Estimation of net radiation from the MODIS data under all sky conditions: Southern Great Plains case study

Bisht, Gautam; Bras, Rafael L.

doi:10.1016/j.rse.2010.02.007

Cited by 187 publications

(137 citation statements)

References 43 publications

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“…Using Aqua satellite data the RMSE of T a was 2.6°C and 3.2 hpa of e a . As shown by the data, the magnitude of the experimental errors generally corresponded to previous studies (Bisht et al 2005;Ryu et al 2008;Bisht and Bras 2010). One intriguing point is that the SMK site yielded a smaller "inter-instrumental" difference (i.e., RMSE) in T a and a larger difference in e a compared to the CFK site.…”

Section: R Ld From Flux Towerssupporting

confidence: 83%

“…3 (Bisht et al 2005;Ryu et al 2008;Bisht and Bras 2010). Ryu et al (2008) compared MODIS air temperature (T a ) and actual vapor pressure (e a ) from the Terra satellite with flux tower measurements.…”

Section: R Ld From Flux Towersmentioning

confidence: 99%

“…Wang and Liang (2009) employed linear and nonlinear models to estimate instantaneous clearsky R ld using a moderate resolution imaging spectroradiometer (MODIS). In addition, Bisht and Bras (2010) employed MODIS data to estimate R ld in Southern Great Plains.…”

Section: Introductionmentioning

confidence: 99%

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Satellite Based Downward Long Wave Radiation by Various Models in Northeast Asia

Sur¹,

Kim²,

Choi³

2014

Terr. Atmos. Ocean. Sci.

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Satellite-based downward long wave radiation measurement under clear sky conditions in Northeast Asia was conducted using five well-known physical models (Brunt 1932, Idso and Jackson 1969, Brutsaert 1975, Satterlund 1979, Prata 1996 with a newly proposed global R ld model (Abramowitz et al. 2012). Data from two flux towers in South Korea were used to validate downward long wave radiation. Moderate resolution imaging spectroradiometer (MODIS) atmospheric profile products were used to develop the R ld models. The overall root mean square error (RMSE) of MODIS R ld with respect to two ecosystem-type flux towers was determined to be ≈ 20 W m -2 . Based on the statistical analyses, MODIS R ld estimates with Brutsaert (1975) and Abramowitz et al. (2012) models were the most applicable for evaluating R ld for clear sky conditions in Northeast Asia. The Abramowitz R ld maps with MODIS T a and e a showed reasonable seasonal patterns, which were wellaligned with other biophysical variables reported by previous studies. The MODIS R ld map developed in this study will be very useful for identifying spatial patterns that are not detectable from ground-based R ld measurement sites.

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Section: R Ld From Flux Towerssupporting

confidence: 83%

Section: R Ld From Flux Towersmentioning

confidence: 99%

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Satellite Based Downward Long Wave Radiation by Various Models in Northeast Asia

Sur¹,

Kim²,

Choi³

2014

Terr. Atmos. Ocean. Sci.

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“…To meet the demands of applications in surface energy balance research, applied meteorology, and other fields, many methods have been developed in previous studies to obtain complete and high-quality LST data both spatially and temporally [33][34][35][36][37][38]. Temporal interpolation is a straightforward method to fill the MODIS LST product gaps.…”

Section: Introductionmentioning

confidence: 99%

“…Another method is to fill the LST retrieval gaps with ancillary meteorological data [35]. However, uncertainties during resampling or simple local averaging are inevitable, which should be attributed to the coarse spatial resolution (~100 km) of the global meteorological data [36,37]. Beyond that, a simple pixel-wise empirical regression method was recently implemented by combining the TIR LST and PMW TB products [38].…”

Section: Introductionmentioning

confidence: 99%

Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method

Kou

Jiang

et al. 2016

Remote Sensing

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Land surface temperature (LST) plays a major role in the study of surface energy balances. Remote sensing techniques provide ways to monitor LST at large scales. However, due to atmospheric influences, significant missing data exist in LST products retrieved from satellite thermal infrared (TIR) remotely sensed data. Although passive microwaves (PMWs) are able to overcome these atmospheric influences while estimating LST, the data are constrained by low spatial resolution. In this study, to obtain complete and high-quality LST data, the Bayesian Maximum Entropy (BME) method was introduced to merge 0.01˝and 0.25˝LSTs inversed from MODIS and AMSR-E data, respectively. The result showed that the missing LSTs in cloudy pixels were filled completely, and the availability of merged LSTs reaches 100%. Because the depths of LST and soil temperature measurements are different, before validating the merged LST, the station measurements were calibrated with an empirical equation between MODIS LST and 0~5 cm soil temperatures. The results showed that the accuracy of merged LSTs increased with the increasing quantity of utilized data, and as the availability of utilized data increased from 25.2% to 91.4%, the RMSEs of the merged data decreased from 4.53˝C to 2.31˝C. In addition, compared with the filling gap method in which MODIS LST gaps were filled with AMSR-E LST directly, the merged LSTs from the BME method showed better spatial continuity. The different penetration depths of TIR and PMWs may influence fusion performance and still require further studies.

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Monitoring daily evapotranspiration in Northeast Asia using MODIS and a regional Land Data Assimilation System

et al. 2013

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[1] We applied an approach for daily estimation and monitoring of evapotranspiration (ET) over the Northeast Asia monsoon region using satellite remote sensing observations from the Moderate Resolution Imaging Spectroradiometer (MODIS). Frequent cloud cover results in a substantial loss of remote sensing information, limiting the capability of continuous ET monitoring for the monsoon region. Accordingly, we applied and evaluated a stand-alone MODIS ET algorithm for representative regional ecosystem types and an alternative algorithm to facilitate continuous regional ET estimates using surface meteorological inputs from the Korea Land Data Assimilation System (KLDAS) in addition to MODIS land products. The resulting ET calculations showed generally favorable agreement (root-mean-square error < 1.3 mm d À1 ) with respect to in situ measurements from eight regional flux tower sites. The estimated mean annual ET for 3 years (2006 to 2008) was approximately 362.0 ± 161.5 mm yr À1 over the Northeast Asia domain. In general, the MODIS and KLDAS-based ET (MODIS-KLDAS ET) results showed favorable performance when compared to tower observations, though the results were overestimated for a forest site by approximately 39.5% and underestimated for a cropland site in South Korea by 0.8%. The MODIS-KLDAS ET data were generally underestimated relative to the MODIS (MOD16) operational global terrestrial ET product for various biome types, excluding cropland; however, MODIS-KLDAS ET showed better agreement than MOD16 ET for forest and cropland sites in South Korea. Our results indicate that MODIS ET estimates are feasible but are limited by satellite optical-infrared remote sensing constraints over cloudy regions, whereas alternative ET estimates using continuous meteorological inputs from operational regional climate systems (e.g., KLDAS) provide accurate ET results and continuous monitoring capability under all-sky conditions.

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Estimation of net radiation from the MODIS data under all sky conditions: Southern Great Plains case study

Cited by 187 publications

References 43 publications

Satellite Based Downward Long Wave Radiation by Various Models in Northeast Asia

Satellite Based Downward Long Wave Radiation by Various Models in Northeast Asia

Estimation of Land Surface Temperature through Blending MODIS and AMSR-E Data with the Bayesian Maximum Entropy Method

Monitoring daily evapotranspiration in Northeast Asia using MODIS and a regional Land Data Assimilation System

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