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The land surface temperature (LST) is a key parameter used to characterize the interaction between land and the atmosphere. Therefore, obtaining highly accurate, spatially consistent and temporally continuous LSTs in large areas is the basis of many studies. The Moderate Resolution Imaging Spectroradiometer (MODIS) LST product is commonly used to a...
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... The TMTC method combined microwave AMSR2 LST with thermal infrared MODIS LST and utilized the clear-sky LST obtained in previous studies to obtain the daily seamless real LST [25]. The method was divided into two steps: The first step involved filling in the orbital gaps and missing values in the microwave data AMSR2 LST. ...
... (i) represents the 1 km resolution real LST of the i-th cloudy-sky pixel to be solved; and represents the 10 km real LST, which is calculated in the first step. ( ) represents the clear-sky LST of the i-th pixel obtained from the Bayesian maximum entropy (BME) method [25], and ∑ ( ) represents the sum of clear-sky LST values of the 100 cloudy-sky pixels. In fact, can be understood as the average LST of the 100 cloudy-sky pixels, and ...
... LST real1 (i) represents the 1 km resolution real LST of the i-th cloudy-sky pixel to be solved; and LST real10 represents the 10 km real LST, which is calculated in the first step. LST BME (i) represents the clear-sky LST of the i-th pixel obtained from the Bayesian maximum entropy (BME) method [25], and ∑ 100 i=1 LST BME (i) represents the sum of clear-sky LST values of the 100 cloudy-sky pixels. In fact, LST real10 can be understood as the average LST of the 100 cloudy-sky pixels, and ...
The land surface temperature (LST), defined as the radiative skin temperature of the ground, plays a critical role in land surface systems, from the regional to the global scale. The commonly utilized daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product at a resolution of one kilometer often contains missing values attributable to atmospheric influences. Reconstructing these missing values and obtaining a spatially complete LST is of great research significance. However, most existing methods are tailored for reconstructing clear-sky LST rather than the more realistic cloudy-sky LST, and their computational processes are relatively complex. Therefore, this paper proposes a simple and effective real LST reconstruction method combining Thermal Infrared and Microwave Remote Sensing Based on Temperature Conservation (TMTC). TMTC first fills the microwave data gaps and then downscales the microwave data by using MODIS LST and auxiliary data. This method maintains the temperature of the resulting LST and microwave LST on the microwave pixel scale. The average Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and R2 of TMTC were 3.14 K, 4.10 K, and 0.88 for the daytime and 2.34 K, 3.20 K, and 0.90 for the nighttime, respectively. The ideal MAE of the TMTC method exhibits less than 1.5 K during daylight hours and less than 1 K at night, but the accuracy of the method is currently limited by the inversion accuracy of microwave LST and whether different LST products have undergone time normalization. Additionally, the TMTC method has spatial generality. This article establishes the groundwork for future investigations in diverse disciplines that necessitate real LSTs.
... The PW-DRRS model uses multisource RS data as the data-driven driving force. Referring to relevant studies of LST retrieval, latitude (LAT) (Zhang et al. 2019), elevation (DEM) (Jamali et al. 2022), normalized difference vegetation index (NDVI) (Liu et al. 2019), and slope (Bayable and Alemu 2022) can affect LST and are often used as auxiliary data for LST retrieval, so this paper selects these data as driving data to improve the accuracy of the WRF LST. The research period of this paper is from February 21 to March 21, 2022, and the data used are from this period. ...
At present, the remote sensing (RS) thermal infrared (TIR) images that are commonly used to obtain land surface temperature (LST) are contaminated by clouds and thus cannot obtain spatiotemporal integrity of LST. To solve this problem, this study combined a physical model with strong interpretability with a data-driven model with high data adaptability. First, the physical model (Weather Research and Forecast (WRF) model) was used to generate LST source data. Then, combined with multisource RS data, a data-driven method (random forest (RF)) was used to improve the accuracy of the LST, and a model framework for a data-driven auxiliary physical model was formed. Finally, all-weather MODIS-like data with a spatial resolution of 1 km were generated. Beijing, China, was used as the study area. The results showed that in cases of more clouds and fewer clouds, the reconstructed all-weather LST had a high spatial continuity and could restore the spatial distribution details of the LST well. The mean absolute error (MAE), root mean square error (RMSE), and correlation coefficient (ρ) in the case of more (fewer) clouds were ranked as follows: MAE < 1 K (< 2 K), RMSE < 2 K (< 2 K), and ρ > 0.9. The errors obeyed an approximately normal distribution. The total MAE, RMSE, and ρ were 0.80 K, 1.09 K, and 0.94 K, respectively. Generally, the LST reconstructed in this paper had a high accuracy, and the model could provide all-weather MODIS-like LST to compensate for the disadvantages of satellite TIR images (i.e., contamination by clouds and inability to obtain complete LST values).
... The combination of the state-of-the-art in the thermal infrared (TIR) domain [1][2][3] with the recent advances in the capabilities provided by operating and new satellites [4][5][6][7][8][9][10], UAVbased [11] or aerial remote sensing are boosting the use of land surface temperature (LST) in a variety of research fields [5,8,9,11,12]. LST plays a key role in soil-vegetation-atmosphere processes and becomes crucial in the estimation of surface energy flux exchanges, actual evapotranspiration, or vegetation and soil properties [8,9]. ...
... Published papers cover a wide range of topics, which can be classified in five groups: algorithms, calibration and validation [1][2][3][4], improving long-term consistency in satellite LST [5][6][7], downscaling LST [8][9][10], LST applications [11,12] and land surface emissivity research [13,14]. ...
... The applicability of remote sensing LSTs is sometimes compromised in areas that are very frequently covered with clouds. Aware of this issue, Zhang et al. [6] and Yoo et al. [7] introduced approaches for the gap-filling of MODIS LST data, by reconstructing 1 km clear-sky LST using Bayesian methods [6] or random forest machine learning [7]. This strategy can improve the applicability of LSTs in a variety of research and practical fields. ...
The combination of the state-of-the-art in the thermal infrared (TIR) domain [...]
... Cloud contamination, therefore, has strong impacts on the availability and the quality of LST products [37]. Methods regarding LST reconstruction for cloudy regions, including the Harmonic Analysis of Time Series (HANTS) algorithm [38], Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST) model [39], Bayesian Maximum Entropy (BME) method [40], and Random Forest (RF) regression approach [41], have been investigated by considering neighboring pixels and nearby dates based on normalized difference vegetation index, enhanced vegetation index, normalized difference water index, solar radiation factor, albedo, elevation, slope, longitude, and latitude. Results showed that all methods for reconstructing LSTs for cloudcovered regions were highly accurate compared with original LST pixels and produced spatial and temporal patterns of LST that were consistent with neighboring clear-sky areas [39,41]. ...
Tea (Camellia sinensis) is one of the most dominant economic plants in China and plays an important role in agricultural economic benefits. Spring tea is the most popular drink due to Chinese drinking habits. Although the global temperature is generally warming, spring frost damage (SFD) to tea plants still occurs from time to time, and severely restricts the production and quality of spring tea. Therefore, monitoring and evaluating the impact of SFD to tea plants in a timely and precise manner is a significant and urgent task for scientists and tea producers in China. The region designated as the Middle and Lower Reaches of the Yangtze River (MLRYR) in China is a major tea plantation area producing small tea leaves and low shrubs. This region was selected to study SFD to tea plants using meteorological observations and remotely sensed products. Comparative analysis between minimum air temperature (Tmin) and two MODIS nighttime land surface temperature (LST) products at six pixel-window scales was used to determine the best suitable product and spatial scale. Results showed that the LST nighttime product derived from MYD11A1 data at the 3×3 pixel window resolution was the best proxy for daily minimum air temperature. A Tmin estimation model was established using this dataset and digital elevation model (DEM) data, employing the standard lapse rate of air temperature with elevation. Model validation with 145,210 ground-based Tmin observations showed that the accuracy of estimated Tmin was acceptable with a relatively high coefficient of determination (R2 = 0.841), low root mean square error (RMSE = 2.15 °C) and mean absolute error (MAE = 1.66 °C), and reasonable normalized RMSE (NRMSE = 25.4%) and Nash–Sutcliffe model efficiency (EF = 0.12), with significantly improved consistency of LST and Tmin estimation. Based on the Tmin estimation model, three major cooling episodes recorded in the "Yearbook of Meteorological Disasters in China" in spring 2006 were accurately identified, and several highlighted regions in the first two cooling episodes were also precisely captured. This study confirmed that estimating Tmin based on MYD11A1 nighttime products and DEM is a useful method for monitoring and evaluating SFD to tea plants in the MLRYR. Furthermore, this method precisely identified the spatial characteristics and distribution of SFD and will therefore be helpful for taking effective preventative measures to mitigate the economic losses resulting from frost damage.
... The reconstructed clear-sky LST data have successfully been used to analyze the surface urban heat island effect [28], investigate the relationship between LSTs and vegetation coverage [29], estimate air temperature [30], [31], calculate the dryness index, and support drought risk management [32]. In addition, the estimation of clear-sky LSTs is an important step in some methods for estimating cloudy-sky LSTs [23], [33], [34]. For example, Zeng et al. [23] first reconstructed clear-sky LSTs for cloudy regions and then corrected the clear-sky LSTs to cloudy-sky LSTs. ...
... 4) The hybrid method [8], [17]- [20], [42], [43], which simultaneously uses at least two kinds of information to fill the gaps in LST data. Hybrid methods that use spatiotemporal information to fill gaps (i.e., spatiotemporal gapfilling) have been widely developed [17]- [20], [33], [42]- [44]. Spatiotemporal gapfilling generally has a higher accuracy than spatial gapfilling or temporal gapfilling do because it uses more information [8], [17], [42]. ...
... Therefore, these three kinds of information were not fully used by the method developed by Li et al. [8]. A comparison study showed that the accuracy of this method was lower than that of the spatiotemporal gapfilling method [33]. ...
Satellite-derived land surface temperatures (LSTs) are a critical parameter in various fields. Unfortunately, there are numerous gaps in LST products due to cloud contamination and orbital gaps. In previous studies, various gapfilling methods have been developed. However, most of those methods use only spatiotemporal information to fill gaps. In this study, a gapfilling method called the enhanced hybrid (EH) method that integrates spatiotemporal information and information from other similar LST products was proposed. The accuracy of the EH method was compared with the accuracies of three other gapfilling methods that only use spatiotemporal information: Remotely Sensed DAily land Surface Temperature reconstruction (RSDAST), interpolation of the mean anomalies (IMAs), and Gapfill. It was found that the correlations between the four LST products were strong, indicating that using information from other products may improve the accuracy of gapfilling. On average, the mean absolute errors (MAEs) of the data filled using the EH method were 23.7%-52.7% lower than those of RSDAST, 35.4%-38.7% lower than those of IMA, and 38.5%-46.9% lower than those of the Gapfill method. The usage of information from other similar LST products was the main reason for the high accuracy observed for the EH method. In addition, the LST images filled using the RSDAST and IMA methods had some outliers, while there were fewer obvious outliers in the LST images filled with the EH method. It was concluded that the EH method is a robust gapfilling method with a high accuracy.
... A great deal of effort has been put into developing clear-sky LST reconstruction methods according to the characteristics of the LST [24]. These methods include methods based on LST temporal correlations [25][26][27], methods that simultaneously use spatial and temporal neighbourhood LSTs [28][29][30][31][32], hybrid methods that consider both LST spatial correlation and auxiliary data [33][34][35], comprehensive methods that consider both spatio-temporal correlations and auxiliary data [36,37], and methods that combine data geostationary and polar orbiting satellite LSTs [38]. ...
... Finally, we input these three data into the BME model and obtained the reconstructed BME clear-sky LST. The specific calculation process followed that of Zhang [37]. In addition to the clear-sky LST, we also selected the cloud-cover duration and downward shortwave radiation as independent variables to consider the impact of atmospheric disturbances on the real LST and selected albedo and NDVI as the independent variables to consider the effect of surface properties on the real LST. ...
Land-surface temperature (LST) plays a key role in the physical processes of surface energy and water balance from local through global scales. The widely used one kilometre resolution daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product has missing values due to the influence of clouds. Therefore, a large number of clear-sky LST reconstruction methods have been developed to obtain spatially continuous LST datasets. However, the clear-sky LST is a theoretical value that is often an overestimate of the real value. In fact, the real LST (also known as cloudy-sky LST) is more necessary and more widely used. The existing cloudy-sky LST algorithms are usually somewhat complicated, and the accuracy needs to be improved. It is necessary to convert the clear-sky LST obtained by the currently better-developed methods into cloudy-sky LST. We took the clear-sky LST, cloud-cover duration, downward shortwave radiation, albedo and normalized difference vegetation index (NDVI) as five independent variables and the real LST at the ground stations as the dependent variable to perform multiple linear regression. The mean absolute error (MAE) of the cloudy-sky LST retrieved by this method ranged from 3.5–3.9 K. We further analyzed different cases of the method, and the results suggested that this method has good flexibility. When we chose fewer independent variables, different clear-sky algorithms, or different regression tools, we also achieved good results. In addition, the method calculation process was relatively simple and can be applied to other research areas. This study preliminarily explored the influencing factors of the real LST and can provide a possible option for researchers who want to obtain cloudy-sky LST through clear-sky LST, that is, a convenient conversion method. This article lays the foundation for subsequent research in various fields that require real LST.
Accurately monitoring the spatial-temporal distribution of drought events is helpful for reducing the meteorological risk. The monthly integrated surface drought index (mISDI) dataset of the Hanjiang River Basin (HJRB) from 2001 to 2017 was established by using the Cubist algorithm, and the Run theory was applied to identify and characterize the drought events. Two gross primary productivity (GPP) datasets, obtained from the Global Land Surface Satellite (GLASS GPP) program and the Numerical Terradynamic Simulation Group (NTSG GPP), were used to investigate the impact of drought events on GPP changes and the relative importance of climate factors in GPP changes. The results showed that (1) the mISDI had good accuracy and reliability in drought events monitoring (R = 0.95; MAE = 0.503; RMSE = 0.707). The problem of missing land surface temperature data was solved, and the filled LST dataset has good accuracy. In addition, the model accuracy was significantly improved by using the time variables; (2) Five drought events were identified by using the Run theory, and the drought event occurred from August 2013 to August 2014 had the largest drought intensity (1.309) and drought affected area (81.12%). Spatially, the western HJRB had more frequent droughts than the eastern region, but the eastern HJRB had longer average drought duration and higher mean drought intensity and severity; (3) During the drought event occurred from August 2013 to August 2014, mainly occurred in the eastern basin. The correlation between GLASS GPP anomalies and mISDI (R = 0.553, p < 0.05) was greater than that between NTSG GPP anomalies and mISDI (R = 0.324, p > 0.1). The decreased area of GLASS GPP (78.36%) was larger than that of NTSG GPP (49.08%), both were mainly distributed in the eastern HJRB. Among different climate factors, temperature was an important drought factor affecting GPP changes. This study provides a good way to understand the evolution process of drought events and the impact of drought events on vegetation productivity.
