Relations between evaporation coefficients and vegetation indices studied by model simulations

Choudhury, Bhaskar J.; Ahmed, Nizam; Idso, Sherwood B.; Reginato, R. J.; Daughtry, Craig S. T.

doi:10.1016/0034-4257(94)90090-6

Cited by 501 publications

(308 citation statements)

References 62 publications

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“…For TSEB, the partitioning of component net radiation is based on the fractional vegetation cover and vegetation structure (Choudhury et al, 1994;Kustas and Norman, 1999). Thus the component energy balance equation can be expressed as:…”

Section: P-tseb and S-tsebmentioning

confidence: 99%

Intercomparison of remote sensing‐based models for estimation of evapotranspiration and accuracy assessment based on SWAT

Gao

Long

2008

Hydrological Processes

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Abstract:An intercomparison of daily actual evapotranspiration (ET) estimates from the single-source models (SEBAL and SEBS) and the two-source models (P-TSEB and S-TSEB) using remotely sensed data was performed to examine their utilities and limitations under a wide range of land covers and different meteorological conditions. The accuracy of ET estimates from remote sensing-based models of a selected watershed on 23 June 2005 (DOY 174) presenting large air drying power and marked contrast in underlying surface characteristics, and 25 July 2005, (DOY 206) in contrast to the meteorological and underlying surface conditions of DOY 174, were evaluated in terms of SWAT-based ET. The ET estimates from the two methodologies are shown to be comparable, indicating that S-TSEB has the highest accuracy with relative errors of 2Ð2% and 5Ð6% with reference to SWAT-based ET for DOY 174 and 206, respectively. Hence it was selected to be the basis for performing an intercomparison with other remote sensing-based models. Single-source models are very sensitive to KB 1 parameter, rendering marked differences in ET estimates because of different treatments of roughness length for heat transfer. SEBAL and SEBS yielded mean absolute percentage difference (MAPD) versus S-TSEB within 26Ð22% and 26Ð56% for DOY 174 and within 9Ð11% and 11Ð69% for DOY 206, respectively, indicating their applicability to higher vegetation cover and soil moisture availability areas under small air drying power condition. P-TSEB generated MAPD versus S-TSEB within 41Ð15% for DOY 174 and 8Ð79% for DOY 206, implying that P-TSEB is highly consistent with S-TSEB under small air drying power and less contrast in soil moisture conditions, whereas noticeable discrepancies occur under large air drying power and marked contrast in soil moisture circumstances, in particular for sparse cover. The performance of P-TSEB largely depends on the meteorological and underlying surface conditions, both exerting significant influences on the extent of coupling between vegetation and soil.

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Section: P-tseb and S-tsebmentioning

confidence: 99%

Intercomparison of remote sensing‐based models for estimation of evapotranspiration and accuracy assessment based on SWAT

Gao

Long

2008

Hydrological Processes

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“…The maximum NDVI (NDVImax) and the minimum NDVI (NDVI min ) values for the study area were determined, which were further used for computing the fractional vegetation cover (FVC) [57,58]: …”

Section: Lst Estimationmentioning

confidence: 99%

Optimized AVHRR land surface temperature downscaling method for local scale observations: case study for the coastal area of the Gulf of Gdańsk

Chybicki¹,

Łubniewski²

2017

Open Geosciences

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Satellite imaging systems have known limitations regarding their spatial and temporal resolution. The approaches based on subpixel mapping of the Earth's environment, which rely on combining the data retrieved from sensors of higher temporal and lower spatial resolution with the data characterized by lower temporal but higher spatial resolution, are of considerable interest. The paper presents the downscaling process of the land surface temperature (LST) derived from low resolution imagery acquired by the Advanced Very High Resolution Radiometer (AVHRR), using the inverse technique. The effective emissivity derived from another data source is used as a quantity describing thermal properties of the terrain in higher resolution, and allows the downsampling of low spatial resolution LST images. The authors propose an optimized downscaling method formulated as the inverse problem and show that the proposed approach yields better results than the use of other downsampling methods. The proposed method aims to find estimation of high spatial resolution LST data by minimizing the global error of the downscaling. In particular, for the investigated region of the Gulf of Gdańsk, the RMSE between the AVHRR image downscaled by the proposed method and the Landsat 8 LST reference image was 2.255 ∘ C with correlation coefficient R equal to 0.828 and Bias = 0.557 ∘ C. For comparison, using the PBIM method, it was obtained RMSE = 2.832 ∘ C, R = 0.775 and Bias = 0.997 ∘ C for the same satellite scene. It also has been shown that the obtained results are also good in local scale and can be used for areas much smaller than the entire satellite imagery scene, depicting diverse biophysical conditions. Specifically, for the analyzed set of small sub-datasets of the whole scene, the obtained RSME between the downscaled and reference image was smaller, by approx. 0.53 ∘ C on average, in the case of applying the proposed method than in the case of using the PBIM method.

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“…We also generated a stack of squared NDVI values because of its relationship to fractional vegetation cover (Choudhury, Ahmed, Idso, Reginato, & Daughtry, 1994). NDVI, squared NDVI, and image acquisition dates were used to generate strata maps.…”

Section: Satellite Datamentioning

confidence: 99%

Improving the precision of dynamic forest parameter estimates using Landsat

Brooks

Coulston

Wynne

et al. 2016

Remote Sensing of Environment

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The use of satellite-derived classification maps to improve post-stratified forest parameter estimates is well established. When reducing the variance of post-stratification estimates for forest change parameters such as forest growth, it is logical to use a change-related strata map. At the stand level, a time series of Landsat images is ideally suited for producing such a map. In this study, we generate strata maps based on trajectories of Landsat Thematic Mapper-based normalized difference vegetation index values, with a focus on post-disturbance recovery and recent measurements. These trajectories, from 1985 to 2010, are converted to harmonic regression coefficient estimates and classified according to a hierarchical clustering algorithm from a training sample. The resulting strata maps are then used in conjunction with measured plots to estimate forest status and change parameters in an Alabama, USA study area. These estimates and the variance of the estimates are then used to calculate the estimated relative efficiencies of the post-stratified estimates. Estimated relative efficiencies around or above 1.2 were observed for total growth, total mortality, and total removals, with different strata maps being more effective for each. Possible avenues for improvement of the approach include the following: (1) enlarging the study area and (2) using the Landsat images closest to the time of measurement for each plot. Multitemporal satellite-derived strata maps show promise for improving the precision of change parameter estimates.

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Relations between evaporation coefficients and vegetation indices studied by model simulations

Cited by 501 publications

References 62 publications

Intercomparison of remote sensing‐based models for estimation of evapotranspiration and accuracy assessment based on SWAT

Intercomparison of remote sensing‐based models for estimation of evapotranspiration and accuracy assessment based on SWAT

Optimized AVHRR land surface temperature downscaling method for local scale observations: case study for the coastal area of the Gulf of Gdańsk

Improving the precision of dynamic forest parameter estimates using Landsat

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