Satellite-based estimation of surface vapor pressure deficits using MODIS land surface temperature data

Hashimoto, Hirofumi; Dungan, Jennifer; White, Michael A.; Yang, Fan; Michaelis, Andrew; Running, Steven W.; Nemani, Ramakrishna R.

doi:10.1016/j.rse.2007.04.016

Cited by 86 publications

(64 citation statements)

References 29 publications

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“…The slope of the saturation vapour pressure curve Δ (Figure 3a) and the soil heat flux G (Figure 3d) (Figure 3b) was calculated based on the T min of MODIS, while the observed e a was calculated from relative humidity data measured by the Cenicaña station; this resulted in an R 2 of 0.047 (p > 0.2), but the RMSE obtained (0.307 kPa) was similar to the reported by different authors who made the same comparison (Cai et al, 2007, Nolan et al, 2016. The e s (Figure 3c) obtained a positive relation (R 2 = 0.372; p < 0.0001) with a RMSE of 0.219 kPa, similar to the reported by Hashimoto et al (2008) when they made the same comparison using MODIS data (0.25 kPa).…”

Section: Parameters Derived From Modis Temperaturessupporting

confidence: 87%

Combination of satellite imagery with meteorological data for estimating reference evapotranspiration

2018

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Abstract:The Food and Agriculture Organization of the United Nations (FAO) in its publication No. 56 of the Irrigation and Drainage Series presents the FAO Penman-Monteith procedure for the estimation of reference evapotranspiration from meteorological data, however, its calculation may be complicated in areas where there are no weather stations. This paper presents an evaluation of the potential of the Land Surface Temperature and Digital Elevation Models products derived from the MODIS and ASTER sensors, both on board the Terra EOS AM-1 satellite, for the estimation of reference evapotranspiration using the Penman-Monteith FAO-56, Hargreaves, Thornthwaite and Blaney-Criddle models. The four models were compared with the method proposed by FAO calculated with the observed data of a ground based meteorological station, finding a significant relation with the models Penman-Monteith FAO-56 and Hargreaves.Key words: Land Surface Temperature, MODIS, ASTER, reference evapotranspiration, FAO. Combinación de imágenes satelitales con datos meteorológicos para la estimación de la evapotranspiración de referenciaResumen: La Organización de las Naciones Unidas para la Alimentación y la Agricultura (FAO) en su publicación No 56 de la Serie de Riego y Drenaje presenta el procedimiento FAO Penman-Monteith para la estimación de la evapotranspiración de referencia a partir de datos meteorológicos, no obstante, su cálculo puede complicarse en zonas donde no se cuenta con estaciones meteorológicas. El presente artículo exhibe una evaluación del potencial de productos de Temperatura Superficial Terrestre y Modelos Digitales de Elevación derivados de imágenes adquiridas por los sensores MODIS y ASTER, ambos a bordo del satélite Terra EOS AM-1, para la estimación de la evapotranspiración de referencia utilizando los modelos de Penman-Monteith FAO-56, Hargreaves, Thornthwaite y Blaney-Criddle. Los cuatro modelos fueron comparados con el método propuesto por la FAO calculado con datos observados de una estación meteorológica en tierra, encontrando una relación significativa con los modelos Penman-Monteith FAO-56 y Hargreaves.Palabras clave: Temperatura Superficial Terrestre, MODIS, ASTER, evapotranspiración de referencia, FAO.

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Section: Parameters Derived From Modis Temperaturessupporting

confidence: 87%

Combination of satellite imagery with meteorological data for estimating reference evapotranspiration

2018

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“…We selected a 3 × 3 km area around each flux tower site based on footprint analysis conducted in Demokeya by Sjöström et al [43] and in Dahra by Tagesson et al [44]. Land surface temperature (LST) has been found to strongly influence ecosystem respiration [45,46] and has been used estimate vapor pressure deficit [47]. Sims et al [48] used LST to characterize temperature and drought stress in a modeling framework that used MODIS-enhanced vegetation index (EVI) as a predictor of GPP.…”

Section: Earth Observation Datamentioning

confidence: 99%

Evaluating Water Controls on Vegetation Growth in the Semi-Arid Sahel Using Field and Earth Observation Data

et al. 2017

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Water loss is a crucial factor for vegetation in the semi-arid Sahel region of Africa. Global satellite-driven estimates of plant CO 2 uptake (gross primary productivity, GPP) have been found to not accurately account for Sahelian conditions, particularly the impact of canopy water stress. Here, we identify the main biophysical limitations that induce canopy water stress in Sahelian vegetation and evaluate the relationships between field data and Earth observation-derived spectral products for up-scaling GPP. We find that plant-available water and vapor pressure deficit together control the GPP of Sahelian vegetation through their impact on the greening and browning phases. Our results show that a multiple linear regression (MLR) GPP model that combines the enhanced vegetation index, land surface temperature, and the short-wave infrared reflectance (Band 7, 2105-2155 nm) of the moderate-resolution imaging spectroradiometer satellite sensor was able to explain between 88% and 96% of the variability of eddy covariance flux tower GPP at three Sahelian sites (overall = 89%). The MLR GPP model presented here is potentially scalable at a relatively high spatial and temporal resolution. Given the scarcity of field data on CO 2 fluxes in the Sahel, this scalability is important due to the low number of flux towers in the region.

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“…MODIS and other satellite sensors provide thermal imagery that can be used to estimate regional D (García et al, 2013;Hashimoto et al, 2008;Nieto et al, 2010;Zhu et al, 2013). Likewise, meteorological agencies provide gridded records of D, which can be used for regional estimates of fuel moisture.…”

Section: Large-scale Estimates Of Fuel Moisturementioning

confidence: 99%

A semi-mechanistic model for predicting the moisture content of fine litter

Dios

Fellows

Nolan

et al. 2015

Agricultural and Forest Meteorology

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a b s t r a c tThe moisture content of vegetation and litter (fuel moisture) is an important determinant of fire risk, and predictions of dead fine fuel moisture content (fuel with a diameter <25.4 mm) are particularly important. A variety of indices, as well as empirical and mechanistic models, have been proposed to predict fuel moisture, but these approaches have seldom been validated across temporally extensive datasets, or widely contrasting vegetation types. Here, we describe a semi-mechanistic model, based on the exponential decline of fuel moisture content with atmospheric vapor pressure deficit, that predicts daily minimum fuel moisture content. We calibrated the model at one site in New South Wales, Australia, and validated it at three contrasting ecosystem types in California, USA, where 10-h fuel moisture content was continuously measured every 30 min over a year. We found that existing drought indices did not accurately predict fuel moisture, and that empirical and equilibrium models provided biased estimates. The mean absolute error (MAE) of the fuel moisture content predicted by our model across sites and years was 3.7%, which was substantially lower than for other, commonly used models. Our model's MAE dropped to 2.9% when fuel moisture was below 20%, and to 1.8% when fuel moisture was below 10%. Our model's MAE was comparable to instrumental MAE (3.1-2.5%), indicating that further improvement may be limited by measurement error. The simplicity, accuracy and precision of our model makes it suitable for a range of applications, such as operational fire management and the prediction of fire risk in vegetation models, without the need for site-specific calibrations.

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Satellite-based estimation of surface vapor pressure deficits using MODIS land surface temperature data

Cited by 86 publications

References 29 publications

Combination of satellite imagery with meteorological data for estimating reference evapotranspiration

Combination of satellite imagery with meteorological data for estimating reference evapotranspiration

Evaluating Water Controls on Vegetation Growth in the Semi-Arid Sahel Using Field and Earth Observation Data

A semi-mechanistic model for predicting the moisture content of fine litter

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