Split-window Method Research Articles

URBAN GREEN SPACE ESTIMATION FOR HEAT HOTSPOT MITIGATION IN A SMALL CITY, BURIRAM MUNICIPALITY, THAILAND

Heat hotspots cause environmental and health problems to residents in urban areas. Green space has been used to reduce the temperature in urban areas. However, the size and location of the green space that can reduce the temperature in those areas are challenging. This study aims to identify the heat hotspot of an urban area and estimate the green space proportion to reduce the heat hotspot. Therefore, the Split Window method (SW) was initially employed to calculate the Land Surface Temperature (LST) data from the Landsat series in the summertime of 2014, 2016, and 2018. The LST data 2018 were used in heat hotspot investigation using Moran's I and Getis-Ord Gi*. The results show the clustering patterns of LST occurring in barren lands, racetracks, and built-up areas in Buriram Municipality. Then, the monthly regression modeling between the green space proportions and LST was analyzed and applied to the hotspot areas. The green space proportions were represented by estimating in regression models showing the ratio of green space and decreasing temperature in hotspot areas. As a result, the green space proportion around 45% of the area is suggested to mitigate the heat hotspot. The explored green space proportion was applied to the 2014 and 2016 data to assess the feasibility of hotspot mitigation. This research presents a simplified technic that will enable urban planners to estimate the green space proportion to reduce the heat hotspots effectively.

Retrieval and validation of the dawn-dusk land surface temperature from FY-3E MERSI-LL

ABSTRACT Land surface temperature (LST) is an important physical parameter that reflects changes in surface processes. Remote sensing makes it possible to obtain global or regional LSTs with high spatial and temporal resolution. Feng-Yun 3E (FY-3E) is the world’s first civilian dawn-dusk orbiting meteorological satellite on board the Medium Resolution Spectral Imager – Low-Light (MERSI-LL) sensor capable of acquiring LST at 250 m spatial resolution at dawn and dusk. By complementing with existing polar-orbiting satellites, FY-3E can fill the satellite observation gap within the 6-hour assimilation window and further enhance the observation capability of LST. To retrieve accurate LST from MERSI-LL, the generalized split-window (GSW) method was developed. The GSW coefficients were derived from the simulation dataset produced by Moderate Resolution Transmittance Code 5.2 with an atmospheric profile database. As a key input to the GSW algorithm, the land surface emissivity was dynamically estimated by the Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Dataset in combination with the fractional vegetation cover. The water vapour content is calculated in real time by the split-window covariance/variance ratio method. The comparison between retrieved LSTs and in situ LSTs shows that the GSW algorithm can effectively retrieve the LSTs of vegetation cover surfaces. The root mean square error ranged from 0.79 to 1.79 K at dawn and from 1.32 to 1.92 K at dusk.

Volcanic Cloud Detection and Retrieval Using Satellite Multisensor Observations

Satellite microwave (MW) and millimetre-wave (MMW) passive sensors can be used to detect volcanic clouds because of their sensitivity to larger volcanic particles (i.e., size bigger than 20 µm). In this work, we combine the MW-MMW observations with thermal-infrared (TIR) radiometric data from the Low Earth Orbit (LEO) spectroradiometer to have a complete characterisation of volcanic plumes. We describe new physical-statistical methods, which combine machine learning techniques, aimed at detecting and retrieving volcanic clouds of two highly explosive eruptions: the 2014 Kelud and 2015 Calbuco test cases. For the detection procedure, we compare the well-known split-window methods with a machine learning algorithm named random forest (RF). Our work highlights how the machine learning method is suitable to detect volcanic clouds using different spectral signatures without fixing a threshold. Moreover, the RF model allows images to be automatically processed with promising results (90% of the area correctly identified). For the retrieval procedure of the mass of volcanic particles, we consider two methods, one based on the maximum likelihood estimation (MLE) and one using the neural network (NN) architecture. Results show a good comparison of the mass obtained using the MLE and NN methods for all the analysed bands. Summing the MW-MMW and TIR estimates, we obtain the following masses: 1.11 ± 0.40 × 1011 kg (MLE method) and 1.32 ± 0.47 × 1011 kg (NN method) for Kelud; 4.48 ± 1.61 × 1010 kg (MLE method) and 4.32 ± 1.56 × 1010 kg (NN method) for Calbuco. This work shows how machine learning techniques can be an effective tool for volcanic cloud detection and how the synergic use of the TIR and MW-MMW observations can give more accurate estimates of the near-source volcanic clouds.

ASSESSING LANDFILLS SURFACE TEMPERATURE DISTRIBUTION USING LANDSAT 8 SATELLITE IMAGE AT BIG CITIES IN VIETNAM

In recent years, rapid urbanization and population growth have led to an overload of waste in big cities. High landfill surface temperature is an important factor causing serious environmental pollution due to speeding up the generation of biogas. This paper presents the results of assessment of land surface temperature distribution in some large landfills in Vietnam, including Nam Son landfill (Hanoi) and Da Phuoc landfill (Ho Chi Minh city). Landsat 8 satellite image data is used to calculate land surface temperatures based on the split-window method. The results showed that the landfill surface temperature was much higher than the surrounding area, even compared to the areas characterized by impervious surfaces. The difference between the highest surface temperature in Nam Son, Da Phuoc landfills and the lowest surface temperature in the surrounding area is about 20ºC. The results obtained in this study provide input information to develop monitoring models of pollution at landfills.

Optimization of Instrumental Spectral Configurations for the Split-Window Method in the Context of the TRISHNA Mission

Optimization of Instrumental Spectral Configurations for the Split-Window Method in the Context of the TRISHNA Mission

Preflight Radiometric Calibration of TIS Sensor Onboard SDG-1 Satellite and Estimation of Its LST Retrieval Ability

The thermal Infrared Spectrometer (TIS) is the thermal infrared (TIR) sensor on-board the first Sustainable Development Goals (SDG-1) satellite. The TIS data can potentially be used to support improved monitoring of ground conditions with high-spatial resolutions, so accurate radiometric calibration is required. A meticulous radiometric calibration was conducted on the prototype of TIS to test its ability to convert a raw digital number (DN) to at-aperture radiance. The initial maximum radiometric error was 2.19 K at 300 K for Band 1(B1) and the minimum radiometric error was 0.25 K at 300 K rooted in Band 3 (B3). The R-Squared (R2) was over 0.99 for each band. The methodology was refined to divide the channel detectable temperature range into three sub-ranges and then the maximum radiometric errors were reduced to less than 1 K at 300 K for three bands. Subsequently, the Generalized Split-Window (SW) algorithm was preformed to estimate the ability of TIS on land surface temperature (LST) retrieval. In order to take advantage of its high-spatial resolution and make full use of TIR data, three-channel SW algorithm was also performed for intercomparison. Results showed that the SW algorithm can obtain LST with root-mean-square error (RMSE) less than 1K. Compared with two-channel algorithm with RMSE = 0.94 K, three-channel algorithm achieves better results in retrieving LST with RMSE = 0.82 K. For different land surface types, water samples achieved the minimum RMSE, and for different atmospheric column water vapor (CWV), dry atmospheres obtained better results. The sensitivity analysis of SW algorithm was considered along with noise-equivalent differential temperature (NEΔT), uncertainty of land surface emissivity (LSE) and input land surface temperature (Ts). Generally, three-channel algorithm was more stable to LSE uncertainties, and the error changes were within 40%. But when NEΔT and Ts uncertainties were included, the error percentage of three-channel SW method increases more, which means three-channel SW method is more sensitive to those two factors. All in all, the methodology and results used for radiometric calibration and LST retrieval in this study provide valuable guidance for the flight model of TIS and post-launch applications.

Comparing different space-borne sensors and methods for the retrieval of land surface temperature

The importance of land surface temperature (LST) is increasingly recognized, and various methodologies have been proposed for the retrieval of LST using space-borne thermal infrared data. However, the selection of LST retrieval from Thermal Infrared Sensor (TIRS) of Landsat-8 based on different methods and the readily available MODIS LST products is still a challenging topic for local and global environmental studies. In this study, the potential of three different methods for retrieving LST using Landsat-8 TIRS data, including Radiative Transfer Equation (RTE), Single Channel (SC), and Split Window (SW) method in comparison with MODIS MOD11A1 LST product was evaluated. For accuracy assessment, 0 cm ground surface temperature (LSTGST) data was used. Our results almost showed same accuracy for RTEB10 with RMSE = 0.35 °C, followed by MODIS with RMSE = 0.36 °C, and SCB10 with RMSE = 0.38 °C. Secondly, SCmean (Mean of B10 and B11), and RTEmean (Mean of B10 and B11) generate nearly the same accuracy with RMSE = 0.53 °C, and RMSE = 0.54 °C, respectively. The other methods viz., SCB11, RTEB11, and SW method slightly showed lower accuracy with RMSE = 0.87 °C, RMSE = 0.88 °C, and RMSE = 0.91 °C, respectively. We found all the methods highly accurate and can be used successfully by climatologists, environmentalists, hydrologists, and urban planners concerning planning, and monitoring of the ever-increasing LST at local and global scale studies.

Surface Temperature Retrieval From Gaofen-5 Observation and its Validation

Surface temperature (ST) plays a great role in urban heat island effect, environment monitoring, earth resources monitoring, and water balance at local and global scales. The Chinese Gaofen-5 (GF5) satellite can capture Earth’s thermal infrared information for use in national high-resolution Earth observations. In this article, the nonlinear split-window algorithm and the refined generalized split-window method are used to retrieve sea surface temperature (SST) and land surface temperature (LST) from GF5 observations. For different atmospheric and surface conditions, the algorithm coefficients are calculated using a statistical regression method from the numerical values simulated with the atmospheric radiative transfer model MODTRAN 5.0. The simulation results show that the root mean square error (RMSE) for SST and LST retrieval ranges from 0.09 K to 0.46 K and 0.19 K to 0.69 K, respectively, with increasing water vapor content (WVC). To validate the retrieved STs, Moderate Resolution Imaging Spectroradiometer (MODIS) STs extracted from the MYD11A1 product are used. Note that the RMSEs of both the LST and SST are less than 3.3 K. The RMSE for SST retrieval varies from 1.2 K to 1.45 K, with a mean value of 1.33 K; the RMSE for LST retrieval ranges from 1.57 K to 3.3 K, with a mean value of 2.41 K.

Remote Sensing Analysis of Surface Temperature from Heterogeneous Data in a Maize Field and Related Water Stress

Precision agriculture aims at optimizing crop production by adapting management actions to real needs and requires that a reliable and extensive description of soil and crop conditions is available, that multispectral satellite images can provide. The purpose of the present study, based on activities carried out in 2019 on an agricultural area north of Ravenna (Italy) within the project LIFE AGROWETLANDS II, is to evaluate the potentials and limitations of freely available satellite thermal images for the identification of water stress conditions and the optimization of irrigation management practices, especially in agricultural areas and wetlands affected by saline soils and salt water capillary rise. Point field surveys and a very-high resolution thermal survey (5 cm) by an unmanned aerial vehicle (UAV) supported thermal camera were performed on a maize field tentatively at every Landsat-8 passage to check land surface temperature (LST) and canopy cover (CC) estimated from satellite. Temperature measured in the soil near ground surface and from UAV flying at 100 m altitude is compared with LST estimated from satellite measurements using three conversion methods: the top of atmosphere brightness temperature based on Landsat-8 band 10 (SB) corrected to account only for surface emissivity, the radiative transfer equation (RTE) for atmosphere effects correction, and the original split window method (SW) using both Thermal Infrared Sensor (TIRS) bands. The comparison shows discrepancies, due to extreme difference in resolution, the systematic hour of satellite passage (11 am solar time), and systematic differences between methods beside the unavoidable inaccuracy of UAV measurements. Satellite derived temperatures result usually lower than UAV measurements; SB produced the lowest values, SW the best (difference = −1.7 ± 1.7), and RTE the median (difference = −2.7 ± 1.6). The correlation between contemporary 30 m resolution temperature values of near pixels and corresponding tile-average temperatures was not significant, due to the purely numerical interpolation from the 100 m resolution TIRS images, whereas the time pattern along the season is consistent among methods, being correlation coefficient always greater than 0.85. Correlation coefficients among temperatures obtained from Landsat-8 by different methods are almost 1, showing that values are almost strictly related by a linear transformation. All the methods are useful to estimate water stress, since its associated Crop Water Stress Index (CWSI) is, from its definition, insensitive to linear transformation of temperatures. Actual evapotranspiration (ETa) maps are evaluated with the Surface Energy Balance Algorithm for Land (SEBAL) based on the three Landsat-8 derived LSTs; the higher is LST, the lower is ETa. Resulting ETa estimates are related with LST but not strictly, due to variation in vegetation cover and soil, therefore patterns result similar but not equivalent, whereas values are dependent on the atmosphere correction method. RTE and SW result in the best methods among the tested ones and the derived ETa values result reliable and appropriate to user needs. For real time application the Normalized Difference Moisture Index (NDMI), which can also be derived from more frequent Sentinel-2 passages, can be profitably used in combination or as a substitute of the CWSI.

Sea ice surface temperature retrieval from Landsat 8/TIRS: Evaluation of five methods against in situ temperature records and MODIS IST in Arctic region

Accurate and high-resolution sea ice surface temperature (IST) data is of great importance for Arctic climate studies. However, the validation of high-resolution IST data using in situ measurements in polar sea ice regions is lacking. This study assesses the accuracy of three split-window (SW) and two single-channel (SC) methods, based on Landsat 8 thermal infrared imagery at 100 m resolution over Arctic sea ice regions. The SW methods are proposed by Jin et al. (2015) (SW-Jin), Jiménez-Muñoz et al. (2014) (SW-JM), and Du et al. (2015) (SW-Du). The SC methods are proposed by Jiménez-Muñoz et al. (2014) (SC-JM) and Barsi et al. (2003, 2005) (SC-Barsi). IST data derived from 58 scenes of the Landsat 8 images were compared with coincident in situ ice skin temperatures and near-surface air temperatures, as measured by a combination of Ice Mass Balance (IMB) buoys, Snow and Ice Mass Balance Array (SIMBA) buoys, and automatic weather stations. SW-Du offers the best accuracy when compared with the skin temperature (bias: −1.06 K; root mean square error (RMSE): 2.08 K) and near-surface air temperature (bias: -0.98 K; RMSE: 2.17 K). SC-Barsi ranks second, with a bias of −1.55 K and RMSE of 2.40 K for the skin temperature. As for precision, IST from the Moderate Resolution Imaging Spectrometer (MODIS) has best performance (standard deviation (STD): 1.69 K), followed by SW-Du, SW-JM, and SC-Barsi (STD: 1.80 K, 1.82 K, and 1.85 K, respectively). The Landsat IST outperforms the MODIS IST in narrow lead areas, owing to its better spatial resolution, and SW-JM and SC-Barsi methods agree best with the MODIS IST in leads and marginal ice zone scenes, respectively. As all three SW methods are constrained by banding effects with different degrees in a lead scene, they are not recommended to be applied on an image scene with severe banding artifacts. The small bias (1.26 K) and high correlation (0.99) between skin temperature and near-surface air temperature prove the capability of using near-surface air temperature as a substitute for validating a satellite IST data if skin temperature data are not available.

A split-window method to retrieving sea surface temperature from landsat 8 thermal infrared remote sensing data in offshore waters

Sea surface temperature (SST) is an important parameter used to describe the air-sea interaction and the state of marine structures. Atmospheric water vapor has a significant attenuation effect on thermal infrared information, which will reduce the accuracy of SST inversion. However, the effect of atmospheric water vapor on the inversion accuracy is not considered carefully in the existing SST inversion algorithm. In this study, a new method is proposed for retrieving SST from Landsat 8 Thermal Infrared Remote Sensing (TIRS) data based on the variation of atmospheric water vapor content (wvc). First, simulating atmospheric conditions by using moderate resolution atmospheric transmission (MODTRAN) based on atmospheric profiles data (air temperature and pressure). Second, calculating the bright temperature based on the radiation transfer equation and Planck's law. Then, constructing the SST retrieval model of thermal infrared remote sensing based on wvc. Finally, evaluating the accuracy of the proposed model by using simulation data. The root mean square errors (RMSEs) are within 0.5 K, indicating that the accuracy of the model is good in theory. In addition, taking the Zhoushan sea area as the research area, SST is retrieved by Landsat 8 TIRS data. The accuracy of inversion results is evaluated by advanced very high-resolution radiometer (AVHRR) SST products. The bias and RMSE based on the AVHRR SST products are within 1 K and 2 K, respectively. The results show that the accurate SST with high spatial resolution was successfully obtained by using the method. The study is of great significance to the acquisition of marine structural parameters, the exploitation of marine resources, and the monitoring of marine disasters.

Towards an Operational, Split Window-Derived Surface Temperature Product for the Thermal Infrared Sensors Onboard Landsat 8 and 9

The split window technique has been used for over thirty years to derive surface temperatures of the Earth with image data collected from spaceborne sensors containing two thermal channels. The latest NASA/USGS Landsat satellites contain the Thermal Infrared Sensor (TIRS) instruments that acquire Earth data in two longwave infrared bands, as opposed to a single band with earlier Landsats. The United States Geological Survey (USGS) will soon begin releasing a surface temperature product for Landsats 4 through 8 based on the single spectral channel methodology. However, progress is being made toward developing and validating a more accurate and less computationally intensive surface temperature product based on the split window method for Landsat 8 and 9 datasets. This work presents the progress made towards developing an operational split window algorithm for TIRS. Specifically, details of how the generalized split window algorithm was tailored for the TIRS sensors are presented, along with geometric considerations that should be addressed to avoid spatial artifacts in the final surface temperature product. Validation studies indicate that the proposed algorithm is accurate to within 2 K when compared to land-based measurements and to within 1 K when compared to water-based measurements, highlighting the improved accuracy that may be achieved over the current single-channel methodology being used to derive surface temperature in the Landsat Collection 2 surface temperature product. Surface temperature products using the split window methodologies described here can be made available upon request for testing purposes.

Tree Water Status in Apple Orchards Measured by Means of Land Surface Temperature and Vegetation Index (LST–NDVI) Trapezoidal Space Derived from Landsat 8 Satellite Images

In this study, the split window (SW) method was applied for land surface temperature (LST) retrieval using Landsat 8 in two apple orchards (Glindow, Altlandsberg). Four images were acquired during high demand of irrigation water from July to August 2018. After pre-processing images, the normalized difference vegetation index (NDVI) and LST were calculated by red, NIR, and thermal bands. The results were validated by interpolated infrared thermometer (IRT) measurements using the inverse distance weighting (IDW) method. In the next step, the temperature vegetation index (TVDI) was calculated based on the trapezoidal NDVI/LST space to determine the water status of apple trees in the case studies. Results show good agreement between interpolated LST using IRT measurements and remotely sensed LST calculation using SW in all satellite overpasses, where the absolute mean error was between 0.08 to 4.00 K and root mean square error (RMSE) values ranged between 0.71 and 4.23 K. The TVDI spatial distribution indicated that the trees suffered from water stress on 7 and 23 July and 8 August 2018 in Glindow apple orchard with the mean value of 0.69, 0.57, and 0.73, whereas in the Altlandsberg orchard on 17 August, the irrigation system compensated the water deficit as indicated by the TVDI value of 0.34. Moreover, a negative correlation between TVDI and vegetation water content (VWC) with correlation coefficient (r) of −0.81 was observed. The corresponding r for LST and VWC was equal to −0.89, which shows the inverse relation between water status and temperature-based indices. The results indicate that the LST and/or TVDI calculation using the proposed methods can be effectively applied for monitoring tree water status and support irrigation management in orchards using Landsat 8 satellite images without requiring ground measurements.

Comparison of Single-channel and Split-window Methods for Estimating Land Surface Temperature from Landsat 8 Data

Abstract: Landsat 8 is the eighth satellite in the Landsat program, which provides images at 11 spectral channels, including 2 thermal infrared bands at a spatial resolution of 100 m (band 10 (10,30÷11,30 µm) and band 11 (11,50÷12,50 µm)). Until now, most studies have used only band 10 of Landsat 8 image to calculate land surface temperature. In this paper, we compare the results of determining a land surface temperature from Landsat 8 thermal infrared data when using a single band (single-channel method) and using both thermal infrared bands (split-window method). 02 Landsat 8 scenes in the dry season 2015 - 2016 in Loc Ninh district (Binh Phuoc province) and Lam Ha district (Lam Dong province) were used to calculate the land surface temperature according to the SC and SW methods. The results obtained in both experiments showed that the land surface temperature, determined from band 10 of Landsat 8 images was significantly higher than using band 11. Meanwhile, the method using both thermal infrared bands of Landsat 8 data (SW method) to calculate land surface temperature has higher accuracy when compared with the method using band 10 or band 11 only (SC method).
 Keywords: Landsat 8, thermal infrared, land surface temperature, split-window algorithm, single-channel algorithm.

A new thermal infrared channel configuration for accurate land surface temperature retrieval from satellite data

Land surface temperature (LST) is an important parameter in many research fields. Many algorithms have been developed to retrieve LST from satellite thermal infrared (TIR) measurements; of these, the most widely used are the split window (SW) and temperature–emissivity separation (TES) methods. However, the performance of the SW and TES methods can be limited by the difficulty in obtaining sufficiently accurate prior knowledge—specifically, input land surface emissivity (LSE) for the SW method and atmospheric parameters for the TES method. In this study, a procedure was proposed for selecting specific channel pairs in the TIR spectral region to accurately retrieve ground brightness temperatures without prior atmospheric knowledge, using a method similar to the SW method. Subsequently, the TES method is applied to the retrieved ground brightness temperatures to separate the LST and LSE. In numerical simulations, the three ground brightness temperatures corresponding to 8.6 μm, 9.0 μm, and 10.4 μm are acquired with an accuracy of about 0.65 K by using five channels centered at 8.6 μm, 9.0 μm, 10.4 μm, 11.3 μm, and 12.5 μm, each with a width of 0.1 μm. When inputting the three retrieved ground brightness temperatures into TES method, LST could be recovered with an accuracy of 0.87 K. Sensitivity analysis shows that LST retrieval accuracy is less affected by channel width and atmospheric downwelling radiance than by the channel center locations and channel noise. Finally, the proposed method is preliminarily applied to actual satellite data from the Atmospheric InfraRed Sounder (AIRS) and the retrieved results are compared with the pixel-aggregated Moderate Resolution Imaging Spectroradiometer (MODIS) LST product. For the study area of Australia, discrepancies between our result and the MODIS LST product appear to be about 1.6 K during the day and 1.0 K at night, indicating that the new channel configuration can be used to retrieve accurate LST from satellite measurements.

Using Landsat-8 Data in Preliminary Exploration for Geothermal Resources

To characterize geothermal potential areas, conventional surface exploration activities involve field surveys, gathering geothermal information from locals and review of any existing geothermal literature. This is not only time consuming and costly but also unreliable for inaccessible geothermal potential areas. Thus, this study explores the cost-effectiveness and powerful tools of satellite remote sensing in preliminary land surface characterization for expansive geothermal exploration. The main approach entailed the use of free-access Landsat-8 and atmospheric data to retrieve land surface temperature (LST) using split-window and single channel algorithm, analysis of retrieved surface products, validation using in-situ ground temperature data, and finally delineation of surface thermal anomalies associated with geothermal features. Gilgil district and Baringo County in Kenya made the study areas. The former is a known and confirmed geothermal area while the latter is only a geothermal prospect. The two areas sit on the central section of the Kenyan rift; geothermal belt, and combined form a suitable case study for preliminary exploration using Landsat-8 data. The main objective of the study was to demonstrate the use of satellite remote sensing data to identify surface thermal anomalies associated with geothermal features as a cost-effective geothermal exploration support tool. Identify the best LST retrieval method between split window and single channel method using Landsat 8 data, and finally employ the better retrieval method to characterize geothermal prospect area and suggest targets for further investigations. Results showed that free-access satellite remote sensing imagery can conveniently be used to identify and map surface thermal anomalies associated with geothermal features and thus can be employed to complement the main geothermal exploration studies namely geological, geochemical and geophysical. Further, single channel method had better LST retrieval results compared to split-window method when using Landsat-8 data

An Operational Precipitable Water Vapor Retrieval Algorithm for Fengyun-2F/VLSSR Using a Modified Three-Band Physical Split-Window Method

The Visible and Infrared Spin-Scan Radiometer (VISSR) onboard the Fengyun-2 (FY-2) satellite can provide valuable thermal infrared observations to help create a precipitable water vapor (PWV) product with high spatial and temporal resolutions. The current FY-2/VISSR PWV product in operation is produced by using a traditional two-band physical split-window (PSW) method, which produces low quality results under dry atmospheric conditions. Based on the sensitivity characteristics of FY-2F/VISSR water vapor channel and two split-window channels to atmospheric water vapor, this study developed a new, robust operational PWV retrieval algorithm for FY-2F to improve the operational precision of the current PWV product. The algorithm uses a modified three-band PSW method, which adds a scale for the water vapor channel in the improved three-band PSW method. Integrated PWV products from the radiosonde data in 2016 are used here to validate the precision of the PWV retrieved by the modified three-band and traditional two-band PSW methods. The mean bias, root mean square error (RMSE), and correlation coefficient of the PWV retrieved by the modified three-band PSW method are 0.28 mm, 4.53 mm, and 0.969, respectively. The accuracy is much better than the PWV retrieved by the two-band method, whose mean bias, RMSE, and correlation coefficient are 12.67 mm, 29.35 mm, and 0.23. Especially, in mid- or high-latitude regions, the RMSE of the PWV is improved from 10 to 2 mm by changing the inversion in the two-band method to the modified three-band PSW method. Furthermore, the modified three-band PSW results show a better consistency with the radiosonde PWV at any zonal belt and season than the two-band PSW results. This new algorithm could significantly improve the quality of the current FY-2F/VISSR PWV product, especially at sites where the actual PWV are lower than 15 mm.

Developed split window method to retrieve land surface temperature from HYTES thermal hyperspectral images using genetic algorithm

ABSTRACTThis article aims to establish a new method to retrieve land surface temperature (LST) from hyperspectral thermal emission spectrometer (HYTES) data with split window (SW) algorithm. First, the optimal bands of HYTES sensor were selected with the genetic algorithm and then were used in the SW algorithm. In the SW algorithm, its coefficients were obtained based on several subranges of atmospheric column water vapours (CWVs) and view zenith angle (VZA) under various land surface conditions, in order to remove the atmospheric effect and improve the retrieval accuracy. Results showed that the root-mean-square error (RMSE) varies for different CWV and VZA, and with the increasing CWV and VZA, the RMSE value also increases. The emissivity, CWV, and VZA were also obtained for pixels. The sensitive analysis of LST retrieval to instrument noise and uncertainty of pixel emissivity and water vapour demonstrated the good performance of the proposed algorithm. Finally, the new algorithm was applied to HYTES sensor data, and the LST was validated using LST product of HYTES sensor obtained by NASA. The results showed that the RMSE of the LST retrieval with the proposed algorithm and the LST product of sensor for data 1 and data 2 is 1.3 K and 1.6 K, respectively.

Downscaling MODIS land surface temperature over a heterogeneous area: An investigation of machine learning techniques, feature selection, and impacts of mixed pixels

Remotely sensed Land Surface Temperature (LST) is of paramount importance in numerous environmental applications. Although, coarse spatial resolution sensors provide frequent LST measurements, their applicability is rather limited for many applications. Downscaling methods are therefore applied to improve the spatial resolution of LST products. A number of Machine Learning (ML) methods have already been used in the LST downscaling studies. Nevertheless, the literature lacks a suitable inter-comparison of different ML methods, as well as the impact of the feature selection process on downscaling results. This study aims at comparing downscaled LST from 1 km daily MODIS LST product (MOD11A1) to 240 m using Random Forest Regression (RFR), Support Vector Regression (SVR), Extreme Learning Machine (ELM) and Temperature Sharpening (TsHARP) approaches with 11 predictor variables at a heterogeneous area in different seasons. In addition, by implementing a feature selection with the Support Vector Machine-Recursive Feature Elimination (SVM-RFE) method, the most important variables were selected and used as inputs of the models. The results were evaluated against the LST derived from Landsat-8 thermal imageries using a split window method, showing that all the ML methods perform well in LST downscaling (with an average RMSE = 2.5 and MAE = 1.74) with marginal differences, outperforming the TsHARP method (RMSE∼ 3.02, MAE∼ 2.19). Among all the methods, ELM required the least computational effort, and when it was combined with SVM-RFE, general efficiency of the downscaling procedure was increased substantially.

Development of Himawari-8/Advanced Himawari Imager (AHI) Land Surface Temperature Retrieval Algorithm

We developed land surface temperature (LST) retrieval algorithms based on the time of day and water vapor content using the Himawari-8/AHI (Advanced Himawari Imager) data, which is the Japanese next generation geostationary satellite. To develop the LST retrieval algorithms, we simulated the spectral radiance using the radiative transfer model (MODTRAN4) by applying the atmospheric profiles (SeeBor), diurnal variation of LST and air temperature, spectral emissivity of land surface, satellite viewing angle, and spectral response function of Himawari-8/AHI. To retrieve the LST from Himawari-8 data, a linear type of split-window method was used in this study. The Himawari-8 LST algorithms showed a high correlation coefficient (0.996), and a small bias (0.002 K) and root mean square error (RMSE) (1.083 K) between prescribed LSTs and estimated LSTs. However, the accuracy of LST algorithms showed a slightly large RMSE when the lapse rate was larger than 10 K, and the brightness temperature difference was greater than 6 K. The cross-validation of Himawari-8/AHI LST using the MODIS (Terra and Aqua Moderate Resolution Imaging Spectroradiometer) LST showed that annual mean correlation coefficient, bias, and RMSE were 0.94, +0.45 K, and 1.93 K, respectively. The performances of LST algorithms were slightly dependent on the season and time of day, generally better during the night (warm season) than during the day (cold season).