface temperature (LST) in thin cirrus cloudy skies, we have developed a three-channel LST retrieval algorithm based on a widely used two-channel LST retrieval algorithm for the clear-sky conditions. In this algorithm, the LST is expressed as a multiple linear function of MODIS channels 29, 31 and 32 with the coefficients of the linear function dependent on the cirrus optical depth (COD) and cirrus effective radius (R). The influences from land surface emissivities (LSEs) are also considered in this algorithm. The simulated dataset shows that the LST could be estimated using the proposed algorithm with the root mean squire error (RMSE) less than 2.2 K in thin cirrus cloudy skies (COD less than 0.7) when viewing zenith angle (VZA) equivalent to 0°. As VZA is equivalent to 60°, the maximum RMSE are 2.7 K. The widely used generalized split-window (GSW) algorithm proposed for clear-sky conditions are used in cirrus cloudy skies, and the RMSEs of GSW algorithm estimated LST are 16.89 K and 22.32 K for VZA =0° and VZA =60° respectively when COD is 0.7. It indicates that the proposed three-channel algorithm can significantly improve the LST retrieval accuracy using thermal infrared data in cirrus cloudy skies. To estimate the LST errors caused by the uncertainties of COD, R, LSE and instrument noise, a sensitivity analysis was performed. It shows that the accuracy of cirrus COD is more important for the retrieval of LST compared with other parameters. The maximum total LST errors, taking into account all the input parameters' uncertainty and algorithm error itself, are 3.8 K and 4.3 K when VZA =0° and VZA =60° respectively.

Quantitative Evaluation of Leaf Inclination Angle Distribution on Leaf Area Index Retrieval of Coniferous Canopies

Both leaf inclination angle distribution (LAD) and leaf area index (LAI) dominate optical remote sensing signals. The G-function, which is a function of LAD and remote sensing geometry, is often set to 0.5 in the LAI retrieval of coniferous canopies even though this assumption is only valid for spherical LAD. Large uncertainties are thus introduced. However, because numerous tiny leaves grow on conifers, it is nearly impossible to quantitatively evaluate such uncertainties in LAI retrieval. In this study, we proposed a method to characterize the possible change of G-function of coniferous canopies as well as its effect on LAI retrieval. Specifically, a Multi-Directional Imager (MDI) was developed to capture stereo images of the branches, and the needles were reconstructed. The accuracy of the inclination angles calculated from the reconstructed needles was high. Moreover, we analyzed whether a spherical distribution is a valid assumption for coniferous canopies by calculating the possible range of the G-function from the measured LADs of branches of Larch and Spruce and the true G-functions of other species from some existing inventory data and three-dimensional (3D) tree models. Results show that the constant G assumption introduces large errors in LAI retrieval, which could be as large as 53% in the zenithal viewing direction used by spaceborne LiDAR. As a result, accurate LAD estimation is recommended. In the absence of such data, our results show that a viewing zenith angle between 45 and 65 degrees is a good choice, at which the errors of LAI retrieval caused by the spherical assumption will be less than 10% for coniferous canopies.

Retrieving Land Surface Temperature From Chinese FY-3D MERSI-2 Data Using an Operational Split Window Algorithm

Medium resolution spectral imager II (MERSI-2) is a payload for the Chinese meteorological satellite Feng Yun 3 (FY-3). China's satellite remote sensing observation capabilities such as climate change research can be improved during MERSI-2's operation in orbit, and the sensor is the world's first imaging instrument that can obtain a global infrared split-window data with a spatial resolution of 250 m. We developed an operational spilt-window (SW) algorithm to retrieve land surface temperature (LST) accurately from the MERSI-2 data. The SW algorithm coefficients were derived from a simulation dataset that was established with the Moderate spectral resolution atmospheric Transmittance model version 5.2 and the thermodynamic initial guess retrieval dataset. In the practical retrieval, the precise algorithm coefficients were determined by view zenith angle and atmospheric water vapor content (WVC), the atmospheric WVC were obtained from the ERA5 dataset, and the land surface emissivity was dynamically estimated using the advanced spaceborne thermal emission and reflection radiometer global emissivity dataset, considering the fractional of vegetation cover and snow cover. The retrieved LST compared with in situ LST, which was highly consistent with the in situ LST and that the root-mean-square error of the two is within 3 K. The retrieved LST was compared with the MYD11_L2 and MYD21_L2 LST products, and the results indicated that MERSI-2 LST was more consistent with the MYD21 LST. The operational SW algorithm for FY-3D MERSI-2 developed in this study could retrieve LST accurately and has a wide range of popularization and application values.

Evaluation of a CONUS-Wide ECOSTRESS DisALEXI Evapotranspiration Product

The Atmosphere-Land Exchange Inverse disaggregation (DisALEXI) algorithm is a multi-scale energy balance model that estimates evapotranspiration (ET) using land-surface temperature (LST) as a driving remote sensing input. Using LST products from ECOSTRESS, a thermal radiometer mounted on the International Space Station, DisALEXI ET products have been produced over the contiguous United States (CONUS) at 70 m resolution. The goal of this study is to demonstrate the accuracy of the CONUS-wide ET produced by the Jet Propulsion Laboratory (JPL) and to compare the results with the original DisALEXI ET produced by researchers at the United States Department of Agriculture (USDA). DisALEXI-USDA has been produced ad-hoc using Landsat LST, and is routinely produced over six target sites using ECOSTRESS LST. DisALEXI-JPL was implemented in order to expand the spatial coverage. DisALEXI-JPL was evaluated at 26 CONUS eddy covariance sites, showing good correlation, with R2 = 0.80 and RMSE = 0.81 mm/day, which is comparable to previous DisALEXI validation studies (RMSE ~1 mm/day). The two DisALEXI implementations compared well, with R2 = 0.92. This work evaluates DisALEXI-JPL and shows that the algorithm is valid over a larger segment of CONUS. We also show the impact of quality flags, as pixels with high view zenith angles or high aerosol optical depth showed greater deviation from field measurements. As a product demonstration, we show a regional map of fine-scale ET, where the fine-scale variation over wider areas can detect small areas of stress much sooner than products with coarse resolution representing average conditions.

Validation of GOES-16 ABI VNIR channel radiometric performance with NPP and NOAA-20 VIIRS over the Sonoran Desert

The advanced baseline imager (ABI) onboard Geostationary Operational Environmental Satellites-16 (GOES-16) provides high-quality visible and near-infrared (VNIR) imagery data. Radiometric performance of the GOES-16 ABI multiple VNIR bands (B1, B2, B3, B5, and B6) is evaluated over the Sonoran Desert by comparing measurements with Suomi National Polar-Orbiting Partnership (S-NPP) and NOAA-20 visible infrared imaging radiometer suite equivalent bands M3, M5, M7, M10, and M11, respectively. In order to minimize the uncertainties due to the difference in spectral response functions of similar bands from the different sensors, spectral band adjustment factor (SBAF) derived from Hyperion data over Sonoran Desert is used. The large viewing angle at the Sonoran Desert by ABI (56.34 deg) and a lack of comprehensive BRDF model at such viewing geometry are the main challenges for the comparison. Two schemes to address such challenges were developed. A data-derived (DD) method, based on confining the matched solar-zenith angle and view-zenith angle between ABI and VIIRS, interpolation in solar-zenithal and relative-azimuthal angles is developed to address this issue. It is shown that there is some residual bias for ABI B1 and its equivalent VIIRS M3 band, which is mainly due to unaccounted angular dependence of atmospheric scattering in this DD method. To address this issue, a radiative transfer modeling (RTM)-based method to account for atmospheric effects is developed to facilitate the comparison. The time series trending and mean bias of reflectance ratio between ABI and VIIRS measurements over the Sonoran Desert after SBAF and bidirectional reflectance distribution function corrections are derived to evaluate the radiometric performance of ABI with respect to net primary productivity and NOAA-20 VIIRS. The analysis shows that the radiometric biases of the five VNIR channels of GOES-16 ABI are all within 5% in comparison to the matched channels of NPP VIIRS after applying the RTM correction. The analysis also detects ∼6 % drop in the radiometric bias of GOES-16 ABI 0.64 μm channel after April 23, 2019, which can be traced to the implementation of a correction of the ABI B2 calibration coefficient around this date. Further, we evaluate the relative radiometric bias for the five VNIR channels between NPP and NOAA-20 VIIIRS using double difference method, and the comparison shows that NPP VIIRS has 2% to 3% higher bias than NOAA-20 VIIRS for these spectral bands.

Effects of clouds on the UV Absorbing Aerosol Index from TROPOMI

Abstract. The ultraviolet (UV) Absorbing Aerosol Index (AAI) is widely used as an indicator for the presence of absorbing aerosols in the atmosphere. Here we consider the TROPOMI AAI based on the 340 nm/380 nm wavelength pair. We investigate the effects of clouds on the AAI observed at small and large scales. The large-scale effects are studied using an aggregate of TROPOMI measurements over an area mostly devoid of absorbing aerosols (Pacific Ocean). The study reveals that several structural features can be distinguished in the AAI, such as the cloud bow, viewing zenith angle dependence, sunglint, and a previously unexplained increase in AAI values at extreme viewing and solar geometries. We explain these features in terms of the bidirectional reflectance distribution function (BRDF) of the scene in combination with the different ratios of diffuse and direct illumination of the surface at 340 and 380 nm. To reduce the dependency on the BRDF and homogenize the AAI distribution across the orbit, we present three different AAI retrieval models: the traditional Lambertian scene model (LSM), a Lambertian cloud model (LCM), and a scattering cloud model (SCM). We perform a model study to assess the propagation of errors in auxiliary databases used in the cloud models. The three models are then applied to the same low-aerosol region. Results show that using the LCM and SCM gives on average a higher AAI than the LSM. Additionally, a more homogeneous distribution is retrieved across the orbit. At the small scale, related to the high spatial resolution of TROPOMI, strong local increases and decreases in AAI are observed in the presence of clouds. The BRDF effect presented here is a first step – more research is needed to explain the small-scale cloud effects on the AAI.

Estimating hourly full-coverage PM2.5 over China based on TOA reflectance data from the Fengyun-4A satellite.

It is challenging to retrieve hourly ground-level PM2.5 on a national scale in China due to the sparse site measurements and the limited coverage of Low Earth Orbit (LEO) satellite observations. The new geostationary meteorological satellite of China, Fengyun-4A (FY-4A), provides a unique opportunity to fill this gap. In this study, the Random Forest (RF) algorithm was applied to retrieve hourly PM2.5 of China directly from FY-4A Top-of-Atmosphere (TOA) reflectance data. A one-year PM2.5 retrieval shows a strong agreement to ground-based measurements, with the averaged R2 approaching 0.92, while the RMSE was only 10.0μg/m³. An analysis of the regional differences of the performance and the dependency on satellite Viewing Zenith Angle (VZA) show that sparse measurements, high VZA, and solar zenith angle (SZA) are the primary sources of the uncertainty. The use of the FY-4A improved 17% spatial coverage compared to the Himawari-8-based PM2.5 retrievals, enabling full-coverage, hourly PM2.5 monitoring over China, and potentially could improve PM2.5 predictions from air quality models after data assimilation.

Simulation of Sentinel-2 Bottom of Atmosphere Reflectance Using Shadow Parameters on a Deciduous Forest in Thailand

The shadow fraction of the canopy is an important factor in Bidirectional Reflectance Distribution Function (BRDF) and in estimating physical quantities, such as tree height and biomass. Shadows are used as a shielding ratio for direct solar irradiance, but, at shorter wavelengths, the amount of diffuse solar irradiance is greater, so the shielding ratio cannot be ignored. The shielding ratio of direct and diffuse solar irradiance is called Cast Shadow (CS) and Self Cast Shadow (SCS), respectively; however, it has been pointed out that the effect of these shadows is greater at higher resolutions, such as Sentinel-2. In addition, the Bottom Of Atmosphere (BOA) reflectance is greatly affected by shadows, because it is corrected for atmospheric effects. Therefore, the main purpose of this study is to investigate the spatial variability of CS and SCS and simulate the Sentinel-2BOA reflectance with these shadows. The target forest was a greenness season of a deciduous broadleaf forest in Thailand. First, we obtained a point cloud of the forest by Structure from Motion while using the Unmanned Aerial Vehicle. Next, we created a voxel model with CS and SCS as attributes. CS was calculated as the percentage of area where the plane that is assumed per voxel is shielded from direct solar irradiance by other voxels. SCS was calculated as the percentage of area where the hemispheric radiant environment is shielded by other voxels. Subsequently, using solar irradiance and leaf spectral reflectance data, the reflectance of each band of Sentinel-2 was simulated. Nine leaves were used to investigate the effect of leaf species on the simulation. The reflectance acquired by Sentinel-2 is not at the leaf level; however, we used this spectral reflectance data because the reflectance was simulated at the same spatial resolution as the voxel size. Voxel sizes of 20 cm, 50 cm, 100 cm, and 200 cm were used. Our result showed that (1) the spatial variability of SCS was smaller than that of CS when the sun position is fixed and the view zenith angle is changed. SCS was mostly 0.12 at different zenith angles, while the CS had a maximum value of 0.45 and a minimum value of 0.15. (2) The accuracy of the simulations was evaluated using the Root Mean Square Error (RMSE). The best RMSE is 0.020 ± 0.015 and the worst one is 0.084 ± 0.044. It was found that the error is larger in short wavelength infrared bands. (3) In this forest, the relative reflectance changed only about 1.2 times as much, as the voxel size was increased from 20 cm to 200 cm. In this study, we have simulated a single Sentinel-2 image. In the future, we will simulate multi-temporal images in order to investigate the effects of phenology and shadow changes on the reflectance that was observed by optical sensors.

DART: Improvement of thermal infrared radiative transfer modelling for simulating top of atmosphere radiance

Land surface temperature (LST) is increasingly needed for studying the functioning of the Earth's surface at local to global scale. Radiative transfer (RT) models that simulate top of atmosphere (TOA) radiance are essential tools to derive accurate LST from thermal infrared (TIR) signals of Earth observation (EO) satellites. DART (Discrete Anisotropic Radiative Transfer) is one of the most accurate and comprehensive three-dimensional models that simulate RT in the Earth-atmosphere system. Up to version 5.7.3, the mean absolute error (MAE) of DART atmospheric TIR radiance of six standard atmospheres (USSTD76, TROPICAL, MIDDLATSUM, MIDDLATWIN, SUBARCSUM, SUBARCWIN) over 3.5 μm - 20 μm was 3.1 K compared to the reference atmospheric RT model MODTRAN, which is much larger than the 1 K accuracy needed by most LST applications. Also, the radiance error reached 2.6 K for some TIR bands whereas the noise equivalent differential temperature (NeDT) of satellite TIR sensor is usually less than 0.4 K. Recently, the DART atmospheric RT modelling was greatly improved by (1) introducing the equivalent absorption cross-section of five most absorbing gases (H2O, CO2, O3, CH4, N2O), and (2) implementing a double-layer thermal emission method. The MAE of DART atmospheric TIR radiance of six standard atmospheres and actual atmospheres over France and the Mediterranean Sea is now better than 1.0 K. The band radiance error is less than 0.2 K in the EO satellite TIR bands. DART is still accurate if the temperature profiles of standard atmospheres are offset by less than 6 K and if the viewing zenith angle is less than 50°. In short, the improved DART meets the requirements of both LST applications, and present and future TIR EO satellite missions. It is already available to scientists (https://dart.omp.eu).

Comparison of Empirical and Physical Modelling for Estimation of Biochemical and Biophysical Vegetation Properties: Field Scale Analysis across an Arctic Bioclimatic Gradient

To evaluate the potential of multi-angle hyperspectral sensors for monitoring vegetation variables in Arctic environments, empirical and physical modelling using field data was implemented for the retrieval of leaf and canopy chlorophyll content (LCC, CCC) and plant area index (PAI) measured at four sites situated across a bioclimatic gradient in the Western Canadian Arctic. Field reflectance data were acquired with an ASD FieldSpec (305–1075 nm) and used to simulate CHRIS Mode1 spectra (411–997 nm). Multi-angle measurements were taken corresponding to CHRIS view zenith angles (VZA) (−55°, −36°, 0°, +36°, +55°). Empirical modelling compared parametric regression based on vegetation indices (VIs) to non-parametric Gaussian Processes Regression (GPR). In physical modelling, PROSAIL was inverted using numerical optimization and look-up table (LUT) approaches. Cross-validation of the empirical models ranked GPR as best, followed by simple ratio (SR) with optimally selected NIR and red wavelengths, and then ROSAVI using its published wavelengths (mean r2cv = 0.62, 0.58, and 0.54, respectively across all sites, variables, and VZAs). However, the best predictive performance was achieved by SR followed by GPR and ROSAVI (NRMSEcv = 0.12, 0.16, 0.16, respectively). PROSAIL simulated the multi-angle top-of-canopy reflectance well with numerical optimization (r2 = ~0.99, RMSE = 0.004 ± 0.002), but best performing LUT models of LCC, CCC and PAI were poorer than the empirical approaches (mean r2 = 0.48, mean NRMSE = 0.22). PROSAIL performed best at the high Arctic sparsely vegetated site (r2 = 0.57–0.86 for all parameters). Overall, the best performing VZA was −55° for empirical modelling and 0° and ±55° for physical modelling; however, these were not significantly better than the other VZAs. Overall, this study demonstrates that, for Arctic vegetation, nadir narrowband reflectance data used to derive simple empirical VIs with optimally selected bands is a more efficient approach for modelling chlorophyll and PAI than more complex empirical and physical approaches.

Intercomparison of atmospheric CO<sub>2</sub> and CH<sub>4</sub> abundances on regional scales in boreal areas using Copernicus Atmosphere Monitoring Service (CAMS) analysis, COllaborative Carbon Column Observing Network (COCCON) spectrometers, and Sentinel-5 Precursor satellite observations

Abstract. We compare the atmospheric column-averaged dry-air mole fractions of carbon dioxide (XCO2) and methane (XCH4) measured with a pair of COllaborative Carbon Column Observing Network (COCCON) spectrometers at Kiruna and Sodankylä (boreal areas). We compare model data provided by the Copernicus Atmosphere Monitoring Service (CAMS) between 2017 and 2019 with XCH4 data from the recently launched Sentinel-5 Precursor (S5P) satellite between 2018 and 2019. In addition, measured and modeled gradients of XCO2 and XCH4 (ΔXCO2 and ΔXCH4) on regional scales are investigated. Both sites show a similar and very good correlation between COCCON retrievals and the modeled CAMS XCO2 data, while CAMS data are biased high with respect to COCCON by 3.72 ppm (±1.80 ppm) in Kiruna and 3.46 ppm (±1.73 ppm) in Sodankylä on average. For XCH4, CAMS values are higher than the COCCON observations by 0.33 ppb (±11.93 ppb) in Kiruna and 7.39 ppb (±10.92 ppb) in Sodankylä. In contrast, the S5P satellite generally measures lower atmospheric XCH4 than the COCCON spectrometers, with a mean difference of 9.69 ppb (±20.51 ppb) in Kiruna and 3.36 ppb (±17.05 ppb) in Sodankylä. We compare the gradients of XCO2 and XCH4 (ΔXCO2 and ΔXCH4) between Kiruna and Sodankylä derived from CAMS analysis and COCCON and S5P measurements to study the capability of detecting sources and sinks on regional scales. The correlations in ΔXCO2 and ΔXCH4 between the different datasets are generally smaller than the correlations in XCO2 and XCH4 between the datasets at either site. The ΔXCO2 values predicted by CAMS are generally higher than those observed with COCCON with a slope of 0.51. The ΔXCH4 values predicted by CAMS are mostly higher than those observed with COCCON with a slope of 0.65, covering a larger dataset than the comparison between S5P and COCCON. When comparing CAMS ΔXCH4 with COCCON ΔXCH4 only in S5P overpass days (slope = 0.53), the correlation is close to that between S5P and COCCON (slope = 0.51). CAMS, COCCON, and S5P predict gradients in reasonable agreement. However, the small number of observations coinciding with S5P limits our ability to verify the performance of this spaceborne sensor. We detect no significant impact of ground albedo and viewing zenith angle on the S5P results. Both sites show similar situations with the average ratios of XCH4 (S5P/COCCON) of 0.9949±0.0118 in Kiruna and 0.9953±0.0089 in Sodankylä. Overall, the results indicate that the COCCON instruments have the capability of measuring greenhouse gas (GHG) gradients on regional scales, and observations performed with the portable spectrometers can contribute to inferring sources and sinks and to validating spaceborne greenhouse gas sensors. To our knowledge, this is the first published study using COCCON spectrometers for the validation of XCH4 measurements collected by S5P.

Estimating leaf chlorophyll content by laser-induced fluorescence technology at different viewing zenith angles.

Leaf chlorophyll content (LCC) is a key indicator of a plant's physiological status. Fast and non-destructive monitoring of chlorophyll content in plants through remote sensing is very important for accurate diagnosis and assessment of plant growth. Through the use of laser-induced fluorescence (LIF) technology, this study aims to compare the predictive ability of different single fluorescence characteristic and fluorescence characteristic combinations at various viewing zenith angles (VZAs) combined with multivariate analysis algorithms, such as principal component analysis (PCA) and support vector machine (SVM), for estimating the LCC of plants. The SVM models of LCC estimation were proposed, and fluorescence characteristics-fluorescence peak (FP), fluorescence ratio (FR), PCA, and first-derivative (FD) parameter-and fluorescence characteristic combinations (FP+FR, FP+FD, FR+FD, FP+FR+FD) were used as input variables for the models. Experimental results demonstrated that the effect of single fluorescence characteristics on the predictive performance of SVM models was: FR>FD>FP>PCA. Compared with other models, 0° SVM was the optimal model for estimating LCC by higher R2. The fluorescence spectra and FD spectra observed at 0° and 30° were superior to those observed at 15°, 45°, and 60°. Thus, appropriate VZA must also be considered, as it can improve the accuracy of LCC monitoring. In addition, compared with single fluorescence characteristic, the FP+FR+FD was the optimal combination of fluorescence characteristics to estimate the LCC for the SVM model by higher R2, indicating better predictive performance. The experimental results show that the combination of LIF technology and multivariate analysis can be effectively used for LCC monitoring and has broad development prospects.

Homogenization of Geostationary Infrared Imager Channels for Cold Cloud Studies Using Megha-Tropiques/ScaRaB

Infrared (IR) observations from the fleet of multiagencies meteorological geostationary satellites have a great potential to support scientific and operational investigations at a quasi-global scale. In particular, such a data record, defined as the GEOring data set, is well suited to document the tropical convective systems life cycles by applying cloud tracking algorithms. Yet, this GEOring data set is far from being homogeneous, preventing the realization of its potential. A number of sources of inhomogeneities are identified ranging from spatiotemporal resolutions to spectral characteristics of the IR channels and calibration methodologies. While previous efforts have attempted to correct such issues, the adjustment of the cold part of the IR spectrum remains unfit for cold cloud studies. Here, a processing method is introduced to minimize the inhomogeneities against a reference observational data set from the Scanner for Radiation Budget (ScaRaB) instrument onboard the Megha-Tropiques satellite. The method relies on the collocations between the geostationary observations and the reference. The techniques exhibit significant sensitivity to the selection of the relevant pairs of observations requiring a dedicated filtering of the data. A second effort is then proposed to account for the limb-darkening effect and a method is developed to correct the brightness temperature (BT) dependence on the geostationary viewing zenith angle (VZA). Overall, results show a residual after the processing of 0 K between any of the geostationary data and the ScaRaB reference. The final calibrated and limb-adjusted IR observations are then homogeneous for cold BT lower than 240 K with a standard deviation lower than 1.5 K throughout the GEOring.

Reconstruction method and optimum range of camera-shooting angle for 3D plant modeling using a multi-camera photography system

BackgroundMeasurement of plant structure is useful in monitoring plant conditions and understanding the responses of plants to environmental changes. 3D imaging technologies, especially the passive-SfM (Structure from Motion) algorithm combined with a multi-camera photography (MCP) system has been studied to measure plant structure due to its low-cost, close-range, and rapid image capturing ability. However, reconstruction of 3D plant models with complex structure is a time-consuming process and some systems have failed to reconstruct 3D models properly. Therefore, an MCP based SfM system was developed and an appropriate reconstruction method and optimal range of camera-shooting angles were investigated.ResultsAn MCP system which utilized 10 cameras and a rotary table for plant was developed. The 3D mesh model of a single leaf reconstruction using a set of images taken at each viewing zenith angle (VZA) from 12° (C2 camera) to 60° (C6 camera) by the MCP based SfM system had less undetected or unstable regions in comparison with other VZAs. The 3D mesh model of a whole plant, which merged 3D dense point cloud models built from a set of images taken at each appropriate VZA (Method 1), had high accuracy. The Method 1 error percentages for leaf area, leaf length, leaf width, stem height, and stem width are in the range of 2.6–4.4%, 0.2–2.2%, 1.0–4.9%, 1.9–2.8%, and 2.6–5.7% respectively. Also, the error of the leaf inclination angle was less than 5°. Conversely, the 3D mesh model of a whole plant built directly from a set of images taken at all appropriate VZAs (Method 2) had lower accuracy than that of Method 1. For Method 2, the error percentages of leaf area, leaf length, and leaf width are in the range of 3.1–13.3%, 0.4–3.3%, and 1.6–8.6%, respectively. It was difficult to obtain the error percentages of stem height and stem width because some information was missing in this model. In addition, the calculation time for Method 2 was 1.97 times longer computational time in comparison to Method 1.ConclusionsIn this study, we determined the optimal shooting angles on the MCP based SfM system developed. We found that it is better in terms of computational time and accuracy to merge partial 3D models from images taken at each appropriate VZA, then construct complete 3D model (Method 1), rather than to construct 3D model by using images taken at all appropriate VZAs (Method 2). This is because utilization of incorporation of incomplete images to match feature points could result in reduced accuracy in 3D models and the increase in computational time for 3D model reconstruction.

A fast and accurate radiative transfer model for aerosol remote sensing

After several decades’ development of retrieval techniques in aerosol remote sensing, no fast and accurate analytical Radiative Transfer Model (RTM) has been developed and applied to create global aerosol products for non-polarimetric instruments such as Ocean and Land Colour Instrument/Sentinel-3 (OLCI/Sentinel-3) and Meteosat Second Generation/Spinning Enhanced Visible and Infrared Imager (MSG/SEVIRI). Global aerosol retrieval algorithms are typically based on a Look-Up–Table (LUT) technique, requiring high-performance computers. The current eXtensible Bremen Aerosol/cloud and surfacE parameters Retrieval (XBAER) algorithm also utilizes the LUT method. In order to have a near-real time retrieval and achieve a quick and accurate “FIRST-LOOK” aerosol product without high-demand of computing resource, we have developed a Fast and Accurate Semi-analytical Model of Atmosphere-surface Reflectance (FASMAR) for aerosol remote sensing. The FASMAR is developed based on a successive order of scattering technique. In FASMAR, the first three orders of scattering are calculated exactly. The contribution of higher orders of scattering is estimated using an extrapolation technique and an additional correction function. The evaluation of FASMAR has been performed by comparing with radiative transfer model SCIATRAN for all typical observation/illumination geometries, surface/aerosol conditions, and wavelengths 412, 550, 670, 870, 1600, 2100 nm used for aerosol remote sensing. The selected observation/illumination conditions are based on the observations from both geostationary satellite (e.g. MSG/SEVIRI) and polar-orbit satellite (e.g. OLCI/Sentinel-3). The percentage error of the top of atmosphere reflectance calculated by FASMAR is within ± 3% for typical polar-orbit/geostationary satellites’ observation/illumination geometries. The accuracy decreases for solar and viewing zenith angles larger than 70∘. However, even in such cases, the error is within the range ± 5%. The evaluation of model performance also shows that FASMAR can be used for all typical surfaces with albedo in the interval [0−1]and aerosol with optical thickness in the range [0.01−1].

Comparing methods for estimating leaf area index by multi-angular remote sensing in winter wheat

The reflectance of wheat’s canopy exhibits angular sensitivity, which can influence the accuracy of different methods for its leaf area index (LAI) estimation through multi-angular remote sensing. The primary objective of this study was to assess and compare the ability of various methods for LAI estimation from 13 view zenith angles (VZAs). The four methods included: (1) common hyper-spectral vegetation indices (VIs), (2) optimal two-band combination VIs (i.e., VIs: normalized difference index, simple ratio index, and difference vegetation index), (3) back-propagation neural network (BPNN), and (4) partial least squares regression (PLSR). Our results demonstrated that the red-edge plays a key role in estimating LAI, in that the traditional VIs, optimal two-band VIs, and PLSR including the red-edge band all showed satisfactory performance, with coefficient of determination (R2) > 0.72 in the nadir direction. However, the estimation accuracy of LAI was not positively related with band number, and BPNN gave unsatisfactory results under a larger viewing angle, with R2 ≤ 0.60 for extreme angles. The predictive ability of all four methods declined with an increasing VZA, with reliable LAI estimation near the nadir direction. Importantly, by comparing the four methods, PLSR emerged as superior in both its estimation accuracy and angular insensitivity, with R2 = 0.83 in the nadir direction and ≥ 0.65 for extreme angles. For this reason, we highly recommend it be used with multi-angular remote sensing data, especially in agricultural applications.

Seasonal Comparisons of Himawari-8 AHI and MODIS Vegetation Indices over Latitudinal Australian Grassland Sites

The Advanced Himawari Imager (AHI) on board the Himawari-8 geostationary (GEO) satellite offers comparable spectral and spatial resolutions as low earth orbiting (LEO) sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS) and Visible Infrared Imaging Radiometer Suite (VIIRS) sensors, but with hypertemporal image acquisition capability. This raises the possibility of improved monitoring of highly dynamic ecosystems, such as grasslands, including fine-scale phenology retrievals from vegetation index (VI) time series. However, identifying and understanding how GEO VI temporal profiles would be different from traditional LEO VIs need to be evaluated, especially with the new generation of geostationary satellites, with unfamiliar observation geometries not experienced with MODIS, VIIRS, or Advanced Very High Resolution Radiometer (AVHRR) VI time series data. The objectives of this study were to investigate the variations in AHI reflectances and normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and two-band EVI (EVI2) in relation to diurnal phase angle variations, and to compare AHI VI seasonal datasets with MODIS VIs (standard and sun and view angle-adjusted VIs) over a functional range of dry grassland sites in eastern Australia. Strong NDVI diurnal variations and negative NDVI hotspot effects were found due to differential red and NIR band sensitivities to diurnal phase angle changes. In contrast, EVI and EVI2 were nearly insensitive to diurnal phase angle variations and displayed nearly flat diurnal profiles without noticeable hotspot influences. At seasonal time scales, AHI NDVI values were consistently lower than MODIS NDVI values, while AHI EVI and EVI2 values were significantly higher than MODIS EVI and EVI2 values, respectively. We attributed the cross-sensor differences in VI patterns to the year-round smaller phase angles and backscatter observations from AHI, in which the sunlit canopies induced a positive EVI/ EVI2 response and negative NDVI response. BRDF adjustments of MODIS VIs to solar noon and to the oblique view zenith angle of AHI resulted in strong cross-sensor convergence of VI values (R2 > 0.94, mean absolute difference <0.02). These results highlight the importance of accounting for cross-sensor observation geometries for generating compatible AHI and MODIS annual VI time series. The strong agreement found in this study shows promise in cross-sensor applications and suggests that a denser time series can be formed through combined GEO and LEO measurement synergies.