A vegetation index based technique for spatial sharpening of thermal imagery

Agam, Nurit; Kustas, William P.; Anderson, Martha C.; Li, Fuqin; Neale, Christopher M. U.

doi:10.1016/j.rse.2006.10.006

Cited by 392 publications

(380 citation statements)

References 29 publications

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“…D0 does not use any ancillary data. D1 is based on the fractional photosynthetically active vegetation cover estimated at high resolution and is the same as the NDVI-based approach of Agam et al (2007). Fractional photosynthetically active vegetation cover is noted f pav and is defined as the surface of green (photosynthetically active) vegetation per soil surface unit.…”

Section: Disaggregation Methodologymentioning

confidence: 99%

“…To bridge the gap between the lowspatial resolution of available thermal data and the high-spatial resolution required over agricultural areas, one may disaggregate low-spatial-resolution thermal images at high-temporal frequency. To date, most disaggregation approaches of remotely sensed surface temperature have been based on the Normalized Difference Vegetation Index (NDVI) available from shortwave data at a spatial resolution finer than that of thermal data Agam et al, 2007;Inamdar et al, 2008). Although the NDVI-based approach has been successfully tested over agricultural areas, Agam et al (2007) and Inamdar et al (2008) emphasized the limitation that the variability of surface temperature is not explained entirely by NDVI.…”

Section: Introductionmentioning

confidence: 99%

“…To date, most disaggregation approaches of remotely sensed surface temperature have been based on the Normalized Difference Vegetation Index (NDVI) available from shortwave data at a spatial resolution finer than that of thermal data Agam et al, 2007;Inamdar et al, 2008). Although the NDVI-based approach has been successfully tested over agricultural areas, Agam et al (2007) and Inamdar et al (2008) emphasized the limitation that the variability of surface temperature is not explained entirely by NDVI. Recently, Inamdar and French (2009) developed a new disaggregation methodology of 5 km resolution GOES data using 1 km resolution MODIS-derived surface emissivity.…”

Section: Introductionmentioning

confidence: 99%

“…The main limitation of the NDVI-based approach is that the relationship between surface temperature and NDVI is not unique (Agam et al, 2007). It is not unique because (i) NDVI is mostly sensitive to green (photosynthetically active) vegetation cover (e.g.…”

Section: Introductionmentioning

confidence: 99%

“…The objective of this study is to complement the classical approach of Agam et al (2007) in the conditions where the temperature difference between photosynthetically and non-photosynthetically active vegetation is significant. The methodology takes advantage of the unique capabilities of the Formosat-2 instrument (Chern et al, 2008) providing shortwave data at high-spatial resolution (8 m) and high-temporal frequency (potentially 1 image per day).…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images

Merlin

Duchemin

Hagolle

et al. 2010

Remote Sensing of Environment

159

107

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The temporal frequency of the thermal data provided by current spaceborne high-resolution imagery systems is inadequate for agricultural applications.As an alternative to the lack of high-resolution observations, kilometric thermal data can be disaggregated using a green (photosynthetically active) veg- . The approach is also tested using the MODIS data re-sampled at 2 km resolution. Aggregation reduces errors in MODIS data and consequently increases the disaggregation accuracy.

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Section: Disaggregation Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images

Merlin

Duchemin

Hagolle

et al. 2010

Remote Sensing of Environment

159

107

View full text Add to dashboard Cite

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Genetic particle filter application to land surface temperature downscaling

et al. 2014

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Thermal infrared data are widely used for surface flux estimation giving the possibility to assess water and energy budgets through land surface temperature (LST). Many applications require both high spatial resolution (HSR) and high temporal resolution (HTR), which are not presently available from space. It is therefore necessary to develop methodologies to use the coarse spatial/high temporal resolutions LST remote-sensing products for a better monitoring of fluxes at appropriate scales. For that purpose, a data assimilation method was developed to downscale LST based on particle filtering. The basic tenet of our approach is to constrain LST dynamics simulated at both HSR and HTR, through the optimization of aggregated temperatures at the coarse observation scale. Thus, a genetic particle filter (GPF) data assimilation scheme was implemented and applied to a land surface model which simulates prior subpixel temperatures. First, the GPF downscaling scheme was tested on pseudoobservations generated in the framework of the study area landscape (Crau-Camargue, France) and climate for the year 2006. The GPF performances were evaluated against observation errors and temporal sampling. Results show that GPF outperforms prior model estimations. Finally, the GPF method was applied on Spinning Enhanced Visible and InfraRed Imager time series and evaluated against HSR data provided by an Advanced Spaceborne Thermal Emission and Reflection Radiometer image acquired on 26 July 2006. The temperatures of seven land cover classes present in the study area were estimated with root-mean-square errors less than 2.4 K which is a very promising result for downscaling LST satellite products.

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A simple model for spatial disaggregation of evaporative fraction: Comparative study with thermal sharpened land surface temperature data over India

2013

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[1] Evaporative fraction (EF) is a measure of the amount of available energy at the earth surface that is partitioned into latent heat flux. The currently operational thermal sensors like the Moderate Resolution Imaging Spectroradiometer (MODIS) on satellite platforms provide data only at 1000 m, which constraints the spatial resolution of EF estimates. A simple model (disaggregation of evaporative fraction (DEFrac)) based on the observed relationship between EF and the normalized difference vegetation index is proposed to spatially disaggregate EF. The DEFrac model was tested with EF estimated from the triangle method using 113 clear sky data sets from the MODIS sensor aboard Terra and Aqua satellites. Validation was done using the data at four micrometeorological tower sites across varied agro-climatic zones possessing different land cover conditions in India using Bowen ratio energy balance method. The root-mean-square error (RMSE) of EF estimated at 1000 m resolution using the triangle method was 0.09 for all the four sites put together. The RMSE of DEFrac disaggregated EF was 0.09 for 250 m resolution. Two models of input disaggregation were also tried with thermal data sharpened using two thermal sharpening models DisTrad and TsHARP. The RMSE of disaggregated EF was 0.14 for both the input disaggregation models for 250 m resolution. Moreover, spatial analysis of disaggregation was performed using Landsat-7 (Enhanced Thematic Mapper) ETM + data over four grids in India for contrasted seasons. It was observed that the DEFrac model performed better than the input disaggregation models under cropped conditions while they were marginally similar under non-cropped conditions. Citation: Eswar, R., M. Sekhar, and B. K. Bhattacharya (2013), A simple model for spatial disaggregation of evaporative fraction: Comparative study with thermal sharpened land surface temperature data over India,

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A vegetation index based technique for spatial sharpening of thermal imagery

Cited by 392 publications

References 29 publications

Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images

Disaggregation of MODIS surface temperature over an agricultural area using a time series of Formosat-2 images

Genetic particle filter application to land surface temperature downscaling

A simple model for spatial disaggregation of evaporative fraction: Comparative study with thermal sharpened land surface temperature data over India

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