A method to estimate maximum and minimum air temperature using MODIS surface temperature and vegetation data: application to the Maipo Basin, Chile

Bustos, E.; Meza, Francisco

doi:10.1007/s00704-014-1167-2

Cited by 35 publications

(16 citation statements)

References 43 publications

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“…Main crops are fruits, vegetables, grapes for wine, and cereals. Natural vegetation in the lower part of the river basin is characterized by grass and shrublands with some native forests [28].…”

Section: Maipo Chilementioning

confidence: 99%

Drought Propagation in Semi-Arid River Basins in Latin America: Lessons from Mexico to the Southern Cone

Oertel

Meza

Gironás

et al. 2018

Water

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Detecting droughts as early as possible is important in avoiding negative impacts on economy, society, and environment. To improve drought monitoring, we studied drought propagation (i.e., the temporal manifestation of a precipitation deficit on soil moisture and streamflow). We used the Standardized Precipitation Evapotranspiration Index (SPEI), Standardized Streamflow Index (SSI), and Standardized Soil Moisture Index (SSMI) in three drought-prone regions: Sonora (Mexico), Maipo (Chile), and Mendoza-Tunuyán (Argentina) to study their temporal interdependence. For this evaluation we use precipitation, temperature, and streamflow data from gauges that are managed by governmental institutions, and satellite-based soil moisture from the ESA CCI SM v03.3 combined data set. Results confirm that effective drought monitoring should be carried out (1) at river-basin scale, (2) including several variables, and (3) considering hydro-meteorological processes from outside its boundaries.

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Section: Maipo Chilementioning

confidence: 99%

Drought Propagation in Semi-Arid River Basins in Latin America: Lessons from Mexico to the Southern Cone

Oertel

Meza

Gironás

et al. 2018

Water

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“…The absorption and reflectivity of the vegetation cover are correlated with their structural properties, such as leaf area index (LAI), fractional vegetation cover (FVC), and their physiological condition [39,40]. The values of NDVI vary between −1 and 1, where the range between 0.2 and 0.9 is mostly common in continuous vegetation cover [39]. The NDVI is defined as:…”

Section: Vegetation Indexmentioning

confidence: 99%

Evaluation of MODIS Land Surface Temperature Data to Estimate Near-Surface Air Temperature in Northeast China

2017

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Air temperature (T air ) near the ground surface is a fundamental descriptor of terrestrial environment conditions and one of the most widely used climatic variables in global change studies. The main objective of this study was to explore the possibility of retrieving high-resolution T air from the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products, covering complex terrain in Northeast China. The All Subsets Regression (ASR) method was adopted to select the predictors and build optimal multiple linear regression models for estimating maximum (T max ), minimum (T min ), and mean (T mean ) air temperatures. The relative importance of predictors in these models was evaluated via the Standardized Regression Coefficients (SRCs) method. The results indicated that the optimal models could estimate the T max , T min , and T mean with relatively high accuracies (Model Efficiency ≥ 0.90). Both LST and day length (DL) predictors were important in estimating T max (SRCs: daytime LST = 0.53, DL = 0.35), T min (SRCs: nighttime LST = 0.74, DL = 0.23), and T mean (SRCs: nighttime LST = 0.72, DL = 0.28). Models predicting T min and T mean had better performance than the one predicting T max . Nighttime LST was better at predicting T min and T mean than daytime LST data at predicting T max . Land covers had noticeable influences on estimating T air , and even seasonal vegetation greening could result in temporal variations of model performance. Air temperature could be accurately estimated using remote sensing, but the model performance was varied across different spatial and temporal scales. More predictors should be incorporated for the purpose of improving the estimation of near surface T air from the MODIS LST production.

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“…Precipitation, which is generally linked to higher soil water availability, can partly explain the regional low δ T values in southern China, Australia, and the African savannas (Figures c and c). Generally, with increasing precipitation, the soil layer gets wetter and therefore decreases the surface resistance, which leads to more evaporative cooling at the land surface [ Coops et al ., ; Mildrexler et al ., ; Bustos and Meza , ], and δ T decreases as a result.…”

Section: Discussionmentioning

confidence: 97%

Spatiotemporal variations in the difference between satellite‐observed daily maximum land surface temperature and station‐based daily maximum near‐surface air temperature

et al. 2017

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There is an increasing demand to integrate land surface temperature (LST) into climate research due to its global coverage, which requires a comprehensive knowledge of its distinctive characteristics compared to near‐surface air temperature (Tair). Using satellite observations and in situ station‐based data sets, we conducted a global‐scale assessment of the spatial and seasonal variations in the difference between daily maximum LST and daily maximum Tair (δT, LST − Tair) during 2003–2014. Spatially, LST is generally higher than Tair over arid and sparsely vegetated regions in the middle‐low latitudes, but LST is lower than Tair in tropical rainforests due to strong evaporative cooling, and in the high‐latitude regions due to snow‐induced radiative cooling. Seasonally, δT is negative in tropical regions throughout the year, while it displays a pronounced seasonality in both the midlatitudes and boreal regions. The seasonality in the midlatitudes is a result of the asynchronous responses of LST and Tair to the seasonal cycle of radiation and vegetation abundance, whereas in the boreal regions, seasonality is mainly caused by the change in snow cover. Our study identified substantial spatial heterogeneity and seasonality in δT, as well as its determinant environmental drivers, and thus provides a useful reference for monitoring near‐surface air temperature changes using remote sensing, particularly in remote regions.

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A method to estimate maximum and minimum air temperature using MODIS surface temperature and vegetation data: application to the Maipo Basin, Chile

Cited by 35 publications

References 43 publications

Drought Propagation in Semi-Arid River Basins in Latin America: Lessons from Mexico to the Southern Cone

Drought Propagation in Semi-Arid River Basins in Latin America: Lessons from Mexico to the Southern Cone

Evaluation of MODIS Land Surface Temperature Data to Estimate Near-Surface Air Temperature in Northeast China

Spatiotemporal variations in the difference between satellite‐observed daily maximum land surface temperature and station‐based daily maximum near‐surface air temperature

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