scholarly journals COMS-Based Retrieval of Daily Actual Evapotranspiration over Korea

2017 ◽  
Vol 2017 ◽  
pp. 1-16 ◽  
Author(s):  
Na-Yeon Park ◽  
Jae-Dong Jang ◽  
Youngmi Kim ◽  
Eun-Ha Sohn ◽  
Mi-Lim Ou ◽  
...  
Keyword(s):  
Satellite Data ◽  
Land Surface ◽  
Mixed Forest ◽  
Data Communication ◽  
Surface Characteristics ◽  
Semiempirical Method ◽  
Rice Paddy ◽  
Flux Tower ◽  
Polar Orbiting Satellite ◽  
Meteorological Satellite

Evapotranspiration (ET) from the land surface is an important hydrometeorological factor in the exchange of energy between the atmosphere and land surface. The accurate quantification for management of water resources and understanding of climate change are crucial, requiring continuous temporal and spatial monitoring. The objective of this study is to apply and estimate daily actual ET using semiempirical method, B-method, which is based on surface energy balance over heterogeneous area, Korea. To estimate daily ET, we used geostationary meteorological satellite data (Communication, Ocean and Meteorological Satellite, COMS) and polar-orbiting satellite data (Système Pour ľObservation de la Terre, SPOT). Estimated daily ET using only satellite data was relatively accurate and reflects land surface characteristics. It had high periodicity and spatial resolution over a wide area on clear-sky days. The daily ET was overestimated by about 1 mm/day at the two flux tower measurements sites, but the simulated seasonal variation and pattern were in good agreement with flux tower measurements. In the mixed forest, the root-mean-square error (RMSE) was 0.94 mm/day and the bias was 1.05 mm/day, while, in the rice paddy, RMSE was 1.12 mm/day and bias was 1.21 mm/day.

Terrestrial CO2 Fluxes, Concentrations, Sources and Budget in Northeast China: Observational and Modeling Studies

2020 ◽  
Author(s):  
Xiaolan Li ◽  
Xiao-Ming Hu ◽  
Changjie Cai ◽  
Qingyu Jia ◽  
Yao Zhang ◽  
...  
Keyword(s):  
Land Surface ◽  
Northeast China ◽  
Ecosystem Respiration ◽  
Mixed Forest ◽  
Net Ecosystem Exchange ◽  
Growth Period ◽  
Rice Paddy ◽  
Light Use Efficiency ◽  
Maximum Light ◽  
Use Efficiency

<p>CO<sub>2</sub> fluxes and concentrations are not well understood in Northeast China, where dominant land surface types are mixed forest and cropland.  Here, we analyzed the CO<sub>2</sub> fluxes and concentrations using Eddy Covariance (EC) measurements, satellite observations, and the Weather Research and Forecasting model coupled with the Vegetation Photosynthesis and Respiration Model (WRF-VPRM).  We also used WRF-VPRM outputs to examine CO<sub>2</sub> transport/dispersion, and to quantify the biogenic and anthropogenic contributions to atmospheric CO<sub>2</sub> concentrations.  Finally, we investigated the uncertainties of simulating CO<sub>2</sub> fluxes related to four VPRM parameters (including maximum light use efficiency, photosynthetically active radiation half-saturation value, and two respiration parameters) using offline ensemble simulations with randomly selected parameter values.  The results indicated that mixed forests acted as a larger CO<sub>2</sub> source and sink than rice paddies on average in 2016 due to a longer growth period and stronger ecosystem respiration, although the minimum EC-measured daily mean net ecosystem exchange (NEE) was smaller at rice paddy (-10 μmol m<sup>-2</sup> s<sup>-1</sup>) than at mixed forest (-6.5 μmol m<sup>-2</sup> s<sup>-1</sup>) during the growing season (May through September).  The monthly fluctuation of column-averaged CO<sub>2</sub> concentrations (XCO<sub>2</sub>) exceeded 10 ppm in Northeast China during 2016.  Biogenic contribution (large negative in summer and insignificant in other months) offset about 70% of anthropogenic contribution of XCO<sub>2</sub> in this region.  WRF-VPRM modeling successfully captured seasonal and episodic variations of NEE and CO<sub>2</sub> concentrations, however, the NEE in mixed forest was overestimated during daytime, mainly due to the uncertainties of VPRM parameters, especially maximum light use efficiency.</p>

scholarly journals Use of AMSR-E microwave satellite data for land surface characteristics and snow cover variation

Data in Brief ◽  
2016 ◽  
Vol 9 ◽  
pp. 1077-1089 ◽  
Author(s):  
Mukesh Singh Boori ◽  
Ralph R. Ferraro ◽  
Komal Choudhary ◽  
Alexander Kupriyanov
Keyword(s):  
Snow Cover ◽  
Satellite Data ◽  
Land Surface ◽  
Surface Characteristics ◽  
Land Surface Characteristics

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.

Visual analysis of meteorological satellite data via model-agnostic meta-learning

2021 ◽  
Author(s):  
Shiyu Cheng ◽  
Hanwei Shen ◽  
Guihua Shan ◽  
Beifang Niu ◽  
Weihua Bai
Keyword(s):  
Satellite Data ◽  
Visual Analysis ◽  
Meta Learning ◽  
Meteorological Satellite

scholarly journals The Gradient Effect on the Relationship between the Underlying Factor and Land Surface Temperature in Large Urbanized Region

Land ◽  
2020 ◽  
Vol 10 (1) ◽  
pp. 20
Author(s):  
Yixu Wang ◽  
Mingxue Xu ◽  
Jun Li ◽  
Nan Jiang ◽  
Dongchuan Wang ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Landscape Planning ◽  
Potential Gradient ◽  
Surface Characteristics ◽  
Ordinary Least Squares ◽  
Urban Agglomeration ◽  
Spatial Gradient ◽  
Gradient Effect

Although research relating to the urban heat island (UHI) phenomenon has been significantly increasing in recent years, there is still a lack of a continuous and clear recognition of the potential gradient effect on the UHI—landscape relationship within large urbanized regions. In this study, we chose the Beijing-Tianjin-Hebei (BTH) region, which is a large scaled urban agglomeration in China, as the case study area. We examined the causal relationship between the LST variation and underlying surface characteristics using multi-temporal land cover and summer average land surface temperature (LST) data as the analyzed variables. This study then further discussed the modeling performance when quantifying their relationship from a spatial gradient perspective (the grid size ranged from 6 to 24 km), by comparing the ordinary least squares (OLS) and geographically weighted regression (GWR) methods. The results indicate that: (1) both the OLS and GWR analysis confirmed that the composition of built-up land contributes as an essential factor that is responsible for the UHI phenomenon in a large urban agglomeration region; (2) for the OLS, the modeled relationship between the LST and its drive factor showed a significant spatial gradient effect, changing with different spatial analysis grids; and, (3) in contrast, using the GWR model revealed a considerably robust and better performance for accommodating the spatial non-stationarity with a lower scale dependence than that of the OLS model. This study highlights the significant spatial heterogeneity that is related to the UHI effect in large-extent urban agglomeration areas, and it suggests that the potential gradient effect and uncertainty induced by different spatial scale and methodology usage should be considered when modeling the UHI effect with urbanization. This would supplement current UHI study and be beneficial for deepening the cognition and enlightenment of landscape planning for UHI regulation.

Temporal behaviour of Asian dust aerosols observed in 2001 using meteorological satellite data

2004 ◽  
Vol 4 (3) ◽  
pp. 208 ◽  
Author(s):  
Naoko Iino ◽  
Toshiaki Yano ◽  
Toshikatsu Masumizu ◽  
Kisei Kinoshita ◽  
Itsushi Uno ◽  
...  
Keyword(s):  
Satellite Data ◽  
Asian Dust ◽  
Temporal Behaviour ◽  
Dust Aerosols ◽  
Meteorological Satellite

scholarly journals Optimization of the Local Split-Window Algorithm for FY-4A Land Surface Temperature Retrieval

2019 ◽  
Vol 11 (17) ◽  
pp. 2016
Author(s):  
Lijuan Wang ◽  
Ni Guo ◽  
Wei Wang ◽  
Hongchao Zuo
Keyword(s):  
Surface Temperature ◽  
Diurnal Variation ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Regional Scale ◽  
Pso Algorithm ◽  
Variation Characteristics ◽  
Split Window Algorithm ◽  
Mean Square Errors

FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). In this paper, different underlying surfaces-observed data were applied to evaluate the applicability of the local split-window algorithm for FY-4A, and the local split-window algorithm parameters were optimized by the artificial intelligent particle swarm optimization (PSO) algorithm to improve the accuracy of retrieved LST. Results show that the retrieved LST can efficiently reproduce the diurnal variation characteristics of LST. However, the estimated values deviate hugely from the observed values when the local split-window algorithms are directly used to process the FY-4A satellite data, and the root mean square errors (RMSEs) are approximately 6K. The accuracy of the retrieved LST cannot be effectively improved by merely modifying the emissivity-estimated model or optimizing the algorithm. Based on the measured emissivity, the RMSE of LST retrieved by the optimized local split-window algorithm is reduced to 3.45 K. The local split-window algorithm is a simple and easy retrieval approach that can quickly retrieve LST on a regional scale and promote the application of FY-4A satellite data in related fields.

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