Spatial heterogeneity of satellite derived land surface parameters and energy flux densities for LITFASS-area

Abstract. Based on satellite data in different temporal and spatial resolution, the current use of frequency distribution functions (PDF) for surface parameters and energy fluxes is one of the most promising ways to describe subgrid heterogeneity of a landscape. Objective of this study is to find typical distribution patterns of parameters (albedo, NDVI) for the determination of the actual latent heat flux (L.E) determined from highly resolved satellite data within pixel on coarser scale. Landsat ETM+, Terra MODIS and NOAA-AVHRR surface temperature and spectral reflectance were used to infer further surface parameters and radiant- and energy flux densities for LITFASS-area, a 20×20 km2 heterogeneous area in Eastern Germany, mainly characterised by the land use types forest, crop, grass and water. Based on the Penman-Monteith-approach L.E, as key quantity of the hydrological cycle, is determined for each sensor in the accordant spatial resolution with an improved parametrisation. However, using three sensors, significant discrepancies between the inferred parameters can cause flux distinctions resultant from differences of the sensor filter response functions or atmospheric correction methods. The approximation of MODIS- and AVHRR- derived surface parameters to the reference parameters of ETM (via regression lines and histogram stretching, respectively), further the use of accurate land use classifications (CORINE and a new Landsat-classification), and a consistent parametrisation for the three sensors were realized to obtain a uniform base for investigations of the spatial variability. The analyses for 4 scenes in 2002 and 2003 showed that for forest clear distribution-patterns for NDVI and albedo are found. Grass and crop distributions show higher variability and differ significantly to each other in NDVI but only marginal in albedo. Regarding NDVI-distribution functions NDVI was found to be the key variable for L.E-determination.

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Spatial heterogeneity of satellite derived land surface parameters and energy flux densities for LITFASS-area

Atmospheric Chemistry and Physics Discussions ◽

10.5194/acpd-8-16219-2008 ◽

2008 ◽

Vol 8 (4) ◽

pp. 16219-16254 ◽

Cited By ~ 1

Author(s):

A. Tittebrand ◽

F. H. Berger

Keyword(s):

Land Use ◽

Spatial Heterogeneity ◽

Distribution Pattern ◽

Spatial Resolution ◽

Energy Flux ◽

Atmospheric Correction ◽

Distribution Functions ◽

Target Area ◽

Surface Parameters ◽

Noaa Avhrr

Abstract. Remote sensing data provide area integrated information of surface properties in different spatial or temporal resolutions according to different sensor features. Landsat ETM+, Terra MODIS and NOAA-AVHRR surface temperature and spectral reflectance were used to infer further surface parameters and radiant- and energy flux densities for LITFASS-area, a 20×20 km2 heterogeneous area in Eastern Germany, mainly characterized by the land use types forest, crop, grass and water. Based on the Penman-Monteith-approach the actual latent heat flux (L.E), as key quantity of the hydrological cycle, is determined for each sensor in the accordant spatial resolution with an improved parametrization. However, using three sensors, significant discrepancies between the inferred parameters can cause flux distinctions resultant from differences of the sensor filter response functions or atmospheric correction methods. The approximation of MODIS- and AVHRR- derived surface parameters to the reference parameters of ETM (via regression lines and histogram stretching, respectively), further the use of accurate land use classifications (CORINE and a new Landsat-classification), and a consistent parametrization for the three sensors were realized to obtain a uniform base for investigations of the spatial variability. For the target area the spatial heterogeneity is analysed investigating frequency distribution functions (PDF) for surface parameters and energy fluxes. PDF is the most promising way to describe subgrid heterogeneity due to the given data in different spatial resolution. Aim of this study is to find typical distribution pattern of parameters (albedo, NDVI) for the determination of L.E determined from the highly resolved ETM data within pixel on coarser scale (MODIS, AVHRR). The analyses for 4 scenes in 2002 and 2003 showed that clear distribution-pattern for forest for NDVI and albedo are found. Grass and crop distributions show higher variability and differ significantly to each other in NDVI but only marginal in albedo. Regarding NDVI-distribution functions NDVI was found to be the key variable for L.E-determination.

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Multi-scale texture analysis for urban land use/cover classification using high spatial resolution satellite data

10.1117/12.761232 ◽

2007 ◽

Cited By ~ 1

Author(s):

Youjing Zhang ◽

Liang Chen ◽

Bing Yu

Keyword(s):

Land Use ◽

Texture Analysis ◽

Spatial Resolution ◽

Satellite Data ◽

High Spatial Resolution ◽

Urban Land ◽

Urban Land Use ◽

Multi Scale

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Critical Analysis of Satellite Data of NSIDC, NOAA NESDIS in Determining the Spatial Distribution of Ice on Lakes

Ekológia (Bratislava) ◽

10.2478/eko-2018-0030 ◽

2018 ◽

Vol 37 (4) ◽

pp. 392-400

Author(s):

Nikolaevich Vyacheslav Baklagin

Keyword(s):

Spatial Distribution ◽

Spatial Resolution ◽

Satellite Data ◽

Hydrological Cycle ◽

Practical Interest ◽

Mean Value ◽

Modis Data ◽

Ice Coverage ◽

Ice Regime

AbstractThe process of formation and rotting of ice on lakes is an integral part of the hydrological cycle of many lakes. The conditions of the ice regime significantly influence the ecological system of lakes. The article includes calculation and analysis of errors in the determination of the spatial ice distribution (spatial resolution of 4–6 km) on Lake Onego, Lake Ladoga, Lake Segozero and Lake Vigozero within the period of 2006−2017 according to National Snow and Ice Data Center (NSIDC), National Oceanic and Atmospheric Administration National Environmental Satellite, Data, and Information Service (NOAA NESDIS) data with regard to reliable Moderate Resolution Imaging Spectroradiometer (MODIS) data (spatial resolution of 500 m). It was established that within the monitoring period, NSIDC data have the minimum mean values of errors in determining the spatial distribution of ice on lakes (3−10%) compared to NOAA NESDIS data (11−19%) and are also of more practical interest in estimating the ice coverage of lakes. The dependence of the mean value of errors that occur in the determination of the spatial distribution of ice (according to NSIDC, NOAA and NESDIS data) on the actual value of ice coverage (according to MODIS) was revealed. The results show that the NSIDC data allow estimating adequately the phases of the ice regime; however, the formation of a daily time series of ice coverage during freeze-up and break-up phases is possible only with a significant error (mean value of absolute deviations according to MODIS data is up to 35%).

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ESTIMATION OF GROSS PRIMARY PRODUCTION USING SATELLITE DATA AND GIS IN URBAN AREA, DENPASAR

International Journal of Remote Sensing and Earth Sciences (IJReSES) ◽

10.30536/j.ijreses.2010.v7.a1544 ◽

2011 ◽

Vol 7 (1) ◽

Cited By ~ 1

Author(s):

A.R. As-syakur ◽

T. Osawa ◽

IW.S. Adnyana

Keyword(s):

Remote Sensing ◽

Land Use ◽

Land Cover ◽

Primary Production ◽

Spatial Resolution ◽

Satellite Data ◽

High Spatial Resolution ◽

Gross Primary Production ◽

Remote Sensing Data ◽

Sensing Data

Remote sensing data with high spatial resolution is very useful to provideinformation about Gross Primary Production (GPP) especially over spatial coverage in theurban area. Most models of ecosystem carbon exchange based on remote sensing data usedlight use efficiency (LUE) model. The aim of this research was to analyze the distributionof annual GPP urban area of Denpasar. Two main satellite data used in this study wereALOS/AVNIR-2 and Aster satellite data. Result showed that annual value of GPP usingALOS/AVNIR-2 varied from 0.130 gC m-2 yr-1 to 2586.181 gC m-2 yr-1. Meanwhile, usingAster the value varied from 0.144 gC m-2 yr-1 to 2595.264 gC m-2 yr-1. The annual value ofGPP ALOS was lower than the value of Aster, because ALOS have high spatial resolutionand smaller interval of spectral resolution compared to Aster. Different land use couldeffect the value of GPP, because the different land use has different vegetation type,distribution, and different photosynthetic pathway type. The high spatial resolution of theremote sensing data is crucial to discriminate different land cover types in urban region.With heterogeneous land cover surface, maximum value of GPP using ALOS/AVNIR-2was smaller than that of Aster, however, the annual mean of GPP value usingALOS/AVNIR-2 was higher than that of Aster.

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