Land Surface Temperature Retrieval Research Articles

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

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.

Land surface emissivity retrieval from multiple vegetation indices: a comparative study over India

ABSTRACTLand surface emissivity (LSE) is an important parameter used in the retrieval of land surface temperature (LST) from thermal infrared (TIR) sensors. One of the widely adopted methods for LSE estimation is to treat the LSE of a pixel as a linear combination of soil and vegetation emissivity and use Normalized Difference Vegetation Index (NDVI) for characterizing vegetation cover of the pixel. However, it is unclear if other existing vegetation indices (VI) can be used for estimating LSE using this method. The aim of this study is to compare multiple VIs: NDVI, Enhanced Vegetation Index (EVI), Normalized Difference Water Index (NDWI), Soil Adjusted Vegetation Index (SAVI) and Modified SAVI (MSAVI) for the retrieval of LSE over four sites in India with data from Landsat-7 and Landsat-8 satellites. The LSE estimated from different VIs were compared with the Advanced Spaceborne Thermal Emission Radiometer-Global Emissivity Dataset (ASTER GED) and MODIS 21 LST and emissivity product. It was found that EVI was marginally better performing than NDVI, consistently. However, EVI gave better results only under non- cropped conditions (NDVI < 0.25). Whereas, during cropped stages, NDWI performed better than other VIs for the retrieval of LSE. SAVI and MSAVI resulted in marginally poorer retrievals of LSE in comparison with other VIs and performed inconsistently.

Quantification of the Adjacency Effect on Measurements in the Thermal Infrared Region

Sensor-observed energy from adjacent pixels, known as the adjacency effect, influences land surface reflectivity retrieval accuracy in optical remote sensing. As the spatial resolution of thermal infrared (TIR) images increases, the adjacency effect may influence land surface temperature (LST) retrieval accuracy in TIR remote sensing. However, to our knowledge, few studies have focused on quantifying this adjacency effect on TIR measurements. In this study, a forward adjacency effect radiative transfer model (FAERTM) was developed to quantify the adjacency effect on high-spatial-resolution TIR measurements. The model was verified to be in good agreement with moderate resolution atmospheric transmission (MODTRAN) code, with a discrepancy 3 K in some cases. These findings indicate that the adjacency effect should be considered when retrieving LSTs from TIR measurements, at least in some specific conditions. The proposed FAERTM provides a useful model for quantifying and addressing the adjacency effect on TIR measurements.

Automated water surface temperature retrieval from Landsat 8/TIRS

Satellite remote sensing of Land and Water Surface Temperature (L/WST) has many applications in studies of terrestrial and aquatic ecology. Retrieval of L/WST requires a well calibrated radiometer and an accurate atmospheric correction. In the present study, the performance of the Thermal InfraRed Sensor (TIRS) on board Landsat 8 is evaluated for the retrieval of L/WST. libRadtran is used to retrieve atmospheric correction parameters based on atmospheric profiles of relative humidity and temperature from three global atmospheric models. Performance of single band retrievals is compared to typical MODTRAN results from the Atmospheric Correction Parameter Calculator (ACPC) and a split-window approach. A multi-temporal land masking method using imagery from the Operational Land Imager (OLI) on board Landsat 8 is demonstrated, and is used to automatically classify imagery in the matchup dataset in three classes of cloud cover. Two sources of in situ data covering the Belgian Coastal Zone (BCZ) are used for validation of the L/WST product: (1) fixed locations in the Flemish Banks measurement network and (2) underway data from regular RV Belgica campaigns. In the present study the single band methods outperformed the split-window approach, and consistent retrievals are found for the MODTRAN and libRadtran simulations. Typical single band surface temperature retrievals in quasi cloud-free conditions have Root Mean Squared Differences (RMSD) of 0.7 K and 1 K for Bands 10 and 11 with low bias, depending on the method and atmospheric profile source. For imagery with scattered clouds, RMSD values increase to 1 K and 2 K respectively with an approximately 0.5 K cold bias, likely caused by cloud proximity. The calibration efforts combined into Collection 1 allows for accurate absolute surface temperature retrievals from B10 on Landsat 8/TIRS for homogeneous targets with known emissivity, such as liquid water. The method is adapted to global processing and can be used for Land Surface Temperature retrieval with a suitable source of emissivity data.

Comparison of Three Algorithms for the Retrieval of Land Surface Temperature from Landsat 8 Images.

The successful launch of the Landsat 8 satellite provides important data for the monitoring of urban heat island effects. Since the Landsat 8 TIRS data has two thermal infrared bands, it is suitable for many algorithms to retrieve the land surface temperature (LST). However, the selection of algorithms for retrieving the LST, the acquisition of algorithm input parameters, and the verification of the results are problems without obvious solutions. Taking Changchun City as an example, this paper used the mono-window algorithm (MWA), the split window algorithm (SWA), and the single-channel (SC) method to extract the LST from the Landsat 8 image and compared the three algorithms in terms of input parameters, accuracy, and sensitivity. The results show that all three algorithms can achieve good results in retrieving the LST. The SWA is the least sensitive to the error of the input parameters. The MWA and the SC method are sensitive to the error of the input parameters, and compared with the error of the LSE, these two algorithms are more sensitive to the error of atmospheric water vapor content. In addition, the MWA is also very sensitive to the error of the effective mean atmospheric temperature.

Monitoring agricultural drought in Australia using MTSAT-2 land surface temperature retrievals

Drought indices based on thermal remote sensing have been developed and have merit for effective early warning of agricultural droughts, but approaches so far are relatively complex or sensitive to land surface temperature (LST) estimation uncertainties. Here, we propose the temperature rise index (TRI), a drought index that is comparatively robust and easy to calculate, as the anomaly of the intrinsic morning rise of LST. The underlying principle is that the rate of LST rise between 1.5 and 3.5 h after the sunrise is approximately linear and occurs more rapidly under dry conditions than under wet conditions over vegetated surfaces as a consequence of stomatal control. TRI during the growing seasons of 2010–2014 was calculated over the Australian wheatbelt from LST retrievals from the geostationary Multifunction Transport Satellite-2 (MTSAT-2) instrument. The calculated TRI was compared with indices based on precipitation integrated over 1-, 3- and 6-month time scales, on Soil Moisture and Ocean Salinity (SMOS) soil moisture derived from passive microwave remote sensing, and on vegetation condition (normalized difference vegetation index, NDVI) derived from optical remote sensing. The various indices were also compared to annual wheat yield over large areas. The correlation coefficient between TRI and precipitation anomaly that serves as an operational drought index in Australia was above 0.6 in general with 3-month integrative time scale for precipitation. TRI produced spatiotemporal dryness patterns that were very similar to those in soil moisture, but with more detail due to its finer resolution. A time lag of >1 month was found between TRI and observed vegetation condition, supporting the use of TRI in early warning. Among the compared drought indices, TRI explained the largest fraction (35%) of wheat yield variations. TRI correlations with wheat yields peaked higher and earlier by almost one month in comparison to other indices. We conclude that the thermal drought index proposed here shows considerable potential for use in drought early warning as an effective complement.

Evapotranspiration Data Product from NESDIS GET-D System Upgraded for GOES-16 ABI Observations

Evapotranspiration (ET) is a major component of the global and regional water cycle. An operational Geostationary Operational Environmental Satellite (GOES) ET and Drought (GET-D) product system has been developed by the National Environmental Satellite, Data and Information Service (NESDIS) in the National Oceanic and Atmospheric Administration (NOAA) for numerical weather prediction model validation, data assimilation, and drought monitoring. GET-D system was generating ET and Evaporative Stress Index (ESI) maps at 8 km spatial resolution using thermal observations of the Imagers on GOES-13 and GOES-15 before the primary operational GOES satellites transitioned to GOES-16 and GOES-17 with the Advanced Baseline Imagers (ABI). In this study, the GET-D product system is upgraded to ingest the thermal observations of ABI with the best spatial resolution of 2 km. The core of the GET-D system is the Atmosphere-Land Exchange Inversion (ALEXI) model, which exploits the mid-morning rise in the land surface temperature to deduce the land surface fluxes including ET. Satellite-based land surface temperature and solar insolation retrievals from ABI and meteorological forcing from NOAA NCEP Climate Forecast System (CFS) are the major inputs to the GET-D system. Ancillary data required in GET-D include land cover map, leaf area index, albedo and cloud mask. This paper presents preliminary results of ET from the upgraded GET-D system after a brief introduction of the ALEXI model and the architecture of GET-D system. Comparisons with in situ ET measurements showed that the accuracy of the GOES-16 ABI based ET is similar to the results from the legacy GET-D ET based on GOES-13/15 Imager data. The agreement with the in situ measurements is satisfactory with a correlation of 0.914 averaged from three Mead sites. Further evaluation of the ABI-based ET product, upgrade efforts of the GET-D system for ESI products, and conclusions for the ABI-based GET-D products are discussed.

Analysis of the Spatial and Temporal Variations of Land Surface Temperature Based on Local Climate Zones: A Case Study in Nanjing, China

The concept of local climate zone (LCZ) has standardized the calculation of urban heat island (UHI) intensity (UHII) and established the connection among urban morphology, surface property, and UHI. In addition, LCZ has provided a new insight into the studies on urban thermal environment. This study selected Nanjing, China, as the study area and utilized a combined method that comprised remote sensing based and geographic information system based methods based on random forest classifier for LCZ classification. Overall accuracy reached 92%, and kappa coefficient was 0.911. In addition, the seasonal and diurnal differences of land surface temperature (LST) were analyzed via LST retrieval from Landsat data and computational fluid dynamics model simulation, respectively. Results confirmed the warmest and coldest zones in four seasons. The LST distribution characteristics of land cover and built types were basically the same during the four seasons. Moreover, the UHII difference of an LCZ class in various seasons and the UHII difference of a season in various LCZs were investigated. The daily LSTs of the simulated LCZs (1 to 6) within 24 h showed the same variation law but different variation extents in a day. The LST variations of built types were related to building elements, such as building height, building density, building layout, and green ratio. This study identified an existing relation between LST and LCZ and analyzed LST on the basis of LCZs from seasonal and diurnal scales, which provides guidance for future researchers.

Development and validation of algorithms for LST measurement from NOAA-11/AVHRR satellite data

The purpose of this project was to develop the algorithms and characterise measurement errors of land surface temperature (LST). An initial investigation of the accuracy of retrieval of LST has employed synthetic radiances, produced using Lowtran7, for a series of land and atmospheric situations representative of the Australian continental climate. The synthetic data have been modelled to incorporate variation of surfaces emissivity, atmospheric temperature, water vapour profile, and the surface or boundary temperature. The study indicated that the success of the scheme depended on the validity of the modelled radiances, particularly with respect to the formulation of the radiative role of the water vapour constituent of the atmosphere. Whirls, the use of regression in deriving the coefficients for LST equation minimised the bias errors in the radiative transfer calculation that may arise from air mass dependence. However, our algorithms derived using statistical regression and synthetic atmospheres indicated good accuracy for LST measurement with and rms error of order 1°C when validated using data for an instrumented field site.

The seasonal and annual impacts of landscape patterns on the urban thermal comfort using Landsat

Abstract The traditional in situ data based temperature-humidity indexes (THIs) have been widely used in the assessment of the quality of urban thermal environment, with the spatial details of thermal comfort currently unavailable. In this study, the THI is modified replacing the required in situ air temperature and relative humidity with remote sensing retrieved Land Surface Temperature and Normalized Difference Moisture Index, being the modified temperature-humidity index. The proposed remote sensing based index is then used to explore the spatio-temporal characteristics of urban thermal comfort, which are used to describe the urban thermal comfort grading in different seasons and the landscape metrics as well as to investigate the response of urban thermal comfort to landscape patterns seasonally and annually. The results show that at the macro scale, the impacts of landscape patterns on thermal comfort are the most important in summer with water bodies and built-up land being the most desirable and uncomfortable types, respectively. The opposite results are found in spring and winter despite the relatively less important effect in winter. At the micro scale, the variance contribution rate of the Percentage of Landscape to the MTHI is above 70%, and that of the Landscape Division Index and Aggregation Index is from 10% to 38.1%. It indicates that the composition of the landscape is the main factor affecting urban thermal comfort and is better than the landscape structure. The MTHI based on remote sensing data can monitor the spatial distribution of urban thermal comfort and is suitable to explain the reasons for the thermal comfort temporal variations.

Validation of Physical Radiative Transfer Equation-Based Land Surface Temperature Using Landsat 8 Satellite Imagery and SURFRAD in-situ Measurements

Land Surface Temperature (LST) is a key criterion in the physics of the Earth surface that controls the interactions between the land and atmosphere. The objective of this study is to evaluate the performance of physics-based Radiative Transfer Equation (RTE) method on LST retrieval using Landsat 8 satellite imagery and simultaneous in-situ LST data. In order to validate the satellite-based LST, in-situ LST measurements were obtained from Surface Radiation Budget Network (SURFRAD) stations simultaneous with satellite data acquisitions. In the study, four SURFRAD stations (BND, FPK, TBL and GWN) and five images for each SURFRAD station, totally twenty cloud-free images, were used for RTE-based LST validation. RTE method uses the atmospheric parameters acquired from radiosounding data simultaneous with satellite pass; however, these parameters were retrieved from NASA's atmospheric correction parameter calculator since radiosounding data are not available every time. Thus, this situation is another contribution of this study. As a result of the validation process of all data, the statistical measures, namely, coefficient of determination (R2), Root Mean Square Error (RMSE), Mean Absolute Error (MAE) and RMSE-observations standard deviation ratio (RSR) were calculated as 0.96, 3.12 K, 2.30 K and 0.33, respectively. However, the accuracy of RTE method on LST retrieval increased (R2 = 0.97, RMSE = 2.17 K, MAE = 1.44 K and RSR = 0.25) after removing TBL station from the analysis, since LST differences in this station were high for all scenes. RSR (ranging from 0 to high positive vlues) is an important measure for model evaluation, and the lower RSR value means high performance of the model. The obtained results revealed that physics-based RTE method is an effective and practical way for LST retrieval from Landsat 8 data despite using interpolated atmospheric parameters instead of radiosounding data.

Land surface temperature retrieval from Landsat 8 OLI/TIRS images based on back-propagation neural network

Land surface temperature (LST) is an important parameter related to the environmental assessment concerning the surface energy, water balance, greenhouse effect, etc. at local and global scales. With the rapid development of the remote sensing technology, various methodologies have been developed to retrieve LST from space-based remote sensing images. Due to the ill-posed problem, the LST retrieval is still a challenge. In this research, a so-called multiple band reflectance (MBR)-LST model has been proposed based on the back-propagation neural (BPN) network, which can be employed to retrieve the LSTs from Landsat 8 Operational Land Imager (OLI)/TIRS images as well as produce continuous spatial LST distributions with a spatial resolution of 30 m. Experiments conducted in two randomly selected areas in mainland China proved that the proposed MBR-LST model has yielded a much better performance than the traditional radiative transfer equation (RTE) method with respect to both the accuracy and stability for the LST retrievals. Moreover, another significant advantage of the proposed MBR-LST is the generic nature – once trained by the sample data in the whole region of mainland China, the proposed MBR-LST model can be utilized for the accurate LST-retrievals in any area of mainland China.

RELATIONSHIPS BETWEEN LAND USE/LAND COVER AND LAND SURFACE TEMPERATURE IN TABRIZ FROM 2000 TO 2017

Abstract. Urban Heat Island (UHI) refers to the development of higher urban temperatures of an urban area compared to the temperatures of surrounding suburban and rural areas. Highly reflective urban materials to solar radiation present a significantly lower surface temperature and contribute to reducing the sensible heat released in the atmosphere and mitigating the urban heat island. Many studies of the UHI effect have been based on Land Surface Temperature (LST) measurements from remote sensors. The remotely sensed UHI has been termed the surface urban heat island (SUHI) effect. This study examines Tabriz city land use/land cover (LULC) and LST changes using Landsat satellite images between 2000 and 2017. Maximum likelihood classification and single channel methods were used for LULC classification and LST retrieval respectively. Results show that impervious surface has increased 13.79% and bare soil area has decreased 16.2%. The results also revealed bare soil class LST after a constant trend become increasing. It also revealed the impervious surface LST has a decreasing trend between 2000 and 2011 and has a little change. Using materials that have low absorption and high reflectance decrease the effect of heat island considerably.

An Experimental Study on Separating Temperature and Emissivity of a Nonisothermal Surface

This letter presents an experiment to explore the nonisothermal effects on temperature and emissivity separation (TES). The innovation of this experiment lies in its design, which highlights the contrast between isothermal and nonisothermal conditions in emissivity measurements. We artificially created a sharply contrasting nonisothermal soil surface using liquid nitrogen cooling and solar heating. The iterative spectrally smooth TES (ISSTES) algorithm was used to process the experimental data. The analyzed results of the experimental data show that the nonisothermal conditions have a significant effect on TES. The bias of retrieved emissivity increases with the component temperature difference as well as with wavelength. The bias around the split window band can reach up to 1% when the difference of the component temperature is 40K. Considering that 1% error in emissivity can cause approximately 1K error of retrieved land surface temperature (LST), the nonisothermal effects on emissivity cannot be ignored. We hope that this experiment will arouse attention of the nonisothermal effects on TES and call for more efforts to be devoted to this issue in the future.

Comparison of the MuSyQ and MODIS Collection 6 Land Surface Temperature Products Over Barren Surfaces in the Heihe River Basin, China

In this study, to improve the accuracy of land surface temperature (LST) products over barren surfaces, we present an operational algorithm to retrieve the LST from Moderate-Resolution Imaging Spectroradiometer (MODIS) thermal infrared data using physically retrieved emissivity products. The LST algorithm involved two steps. First, the emissivity in the two MODIS split-window (SW) channels was estimated using the vegetation cover method, with the bare soil component emissivity derived from the ASTER global emissivity data set. Then, the LST was retrieved using a modified generalized SW algorithm. This algorithm was implemented in the MUlti-source data SYnergized Quantitative (MuSyQ) remote sensing product system. The MuSyQ MODIS LST product and the Collection 6 MODIS LST product (MxD11_L2) were compared and validated using ground measurements collected from four barren surface sites in Northwest China during the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) experiment from June 2012 to December 2015. In total, 2268 and 2715 clear-sky samples were used in the validation for Terra and Aqua, respectively. The evaluation results indicate that the MuSyQ LST products provide better accuracy than the C6 MxD11 product during both daytime and nighttime at all four sites. For the daytime results, the LST is underestimated by the C6 MxD11 products at all four sites, with a mean bias of -1.78 and -2.86 K and a mean root-mean-square error (RMSE) of 3.16 and 3.94 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are within 1 K, with a mean bias of -0.26 and -1.03 K and a mean RMSE of 2.45 and 2.71 K for Terra and Aqua, respectively. For the nighttime results, the LST is also underestimated by the C6 MxD11 products at all four sites, with a mean bias of -1.60 and -1.26 K and a mean RMSE of 1.93 and 1.60 K for Terra and Aqua, respectively, whereas the mean biases of the MuSyQ LST products are 0.16 and 0.58 K and the mean RMSEs are 1.12 and 1.25 K for Terra and Aqua, respectively. The results indicate that the underestimation of the C6 MxD11 LST product at all four sites mainly results from the overestimation of the emissivities in MODIS bands 31 and 32. This study demonstrates that physically retrieved emissivity products are a useful source for LST retrieval over barren surfaces and can be used to improve the accuracy of global LST products.

Assessing the Impact of Land Cover Changes on Surface Urban Heat Islands with High-Spatial-Resolution Imagery on a Local Scale: Workflow and Case Study

Low-altitude remote sensing platform has been increasingly applied to observing local thermal environments due to its obvious advantage in spatial resolution and apparent flexibility in data acquisition. However, there is a general lack of systematic analysis for land cover (LC) classification, surface urban heat island (SUHI), and their spatial and temporal change patterns. In this study, a workflow is presented to assess the LC’s impact on SUHI, based on the visible and thermal infrared images with high spatial resolution captured by an unmanned airship in the central area of the Sino-Singapore Guangzhou Knowledge City in 2012 and 2015. Then, the accuracy assessment of LC classification and land surface temperature (LST) retrieval are performed. Finally, the commonly-used indexes in the field of satellites are applied to analyzing the spatial and temporal changes in the SUHI pattern on a local scale. The results show that the supervised maximum likelihood algorithm can deliver satisfactory overall accuracy and Kappa coefficient for LC classification; the root mean square error of the retrieved LST can reach 1.87 °C. Moreover, the LST demonstrates greater consistency with land cover type (LCT) and more fluctuation within an LCT on a local scale than on an urban scale. The normalized LST classified by the mean and standard deviation (STD) is suitable for the high-spatial situation; however, the thermal field level and the corresponded STD multiple need to be judiciously selected. This study exhibits an effective pathway to assess SUHI pattern and its changes using high-spatial-resolution images on a local scale. It is also indicated that proper landscape composition, spatial configuration and materials on a local scale exert greater impacts on SUHI.

Land Surface Emissivity Product for NOAA JPSS and GOES-R Missions: Methodology and Evaluation

Land surface emissivity (LSE) is a key parameter for the determination of land surface temperature (LST) from thermal remotely sensed data. A new LSE product has been developed at the National Oceanic and Atmospheric Administration (NOAA), College Park, MD, USA, to enhance the LST product for the Joint Polar Satellite System (JPSS) and the Geostationary Operational Environmental Satellite R-Series (GOES-R) missions as well as to support the forecasting models. A 1-km resolution bare ground emissivity was derived from the historical Advanced Spaceborne Thermal Emission and Reflection Radiometer Global Emissivity Dataset (ASTER-GED) and Moderate Resolution Imaging Spectroradiometer (MODIS) LSE product. It is then spectrally adjusted to the split window (SW) channels of Visible/Infrared Imager Radiometer Suite (VIIRS) onboard JPSS and Advanced Baseline Imager (ABI) from GOES-R. VIIRS daily green vegetation fraction and snow fraction products are subsequently used to account for the dynamic variations. Validation results indicate that the product has a good agreement with in situ observations, with an emissivity difference less than 0.006 at the bare surface sites and a difference less than 0.007 at one cropland site with three growth stages. The intercomparison with NASA VIIRS daily LSE product proves a good agreement with a standard deviation of less than 0.012. To evaluate the LSE performance in LST retrieval, a whole year VIIRS LST is produced and validated over the SURFace RADiation budget observing network (SURFRAD) sites. The results indicate that the LSE works well with LST RMSE of 1.78 (daytime) and 1.58 K (nighttime). The new LSE algorithm has been integrated to NOAA-20 LST with the status of provisional maturity and will be applied on GOES-16/17 LST product in the near future.

A Split Window Algorithm for Retrieving Land Surface Temperature from FY-3D MERSI-2 Data

The thermal infrared (TIR) data from the Medium Resolution Spectral Imager II (MERSI-2) on the Chinese meteorological satellite FY-3D have high spatiotemporal resolution. Although the MERSI-2 land surface temperature (LST) products have good application prospects, there are some deviations in the TIR band radiance from MERSI-2. To accurately retrieve LSTs from MERSI-2, a method based on a cross-calibration model and split window (SW) algorithm is proposed. The method is divided into two parts: cross-calibration and LST retrieval. First, the MODTRAN program is used to simulate the radiation transfer process to obtain MERSI-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) simulation data, establish a cross-calibration model, and then calculate the actual brightness temperature (BT) of the MERSI-2 image. Second, according to the characteristics of the near-infrared (NIR) bands, the atmospheric water vapor content (WVC) is retrieved, and the atmospheric transmittance is calculated. The land surface emissivity is estimated by the NDVI-based threshold method, which ensures that both parameters (transmittance and emissivity) can be acquired simultaneously. The validation shows the following: 1) The average accuracy of our algorithm is 0.42 K when using simulation data; 2) the relative error of our algorithm is 1.37 K when compared with the MODIS LST product (MYD11A1); 3) when compared with ground-measured data, the accuracy of our algorithm is 1.23 K. Sensitivity analysis shows that the SW algorithm is not sensitive to the two main parameters (WVC and emissivity), which also proves that the estimation of LST from MERSI-2 data is feasible. In general, our algorithm exhibits good accuracy and applicability, but it still requires further improvement.

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

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.

SOIL MOISTURE ANALYSIS OF LALITPUR DISTRICT UTTAR PRADESH INDIA USING LANDSAT AND SENTINEL DATA

Abstract. Soil moisture is the available water content within the voids of soil particles. Remote sensing and GIS technique provide an advance &amp;amp; better information to extract the soil moisture of Lalitpur district Uttar Pradesh. The Landsat-8 OLI+TIRS (Optical Level Imager +Thermal Infrared Sensor) data (2013–18) and Sentinel 2A &amp;amp; 2B data (2015–18) was used to retrieve soil moisture content for the period 2013–2018. The optical and thermal bands were used to retrieve Land surface temperature (LST), NDVI and NDWI of Lalitpur district for the different years. Using land surface temperature encompasses with NDVI and NDWI the moisture content of the soil was estimated for the study area.Using Advance Microwave Scanning Radiometer-2, (2013–18) (AMSR-2) data and measurement of soil moisture through in-situ (Field) soil samples collections for soil moisture estimation was used to check the accuracy of the output resulted from Landsat and Sentinel data. This study results that; the output obtained from Landsat-8 in comparison to Sentinel data provide an accurate and better information of soil moisture at a high resolution.