scholarly journals Evaluation of Satellite Estimates of Land Surface Temperature from GOES over the United States

2009 ◽  
Vol 48 (1) ◽  
pp. 167-180 ◽  
Author(s):  
Rachel T. Pinker ◽  
Donglian Sun ◽  
Meng-Pai Hung ◽  
Chuan Li ◽  
Jeffrey B. Basara
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Comprehensive Evaluation ◽  
Information Service ◽  
The United States ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Oklahoma Mesonet ◽  
Split Window Algorithm

Abstract A comprehensive evaluation of split-window and triple-window algorithms to estimate land surface temperature (LST) from Geostationary Operational Environmental Satellites (GOES) that were previously described by Sun and Pinker is presented. The evaluation of the split-window algorithm is done against ground observations and against independently developed algorithms. The triple-window algorithm is evaluated only for nighttime against ground observations and against the Sun and Pinker split-window (SP-SW) algorithm. The ground observations used are from the Atmospheric Radiation Measurement Program (ARM) Central Facility, Southern Great Plains site (April 1997–March 1998); from five Surface Radiation Budget Network (SURFRAD) stations (1996–2000); and from the Oklahoma Mesonet. The independent algorithms used for comparison include the National Oceanic and Atmospheric Administration/National Environmental Satellite, Data and Information Service operational method and the following split-window algorithms: that of Price, that of Prata and Platt, two versions of that of Ulivieri, that of Vidal, two versions of that of Sobrino, that of Coll and others, the generalized split-window algorithm as described by Becker and Li and by Wan and Dozier, and the Becker and Li algorithm with water vapor correction. The evaluation against the ARM and SURFRAD observations indicates that the LST retrievals from the SP-SW algorithm are in closer agreement with the ground observations than are the other algorithms tested. When evaluated against observations from the Oklahoma Mesonet, the triple-window algorithm is found to perform better than the split-window algorithm during nighttime.

scholarly journals Sensitivity Analysis and Validation of Daytime and Nighttime Land Surface Temperature Retrievals from Landsat 8 Using Different Algorithms and Emissivity Models

2020 ◽  
Vol 12 (17) ◽  
pp. 2776 ◽  
Author(s):  
Aliihsan Sekertekin ◽  
Stefania Bonafoni
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
The United States ◽  
In Situ Measurements ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Sensitivity Analyses ◽  
Landsat 8

Land Surface Temperature (LST) is a substantial element indicating the relationship between the atmosphere and the land. This study aims to examine the efficiency of different LST algorithms, namely, Single Channel Algorithm (SCA), Mono Window Algorithm (MWA), and Radiative Transfer Equation (RTE), using both daytime and nighttime Landsat 8 data and in-situ measurements. Although many researchers conducted validation studies of daytime LST retrieved from Landsat 8 data, none of them considered nighttime LST retrieval and validation because of the lack of Land Surface Emissivity (LSE) data in the nighttime. Thus, in this paper, we propose using a daytime LSE image, whose acquisition is close to nighttime Thermal Infrared (TIR) data (the difference ranges from one day to four days), as an input in the algorithm for the nighttime LST retrieval. In addition to evaluating the three LST methods, we also investigated the effect of six Normalized Difference Vegetation Index (NDVI)-based LSE models in this study. Furthermore, sensitivity analyses were carried out for both in-situ measurements and LST methods for satellite data. Simultaneous ground-based LST measurements were collected from Atmospheric Radiation Measurement (ARM) and Surface Radiation Budget Network (SURFRAD) stations, located at different rural environments of the United States. Concerning the in-situ sensitivity results, the effect on LST of the uncertainty of the downwelling and upwelling radiance was almost identical in daytime and nighttime. Instead, the uncertainty effect of the broadband emissivity in the nighttime was half of the daytime. Concerning the satellite observations, the sensitivity of the LST methods to LSE proved that the variation of the LST error was smaller than daytime. The accuracy of the LST retrieval methods for daytime Landsat 8 data varied between 2.17 K Root Mean Square Error (RMSE) and 5.47 K RMSE considering all LST methods and LSE models. MWA with two different LSE models presented the best results for the daytime. Concerning the nighttime accuracy of the LST retrieval, the RMSE value ranged from 0.94 K to 3.34 K. SCA showed the best results, but MWA and RTE also provided very high accuracy. Compared to daytime, all LST retrieval methods applied to nighttime data provided highly accurate results with the different LSE models and a lower bias with respect to in-situ measurements.

scholarly journals Low-Cost Radiometer for Landsat Land Surface Temperature Validation

2020 ◽  
Vol 12 (3) ◽  
pp. 416
Author(s):  
Jonathan Miller ◽  
Aaron Gerace ◽  
Rehman Eon ◽  
Matthew Montanaro ◽  
Robert Kremens ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Low Cost ◽  
Field Measurements ◽  
Global Scale ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Landsat 8 ◽  
Initial Field

Land Surface Temperature (ST) represents the radiative temperature of the Earth’s surface and is used as input to hydrological, agricultural, and meteorological science applications. Due to the synoptic nature of satellite imaging systems, ST products derived from space-borne platforms are invaluable for estimating ST at the local, regional, and global scale. In the past two decades, an emphasis has been placed on the need to develop algorithms necessary to deliver accurate surface temperature products to support the needs of science users. However, corresponding efforts to validate these products are hindered by the availability of quality ground-based reference measurements. The NOAA Surface Radiation Budget (SURFRAD) network is commonly used to support ST validation efforts, but their instrumentation is broadband (4–50 μ m) and several of their sites lack spatial uniformity. To address the apparent deficiencies within existing validation networks, this work discusses a prototype radiometer that was developed to provide surface temperature estimates to support validation efforts for spaceborne thermal instruments. Specifically, a prototype radiometer was designed, built, and calibrated to acquire ground reference data to be used to validate ST product(s) derived from Landsat 8 image data. Lab-based efforts indicate that these prototype instruments are accurate to within 1.28 K and initial field measurements demonstrate agreement to Landsat-derived ST products to within 1.37 K.

scholarly journals Toward an Operational Land Surface Temperature Algorithm for GOES

2013 ◽  
Vol 52 (9) ◽  
pp. 1974-1986 ◽  
Author(s):  
Donglian Sun ◽  
Yunyue Yu ◽  
Li Fang ◽  
Yuling Liu
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Regression Tree ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Surface Radiation Budget ◽  
Shortwave Infrared ◽  
Dual Window

AbstractFor most land surface temperature (LST) regression algorithms, a set of optimized coefficients is determined by manual separation of the different subdivisions of atmospheric and surface conditions. In this study, a machine-learning technique, the regression tree (RT) technique, is introduced with the aim of automatically finding these subranges and the thresholds for the stratification of regression coefficients. The use of RT techniques in LST retrieval has the potential to contribute to the determination of optimal regression relationships under different conditions. Because of the lack of split-window channels for the Geostationary Operational Environmental Satellite (GOES) M–Q series (GOES-12–GOES-15, plus GOES-Q), a dual-window LST algorithm was developed by combining the infrared 11-μm channel with the shortwave-infrared (SWIR) 3.9-μm channel, which presents lower atmospheric absorption than does the infrared split-window channels (11 and 12 μm). The RT technique was introduced to derive the regression models under different conditions. The algorithms were used to derive the LST product from GOES observations and were evaluated against the 2004 Surface Radiation budget network. The results indicate that the RT technique outperforms the traditional regression method.

scholarly journals Using SURFRAD to Verify the NOAA Single-Channel Land Surface Temperature Algorithm

2013 ◽  
Vol 30 (12) ◽  
pp. 2868-2884 ◽  
Author(s):  
Andrew K. Heidinger ◽  
Istvan Laszlo ◽  
Christine C. Molling ◽  
Dan Tarpley
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Weather Prediction ◽  
Information Service ◽  
Surface Radiation ◽  
Cloud Detection ◽  
Spectral Shifts ◽  
Radiative Transfer Modeling

Abstract Because of spectral shifts from instrument to instrument in the operational NOAA satellite imager longwave infrared channels, the NOAA/National Environmental Satellite, Data, and Information Service (NESDIS) has developed a single-channel land surface temperature (LST) algorithm based on the observed 11-μm radiances, numerical weather prediction data, and radiative transfer modeling that allows for consistent results from the Geostationary Operational Environmental Satellite-I/L (GOES-I/L), GOES-M–P, and Advanced Very High Resolution Radiometer (AVHRR)/1 through 3 sensor versions. This approach is implemented in the real-time NESDIS processing systems [GOES Surface and Insolation Products (GSIP) and Clouds from AVHRR Extended (CLAVR-x)], and in the Pathfinder Atmospheres–Extended (PATMOS-x) climate dataset. An analysis of the PATMOS-x LST against that derived from the upwelling broadband longwave flux at each Surface Radiation Network (SURFRAD) site showed that biases in PATMOS-x were approximately 1 K or less. The standard deviations of the PATMOS-x minus SURFRAD LST biases are generally 2.5 K or less at all sites for all sensors. Using the PATMOS-x minus SURFRAD LST distributions to validate the PATMOS-x cloud detection, the PATMOS-x cloud probability of correct detection values were shown to meet the GOES-R specifications for all sites.

scholarly journals A global long-term (1981–2000) land surface temperature product for NOAA AVHRR

2020 ◽  
Vol 12 (4) ◽  
pp. 3247-3268
Author(s):  
Jin Ma ◽  
Ji Zhou ◽  
Frank-Michael Göttsche ◽  
Shunlin Liang ◽  
Shaofei Wang ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Mean Bias Error ◽  
Noaa Avhrr ◽  
Orbital Drift

Abstract. Land surface temperature (LST) plays an important role in the research of climate change and various land surface processes. Before 2000, global LST products with relatively high temporal and spatial resolutions are scarce, despite a variety of operational satellite LST products. In this study, a global 0.05∘×0.05∘ historical LST product is generated from NOAA advanced very-high-resolution radiometer (AVHRR) data (1981–2000), which includes three data layers: (1) instantaneous LST, a product generated by integrating several split-window algorithms with a random forest (RF-SWA); (2) orbital-drift-corrected (ODC) LST, a drift-corrected version of RF-SWA LST; and (3) monthly averages of ODC LST. For an assumed maximum uncertainty in emissivity and column water vapor content of 0.04 and 1.0 g cm−2, respectively, evaluated against the simulation dataset, the RF-SWA method has a mean bias error (MBE) of less than 0.10 K and a standard deviation (SD) of 1.10 K. To compensate for the influence of orbital drift on LST, the retrieved RF-SWA LST was normalized with an improved ODC method. The RF-SWA LST were validated with in situ LST from Surface Radiation Budget (SURFRAD) sites and water temperatures obtained from the National Data Buoy Center (NDBC). Against the in situ LST, the RF-SWA LST has a MBE of 0.03 K with a range of −1.59–2.71 K, and SD is 1.18 K with a range of 0.84–2.76 K. Since water temperature only changes slowly, the validation of ODC LST was limited to SURFRAD sites, for which the MBE is 0.54 K with a range of −1.05 to 3.01 K and SD is 3.57 K with a range of 2.34 to 3.69 K, indicating good product accuracy. As global historical datasets, the new AVHRR LST products are useful for filling the gaps in long-term LST data. Furthermore, the new LST products can be used as input to related land surface models and environmental applications. Furthermore, in support of the scientific research community, the datasets are freely available at https://doi.org/10.5281/zenodo.3934354 for RF-SWA LST (Ma et al., 2020a), https://doi.org/10.5281/zenodo.3936627 for ODC LST (Ma et al., 2020c), and https://doi.org/10.5281/zenodo.3936641 for monthly averaged LST (Ma et al., 2020b).

scholarly journals Retrieval of Global Orbit Drift Corrected Land Surface Temperature from Long-term AVHRR Data

2019 ◽  
Vol 11 (23) ◽  
pp. 2843 ◽  
Author(s):  
Liu ◽  
Tang ◽  
Yan ◽  
Li ◽  
Liang
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Surface Radiation ◽  
Bayesian Optimization ◽  
Radiation Budget ◽  
Temperature Cycle ◽  
Avhrr Data ◽  
Mean Square Errors

Advanced Very High Resolution Radiometer (AVHRR) sensors provide a valuable data source for generating long-term global land surface temperature (LST). However, changes in the observation time that are caused by satellite orbit drift restrict their wide application. Here, a generalized split-window (GSW) algorithm was implemented to retrieve the LST from the time series AVHRR data. Afterwards, a novel orbit drift correction (ODC) algorithm, which was based on the diurnal temperature cycle (DTC) model and Bayesian optimization algorithm, was also proposed for normalizing the estimated LST to the same local time. This ODC algorithm is pixel-based and it only needs one observation every day. The resulting LSTs from the six-year National Oceanic and Atmospheric Administration (NOAA)-14 satellite data were validated while using Surface Radiation Budget Network (SURFRAD) in-situ measurements. The average accuracies for LST retrieval varied from −0.4 K to 2.0 K over six stations and they also depended on the viewing zenith angle and season. The simulated data illustrate that the proposed ODC method can improve the LST estimate at a similar magnitude to the accuracy of the LST retrieval, i.e., the root-mean-square errors (RMSEs) of the corrected LSTs were 1.3 K, 2.2 K, and 3.1 K for the LST with a retrieval RMSE of 1 K, 2 K, and 3 K, respectively. This method was less sensitive to the fractional vegetation cover (FVC), including the FVC retrieval error, size, and degree of change within a neighboring area, which suggested that it could be easily updated by applying other LST expression models. In addition, ground validation also showed an encouraging correction effect. The RMSE variations of LST estimation that were introduced by ODC were within ±0.5 K, and the correlation coefficients between the corrected LST errors and original LST errors could approach 0.91.

scholarly journals Land Surface Temperature Retrieval from Landsat 5, 7, and 8 over Rural Areas: Assessment of Different Retrieval Algorithms and Emissivity Models and Toolbox Implementation

2020 ◽  
Vol 12 (2) ◽  
pp. 294 ◽  
Author(s):  
Aliihsan Sekertekin ◽  
Stefania Bonafoni
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Single Channel ◽  
Thematic Mapper ◽  
Surface Radiation ◽  
Radiation Budget ◽  
Bias Error ◽  
Landsat 8 ◽  
Retrieval Algorithms

Land Surface Temperature (LST) is an important parameter for many scientific disciplines since it affects the interaction between the land and the atmosphere. Many LST retrieval algorithms based on remotely sensed images have been introduced so far, where the Land Surface Emissivity (LSE) is one of the main factors affecting the accuracy of the LST estimation. The aim of this study is to evaluate the performance of LST retrieval methods using different LSE models and data of old and current Landsat missions. Mono Window Algorithm (MWA), Radiative Transfer Equation (RTE) method, Single Channel Algorithm (SCA) and Split Window Algorithm (SWA) were assessed as LST retrieval methods processing data of Landsat missions (Landsat 5, 7 and 8) over rural pixels. Considering the LSE models introduced in the literature, different Normalized Difference Vegetation Index (NDVI)-based LSE models were investigated in this study. Specifically, three LSE models were considered for the LST estimation from Landsat 5 Thematic Mapper (TM) and seven Enhanced Thematic Mapper Plus (ETM+), and six for Landsat 8. For the accurate evaluation of the estimated LST, in-situ LST data were obtained from the Surface Radiation Budget Network (SURFRAD) stations. In total, forty-five daytime Landsat images; fifteen images for each Landsat mission, acquired in the Spring-Summer-Autumn period in the mid-latitude region in the Northern Hemisphere were acquired over five SURFRAD rural sites. After determining the best LSE model for the study case, firstly, the LST retrieval accuracy was evaluated considering the sensor type: when using Landsat 5 TM, 7 ETM+, and 8 Operational Land Imager (OLI), and Thermal Infrared Sensor (TIRS) data separately, RTE, MWA, and MWA presented the best results, respectively. Then, the performance was evaluated independently of the sensor types. In this case, all LST methods provided satisfying results, with MWA having a slightly better accuracy with a Root Mean Square Error (RMSE) equals to 2.39 K and a lower bias error. In addition, the spatio-temporal and seasonal analyses indicated that RTE and SCA presented similar results regardless of the season, while MWA differed from RTE and SCA for all seasons, especially in summer. To efficiently perform this work, an ArcGIS toolbox, including all the methods and models analyzed here, was implemented and provided as a user facility for the LST retrieval from Landsat data.

scholarly journals Optimization of the Local Split-Window Algorithm for FY-4A Land Surface Temperature Retrieval

2019 ◽  
Vol 11 (17) ◽  
pp. 2016
Author(s):  
Lijuan Wang ◽  
Ni Guo ◽  
Wei Wang ◽  
Hongchao Zuo
Keyword(s):  
Surface Temperature ◽  
Diurnal Variation ◽  
Land Surface Temperature ◽  
Satellite Data ◽  
Land Surface ◽  
Regional Scale ◽  
Pso Algorithm ◽  
Variation Characteristics ◽  
Split Window Algorithm ◽  
Mean Square Errors

FY-4A is a second generation of geostationary orbiting meteorological satellite, and the successful launch of FY-4A satellite provides a new opportunity to obtain diurnal variation of land surface temperature (LST). In this paper, different underlying surfaces-observed data were applied to evaluate the applicability of the local split-window algorithm for FY-4A, and the local split-window algorithm parameters were optimized by the artificial intelligent particle swarm optimization (PSO) algorithm to improve the accuracy of retrieved LST. Results show that the retrieved LST can efficiently reproduce the diurnal variation characteristics of LST. However, the estimated values deviate hugely from the observed values when the local split-window algorithms are directly used to process the FY-4A satellite data, and the root mean square errors (RMSEs) are approximately 6K. The accuracy of the retrieved LST cannot be effectively improved by merely modifying the emissivity-estimated model or optimizing the algorithm. Based on the measured emissivity, the RMSE of LST retrieved by the optimized local split-window algorithm is reduced to 3.45 K. The local split-window algorithm is a simple and easy retrieval approach that can quickly retrieve LST on a regional scale and promote the application of FY-4A satellite data in related fields.

Study on the Application and Comparative Analysis of Land Surface Temperature Retrieval Method Based on Multi-Sensor Remote Sensing Data

2014 ◽  
Vol 1010-1012 ◽  
pp. 1276-1279 ◽  
Author(s):  
Yin Tai Na
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Remote Sensing Data ◽  
Sensing Data ◽  
Advantages And Disadvantages ◽  
Urban Heat ◽  
Split Window Algorithm ◽  
Temperature Retrieval

The three commonly used remote sensing land surface temperature retrieval methods are described, namely single-window algorithm, split window algorithm and multi-channel algorithm, which have their advantages and disadvantages. The land surface temperature (LST) of study area was retrieved with multi-source remote sensing data. LST of study area was retrieved with the split window algorithm on January 10, 2003 and November 19, 2003 which is comparatively analyzed with the LST result of ETM+data with the single-window algorithm and the LST result of ASTER data with multi channel algorithm in the same period. The results show that land surface temperature of different land features are significantly different, where the surface temperature of urban land is the highest, and that of rivers and lakes is the lowest, followed by woodland. It is concluded that the expansion of urban green space and protection of urban water can prevent or diminish the urban heat island.

scholarly journals Land Surface Temperature Retrieval from Sentinel-3A Sea and Land Surface Temperature Radiometer, Using a Split-Window Algorithm

2019 ◽  
Vol 11 (6) ◽  
pp. 650 ◽  
Author(s):  
Yitong Zheng ◽  
Huazhong Ren ◽  
Jinxin Guo ◽  
Darren Ghent ◽  
Kevin Tansey ◽  
...  
Keyword(s):  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Northern China ◽  
Lower Sensitivity ◽  
Ground Validation ◽  
The Usa ◽  
Split Window Algorithm ◽  
Atmospheric Transmittance ◽  
Temperature Retrieval

Land surface temperature (LST) is a crucial parameter in the interaction between the ground and the atmosphere. The Sentinel-3A Sea and Land Surface Temperature Radiometer (SLSTR) provides global daily coverage of day and night observation in the wavelength range of 0.55 to 12.0 μm. LST retrieved from SLSTR is expected to be widely used in different fields of earth surface monitoring. This study aimed to develop a split-window (SW) algorithm to estimate LST from two-channel thermal infrared (TIR) and one-channel middle infrared (MIR) images of SLSTR observation. On the basis of the conventional SW algorithm, using two TIR channels for the daytime observation, the MIR data, with a higher atmospheric transmittance and a lower sensitivity to land surface emissivity, were further used to develop a modified SW algorithm for the nighttime observation. To improve the retrieval accuracy, the algorithm coefficients were obtained in different subranges, according to the view zenith angle, column water vapor, and brightness temperature. The proposed algorithm can theoretically estimate LST with an error lower than 1 K on average. The algorithm was applied to northern China and southern UK, and the retrieved LST captured the surface features for both daytime and nighttime. Finally, ground validation was conducted over seven sites (four in the USA and three in China). Results showed that LST could be estimated with an error mostly within 1.5 to 2.5 K from the algorithm, and the error of the nighttime algorithm involved with MIR data was about 0.5 K lower than the daytime algorithm.

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