Determination of land surface temperature distributions from single channel IR measurements: An effective spatial interpolation method for the use of TOVS, ECMWF and radiosonde profiles in the atmospheric correction scheme

2003 ◽  
Vol 24 (6) ◽  
pp. 1189-1196 ◽  
Author(s):  
M. Schroedter ◽  
F. Olesen ◽  
H. Fischer
Keyword(s):  
Land Surface Temperature ◽  
Land Surface ◽  
Spatial Interpolation ◽  
Single Channel ◽  
Interpolation Method ◽  
Atmospheric Correction ◽  
Temperature Distributions ◽  
Correction Scheme ◽  
Spatial Interpolation Method

scholarly journals AN IMPROVED TEMPERATURE SPATIAL INTERPOLATION METHOD FOR SPACEBORNE LIDAR ATMOSPHERIC CORRECTION

2021 ◽  
Vol V-4-2021 ◽  
pp. 83-88
Author(s):  
M. Zhou ◽  
K. Li ◽  
M. Pan ◽  
J. Chen ◽  
C. Li ◽  
...  
Keyword(s):  
Spatial Interpolation ◽  
Interpolation Method ◽  
Meteorological Parameter ◽  
Atmospheric Correction ◽  
Absolute Error ◽  
Lapse Rate ◽  
Interpolation Methods ◽  
Spatial Interpolation Method ◽  
Meteorological Elements ◽  
Inverse Distance

Abstract. As one of the most important meteorological elements, temperature is an indispensable meteorological parameter for the atmospheric correction of spaceborne LiDAR ranging. Given a limited number of surface meteorological observation stations, the temperature values for all region of LiDAR observation need to be interpolated using appropriate spatial interpolation methods. In this paper, based on the monthly surface observation values in individual years (1981–2010) of Sichuan province observation stations, we firstly analyze the effects of three common interpolation methods, including inverse distance weighting (IDW), ordinary kriging (OK) and gradient plus inverse distance squared (GIDS). To solve the problem of low interpolation accuracy in severely undulating terrain area, an improved gradient distance inverse square method based on the adiabatic lapse rate (GIDS-ALR) is proposed. The experimental results show that the GIDS-ALR has an obvious improvement in the effect of severely undulating terrain, where the absolute error has been improved by more than 43% in average. Additionally, the temperature-interpolated MAE is reduced by more than 30%. The effectiveness and applicability of the proposed method is verified in this paper.

Spatial Simulation of Precipitation Resources in Chongqing

2012 ◽  
Vol 518-523 ◽  
pp. 4261-4265
Author(s):  
Xiao Song Lin ◽  
Sha Sha Yu ◽  
Hai Yan Wang
Keyword(s):  
Spatial Distribution ◽  
Spatial Interpolation ◽  
Regional Difference ◽  
Interpolation Method ◽  
Annual Precipitation ◽  
Regression Equations ◽  
Spatial Simulation ◽  
Simulation Data ◽  
Spatial Interpolation Method ◽  
Precise Simulation

Years’ precipitation data of Chongqing from 101 metrological stations has been adopted in the paper and the regression equations between annual precipitation and altitude, longitude, and height have been obtained by the use of SPSS, then elaborate simulation of Chongqing’s precipitation resources based on regression analysis was completed through the 1km×1km grid system and fitted equation. Elaborated simulation of precipitation resources was realized by best spatial interpolation method with the support of GIS; then the results of two different simulation methods were coupled in the form of linear combination to obtain the coupling simulation of spatial distribution of Chongqing’s precipitation resources, finally the precipitation resources were summed up and distributed according to different administration areas at county level and thus obtain precise simulation data of precipitation resources in each county of Chongqing. The results showed that there is a remarkable regional difference in the spatial distribution of precipitation resources of Chongqing, and it decreases from the southeast to the northwest in general, with the annual precipitation higher than 1270mm in southeast and lower than 1080mm in northwest.

scholarly journals COMPARATIVE ANALYSIS OF SPLIT-WINDOW AND SINGLE-CHANNEL ALGORITHMS FOR LAND SURFACE TEMPERATURE RETRIEVAL OF A PSEUDO-INVARIANT TARGET

2020 ◽  
Vol 26 (2) ◽  
Author(s):  
Pâmela Suélen Käfer ◽  
Silvia Beatriz Alves Rolim ◽  
Lucas Ribeiro Diaz ◽  
Nájila Souza da Rocha ◽  
María Luján Iglesias ◽  
...  
Keyword(s):  
Comparative Analysis ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Temperature Retrieval

scholarly journals SPATIOLTEMPORAL MODELING THE IMPACT OF SURFACE CHARACTERISTICS VARIATIONS ON LAND SURFACE TEMPERATURE VARIATIONS: A CASE STUDY OF SAMALGHAN VALLY

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 401-405
Author(s):  
M. K. Firozjaei ◽  
M. Makki ◽  
J. Lentschke ◽  
M. Kiavarz ◽  
S. K. Alavipanah
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Incident Angle ◽  
Surface Characteristics ◽  
Slope Aspect ◽  
Biophysical Parameters ◽  
The Impact

Abstract. Spatiotemporal mapping and modeling of Land Surface Temperature (LST) variations and characterization of parameters affecting these variations are of great importance in various environmental studies. The aim of this study is a spatiotemporal modeling the impact of surface characteristics variations on LST variations for the studied area in Samalghan Valley. For this purpose, a set of satellite imagery and meteorological data measured at the synoptic station during 1988–2018, were used. First, single-channel algorithm, Tasseled Cap Transformation (TCT) and Biophysical Composition Index (BCI) were employed to estimate LST and surface biophysical parameters including brightness, greenness and wetness and BCI. Also, spatial modeling was used to modeling of terrain parameters including slope, aspect and local incident angle based on DEM. Finally, the principal component analysis (PCA) and the Partial Least Squares Regression (PLSR) were used to modeling and investigate the impact of surface characteristics variations on LST variations. The results indicated that surface characteristics vary significantly for case study in spatial and temporal dimensions. The correlation coefficient between the PC1 of LST and PC1s of brightness, greenness, wetness, BCI, DEM, and solar local incident angle were 0.65, −0.67, −0.56, 0.72, −0.43 and 0.53, respectively. Furthermore, the coefficient coefficient and RMSE between the observed LST variation and modelled LST variation based on PC1s of brightness, greenness, wetness, BCI, DEM, and local incident angle were 0.83 and 0.14, respectively. The results of study indicated the LST variation is a function of s terrain and surface biophysical parameters variations.

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