scholarly journals ESTIMATION OF LAND SURFACE ALBEDO FROM GCOM-C/SGLI SURFACE REFLECTANCE

2021 ◽  
Vol V-3-2021 ◽  
pp. 227-234
Author(s):  
J. Susaki ◽  
H. Sato ◽  
A. Kuriki ◽  
K. Kajiwara ◽  
Y. Honda
Keyword(s):  
Land Surface ◽  
Regression Models ◽  
Surface Albedo ◽  
Model Parameters ◽  
Climate Change Research ◽  
Barren Land ◽  
Multi Temporal ◽  
Angular Reflectance ◽  
Observation Geometry ◽  
Mean Square Errors

Abstract. This paper examines algorithms for estimating terrestrial albedo from the products of the Global Change Observation Mission – Climate (GCOM-C) / Second-generation Global Imager (SGLI), which was launched in December 2017 by the Japan Aerospace Exploration Agency. We selected two algorithms: one based on a bidirectional reflectance distribution function (BRDF) model and one based on multi-regression models. The former determines kernel-driven BRDF model parameters from multiple sets of reflectance and estimates the land surface albedo from those parameters. The latter estimates the land surface albedo from a single set of reflectance with multi-regression models. The multi-regression models are derived for an arbitrary geometry from datasets of simulated albedo and multi-angular reflectance. In experiments using in situ multi-temporal data for barren land, deciduous broadleaf forests, and paddy fields, the albedos estimated by the BRDF-based and multi-regression-based algorithms achieve reasonable root-mean-square errors. However, the latter algorithm requires information about the land cover of the pixel of interest, and the variance of its estimated albedo is sensitive to the observation geometry. We therefore conclude that the BRDF-based algorithm is more robust and can be applied to SGLI operational albedo products for various applications, including climate-change research.

scholarly journals Developing Land Surface Directional Reflectance and Albedo Products from Geostationary GOES-R and Himawari Data: Theoretical Basis, Operational Implementation, and Validation

2019 ◽  
Vol 11 (22) ◽  
pp. 2655 ◽  
Author(s):  
He ◽  
Zhang ◽  
Liang ◽  
Yu ◽  
Wang
Keyword(s):  
Land Surface ◽  
Surface Albedo ◽  
Infrared Imaging ◽  
Atmospheric Correction ◽  
Satellite Observation ◽  
Model Parameters ◽  
Surface Reflectance ◽  
Operational Environment ◽  
Mean Square Errors ◽  
Directional Reflectance

The new generation of geostationary satellite sensors is producing an unprecedented amount of Earth observations with high temporal, spatial and spectral resolutions, which enable us to detect and assess abrupt surface changes. In this study, we developed the land surface directional reflectance and albedo products from Geostationary Operational Environment Satellite-R (GOES-R) Advanced Baseline Imager (ABI) data using a method that was prototyped with the Moderate Resolution Imaging Spectroradiometer (MODIS) data in a previous study, and was also tested with data from the Advanced Himawari Imager (AHI) onboard Himawari-8. Surface reflectance is usually retrieved through atmospheric correction that requires the input of aerosol optical depth (AOD). We first estimated AOD and the surface bidirectional reflectance factor (BRF) model parameters simultaneously based on an atmospheric radiative transfer formulation with surface anisotropy, and then calculated the “blue-sky” surface broadband albedo and directional reflectance. This algorithm was implemented operationally by the National Oceanic and Atmospheric Administration (NOAA) to generate the GOES-R land surface albedo product suite with a daily updated clear-sky satellite observation database. The “operational” land surface albedo estimation from ABI and AHI data was validated against ground measurements at the SURFRAD sites and OzFlux sites and compared with the existing satellite products, including MODIS, Visible infrared Imaging Radiometer (VIIRS), and Global Land Surface Satellites (GLASS) albedo products, where good agreement was found with bias values of −0.001 (ABI) and 0.020 (AHI) and root-mean-square-errors (RMSEs) less than 0.065 for the hourly albedo estimation. Directional surface reflectance estimation, evaluated at more than 74 sites from the Aerosol Robotic Network (AERONET), was proven to be reliable as well, with an overall bias very close to zero and RMSEs within 0.042 (ABI) and 0.039 (AHI). Results show that the albedo and reflectance estimation can satisfy the NOAA accuracy requirements for operational climate and meteorological applications.

scholarly journals Utilizing Multi-Temporal Thermal Data To Assess Environmental Land Degradation Impacts: Example From Suez Canal Region, Egypt.

2022 ◽  
Author(s):  
Mohamed Arnous ◽  
Basma Mansour
Keyword(s):  
Satellite Data ◽  
Land Degradation ◽  
Land Surface ◽  
Agricultural Land ◽  
Suez Canal ◽  
Water Bodies ◽  
Ground Field ◽  
Barren Land ◽  
Thermal Data ◽  
Multi Temporal

Abstract Land surface temperature (LST) analysis of Satellite data is critical for studying the impacts of geo-environmental, hydrometeorological, and land degradation. However, challenges arise to resolve the LST and ground field data resulting from the constant development of land use and land cover (LULC). This study aims to monitor, analyze, assess, and map the environmental land degradation impacts utilizing image processing and GIS tools of space-borne thermal data and fieldwork. Two thermal and optical sets of multi-temporal Landsat TM+5 and TIRS+8 satellite data dated 1984 and 2018 were used to test, detect, and map the thermal and LULC change and their land degradation in the Suez Canal region (SCR). The LULC classification was categorized into seven classes: water bodies, urban, agricultural land, barren land, wetland, clay, and salt crust. LULC and LST change detection and mapping results revealed that the impervious surface, industrial area, saline soil, and urban area have high LST, while wetlands, vegetation cover, and water bodies suffered low LST. The spectral, LST profiles and statistical analyses examined the association between LST and LULC deriving factors. The cluster analyses defined the relationship between LST and LC patterns at the LU level, where the fast transformation of LULC had significant changes in LST. According to these analyses and the fieldwork observations, the SCR was divided into six main areas. These areas vary in LST in association with land degradation and hydro-environmental impacts such as rising groundwater levels, salt accumulation, active seismic fault zones, water pollution, and urban and agricultural activities.

scholarly journals Development of Land Surface Albedo Algorithm for the GK-2A/AMI Instrument

2020 ◽  
Vol 12 (15) ◽  
pp. 2500
Author(s):  
Kyeong-Sang Lee ◽  
Sung-Rae Chung ◽  
Changsuk Lee ◽  
Minji Seo ◽  
Sungwon Choi ◽  
...  
Keyword(s):  
Land Surface ◽  
Surface Albedo ◽  
Monsoon Season ◽  
Atmospheric Correction ◽  
Optimization Method ◽  
Retrieval Algorithm ◽  
High Temporal Resolution ◽  
Temporal Consistency ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Mean Square Errors

The Korea Meteorological Administration successfully launched Korea’s next-generation meteorological satellite, Geo-KOMPSAT-2A (GK-2A), on 5 December 2018. It belongs to the new generation of GEO (Geostationary Elevation Orbit) satellite which offers capabilities to disseminate high spatial- (0.5–2 km) and high temporal-resolution (10 min) observations over a broad area, herein a geographic disk encompassing the Asia–Oceania region. The targeted objective is to enhance our understanding of climate change, owing to a bulk of coherent observations. For such, we developed an algorithm to map the land surface albedo (LSA), which is a major Essential Climate Variable (ECV). The retrieval algorithm devoted to GK-2A/Advanced Meteorological Imager (AMI) data considered Japan’s Himawari-8/Advanced Himawari Imager (AHI) data for prototyping, as this latter owns similar specifications to AMI. Our proposed algorithm is decomposed in three major steps: atmospheric correction, bidirectional reflectance distribution function (BRDF) modeling and angular integration, and narrow-to-broadband conversion. To perform BRDF modeling, the optimization method using normalized reflectance was applied, which improved the quality of BRDF modeling results, particularly when the number of observations was less than 15. A quality assessment was performed to compare our results to those of Moderate Resolution Imaging Spectroradiometer (MODIS) LSA products and ground measurement from Aerosol Robotic Network (AERONET) sites, Australian and New Zealand flux tower network (OzFlux) site and the Korea Flux Network (KoFlux) site from throughout 2017. Our results show dependable spatial and temporal consistency with MODIS broadband LSA data, and rapid changes in LSA due to snowfall and snow melting were well expressed in the temporal profile of our results. Our outcomes also show good agreement with the ground measurements from AERONET, OzFlux and KoFlux ground-based network with root mean square errors (RMSE) of 0.0223 and 0.0306, respectively, which is close to the accuracy of MODIS broadband LSA. Moreover, our results reveal still more reliable LSA products even when clouds are frequently present, such as during the summer monsoon season. It shows that our results are useful for continuous LSA monitoring.

scholarly journals Assimilation of Cosmogenic Neutron Counts for Improved Soil Moisture Prediction in a Distributed Land Surface Model

2021 ◽  
Vol 3 ◽  
Author(s):  
Amol Patil ◽  
Benjamin Fersch ◽  
Harrie-Jan Hendricks Franssen ◽  
Harald Kunstmann
Keyword(s):  
Soil Moisture ◽  
Land Surface ◽  
Land Surface Model ◽  
Cosmic Ray ◽  
Added Value ◽  
Surface Model ◽  
Model Parameters ◽  
Validation Period ◽  
Field Scale ◽  
Mean Square Errors

Cosmic-Ray Neutron Sensing (CRNS) offers a non-invasive method for estimating soil moisture at the field scale, in our case a few tens of hectares. The current study uses the Ensemble Adjustment Kalman Filter (EAKF) to assimilate neutron counts observed at four locations within a 655 km2 pre-alpine river catchment into the Noah-MP land surface model (LSM) to improve soil moisture simulations and to optimize model parameters. The model runs with 100 m spatial resolution and uses the EU-SoilHydroGrids soil map along with the Mualem–van Genuchten soil water retention functions. Using the state estimation (ST) and joint state–parameter estimation (STP) technique, soil moisture states and model parameters controlling infiltration and evaporation rates were optimized, respectively. The added value of assimilation was evaluated for local and regional impacts using independent root zone soil moisture observations. The results show that during the assimilation period both ST and STP significantly improved the simulated soil moisture around the neutron sensors locations with improvements of the root mean square errors between 60 and 62% for ST and 55–66% for STP. STP could further enhance the model performance for the validation period at assimilation locations, mainly by reducing the Bias. Nevertheless, due to a lack of convergence of calculated parameters and a shorter evaluation period, performance during the validation phase degraded at a site further away from the assimilation locations. The comparison of modeled soil moisture with field-scale spatial patterns of a dense network of CRNS observations showed that STP helped to improve the average wetness conditions (reduction of spatial Bias from –0.038 cm3 cm−3 to –0.012 cm3 cm−3) for the validation period. However, the assimilation of neutron counts from only four stations showed limited success in enhancing the field-scale soil moisture patterns.

scholarly journals New Models Used to Determine the Dioxins Total Amount and Toxicity (TEQ) in Atmospheric Emissions from Thermal Processes

Energies ◽  
2019 ◽  
Vol 12 (23) ◽  
pp. 4434
Author(s):  
Damià Palmer ◽  
Josep O. Pou ◽  
L. Gonzalez-Sabaté ◽  
Jordi Díaz-Ferrero ◽  
Juan A. Conesa ◽  
...  
Keyword(s):  
Regression Models ◽  
Bootstrap Method ◽  
Waste Incineration ◽  
Model Parameters ◽  
Thermal Processes ◽  
Multilinear Regression ◽  
Linearly Independent ◽  
Dioxins And Furans ◽  
Mean Square Errors ◽  
The Bootstrap Method

In order to reduce the calculation effort during the simulation of the emission of polychlorinated dibenzo-p-dioxins and furans (PCDD/F) during municipal solid waste incineration, minimizing the number of simulated components is mandatory. For this purpose, two new multilinear regression models capable of determining the dioxins total amount and toxicity of an atmospheric emission have been adjusted based on previously published ones. The new source of data used (almost 200 PCDD/F analyses) provides a wider range of application to the models, increasing also the diversity of the emission sources, from industrial and laboratory scale thermal processes. Only three of the 17 toxic congeners (1,2,3,6,7,8-HxCDD, 2,3,7,8-TCDF and OCDF), whose formation was found to be linearly independent, were necessary as inputs for the models. All model parameters have been statistically validated and their confidence intervals have been calculated using the Bootstrap method. The resulting coefficients of determination (R2) for the models are 0.9711 ± 0.0056 and 0.9583 ± 0.0085; its root mean square errors (RMSE) are 0.2115 and 0.2424, and its mean absolute errors (MAE) are 0.1541 and 0.1733 respectively.

scholarly journals A land surface model combined with a crop growth model for paddy rice (MATCRO-Rice v. 1) – Part 2: Model validation

2016 ◽  
Vol 9 (11) ◽  
pp. 4155-4167 ◽  
Author(s):  
Yuji Masutomi ◽  
Keisuke Ono ◽  
Takahiro Takimoto ◽  
Masayoshi Mano ◽  
Atsushi Maruyama ◽  
...  
Keyword(s):  
Heat Flux ◽  
Crop Yield ◽  
Land Surface ◽  
Crop Yields ◽  
Sensible Heat Flux ◽  
Paddy Rice ◽  
Surface Model ◽  
Model Parameters ◽  
Carbon Uptake ◽  
Mean Square Errors

Abstract. We conducted two types of validation for the simulations by MATCRO-Rice developed by Masutomi et al. (2016). In the first validation, we compared simulations with observations for latent heat flux (LHF), sensible heat flux (SHF), net carbon uptake by crop, and paddy rice yield from 2003 to 2006 at the site where model parameters are parameterized. In the second validation, we compared the observed and simulated paddy rice yields over Japan from 1991 to 2010 between observations and simulations. The 4-year average root mean square errors (RMSEs) of the first validation for LHF and SHF were 18.20 and 15.47 W m−2, respectively. These values for errors are comparable to those reported in earlier studies. The comparison of biomass growth during growing periods from 2003 to 2006 at the parameterization site shows that the simulations were in agreement with the observations, indicating that the model can reproduce the net carbon uptake by crops well. The 4-year average RMSE of the first validation for crop yield in the same period was 410.6 kg ha−1, which accounted for 8.1 % of the mean observed yields. The error of the second validation for crop yield was 16.7 % and the correlation of crop yields between observations and simulations from 1991 to 2010 was significant at 0.663 (P < 0.01). These results indicate that MATCRO-Rice has high ability to accurately and consistently simulate LHF, SHF, net carbon uptake by crop, and crop yield.

scholarly journals ALBEDO RETRIEVING FROM DSCOVR/EPIC DATA AND PRELIMINARY VALIDATION

2019 ◽  
Vol XLII-3/W9 ◽  
pp. 147-152
Author(s):  
Q. Y. Tian ◽  
Q. Liu ◽  
H. W. Zhang ◽  
Y. H. Che ◽  
Y. N. Wen ◽  
...  
Keyword(s):  
Climate Change ◽  
Remote Sensing ◽  
Time Resolution ◽  
Land Surface ◽  
Surface Albedo ◽  
High Temporal Resolution ◽  
High Time Resolution ◽  
Observation Data ◽  
Climate Change Research ◽  
Wide Range

Abstract. Land surface albedo plays an important role in climate change research. Satellite remote sensing has the characteristic of wide observation range, and it can make repeated observations on the same area. Therefore, using the remote sensing data to retrieve surface albedo becomes a main method to obtain the surface albedo in a wide range or even on a global scale. However, the time resolution of existing albedo products is usually low, which has a great impact on the analysis of rapid changes in surface vegetation and the climate change research. The Deep Space Climate Observatory (DSCOVR) was launched to a sun-earth first Lagrange point (L1) orbit, which is a new and unique vantage point to observe the continuously full, sunlit disk of Earth. DSCOVR can provide observation data with high time resolution, therefore, it is necessary to explore the feasibility of the new sensor DSCOVR/EPIC inversion of the daily albedo product. The relationship between the surface broadband albedo and the surface reflectance was established, and then the surface albedo with high temporal resolution was calculated using the DSCOVR/EPIC data. The Inner Mongolia Autonomous Region and parts of the Sahara Desert were selected to verify the accuracy of DSCOVR albedo compared with MODIS albedo. The results show that the correlation coefficients between DSCOVR albedo and MODIS albedo are greater than 0.7 and RMSE are less than 0.05 both in visible band and shortwave band. It can be seen that this method can be used for the albedo retrieval using DSCOVR/EPIC data.

L'apport de la télédétection spatiale à la modélisation des surfaces continentales

2020 ◽  
pp. 052
Author(s):  
Jean-Christophe Calvet ◽  
Jean-Louis Champeaux
Keyword(s):  
Satellite Data ◽  
Land Surface ◽  
Land Surface Model ◽  
Static Model ◽  
Geographic Information ◽  
Surface Model ◽  
Model Parameters

Cet article présente les différentes étapes des développements réalisés au CNRM des années 1990 à nos jours pour spatialiser à diverses échelles les simulations du modèle Isba des surfaces terrestres. Une attention particulière est portée sur l'intégration, dans le modèle, de données satellitaires permettant de caractériser la végétation. Deux façons complémentaires d'introduire de l'information géographique dans Isba sont présentées : cartographie de paramètres statiques et intégration au fil de l'eau dans le modèle de variables observables depuis l'espace. This paper presents successive steps in developments made at CNRM from the 1990s to the present-day in order to spatialize the simulations of the Isba land surface model at various scales. The focus is on the integration in the model of satellite data informative about vegetation. Two complementary ways to integrate geographic information in Isba are presented: mapping of static model parameters and sequential assimilation of variables observable from space.

THE PREDICTIVE CONTENT OF DISAGGREGATED NORMAL INCOME: An Empirical Study in the JSX

2003 ◽  
Vol 5 (3) ◽  
pp. 363 ◽  
Author(s):  
Slamet Sugiri
Keyword(s):  
Linear Regression ◽  
Regression Models ◽  
Stock Exchange ◽  
Manufacturing Firms ◽  
Single Step ◽  
Model Parameters ◽  
Linear Regression Models ◽  
Operating Income ◽  
Predictive Content ◽  
Multiple Step

The main objective of this study is to examine a hypothesis that the predictive content of normal income disaggregated into operating income and nonoperating income outperforms that of aggregated normal income in predicting future cash flow. To test the hypothesis, linear regression models are developed. The model parameters are estimated based on fifty-five manufacturing firms listed in the Jakarta Stock Exchange (JSX) up to the end of 1997.This study finds that empirical evidence supports the hypothesis. This evidence supports arguments that, in reporting income from continuing operations, multiple-step approach is preferred to single-step one.

scholarly journals Assessment of the EUMETSAT Multi Decadal Land Surface Albedo Data Record from Meteosat Observations

2021 ◽  
Vol 13 (10) ◽  
pp. 1992
Author(s):  
Alessio Lattanzio ◽  
Michael Grant ◽  
Marie Doutriaux-Boucher ◽  
Rob Roebeling ◽  
Jörg Schulz
Keyword(s):  
Land Surface ◽  
Surface Albedo ◽  
Time Range ◽  
Essential Variable ◽  
Spatial Coverage ◽  
Data Record ◽  
Moderate Resolution Imaging Spectroradiometer ◽  
Cloud Mask ◽  
Cloud Screening

Surface albedo, defined as the ratio of the surface-reflected irradiance to the incident irradiance, is one of the parameters driving the Earth energy budget and it is for this reason an essential variable in climate studies. Instruments on geostationary satellites provide suitable observations allowing long-term monitoring of surface albedo from space. In 2012, EUMETSAT published Release 1 of the Meteosat Surface Albedo (MSA) data record. The main limitation effecting the quality of this release was non-removed clouds by the incorporated cloud screening procedure that caused too high albedo values, in particular for regions with permanent cloud coverage. For the generation of Release 2, the MSA algorithm has been replaced with the Geostationary Surface Albedo (GSA) one, able to process imagery from any geostationary imager. The GSA algorithm exploits a new, improved, cloud mask allowing better cloud screening, and thus fixing the major limitation of Release 1. Furthermore, the data record has an extended temporal and spatial coverage compared to the previous release. Both Black-Sky Albedo (BSA) and White-Sky Albedo (WSA) are estimated, together with their associated uncertainties. A direct comparison between Release 1 and Release 2 clearly shows that the quality of the retrieval improved significantly with the new cloud mask. For Release 2 the decadal trend is less than 1% over stable desert sites. The validation against Moderate Resolution Imaging Spectroradiometer (MODIS) and the Southern African Regional Science Initiative (SAFARI) surface albedo shows a good agreement for bright desert sites and a slightly worse agreement for urban and rain forest locations. In conclusion, compared with MSA Release 1, GSA Release 2 provides the users with a significantly more longer time range, reliable and robust surface albedo data record.

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