Moderate Resolution Imaging Spectroradiometer Land Surface Temperature Data Research Articles

LONG-TERM THERMAL ANOMALY DETECTION AND MAPPING AT PIXEL LEVEL USING A GOOGLE EARTH ENGINE TOOL

Abstract. Frequency of extreme weather events such as cloudbursts, heatwaves etc. have increased as an outcome of changing climate. Identification of the pattern of extreme temperature events is important since it governs various events such as heatwaves, wildfires, droughts, storms, coldwaves etc. Moderate Resolution Imaging Spectroradiometer (MODIS) provides Land Surface Temperature (LST) data at 1 kilometre of spatial resolution at daily interval that can help in the identification and mapping of the anomalies in the temperature at pixel level. This study proposes a global-scale daily long-term thermal anomaly detection tool made using Google Earth Engine (GEE) App. This open source tool with the name of ‘Deviation from Mean’ uses the MODIS LST data available from 2000 till date to detect temperature anomaly based on the deviation of temperature of any day (chosen by the user) from the long-term climatological mean. It also generates a time-series plot of temperature values of any pixel for any date for last 24 years i.e. 2000–2023 in the graphical form to analyze the variation in the temperature over the time. A case study has also been done using the tool to highlight the thermal anomaly experienced over the Indian sub-continent during March-April, 2022 and 2023. This tool is capable of providing thermal anomaly information at global, regional as well as local level that can help in taking region-specific mitigation measures.

Empirical Models for Estimating Air Temperature Using MODIS Land Surface Temperature (and Spatiotemporal Variables) in the Hurd Peninsula of Livingston Island, Antarctica, between 2000 and 2016

In this article, we present empirical models for estimating daily mean air temperature (Ta) in the Hurd Peninsula of Livingston Island (Antarctica) using Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) data and spatiotemporal variables. The models were obtained and validated using the daily mean Ta from three Spanish in situ meteorological stations (AEMET stations), Juan Carlos I (JCI), Johnsons Glacier (JG), and Hurd Glacier (HG), and three stations in our team’s monitoring sites, Incinerador (INC), Reina Sofía (SOF), and Collado Ramos (CR), as well as daytime and nighttime Terra-MODIS LST and Aqua-MODIS LST data between 2000 and 2016. Two types of multiple linear regression (MLR) models were obtained: models for each individual station (for JCI, INC, SOF, and CR—not for JG and HG due to a lack of data) and global models using all stations. In the study period, the JCI and INC stations were relocated, so we analyzed the data from both locations separately (JCI1 and JCI2; INC1 and INC2). In general, the best individual Ta models were obtained using daytime Terra LST data, the best results for CR being followed by JCI2, SOF, and INC2 (R2 = 0.5–0.7 and RSE = 2 °C). Model cross validation (CV) yielded results similar to those of the models (for the daytime Terra LST data: R2CV = 0.4–0.6, RMSECV = 2.5–2.7 °C, and bias = −0.1 to 0.1 °C). The best global Ta model was also obtained using daytime Terra LST data (R2 = 0.6 and RSE = 2 °C; in its validation: R2CV = 0.5, RMSECV = 3, and bias = −0.03), along with the significant (p < 0.05) variables: linear time (t) and two time harmonics (sine-cosine), distance to the coast (d), slope (s), curvature (c), and hour of LST observation (H). Ta and LST data were carefully corrected and filtered, respectively, prior to its analysis and comparison. The analysis of the Ta time series revealed different cooling/warming trends in the locations, indicating a complex climatic variability at a spatial scale in the Hurd Peninsula. The variation of Ta in each station was obtained by the Locally Weighted Regression (LOESS) method. LST data that was not “good quality” usually underestimated Ta and were filtered, which drastically reduced the LST data (<5% of the studied days). Despite the shortage of “good” MODIS LST data in these cold environments, all months were represented in the final dataset, demonstrating that the MODIS LST data, through the models obtained in this article, are useful for estimating long-term trends in Ta and generating mean Ta maps at a global level (1 km2 spatial resolution) in the Hurd Peninsula of Livingston Island.

Analyzing Changes in Frozen Soil in the Source Region of the Yellow River Using the MODIS Land Surface Temperature Products

The degradation of the frozen soil in the Qinghai–Tibetan Plateau (QTP) caused by climate warming has attracted extensive worldwide attention due to its significant effects on the ecosystem and hydrological processes. In this study, we propose an effective approach to estimate the spatial distribution and changes in the frozen soil using the moderate-resolution imaging spectroradiometer (MODIS) land surface temperature products as inputs. A comparison with in-situ observations suggests that this method can accurately estimate the mean daily land surface temperature, the spatial distribution of the permafrost, and the maximum thickness of the seasonally-frozen ground in the source region of the Yellow River, located in the northeastern area of the QTP. The results of The Temperature at the Top of the Permafrost model indicates that the area of permafrost in the source region of the Yellow River decreased by 4.82% in the period from 2003 to 2019, with an increase in the areal mean air temperature of 0.35 °C/10 years. A high spatial heterogeneity in the frozen soil changes was revealed. The basin-averaged active layer thickness of the permafrost increased at a rate of 5.46 cm/10 years, and the basin-averaged maximum thickness of the seasonally-frozen ground decreased at a rate of 3.66 cm/10 years. The uncertainties in calculating the mean daily land surface temperature and the soil’s thermal conductivity were likely to influence the accuracy of the estimation of the spatial distribution of the permafrost and the maximum thickness of the seasonally-frozen ground, which highlight the importance of the better integration of field observations and multi-source remote sensing data in order to improve the modelling of frozen soil in the future. Overall, the approach proposed in this study may contribute to the improvement of the application of the MODIS land surface temperature data in the study of frozen soil changes in large catchments with limited in-situ observations in the QTP.

Monitoring for Changes in Spring Phenology at Both Temporal and Spatial Scales Based on MODIS LST Data in South Korea

This study aims to monitor spatiotemporal changes of spring phenology using the green-up start dates based on the accumulated growing degree days (AGDD) and the enhanced vegetation index (EVI), which were deducted from moderate resolution imaging spectroradiometer (MODIS) land surface temperature (LST) data. The green-up start dates were extracted from the MODIS-derived AGDD and EVI for 30 Mongolian oak (Quercus mongolica Fisch.) stands throughout South Korea. The relationship between green-up day of year needed to reach the AGDD threshold (DoYAGDD) and air temperature was closely maintained in data in both MODIS image interpretation and from 93 meteorological stations. Leaf green-up dates of Mongolian oak based on the AGDD threshold obtained from the records measured at five meteorological stations during the last century showed the same trend as the result of cherry observed visibly. Extrapolating the results, the spring onset of Mongolian oak and cherry has become earlier (14.5 ± 4.3 and 10.7 ± 3.6 days, respectively) with the rise of air temperature over the last century. The temperature in urban areas was consistently higher than that in the forest and the rural areas and the result was reflected on the vegetation phenology. Our study expanded the scale of the study on spring vegetation phenology spatiotemporally by combining satellite images with meteorological data. We expect our findings could be used to predict long-term changes in ecosystems due to climate change.

Filling Gaps of Monthly Terra/MODIS Daytime Land Surface Temperature Using Discrete Cosine Transform Method

Land surface temperature (LST) is a key parameter in geophysical fields. The Moderate Resolution Imaging Spectroradiometer (MODIS) onboard Terra provides an accurate LST dataset with global coverage and monthly series, but the monthly MODIS LST data are often obscured by clouds and other atmospheric disturbances and consequently exhibit significant data gaps at a global scale, resulting in a difficult interpretation of LST trends and climatological characteristics. In this paper, an effective and fast LST reconstruction method to fill data gaps in monthly MODIS LST is presented. The proposal combines the Discrete Cosine Transform (DCT) and the Penalized Least Square approach (PLS) together with the Generalized Cross-Validation (GCV) criterion. It depends only on the spatial high-frequency information from original LST estimates and allows a fast and automatic filling process without the help of any other ancillary data. To analyze its performance, the method is applied to fill data gaps on three continents with synthetic random missing values introduced as validation sets. The statistical evaluation shows that this method is capable of filling a large number of missing values in MODIS LST datasets with very high accuracy. In addition, the trend differences between the original LST and reconstructed LST have assessed the significance by computing 95% confidence intervals for a time series of trend differences is examined. Simulated experiments show that data gaps with large missing counts lead to significant differences in trend patterns and the patterns on validation sets are well estimated by this method, which confirms that the filling process of MODIS LST is necessary and favorable results can be produced for substantial data gaps by the DCT-PLS method.

Implications of diurnal variations in land surface temperature to data assimilation using MODIS LST data

Based on the Beijing Climate Center’s land surface model BCC_AVIM (Beijing Climate Center Atmosphere-Vegetation Interaction Model), the ensemble Kalman filter (EnKF) algorithm has been used to perform an assimilation experiment on the Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) product to study the influence of satellite LST data frequencies on surface temperature data assimilations. The assimilation results have been independently tested and evaluated by Global Land Data Assimilation System (GLDAS) LST products. The results show that the assimilation scheme can effectively reduce the BCC_AVIM model simulation bias and the assimilation results reflect more reasonable spatial and temporal distributions. Diurnal variation information in the observation data has a significant effect on the assimilation results. Assimilating LST data that contain diurnal variation information can further improve the accuracy of the assimilation analysis. Overall, when assimilation is performed using observation data at 6-hour intervals, a relatively good assimilation result can be obtained, indicated by smaller bias (<2.2K) and root-mean-square-error (RMSE) (<3.7K) and correlation coefficients larger than 0.60. Conversely, the assimilation using 24-hour data generally showed larger bias (>2.2K) and RMSE (>4K). Further analysis showed that the sensitivity of assimilation effect to diurnal variations in LST varies with time and space. The assimilation using observations with a time interval of 3 hours has the smallest bias in Oceania and Africa (both<1K); the use of 24-hour interval observation data for assimilation produces the smallest bias (<2.2K) in March, April and July.

Mapping Regional Turbulent Heat Fluxes via Assimilation of MODIS Land Surface Temperature Data into an Ensemble Kalman Smoother Framework

AbstractEstimation of turbulent heat fluxes via variational data assimilation (VDA) approaches has been the subject of several studies. The VDA approaches need an adjoint model that is difficult to derive. In this study, remotely sensed land surface temperature (LST) data from the Moderate Resolution Imaging Spectroradiometer (MODIS) are assimilated into the heat diffusion equation within an ensemble Kalman smoother (EnKS) approach to estimate turbulent heat fluxes. The EnKS approach is tested in the Heihe River Basin (HRB) in northwest China. The results show that the EnKS approach can estimate turbulent heat fluxes by assimilating low temporal resolution LST data from MODIS. The findings indicate that the EnKS approach performs fairly well in various hydrological and vegetative conditions. The estimated sensible (H) and latent (LE) heat fluxes are compared with the corresponding observations from large aperture scintillometer systems at three sites (namely, Arou, Daman, and Sidaoqiao) in the HRB. The turbulent heat flux estimates from EnKS agree reasonably well with the observations, and are comparable to those of the VDA approach. The EnKS approach also provides statistical information on the H and LE estimates. It is found that the uncertainties of H and LE estimates are higher over wet and/or densely vegetated areas (grassland and forest) compared to the dry and/or slightly vegetated areas (cropland, shrubland, and barren land).

New ECOSTRESS and MODIS Land Surface Temperature Data Reveal Fine-Scale Heat Vulnerability in Cities: A Case Study for Los Angeles County, California

Rapid 21st century urbanization combined with anthropogenic climate warming are significantly increasing heat-related health threats in cities worldwide. In Los Angeles (LA), increasing trends in extreme heat are expected to intensify and exacerbate the urban heat island effect, leading to greater health risks for vulnerable populations. Partnerships between city policymakers and scientists are becoming more important as the need to provide data-driven recommendations for sustainability and mitigation efforts becomes critical. Here we present a model to produce heat vulnerability index (HVI) maps driven by surface temperature data from National Aeronautics and Space Administration’s (NASA) new Ecosystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) thermal infrared sensor. ECOSTRESS was launched in June 2018 with the capability to image fine-scale urban temperatures at a 70 m resolution throughout different times of the day and night. The HVI model further includes information on socio-demographic data, green vegetation abundance, and historical heatwave temperatures from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor onboard the Aqua spacecraft since 2002. During a period of high heat in July 2018, we identified the five most vulnerable communities at a sub-city block scale in the LA region. The persistence of high HVI throughout the day and night in these areas indicates a clear and urgent need for implementing cooling technologies and green infrastructure to curb future warming.

Application of Long Short-Term Memory neural network model for the reconstruction of MODIS Land Surface Temperature images

Land surface temperature (LST) is an important parameter that supplies information about the skin temperature of the Earth surface. Remote sensing satellite systems with thermal bands can be used to obtain LST information. One of these satellite systems, namely, Moderate Resolution Imaging Spectroradiometer (MODIS) is mostly utilized in LST studies. One of the problems of obtaining LST from the MODIS data is missing pixels because of the effects such as cloud coverage. This drawback can be encountered by applying Long Short-Term Memory (LSTM) network with one-step-ahead prediction of MODIS data to reconstruct daily LST through the previous data. In this study, LSTM network was applied to the daytime and nighttime MODIS time-series, separately. MODIS LST data (MYD11A1) have the spatial resolution of 1 km × 1 km with 1-day temporal resolution. The selected data range from Day of Year (DOY) 1 in 2017 (01 January 2017) to DOY 59 in 2019 (28 February 2019). MODIS images were processed for the reconstruction of daily LST images concerning an agricultural region in Ceyhan, Adana, Turkey. 82% of data were chosen as the training data while the remaining data were used for testing purposes. The data were reconstructed by feeding the network adding the new data in a moving window in each prediction step. The produced Root Mean Square Error (RMSE) map regarding all reconstruction errors from daytime and nighttime images varied between 2 K to 9 K and 1 K–5 K, respectively. Besides, the coefficients of determination (R2) at a selected pixel of time-series analysis were obtained as 0.894 and 0.905 for daytime and nighttime LST image, respectively. The results revealed that the LSTM network could be used to fix the missing pixels in LST images.

A statistical analysis of TIR anomalies extracted by RSTs in relation to an earthquake in the Sichuan area using MODIS LST data

Abstract. Research in the field of earthquake prediction has a long history, but the inadequacies of traditional approaches to the study of seismic threats have become increasingly evident. Remote sensing and Earth observation technology, an emerging method that can rapidly capture information concerning anomalies associated with seismic activity across a wide geographic area, has for some time been believed to be the key to overcoming the bottleneck in earthquake prediction studies. However, a multi-parametric method appears to be the most promising approach for increasing the reliability and precision of short-term seismic hazard forecasting, and thermal infrared (TIR) anomalies are important earthquake precursors. While several studies have investigated the correlation among TIR anomalies identified by the robust satellite techniques (RSTs) methodology and single earthquakes, few studies have extracted TIR anomalies over a long period within a large study area. Moreover, statistical analyses are required to determine whether TIR anomalies are precursors to earthquakes. In this paper, RST data analysis and the Robust Estimator of TIR Anomalies (RETIRA) index were used to extract the TIR anomalies from 2002 to 2018 in the Sichuan region using Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data, while the earthquake catalog was used to ascertain the correlation between TIR anomalies and earthquake occurrences. Most TIR anomalies corresponded to earthquakes, and statistical methods were used to verify the correlation between the extracted TIR anomalies and earthquakes. This is the first time that the ability to predict earthquakes has been evaluated based on the positive predictive value (PPV), false discovery rate (FDR), true-positive rate (TPR), and false-negative rate (FNR). The statistical results indicate that the prediction potential of RSTs with use of MODIS is limited with regard to the Sichuan region.

An Integrated Method for Reconstructing Daily MODIS Land Surface Temperature Data

Land surface temperature (LST) is a critical parameter in land surface process. The Moderate Resolution Imaging Spectroradiometer (MODIS) can be used to generate various LST data products, and these data have been widely applied in many studies. Unfortunately, cloud contamination brings about numerous missing or abnormal values, which negatively affect the application of LST data. To reconstruct missing values and improve data quality, this paper proposes an integrated method for reconstructing LST data under two conditions: Clear sky and cloudy sky. For the clear-sky condition, the MODIS eight-day LST (MOD11A2) product is used to be interpolated into the low-quality daily LST dataset using the harmonic analysis of time series (HANTS) algorithm. And then the linear regression algorithm is implemented on the original good-quality pixels of the MODIS daily LST (MOD11A1) product. After that, seamless processing on the reconstructed low-quality daily LST dataset is carried using the Poisson image editing method, and finally the high-quality daily LST dataset under clear-sky condition are then obtained. For the cloudy-sky conditions, the revised neighboring-pixel (NP) algorithm that originates from the surface energy balance theory is used to reconstruct the real LST data. To evaluate the reconstruction performance under clear-sky condition, a simulated dataset is generated from simulated missing pixels with good quality that are randomly chosen from 98 available LST images in the year 2010. Meanwhile, the real LST measurements collected from ground sites are used to assess the reconstructed results under cloudy-sky condition. Satisfactory validation results show that the proposed integrated method effectively reconstructs the missing information and low-quality pixels caused by cloud cover and other factors. The filled data can seamlessly preserve the temporal and spatial consistence of the daily LST data, which do promote the practical utility of the MODIS LST product.

Evaluation of MODIS land surface temperature products for daily air surface temperature estimation in northwest Vietnam

ABSTRACT Recently, the MODerate resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) is considered one of the most suitable ways to retrieve air surface temperature (T a) – one of the most important and widely used climate variables for a wide range of applications. In fact, many successful studies have been reported in many regions of the world. Each day, four MODIS LST data are available; from two sensors (Terra and Aqua) at two local overpass times (daytime and night-time). However, due to their different overpass times, most studies have used LST daytime and night-time for daily maximum (T max) and minimum (T min) air surface temperature estimation, respectively. Therefore, the performance of each individual LST data, the effect of the dynamic combination of these four LST data, the effect of land surface characteristics, and the effect of LST quality on the same estimation in the same region on the accuracy of estimated T a remains unclear. In this study, we evaluated and tested all individual LST data as well as all possible combinations of the four MODIS LST data from two separate stations with distinct land surface characteristics in northwest Vietnam for 10 years (from 2004 to 2013) under two sky conditions (all clear sky conditions and only good data – i.e. Quality Control (QC) value of 0) for daily T a (T max, T min, and T mean) estimations. In addition, the mixed data of the two stations were also evaluated. Our results showed that Terra LST data have a higher correlation with T a than Aqua LST; which is consistent for both stations and both quality conditions (all clear sky and only good data). A closer overpass time with T max or T min occurrence time did not guarantee a higher accuracy of T a estimation. Using only good LST data produced a higher accuracy of T a estimation than using all clear sky data. However, if the percentage of good data is low (i.e. less than 30%), then the all clear sky data will provide better results for T max estimation. Comparing the performance of the different combinations when using the single station and mixed station data, combinations including at least one night-time LST produced stable and high accuracy T min and T mean estimates, while the combinations with only daytime LST produced very low accuracy results. For T max estimation, the results were less impacted by LST quality; however, they were strongly impacted by different combinations and land surface characteristics.

Application of MODIS land surface temperature data: a systematic literature review and analysis

The Moderate Resolution Imaging Spectroradiometer (MODIS) aboard the Terra and Aqua satellites, which provides a very high temporal (four times per day) and spatial (1 km) resolution, has become one of the most important and widely used sensors for a broad range of applications. We analyze 529 articles from 159 journals in the Scopus database from 2009 to 2018 to understand the global and longitudinal trends of MODIS land surface temperature (LST) data applications. The results show that the publications of papers related to MODIS LST data have been steadily rising annually. They spanned 19 subject areas from environmental, agricultural, and biological science to social science and medicine, indicating a wide range of MODIS LST data applications. Among the 159 journals, Remote Sensing of Environment, Remote Sensing, and the International Journal of Remote Sensing published the most articles. The study also showed that urban heat island (UHI), air temperature estimation/mapping (Ta estimation), soil moisture, evapotranspiration estimation, and drought monitoring/estimation were the most popular applications of MODIS LST data. Furthermore, we discuss the strengths, limitations, and future direction of research using MODIS LST data.

Reconstruction of MODIS Land Surface Temperature Products Based on Multi-Temporal Information

Land surface temperature (LST) products derived from the moderate resolution imaging spectroradiometer (MODIS) sensor are one of the most important data sources used to research land surface energy and water balance at regional and global scales. However, MODIS data are severely contaminated by cloud cover, which limits the applications of LST products. In this paper, based on the spatio-temporal autocorrelation of land surface variables, a reconstruction algorithm depending on the correlations between spatial pixels in multiple time phases from available MODIS LST data is developed to reconstruct clear-sky LST values for missing pixels. Considering the impacts of correlation and bias between predictors and reconstructed data on the modeling error, the known data in the reconstructed time phase are combined with the data temporally nearest to them as predictor variables to establish their temporal relationships with the reconstructed data. The reconstructed results are validated by a series of evaluation indices. The average correlation coefficient between the reconstructed results and ground-based observations is 0.87, showing high temporal change accuracy. The difference in Moran’s I, representing spatial structure characteristics between the known and reconstructed data, is 0.03 on average, indicating a slight loss of spatial accuracy. The average reconstruction rate is approximately 87.0%. The modeling error, as part of the reconstruction error, is only 1.40 K on average and accounts for 5.0% of the total error. If the product and modeling errors are removed, the residual error represents approximately 3.5 K and 5.6 K of the annual mean difference between the cloudy and cloudless LST at night and during the day, respectively. In addition, different reconstruction cases are demonstrated using various predictor data, including many combinations of multi-temporal MODIS LST data, the microwave brightness temperature, and the combination of the normalized difference vegetation index and terrain data. Comparisons among cases show that the known MODIS LST data are more reliable as predictor variables and that the data combination advocated in this paper is optimal.

Daily air temperature estimation on glacier surfaces in the Tibetan Plateau using MODIS LST data

ABSTRACTThe MODerate resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) data have been widely used for air temperature estimation in mountainous regions where station observations are sparse. However, the performance of MODIS LST in high-elevation glacierized areas remains unclear. This study investigates air temperature estimation in glacierized areas based on ground observations at four glaciers across the Tibetan Plateau. Before being used to estimate the air temperature, MODIS LST data are evaluated at two of the glaciers, which indicates that MODIS night-time LST is more reliable than MODIS daytime LST data. Then, linear models based on each of the individual MODIS LST products from two platforms (Terra and Aqua) and two overpasses (night-time and daytime) are built to estimate daily mean, minimum and maximum air temperatures in glacierized areas. Regional glacier surface (RGS) models (mean /-mean-square differences: 3.3, 3.0 and 4.8°C for daily mean, minimum and maximum air temperatures, respectively) show higher accuracy than local non-glacier surface models (mean root-mean-square differences: 4.2, 4.7 and 5.7°C). In addition, the RGS models based on MODIS night-time LST perform better to estimate daily mean, minimum and maximum air temperatures than using temperature lapse rate derived from local stations.

A Study of Spatial Soil Moisture Estimation Using a Multiple Linear Regression Model and MODIS Land Surface Temperature Data Corrected by Conditional Merging

This study attempts to estimate spatial soil moisture in South Korea (99,000 km2) from January 2013 to December 2015 using a multiple linear regression (MLR) model and the Terra moderate-resolution imaging spectroradiometer (MODIS) land surface temperature (LST) and normalized distribution vegetation index (NDVI) data. The MODIS NDVI was used to reflect vegetation variations. Observed precipitation was measured using the automatic weather stations (AWSs) of the Korea Meteorological Administration (KMA), and soil moisture data were recorded at 58 stations operated by various institutions. Prior to MLR analysis, satellite LST data were corrected by applying the conditional merging (CM) technique and observed LST data from 71 KMA stations. The coefficient of determination (R2) of the original LST and observed LST was 0.71, and the R2 of corrected LST and observed LST was 0.95 for 3 selected LST stations. The R2 values of all corrected LSTs were greater than 0.83 for total 71 LST stations. The regression coefficients of the MLR model were estimated seasonally considering the five-day antecedent precipitation. The p-values of all the regression coefficients were less than 0.05, and the R2 values were between 0.28 and 0.67. The reason for R2 values less than 0.5 is that the soil classification at each observation site was not completely accurate. Additionally, the observations at most of the soil moisture monitoring stations used in this study started in December 2014, and the soil moisture measurements did not stabilize. Notably, R2 and root mean square error (RMSE) in winter were poor, as reflected by the many missing values, and uncertainty existed in observations due to freezing and mechanical errors in the soil. Thus, the prediction accuracy is low in winter due to the difficulty of establishing an appropriate regression model. Specifically, the estimated map of the soil moisture index (SMI) can be used to better understand the severity of droughts with the variability of soil moisture.

Evaluation of MODIS land surface temperature with in-situ snow surface temperature from CREST-SAFE

ABSTRACTThis article presents the procedure and results of a temperature-based validation approach for the Moderate Resolution Imaging Spectroradiometer (MODIS) Land Surface Temperature (LST) product provided by the National Aeronautics and Space Administration Terra and Aqua Earth Observing System satellites using in-situ LST observations recorded at the Cooperative Remote Sensing Science and Technology Center – Snow Analysis and Field Experiment (CREST-SAFE) during the years of 2013 (January–April) and 2014 (February–April). A total of 314 day-and-night clear-sky thermal images, acquired by the Terra and Aqua satellites, were processed and compared to ground-truth data from CREST-SAFE with a frequency of one measurement every 3 min. CREST-SAFE is a synoptic ground station, located in the cold county of Caribou in Maine, USA, with a distinct advantage over most meteorological stations because it provides automated and continuous LST observations via an Apogee Model SI-111 Infrared Radiometer. This article also attempts to answer the question of whether a single pixel (1 km2) or several spatially averaged pixels should be used for satellite LST validation by increasing the MODIS window size to 5 × 5, 9 × 9, and 25 × 25 windows.Several trends in the MODIS LST data were observed, including the underestimation of daytime values and night-time values. Results indicate that although all the data sets (Terra and Aqua, diurnal and nocturnal) showed high correlation with ground measurements, day values yielded slightly higher accuracy (about 1°C), both suggesting that MODIS LST retrievals are reliable for similar land-cover classes and atmospheric conditions. Increasing the MODIS window size showed an overestimation of in-situ LST and some improvement in the daytime Terra and night-time Aqua biases, with the highest accuracy achieved with the 5 × 5 window. A comparison between MODIS emissivity from bands 31, 32, and in-situ emissivity showed that emissivity errors (relative error = −0.30%) were insignificant.

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

Air temperature (Tair) 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 Tair 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 (Tmax), minimum (Tmin), and mean (Tmean) 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 Tmax, Tmin, and Tmean with relatively high accuracies (Model Efficiency ≥ 0.90). Both LST and day length (DL) predictors were important in estimating Tmax (SRCs: daytime LST = 0.53, DL = 0.35), Tmin (SRCs: nighttime LST = 0.74, DL = 0.23), and Tmean (SRCs: nighttime LST = 0.72, DL = 0.28). Models predicting Tmin and Tmean had better performance than the one predicting Tmax. Nighttime LST was better at predicting Tmin and Tmean than daytime LST data at predicting Tmax. Land covers had noticeable influences on estimating Tair, 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 Tair from the MODIS LST production.

Empirical Estimation of Near-Surface Air Temperature in China from MODIS LST Data by Considering Physiographic Features

Spatially and temporally resolved observations of near-surface air temperatures (Ta, 1.5–2 m above ground) are essential for understanding hydrothermal circulation at the land–atmosphere interface. However, the uneven spatial distribution of meteorological stations may not effectively capture the true nature of the overall climate pattern. Several studies have attempted to retrieve spatially continuous Ta from remotely sensed and continuously monitored Land Surface Temperature (LST). However, the topographical control of the relationship between LST and Ta in regions with complex topographies and highly variable weather station densities is poorly understood. The aim of this study is to improve the accuracy of Ta estimations from the Moderate Resolution Imaging Spectroradiometer (MODIS) LST via parameterization of the physiographic variables according to the terrain relief. The performances of both Terra and Aqua MODIS LST in estimating Ta have been explored in China. The results indicated that the best agreement was found between Terra nighttime LST (LSTmodn) and the observed Ta in China. In flat terrain areas, the LSTmodn product is significantly linearly correlated with Ta (R2 &gt; 0.80), while, in mountainous areas, the LSTmodn-Ta relationship differed significantly from simple linear correlation. By taking the physiographic features into account, including the seasonal vegetation cover (NDVI), the altitudinal gradient (RDLS), and the ambient absolute humidity (AH), the accuracy of the estimation was substantially improved. The study results indicated that the relevant environmental factors must be considered when interpreting the spatiotemporal variation of the surface energy flux over complex topography.

Improvements of a COMS Land Surface Temperature Retrieval Algorithm Based on the Temperature Lapse Rate and Water Vapor/Aerosol Effect

The National Meteorological Satellite Center in Korea retrieves land surface temperature (LST) by applying the split-window LST algorithm (CSW_v1.0) to Communication, Ocean, and Meteorological Satellite (COMS) data. Considerable errors were detected under conditions of high water vapor content or temperature lapse rates during validation with Moderate Resolution Imaging Spectroradiometer (MODIS) LST because of the too simplified LST algorithm. In this study, six types of LST retrieval equations (CSW_v2.0) were developed to upgrade the CSW_v1.0. These methods were developed by classifying “dry,” “normal,” and “wet” cases for day and night and considering the relative sizes of brightness temperature difference (BTD) values. Similar to CSW_v1.0, the LST retrieved by CSW_v2.0 had a correlation coefficient of 0.99 with the prescribed LST and a slightly larger bias of −0.03 K from 0.00K; the root mean square error (RMSE) improved from 1.41 K to 1.39 K. In general, CSW_v2.0 improved the retrieval accuracy compared to CSW_v1.0, especially when the lapse rate was high (mid-day and dawn) and the water vapor content was high. The spatial distributions of LST retrieved by CSW_v2.0 were found to be similar to the MODIS LST independently of the season, day/night, and geographic locations. The validation using one year’s MODIS LST data showed that CSW_v2.0 improved the retrieval accuracy of LST in terms of correlations (from 0.988 to 0.989), bias (from −1.009 K to 0.292 K), and RMSEs (from 2.613 K to 2.237 K).