Land Surface Temperature Retrieval from Landsat-8 Data with the Generalized Split Window Algorithm

This work addresses the LST retrieval from Landsat\|8 data with the generalized split\|window algorithm.Firstly,radiative transfer modeling experiment is conducted using MODTRAN 4.0,fed with SeeBor V5 atmospheric profile database to build a data set of LST related to brightness temperatures in the bands 10 and 11 of Thermal Infrared Sensor(TIRS) on Landsat-8,Land Surface Emissivities(LSEs),viewing zenith angle and Total Precipitable Water(TPW).Secondly,based on the modeling data set,the unknown coefficients of the generalized split-window algorithm are obtained,and the algorithm sensitivity is analyzed.Then,LSTs are derived from the inter-calibrated and clear sky Landsat\|8 data with the generalized split\|window algorithm,in which LSEs are estimated from Landsat\|8 Operational Land Imager(OLI) data,and TPWs are extracted from the European Centre for Medium-range Weather Forecasts(ECMWF) reanalysis data.Finally,the results are validated with the Moderate resolution Imaging Spectroradiometer(MODIS) LST/LSE product(MOD11_L2 V5).The results show that the generalized split window algorithm developed in this work can accurately retrieve LST from the Landsat\|8 data,and the error is mainly come from the uncertainty of LSEs and TPW.Before and after correction of LSEs and TPW,the LST errors in this work are,respectively,-0.64 ±0.81 K and 0.10±0.68 K against the MOD11_L2 V5 product.     

Evaluation of land surface temperature and emissivities retrieved from MSG/SEVIRI data with MODIS land surface temperature and emissivity products

Land surface temperature (LST) and land surface emissivity (LSE) are two key parameters in global climate study. This article aims to cross-validate LST/LSE products retrieved from data of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) on board the first geostationary satellite, Meteosat Second Generation (MSG), with Moderate Resolution Imaging Spectroradiometer (MODIS) LST/LSE version 5 products over the Iberian Peninsula and over Egypt and the Middle East. Besides time matching, coordinate matching is another requirement of the cross-validation. An area-weighted aggregation algorithm was used to aggregate SEVIRI and MODIS LST/LSE products into the same spatial resolution. According to the quality control (QC) criterion and the view angle, the cross-validation was completed under clear-sky conditions and within a view angle difference of less than 5° for the two instruments to prevent land surface anisotropic effects. The results showed that the SEVIRI LST/LSE products are consistent with MODIS LST/LSE products and have the same trend over the two study areas during both the daytime and the night-time. The SEVIRI LST overestimates the temperature by approximately 1.0 K during the night-time and by approximately 2.0 K during the daytime compared to MODIS products over these two study areas. The SEVIRI LSE underestimates by about 0.015 in 11 μm and by about 0.025 in 12 μm over the Iberian Peninsula. However, both LSEs agree and show a difference of less than 0.01 over Egypt and the Middle East.     

Evaluation and improvement of the MODIS land surface temperature/emissivity products using ground‐based measurements at a semi‐desert site on the western Tibetan Plateau

Current MODerate‐resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST, surface skin temperature)/emissivity products are evaluated and improvements are investigated. The ground‐based measurements of LST at Gaize (32.30° N, 84.06° E, 4420 m) on the western Tibetan Plateau from January 2001 to December 2002 agree well (mean and standard deviation of differences of 0.27 K and 0.84 K) with the 1‐km Version 004 (V4) Terra MODIS LST product (MOD11A1) generated by the split‐window algorithm. Spectral emissivities measured from surface soil samples collected at and around the Gaize site are in close agreement with the landcover‐based emissivities in bands 31 and 32 used by the split‐window algorithm. The LSTs in the V4 MODIS LST/emissivity products (MYD11B1 for Aqua and MOD11B1 for Terra) from the day/night LST algorithm are higher by 1–1.7 K (standard deviation around 0.6 K) in comparisons to the 5‐km grid aggregated values of the LSTs in the 1‐km products, which is consistent with the results of a comparison of emissivities. On average, the emissivity in MYD11B1 (MOD11B1) is 0.0107 (0.0167) less than the ground‐based measurements, which is equivalent to a 0.64 K (1.25 K) overestimation of LST around the average value of 285 K. Knowledge obtained from the evaluation of MODIS LST/emissivity retrievals provides useful information for the improvement of the MODIS LST day/night algorithm. Improved performance of the refined (V5) day/night algorithm was demonstrated with the Terra MODIS data in May–June 2004.     

Validating MODIS land surface temperature products using long-term nighttime ground measurements

The Moderate Resolution Imaging Spectroradiometer (MODIS), onboard the NASA Terra and Aqua Earth Observing System satellites, provides multiple land surface temperature (LST) products on a daily basis. However, these products have not been adequately validated. This paper presents preliminary results of validating two MODIS Terra daily LST products, MOD11_L2 (version 4) and MOD07_L2 (version 4), using the FLUXNET and Carbon Europe Integrated Project (CarboEurope-IP) long-term ground measurements over eight vegetated sites. Since ground-measured LSTs were only available over one fixed point in each validation site, the study was carefully designed to mitigate the scale mismatch issue by using nighttime ground measurements concurrent to more than 1800 MODIS Terra overpasses.The preliminary results show that MOD11_L2 LSTs have smaller absolute biases and root mean squared errors (RMSE) than those of MOD07_L2 LSTs in most cases. The match of MOD11_L2 LSTs with ground measurements in the Brookings, Audubon, Canaan Valley, and Black Hills sites is good, yielding absolute biases less than 0.8 °C and RMSEs less than 1.7 °C. In the Fort Peck, Hainich, Tharandt, and Bondville sites, MOD11_L2 LSTs were underestimated by 2-3 °C. Biases in MOD11_L2 LSTs correlate to those in MOD07_L2 LSTs. Since the MOD07_L2 LST product is one of the input parameters to the MOD11_L2 LST algorithm, biases in MOD11_L2 LSTs may be influenced by biases in MOD07_L2 LSTs. The errors in both products depend weakly on sensor view zenith angle but are independent of surface air temperature, humidity, wind speed, and soil moisture.     

Synergistic use of the two-temperature and split-window methods for land-surface temperature retrieval

A strategy is presented with the aim of achieving an operational accuracy of 2.0 K in land-surface temperature (LST) from METEOSAT Second Generation (MSG)/Spinning Enhanced Visible and Infrared Imager (SEVIRI) data. The proposed method is based on a synergistic usage of the split-window (SW) and the two-temperature method (TTM) and consists in combining the use of a priori land-surface emissivity (LSE) estimates from emissivity maps with LST estimates obtained from SW method with the endeavour of defining narrower and more reliable ranges of admissible solutions before applying TTM. The method was tested for different surface types, according to SEVIRI spatial resolution, and atmospheric conditions occurring within the MSG disc. Performance of the method was best in the case of relatively dry atmospheres (water-vapour content less than 3 g cm^?2), an important feature since in this case SW algorithms provide the worst results because of their sensitivity to uncertainties in surface emissivity. The hybrid method was also applied using real MSG/SEVIRI data and then validated with the Moderate resolution Imaging Spectroradiometer (MODIS)/Terra LST/LSE Monthly Global 0.05° geographic climate modeling grid (CMG) product (MOD11C3) generated by the day/night algorithm. The LST and LSE retrievals from the hybrid-method agree well (bias and root mean square error (RMSE) of??0.2 K and 1.4 K for LST, and around 0.003–0.02 and 0.009–0.02 for LSE) with the MOD11C3 product. These figures are also in conformity with the MOD11C3 performance at a semi-desert where LST (LSE) values is 1–1.7 K (0.017) higher (less) than the ground-based measurements.     

A three-channel algorithm for retrieving night-time land surface temperature from MODIS data under thin cirrus clouds

Thin cirrus clouds are dominated by non-spherical ice crystals with an effective emissivity of less than 0.5. Until now, the influences of clouds were not commonly considered in the development of algorithms for retrieving land-surface temperature (LST). However, numerical simulations showed that the influence of thin cirrus clouds could lead to a maximum LST retrieval error of more than 14 K at night if the cirrus optical depth (COD) at 12 μm was equal to 0.7 (cirrus emissivity equivalent to 0.5). To obtain an accurate estimate of the LST under thin cirrus using satellite infrared data, a nonlinear three-channel LST retrieval algorithm was proposed based on a widely used two-channel algorithm for clear-sky conditions. The variations in the cloud top height, COD, and effective radius of cirrus clouds were considered in this three-channel LST retrieval algorithm. Using Moderate Resolution Imaging Spectroradiometer (MODIS) channels 20, 31, and 32 (centred at 3.8, 11.0, and 12.0 μm, respectively) and the corresponding land surface emissivities (LSEs), the simulated data showed that this algorithm could obtain LSTs with root mean square errors (RMSEs) of less than 2.8 K when the COD at 12 μm is less than 0.7 and the viewing zenith angle (VZA) is less than 60°. In addition, a sensitivity analysis of the proposed algorithm showed that the total LST errors, including errors from the uncertainties in input parameters and algorithm error, were nearly the same as the algorithm error itself. Some lake surface water temperatures measured in Lake Superior and Lake Erie were used to test the performance of the proposed LST retrieval algorithm. The results showed that the proposed nonlinear three-channel algorithm could be used for estimating LST under thin cirrus with an RMSE of less than 2.8 K.     

A generalized split-window algorithm for land surface temperature estimation from MSG-2/SEVIRI data

This paper aims to determine land surface temperature (LST) using data from a spinning enhanced visible and infrared imager (SEVIRI) on board Meteosat Second Generation 2 (MSG-2) by using the generalized split-window (GSW) algorithm. Coefficients in the GSW algorithm are pre-determined for several overlapping sub-ranges of the LST, land surface emissivity (LSE), and atmospheric water vapour content (WVC) using the data simulated with the atmospheric radiative transfer model MODTRAN 4.0 under various surface and atmospheric conditions for 11 view zenith angles (VZAs) ranging from 0° to 67°. The results show that the root mean square error (RMSE) varies with VZA and atmospheric WVC and that the RMSEs are within 1.0 K for the sub-ranges in which the VZA is less than 30° and the atmospheric WVC is less than 4.25 g cm^?2. A sensitivity analysis of LSE uncertainty, atmospheric WVC uncertainty, and instrumental noise (NEΔT) is also performed, and the results demonstrate that LSE uncertainty can result in a larger LST error than other uncertainties and that the total error for the LST is approximately 1.21 and 1.45 K for dry atmosphere and 0.86 and 2.91 K for wet atmosphere at VZA = 0° and at VZA = 67°, respectively, if the uncertainty in the LSE is 1% and that in the WVC is 20%. The GSW algorithm is then applied to the MSG-2 – SEVIRI data with the LSE determined using the temperature-independent spectral indices method and the WVC either determined using the measurements in two split-window channels or interpolated temporally and spatially using European Centre for Medium Range Weather Forecasting (ECMWF) data. Finally, the SEVIRI LST derived in this paper (SEVIRI LST1) is evaluated through comparisons with the SEVIRI LST provided by the land surface analysis satellite applications facility (LSA SAF) (SEVIRI LST2) and the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product (MOD11B1 LST product). The results show that more than 80% of the differences between SEVIRI LST1 and SEVIRI LST2 are within 2 K, and approximately 70% of the differences between SEVIRI LST1 and MODIS LST are within 4 K. Furthermore, compared to MODIS LST, for four specific areas with different land surfaces, our GSW algorithm overestimates the LST by up to 1.0 K for vegetated surfaces and by 1.3 K for bare soil.     

针对MODIS 数据的地表温度非线性迭代反演方法

地表温度是气象、水文、生态等研究领域中的一个重要参数。构建了MODIS31/ 32 波段的热辐射传输方程, 讨论了方程的数值迭代解法, 提出了针对MODIS 数据地表温度的非线性迭代反演方法, 并介绍了大气透过率和地表比辐射率这两个中间参数的估计方法。误差及敏感性分析表明,提出的方法对大气透过率和地表比辐射率都不敏感, 反演精度优于传统的线性分裂窗算法。     

Downscaling MODIS LST in the East African mountains using elevation gradient and land-cover information

Land-surface temperature (LST) is strongly affected by altitude and surface albedo. In mountain regions where steep slopes and heterogeneous land cover are predominant, LST can vary significantly within short distances. Although remote sensing currently provides opportunities for monitoring LST in inaccessible regions, the coarse resolution of some sensors may result in large uncertainties at sub-pixel scales. This study aimed to develop a simple methodology for downscaling 1 km Moderate Resolution Spectroradiometer (MODIS) LST pixels, by accounting for sub-pixel LST variation associated with altitude and land-cover spatial changes. The approach was tested in Mount Kilimanjaro, Tanzania, where changes in altitude and vegetation can take place over short distances. Daytime and night-time MODIS LST estimates were considered separately. A digital elevation model (DEM) and normalized difference vegetation index (NDVI), both at 250 m spatial resolution, were used to assess altitude and land-cover changes, respectively. Simple linear regressions and multivariate regressions were used to quantify the relationship between LST and the independent variables, altitude and NDVI. The results show that, in Kilimanjaro, altitude variation within the area covered by a 1 km MODIS LST pixel can be up to ±300 m. These altitude changes can cause sub-pixel variation of up to ±2.13°C for night-time and ±2.88°C for daytime LST. NDVI variation within 1 km pixels ranged between –0.2 and 0.2. For night-time measurements, altitude explained up to 97% of LST variation, while daytime LST was strongly affected by land cover. Using multivariate regressions, the combination of altitude and NDVI explained up to 94% of daytime LST variation in Kilimanjaro. Finally, the downscaling approach proposed in this study allowed an improved representation of the influence of landscape features on local-scale LST patterns.     

A semi-empirical approach for the estimation of land-surface emissivity from satellite data based on spectral index fusion using ensemble regression

Land Surface Emissivity (LSE) is a key parameter in the thermal remote sensing, with several important applications, most notably in Land Surface Temperature (LST) estimation. This paper presents a semi-empirical method of LSE estimation from remote sensing data based on a fusion of spectral indices using the ensemble regression methods. The performance of the proposed method for Moderate Resolution Imaging Spectroradiometer (MODIS), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Operational Land Imager (OLI) and Thermal Infrared Sensor (TIRS) data was evaluated and compared with other semi-empirical methods developed for these sensors. The proposed method was designed in four stages. In the first stage, the reflectance of non-thermal bands and emissivity of thermal bands were simulated for different classes using the ASTER spectral library and the spectral response function of each sensor. In the second stage, the dataset to be used for the training of ensemble regression was arranged by calculating a number of spectral indices, which constitute the feature space along with non-thermal bands. In the third stage, the regression between emissivity of thermal bands of each sensor and the features extracted in the second stage was derived by the use of bagging, boosting and Random Forest (RF) regression methods. In the final stage Using Normalized Difference Vegetation Index (NDVI) values, the image was categorized into three classes including vegetation, non-vegetation and mixture areas using conditions NDVI > 0.5, NDVI < 0.2 and 0.2 ≤ NDVI ≤ 0.5, respectively. The non-vegetation class was then categorized to soil, rock, and man-made classes using land use map. The spectral indices of these classes were then calculated, and the corresponding model trained in the third stage was used to estimate the LSE for that band. The results of LSE estimations were compared with the standard product of each sensor. Due to the lack of standard product for Landsat-8, the ASTER product was used as a substitute. For better analysis, the proposed method was also evaluated with other semi-empirical methods developed for MODIS, ASTER and OLI/TIRS sensors. This evaluation showed that the lowest Root Mean Square Error (RMSE) values for OLI/TIRS bands 10 and 11 are 0.0070 and 0.0075 obtained, respectively, by bagging and RF regression methods. For ASTER bands 13 and 14, the lowest RMSE values of 0.0078 and 0.0077 are both obtained by RF regression. For MODIS bands 31 and 32, the lowest RMSE values are 0.0053 and 0.0049 and obtained by boosting method. A comparison between the proposed method and other semi-empirical methods provided for these sensors demonstrated the ability of the method to improve the RMSE by up to 0.5%. Regarding the higher accuracy and applicability of the proposed method, it can serve as an effective and efficient means of estimating LSE using remote sensing data.     

Evaluating NDVI-based emissivities of MODIS bands 31 and 32 using emissivities derived by Day/Night LST algorithm

Surface emissivity estimation is a significant factor for the land surface temperature estimation from remotely sensed data. For fully vegetated surfaces, the emissivity estimation is performed in a simple manner since the emissivity is relatively uniform. However, for arid land with sparse vegetation, the estimation is more complicated since the emissivity of the exposed soil and rock is highly variable. In this study, mean and difference emissivity for bands 31 and 32 of MODIS sensor have been derived based on NDVI values. First, the NDVI thresholds have been determined to separate bare soil, partially vegetated soil and fully vegetated land. Then regression relations have been derived to estimate mean and difference emissivity of the bare soil samples and partially vegetated surfaces. A constant emissivity is also used for fully vegetated area. Along with the correlations, standard deviations of the regression relations have been examined for a set of representative soil types. Standard deviations smaller than 0.003 in mean emissivity and smaller than 0.004 in difference emissivity are resulted in regression linear relations. Evaluation of the NDVI derived regression relations has been performed using the results of MODIS Day/Night Land Surface Temperature (LST) algorithm on a pair of MODIS images. Using around 45,500 pixels with different soil and land cover types, emissivity of each pixel in bands 31 and 32 have been estimated. The calculated emissivities have been compared with emissivities calculated by MODIS Day/Night LST algorithm. Biases and standard deviations of NDVI-based relations show relatively high agreement for mean and difference emissivity relations with Day/Night method results. It may be concluded that the proposed algorithm can be used as a rather simple alternative to complex emissivity estimation algorithms.     

Investigating the effects of soil moisture on thermal infrared land surface temperature and emissivity using satellite retrievals and laboratory measurements

This study investigates the effects of soil moisture (SM) on thermal infrared (TIR) land surface emissivity (LSE) using field- and satellite-measurements. Laboratory measurements were used to simulate the effects of rainfall and subsequent surface evaporation on the LSE for two different sand types. The results showed that the LSE returned to the dry equilibrium state within an hour after initial wetting, and during the drying process the SM changes were uncorrelated with changes in LSE. Satellite retrievals of LSE from the Atmospheric Infrared Sounder (AIRS) and Moderate Resolution Imaging Spectroradiometer (MODIS) were examined for an anomalous rainfall event over the Namib Desert in Namibia during April, 2006. The results showed that increases in Advanced Microwave Scanning Radiometer (AMSR-E) derived soil moisture and Tropical Rainfall Measuring Mission (TRMM) rainfall estimates corresponded closely with LSE increases of between 0.08-0.3 at 8.6 µm and up to 0.03 at 11 µm for MODIS v4 and AIRS products. This dependence was lost in the more recent MODIS v5 product which artificially removed the correlation due to a stronger coupling with the split-window algorithm, and is lost in any algorithms that force the LSE to a pre-determined constant as in split-window type algorithms like those planned for use with the NPOESS Visible Infrared Imager Radiometer Suite (VIIRS). Good agreement was found between MODIS land surface temperatures (LSTs) derived from the Temperature Emissivity Separation (TES) and day/night v4 algorithm (MOD11B1 v4), while the split-window dependent products (MOD11B1 v5 and MOD11A1) had cooler mean temperatures on the order of 1-2 K over the Namib Desert for the month of April 2006.     

Disaggregation of LST over India: comparative analysis of different vegetation indices

The non-availability of high-spatial-resolution thermal data from satellites on a consistent basis led to the development of different models for sharpening coarse-spatial-resolution thermal data. Thermal sharpening models that are based on the relationship between land-surface temperature (LST) and a vegetation index (VI) such as the normalized difference vegetation index (NDVI) or fraction vegetation cover (FVC) have gained much attention due to their simplicity, physical basis, and operational capability. However, there are hardly any studies in the literature examining comprehensively various VIs apart from NDVI and FVC, which may be better suited for thermal sharpening over agricultural and natural landscapes. The aim of this study is to compare the relative performance of five different VIs, namely NDVI, FVC, the normalized difference water index (NDWI), soil adjusted vegetation index (SAVI), and modified soil adjusted vegetation index (MSAVI), for thermal sharpening using the DisTrad thermal sharpening model over agricultural and natural landscapes in India. Multi-temporal LST data from Landsat-7 Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS) sensors obtained over two different agro-climatic grids in India were disaggregated from 960 m to 120 m spatial resolution. The sharpened LST was compared with the reference LST estimated from the Landsat data at 120 m spatial resolution. In addition to this, MODIS LST was disaggregated from 960 m to 480 m and compared with ground measurements at five sites in India. It was found that NDVI and FVC performed better only under wet conditions, whereas under drier conditions, the performance of NDWI was superior to other indices and produced accurate results. SAVI and MSAVI always produced poorer results compared with NDVI/FVC and NDWI for wet and dry cases, respectively.     

Soil moisture estimation using MODIS and ground measurements in eastern China

Recent technological advances in remote sensing have shown that soil moisture can be measured by microwave remote sensing under some topographic and vegetation cover conditions. However, current microwave technology limits the spatial resolution of soil moisture data. It has been found that the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) are related to surface soil moisture; therefore, a relationship between ground observed soil moisture and satellite NDVI and LST products can be developed. Three years of 1 km NDVI and LST products from the Moderate Resolution Imaging Spectroradiometer (MODIS) have been combined with ground measured soil moisture to determine regression relationships at a 1 km scale. Results show that MODIS NDVI and LST are strongly correlated with the ground measured soil moisture, and regression relationships are land cover and soil type dependent. These regression relationships can be used to generate soil moisture estimates at moderate resolution for study area.     

Estimation of instantaneous net surface longwave radiation from MODIS cloud-free data

This paper develops a statistical regression method to estimate the instantaneous Downwelling Surface Longwave Radiation (DSLR) for cloud-free skies using only the satellite-based radiances measured at the Top Of the Atmosphere (TOA), and subsequently combines the DSLR with the MODIS land surface temperature/emissivity products (MOD11_L2) to estimate the instantaneous Net Surface Longwave Radiation (NSLR). The proposed method relates the DSLR directly to the TOA radiances in the MODIS Thermal InfraRed (TIR) channels provided that the terrain altitude and the satellite Viewing Zenith Angle (VZA) are known. The simulation analysis shows that the instantaneous DSLR could be estimated by the proposed method with the Root Mean Square Error (RMSE) of 12.4 W/m² for VZA = 0 and terrain altitude z = 0 km. Similar results are obtained for the other VZAs and altitudes. Considering the MODIS instrumental errors of 0.25 K for the TOA brightness temperatures in channels 28, 33 and 34, and of 0.05 K for channels 29 and 31, and of 0.35 K for channel 36, the overall retrieval accuracy in terms of the RMSE is decreased to 13.1 W/m² for the instantaneous DSLR. Moreover, a comparison of MODIS derived DSLR and NSLR are done with the field measurements made at six sites of the Surface Radiation Budget Network (SURFRAD) in the United States for days with cloud-free conditions at the moment of MODIS overpass in 2006. The results show that the bias, RMSE and the square of the correlation coefficient (R²) between the MODIS derived DSLR with the proposed method and the field measured DSLR are 20.3 W/m², 30.1 W/m² and 0.91 respectively, and bias = 11.7 W/m², RMSE = 26.1 W/m² and R² = 0.94 for NSLR. In addition, the scheme proposed by Bisht et al. [Bisht, G., Venturini, V., Islam, S., & Jiang, L. (2005). Estimation of the net radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data for clear-sky days. Remote Sensing of Environment, 97, 52-67], which requires the MODIS atmospheric profile product (MOD07) and also the MODIS land surface temperature/emissivity products (MOD11_L2) as inputs, is used to estimate the instantaneous DSLR and NSLR for comparison with the field measurements as well as the MODIS derived DSLR and NSLR using our proposed method. The results of the comparisons show that, at least for our cases, our proposed method for estimating DSLR from the MODIS radiances at the TOA and the resultant NSLR gives results comparable to those estimated with Bisht et al.'s scheme [Bisht, G., Venturini, V., Islam, S., & Jiang, L. (2005). Estimation of the net radiation using MODIS (Moderate Resolution Imaging Spectroradiometer) data for clear-sky days. Remote Sensing of Environment, 97, 52-67].     

Derivation of land surface temperatures from MODIS data using the general split‐window technique

Fast Atmospheric Signature Code (FASCODE), a line‐by‐line radiative transfer programme, was used to simulate Moderate Resolution Imaging Spectroradiometer (MODIS) data at wavelengths 11.03 and 12.02 µm to ascertain how accurately the land surface temperature (LST) can be inferred, by the split‐window technique (SWT), for a wide range of atmospheric and terrestrial conditions. The approach starts from the Ulivieri algorithm, originally applied to Advanced Very High Resolution Radiometer (AVHRR) channels 4 and 5. This algorithm proved to be very accurate compared to several others and takes into account the atmospheric effects, in particular the water vapour column (WVC) amount and a non‐unitary surface emissivity. Extended simulations allowed the determination of new coefficients of this algorithm appropriate to MODIS bands 31 and 32, using different atmospheric conditions. The algorithm was also improved by removing some of the hypothesis on which its original expression was based. This led to the addition of a new corrective term that took into account the interdependence between water vapour and non‐unitary emissivity values and their effects on the retrieved surface temperature. The LST products were validated within 1 K with in situ LSTs in 11 cases.     

Disaggregation of land surface temperature over a heterogeneous urban and surrounding suburban area: a case study in Shanghai,China

Many application fields need land surface temperature (LST) with simultaneous high spatial and temporal resolution, which can be achieved through the disaggregation technique. Most published methods built an assumed scale-independent relationship between LST and predictor variables derived from coarse spatial resolution data. However, LST disaggregation in the heterogeneous areas, especially urban areas, is very difficult to achieve and there are few studies on it. In this article, we propose an adjusted stratified stepwise regression method for temperature disaggregation in urban areas. Landsat Enhanced Thematic Plus (ETM+) data from Shanghai, China, were used to construct remote-sensing indices that are related to LST variance and retrieve LST at 60 and 480 m spatial resolution, respectively. Different stepwise regression models at 480 m resolution were built for two stratified regions according to normalized difference vegetation index (NDVI) distribution, and then each independent variable at 60 m resolution was adjusted to calculate disaggregated LST by considering its relationship with the 480 m resolution image. By using LST retrieved directly from ETM+ band 6 at 60 m spatial resolution as the reference, the proposed method comprising resampling disaggregation, the thermal data sharpening model (TsHARP)/disaggregation procedure for radiometric surface temperature (DisTrad) technique, and the LST-principal component analysis (PCA) regression algorithm were verified and compared. The results show that the temperature distribution estimated using the proposed method is most consistent with that of the reference LST in this heterogeneous study area, and that the precision improves significantly, especially for the low vegetation fraction region.     

Multi-index-based soil moisture estimation using MODIS images

The suitability of using Moderate Resolution Imaging Spectroradiometer (MODIS) images for surface soil moisture estimation to investigate the importance of soil moisture in different applications, such as agriculture, hydrology, meteorology and natural disaster management, is evaluated in this study. Soil moisture field measurements and MODIS images of relevant dates have been acquired. Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Normalized Difference Water Index (NDWI) are calculated from MODIS images. In addition, MODIS Land Surface Temperature (LST) data (MOD11A1) are used in this analysis. Four different soil moisture estimation models, which are based on NDVI–LST, EVI–LST, NDVI–LST–NDWI and EVI–LST–NDWI, are developed and their accuracies are assessed. Statistical analysis shows that replacing EVI with NDVI in the model that is based on LST and NDVI increases the accuracy of soil moisture estimation. Accuracy evaluation of soil moisture estimation using check points shows that the model based on LST, EVI and NDWI values gives a higher accuracy than that based on LST and EVI values. It is concluded that the model based on the three indices is a suitable model to estimate soil moisture through MODIS imagery.     

Surface temperature and water vapour retrieval from MODIS data

This paper gives operational algorithms for retrieving sea (SST), land surface temperature (LST) and total atmospheric water vapour content (W) using Moderate Resolution Imaging Spectroradiometer (MODIS) data. To this end, the MODTRAN 3.5 radiative transfer program was used to predict radiances for MODIS channels 31, 32, 2, 17, 18 and 19. To analyse atmospheric effects, a simulation with a set of radiosonde observations was used to cover the variability of surface temperature and water vapour concentration on a worldwide scale. These simulated data were split into two sets (DB1 and DB2), the first one (DB1) was used to fit the coefficients of the algorithms, while the second one (DB2) was used to test the fitted coefficients. The results show that the algorithms are capable of producing SST and LST with a standard deviation of 0.3 K and 0.7 K if the satellite data are error free. The LST product has been validated with in situ data from a field campaign carried out in the Mississippi (USA), the results show for the LST algorithm proposed a root mean square error lower than 0.5K. Regarding water vapour content, a ratio technique is proposed, which is capable of estimating W from the absorbing channels at 0.905, 0.936, and 0.94,µm, and the atmospheric window channel at 0.865,µm, with a standard deviation (in the comparison with radiosonde observations) of 0.4 g cm^?2.     

Classification-based emissivity for land surface temperature measurement from space

Classification-based global emissivity is needed for the National Aeronautics and Space Administration Earth Observing System Moderate Resolution Imaging Spectrometer (NASA EOS/MODIS) satellite instrument land surface temperature (LST) algorithm. It is also useful for Landsat, the Advanced Very High Resolution Radiometer (AVHRR) and other thermal infrared instruments and studies. For our approach, a pixel is classified as one of fourteen 'emissivity classes' based on the conventional land cover classification and dynamic and seasonal factors, such as snow cover and vegetation index. The emissivity models we present provide a range of values for each emissivity class by combining various spectral component measurements with structural factors. Emissivity statistics are reported for the EOS/MODIS channels 31 and 32, which are the channels that will be used in the LST split-window algorithm.