A statistical framework for estimating air temperature using <scp>MODIS</scp> land surface temperature data

Janatian, Nasime; Sadeghi, Morteza; Nejad, Seyed Hossein Sanaei; Bakhshian, Elham; Faridhosseini, Alireza; Hasheminia, Seyed Majid; Ghazanfari, Sadegh

doi:10.1002/joc.4766

Cited by 98 publications

(56 citation statements)

References 58 publications

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“…The difference between the results of Datasets A and B indicates that elevation and Julian day (i.e., season) also affect the relationship between LST and T a . This is consistent with the results from [15,23,48,49]. The high accuracy of T a estimation using the RF and CB algorithms in Figure 4b also indicates that RF and CB can account for the complicated relationship between predictor and response variables under different conditions, especially in mountainous area.…”

Section: Combinations Using Two-lst Variablessupporting

confidence: 87%

Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data

2017

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Abstract:Recently, several methods have been introduced and applied to estimate daily air surface temperature (T a ) using MODIS land surface temperature data (MODIS LST). Among these methods, the most common used method is statistical modeling, and the most applied algorithms are linear/multiple linear regression models (LM). There are only a handful of studies using machine learning algorithm models such as random forest (RF) or cubist regression (CB). In particular, there is no study comparing different combinations of four MODIS LST datasets with or without auxiliary data using different algorithms such as multiple linear regression, random forest, and cubist regression for daily T a-max , T a-min , and T a-mean estimation. Our study examines the mentioned combinations of four MODIS-LST datasets and shows that different combinations and differently applied algorithms produce various T a estimation accuracies. Additional analysis of daily data from three climate stations in the mountain area of North West of Vietnam for the period of five years (2009 to 2013) with four MODIS LST datasets (AQUA daytime, AQUA nighttime, TERRA daytime, and TERRA nighttime) and two additional auxiliary datasets (elevation and Julian day) shows that CB and LM should be applied if MODIS LST data is used solely. If MODIS LST is used together with auxiliary data, especially in mountainous areas, CB or RF is highly recommended. This study proved that the very high accuracy of T a estimation (R 2 > 0.93/0.80/0.89 and RMSE~1.5/2.0/1.6 • C of T a-max , T a-min , and T a-mean , respectively) could be achieved with a simple combination of four LST data, elevation, and Julian day data using a suitable algorithm.

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Section: Combinations Using Two-lst Variablessupporting

confidence: 87%

Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data

2017

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“…As MODIS LST is only available under clear sky in the land products (MOD11 and MYD11), many previous studies [25,27,35,36,59] estimated Ta by integrating MODIS LST and station observations under clear sky. To estimate Ta under both clear and cloudy sky conditions, Zhu et al [16] used the clear sky LST from the MODIS cloud product (MOD06) and Ta from the MODIS atmosphere profile products (MOD07) to build the relationship between LST and Ta at 5-km scale, and then applied this relationship under cloudy sky.…”

Section: Impact Of Weather Condition On the Ta Estimation Accuracymentioning

confidence: 99%

“…The TVX method also needs ground observation and could not be used in ungauged basins and vegetation sparse watersheds. Statistical regression methods usually try to build the relationship between station observed Ta and remotely sensed LST, elevation, NDVI, surface albedo and other explaining variables (such as longitude/latitude) using multi-variable linear regression model or machine learning approaches [25][26][27][28][29][30][31][32][33][34][35]. Using MODIS LST products and observed daily mean temperature from 95 meteorological stations in the Tibetan Plateau, Zhang et al [36] compared the performances of several regression methods, including MLR (multiple linear regression), PLS (partial least squares regression), BPNN (back propagation neural network), SVR (support vector regression), RF (random forests) and CR (Cubist regression), and 15 different combination schemes of the four MODIS observations per day over the Tibet Plateau.…”

Section: Introductionmentioning

confidence: 99%

Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal

et al. 2017

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Near surface air temperature (Ta) is one of the key input parameters in land surface models and hydrological models as it affects most biogeophysical and biogeochemical processes of the earth surface system. For distributed hydrological modeling over glacierized basins, obtaining high resolution Ta forcing is one of the major challenges. In this study, we proposed a new high resolution daily Ta estimation scheme under both clear and cloudy sky conditions through integrating the moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and China Meteorological Administration (CMA) land data assimilation system (CLDAS) reanalyzed daily Ta. Spatio-temporal continuous MODIS LST was reconstructed through the data interpolating empirical orthogonal functions (DINEOF) method. Multi-variable regression models were developed at CLDAS scale and then used to estimate Ta at MODIS scale. The new Ta estimation scheme was tested over the Langtang Valley, Nepal as a demonstrating case study. Observations from two automatic weather stations at Kyanging and Yala located in the Langtang Valley from 2012 to 2014 were used to validate the accuracy of Ta estimation. The RMSEs are 2.05, 1.88, and 3.63 K, and the biases are 0.42, −0.68 and −2.86 K for daily maximum, mean and minimum Ta, respectively, at the Kyanging station. At the Yala station, the RMSE values are 4.53, 2.68 and 2.36 K, and biases are 4.03, 1.96 and −0.35 K for the estimated daily maximum, mean and minimum Ta, respectively. Moreover, the proposed scheme can produce reasonable spatial distribution pattern of Ta at the Langtang Valley. Our results show the proposed Ta estimation scheme is promising for integration with distributed hydrological model for glacier melting simulation over glacierized basins.

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“…The models used for air temperature estimation were developed taking into account the above-mentioned factors that affect the LST-TA relationship. Stepwise linear regression has been widely used to estimate air temperature due to its advantage of simplicity and efficiency [14,55,57]. In this study, stepwise linear regression was used to find the optimal model for the estimation of TA at the four overpass times of Terra and Aqua (hereafter, TA TD , TA AD , TA TN , and TA AN , respectively) based on LST, EVI, NDWI, SZA, and DTC during the period 2006-2013.…”

Section: Model Calibration and Validationmentioning

confidence: 99%

“…The air temperature is mostly estimated based on remotely sensed land surface temperature. The temperature-vegetation index (TVX) method [52][53][54] and the statistical methods [43,51,[55][56][57][58] are two commonly used methods for estimating air temperature. To link LST and air temperature, many factors must be considered, such as the surface properties, atmospheric conditions and solar angles [59,60].…”

Section: Introductionmentioning

confidence: 99%

A Satellite-Derived Climatological Analysis of Urban Heat Island over Shanghai during 2000–2013

Huang

Guo

et al. 2017

Remote Sensing

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Abstract:The urban heat island is generally conducted based on ground observations of air temperature and remotely sensing of land surface temperature (LST). Satellite remotely sensed LST has the advantages of global coverage and consistent periodicity, which overcomes the weakness of ground observations related to sparse distributions and costs. For human related studies and urban climatology, canopy layer urban heat island (CUHI) based on air temperatures is extremely important. This study has employed remote sensing methodology to produce monthly CUHI climatology maps during the period 2000-2013, revealing the spatiotemporal characteristics of daytime and nighttime CUHI during this period of rapid urbanization in Shanghai. Using stepwise linear regression, daytime and nighttime air temperatures at the four overpass times of Terra/Aqua were estimated based on time series of Terra/Aqua-MODIS LST and other auxiliary variables including enhanced vegetation index, normalized difference water index, solar zenith angle and distance to coast. The validation results indicate that the models produced an accuracy of 1.6-2.6 • C RMSE for the four overpass times of Terra/Aqua. The models based on Terra LST showed higher accuracy than those based on Aqua LST, and nighttime air temperature estimation had higher accuracy than daytime. The seasonal analysis shows daytime CUHI is strongest in summer and weakest in winter, while nighttime CUHI is weakest in summer and strongest in autumn. The annual mean daytime CUHI during 2000-2013 is 1.0 and 2.2 • C for Terra and Aqua overpass, respectively. The annual mean nighttime CUHI is about 1.0 • C for both Terra and Aqua overpass. The resultant CUHI climatology maps provide a spatiotemporal quantification of CUHI with emphasis on temperature gradients. This study has provided information of relevance to urban planners and environmental managers for assessing and monitoring urban thermal environments which are constantly being altered by natural and anthropogenic influences.

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A statistical framework for estimating air temperature using MODIS land surface temperature data

Cited by 98 publications

References 58 publications

Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data

Comparison of Multiple Linear Regression, Cubist Regression, and Random Forest Algorithms to Estimate Daily Air Surface Temperature from Dynamic Combinations of MODIS LST Data

Estimating High Resolution Daily Air Temperature Based on Remote Sensing Products and Climate Reanalysis Datasets over Glacierized Basins: A Case Study in the Langtang Valley, Nepal

A Satellite-Derived Climatological Analysis of Urban Heat Island over Shanghai during 2000–2013

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