A practical split‐window algorithm for retrieving land‐surface temperature from MODIS data

This paper presents a practical split‐window algorithm utilized to retrieve land‐surface temperature (LST) from Moderate‐resolution Imaging Spectroradiometer (MODIS) data, which involves two essential parameters (transmittance and emissivity), and a new method to simplify Planck function has been proposed. The method for linearization of Planck function, how to obtain atmosphere transmittance from MODIS near‐infrared (NIR) bands and the method for estimating of emissivity of ground are discussed with details. Sensitivity analysis of the algorithm has been performed for the evaluation of probable LST estimation error due to the possible errors in water content and emissivity. Analysis indicates that the algorithm is not sensitive to these two parameters. Especially, the average LST error is changed between 0.19–1.1°C when the water content error in the simulation standard atmosphere changes between ?80 and 130%. We confirm the conclusion by retrieving LST from MODIS image data through changing retrieval water content error. Two methods have been used to validate the proposed algorithm. Results from validation and comparison using the standard atmospheric simulation and the comparison with the MODIS LST product demonstrate the applicability of the algorithm. Validation with standard atmospheric simulation indicates that this algorithm can achieve the average accuracy of this algorithm is about 0.32°C in LST retrieval for the case without error in both transmittance and emissivity estimations. The accuracy of this algorithm is about 0.37°C and 0.49°C respectively when the transmittance is computed from the simulation water content by exponent fit and linear fit respectively.     

Split‐window algorithm for land surface temperature estimation from MSG1‐SEVIRI data

This letter addresses the land surface temperature (LST) estimation from the data acquired by the spinning enhanced visible and infra‐red imager (SEVIRI) on board the first geostationary satellite meteosat second generation (MSG1) using the generalized split‐window algorithm proposed by Wan and Dozier (1996 Wan, Z. and Dozier, J. 1996. A generalized split‐window algorithm for retrieving land‐surface temperature from space.. IEEE Transactions on Geoscience and Remote Sensing, 34: 892–905. [Crossref], [Web of Science ®] , [Google Scholar]). The generalized split‐window algorithm was developed for eight view zenith angles (VZAs) by dividing the LST, the average emissivity (ε) and the column water vapour (W) into several sub‐ranges to improve the LST estimating accuracy. The simulated results show that the root mean square errors (RMSEs) increase with VZAs and W, and they are less than 1.0 K for all sub‐ranges with the VZA less than 45°, or for the sub‐ranges with VZA less than 60° and W less than 3.5 cm. The land surface emissivities (LSEs) and W used in the generalized split‐window algorithm were estimated from MSG1‐SEVIRI data by the method developed by us in previous studies. The results at the four specific locations show that the LSEs were well derived, and the LSTs estimated from MSG1‐SEVIRI data are basically consistent with the ones extracted from MODIS/Terra LST products.     

Implementation of GOES‐based land surface temperature diurnal cycle to AVHRR

To enable frequent estimates of land surface temperature (LST) from satellite measurements, and to characterize the land surface temperature diurnal (LSTD) cycle, two new LST retrieval algorithms are applied to observations from the Geostationary Operational Environmental Satellite (GOES). Evaluation against the atmospheric radiation measurement (ARM) observations indicates that LST can be determined from the real‐time GOES‐8 observations within r.m.s. accuracy of about 2 K. In order to combine the advantages of geostationary and polar orbiting instruments, the LSTD estimated from GOES can be incorporated into LST retrievals from polar orbiting imager National Oceanic and Atmospheric Administration/Advanced Very High Resolution Radiometer (NOAA/AVHRR) using a newly proposed fitting algorithm, with r.m.s. errors close to those obtained directly from GOES‐8.     

Near‐surface air temperature estimation from ASTER data based on neural network algorithm

An algorithm based on the radiance transfer model (MODTRAN4) and a dynamic learning neural network for estimation of near‐surface air temperature from ASTER data are developed in this paper. MODTRAN4 is used to simulate radiance transfer from the ground with different combinations of land surface temperature, near surface air temperature, emissivity and water vapour content. The dynamic learning neural network is used to estimate near surface air temperature. The analysis indicates that near surface air temperature cannot be directly and accurately estimated from thermal remote sensing data. If the land surface temperature and emissivity were made as prior knowledge, the mean and the standard deviation of estimation error are both about 1.0 K. The mean and the standard deviation of estimation error are about 2.0 K and 2.3 K, considering the estimation error of land surface temperature and emissivity. Finally, the comparison of estimation results with ground measurement data at meteorological stations indicates that the RM‐NN can be used to estimate near surface air temperature from ASTER data.     

ASTER‐derived emissivity and coal‐fire related surface temperature anomaly: a case study in Wuda,north China

Subsurface and surface coal fires form serious environmental, economic and safety problems in coal‐producing countries like China and India. Remote sensing offers the possibility of detecting and studying thermal anomalies due to coal fires. Emissivity plays an important role in determining the surface temperature of a body using remotely sensed data. In the present study an attempt is made to use satellite‐derived emissivity to estimate the surface temperature in Wuda, north China. With the use of multispectral thermal Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data (five bands in 8.125–11.65 µm region) in combination with a Temperature/Emissivity Separation (TES) algorithm, the anomalous pixels due to coal fires can be extracted from the background to achieve a qualitative study of coal fires. In the present study, during night‐time overpass of ASTER, satellite images have been recorded and simultaneous field measurements were collected. These field measurements were used to process the satellite thermal data and to validate the results obtained. Using the TES approach, satellite‐based temperature corresponded well with actual field measurements at selected locations.     

Simultaneous non‐linear retrieval of atmospheric profiles and surface skin temperature from MODIS infrared radiances in Taiwan Strait

A non‐linear iterative physical algorithm that simultaneously retrieves atmospheric temperature, water vapour distribution and surface skin temperature from Moderate Resolution Imaging Spectroradiometer (MODIS) longwave infrared radiances is presented. The retrieval algorithm uses clear‐sky radiances measured by MODIS in Taiwan Strait for both day and night, and shows that it is capable of retrieving medium‐scale atmospheric temperature and water vapour. Sea surface temperature is retrieved with an accuracy similar to that achieved by MOD07 products. Evaluation of retrieval total precipitable water vapour (TPW) is performed by comparisons with retrievals from MOD07 products and data from a ground‐based sunphotometer. These show that MODIS retrieval of TPW, in general, agrees with other sounder retrievals of TPW. The total totals index (TTI) distribution retrieved from MODIS data is similar to that from MOD07 products.     

Sea surface effects on the sea surface temperature estimation by remote sensing—Part 2

By referring to the sea surface temperature profiler buoy (SSTPB) data observed in Mutsu Bay, this study showed under calm and strong sunshine conditions, the vertical water temperature profile near the sea surface bends abruptly, and sea surface temperature detected by satellite remote sensing is not necessarily coincident to the bulk sea surface temperature. Besides the atmospheric effect, this effect causes another error in the estimation of sea surface temperature by remote sensing data known as the sea surface effect (SSE). As a sequel to a former paper, this paper is concerned with the investigation of the conditions which occur in the apparent SSE. Statistical analyses were directed to the total data set of SSTPB. The amount of SSE was evaluated by the water temperature difference between the uppermost surface and 1 m depth, and apparent SSE was identified to when the absolute value of the difference is larger than 0·5 °C. Apparent SSE was observed in the season from May to September. Its occurrence rate in May and June was about 40 per cent of the total days, and about 20 per cent in July. SSE grew when wind speed was less than 2 m s^?1 and the solar zenith angle is smaller than 30°. If either of those two conditions were violated, SSE easily transferred into reducing phase.     

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.     

Spatial variability of air temperature and appropriate resolution for satellite‐derived air temperature estimation

This study investigates the spatial variability of air temperature over Hong Kong using in situ air temperature recorded from a mobile traverse combined with an ASTER thermal satellite image. Three different degrees of urbanization in Hong Kong, including city downtown (Kowloon), suburban areas (Yuen Long and Shatin), and rural countryside (Tai Mo Shan and Lam Tsuen) are analysed. The spatially variable relationship between air and surface temperature was evaluated using two spatial averaging techniques, namely spatial resampling and buffering around air temperature points. The strength of the correlation coefficient was tested for every decreasing resolution and the appropriate spatial scales of air temperature in urban, suburban and rural areas were found to be 200 m, 450 m and 700 m, respectively. The differences in the spatial scales of air temperature in these regions are attributed mainly to structural factors of land cover such as city block size, building density and percentage of green areas, and secondarily to the climatic conditions being operating in, and which commonly typify these individual regions. Thus small scale lengths in the urban area corresponded to heterogeneous land cover, a well developed urban boundary layer, low wind speeds and a low lapse rate, whereas longer scale lengths were observed in suburban and rural areas having more homogeneous land cover, higher wind speeds and higher lapse rate.     

Generation of Landsat surface temperature product for China, 2000–2010

Land surface temperature (LST) is a key parameter in the physics of land surface processes on regional and global scales. Although there are MODIS and Landsat land surface reflectance products, there is no LST product for Landsat data due in part to many challenges in the development of an operational Landsat LST product generating system because Landsat possesses only one thermal infrared channel. The aim of this article is to describe the Landsat LST product generation project launched by the Centre for Earth Observation and Digital Earth (CEODE), Chinese Academy of Sciences. The generalized single-channel (SC) algorithm proposed by Jiménez-Muñoz et al. is used for LST retrieval. It is fully operational, requires minimal input data requirements, and has acceptable precision. Total atmospheric water vapour content is the key input parameter required by the SC algorithm. In this project, the MODIS water vapour product is employed to derive total atmospheric water vapour content. In this way, an operational Landsat LST product generation program was constructed by integration of MODIS and Landsat satellite imagery.     

Short‐term response of arctic vegetation NDVI to temperature anomalies

The effects of climate change on northern vegetation productivity need to be fully understood in order to reduce uncertainties in predicting vegetation distributions under different climate warming scenarios. Knowledge of the relationship between northern climate and vegetation productivity will also help provide a better understanding of changes in vegetation distributions as an indicator of climate change and variability. Vegetation productivity and biomass have been monitored using long‐term satellite earth observations, mostly using the Normalized Difference Vegetation Index (NDVI), as a cumulative indicator of all effects resulting from processes related to climate change, including changes in temperature, precipitation, and disturbance. In this paper, the investigation is focused on the short‐term effect of temperature anomalies on arctic and tree‐line transition vegetation productivity in both dry and humid regions of Canada. The analysis shows that several land‐cover types composed mainly of trees and shrubs exhibit a significant increase in NDVI with higher‐than‐normal temperatures in the preceding 10–40‐day period, while land‐cover types consisting of lichen and moss growing on mostly barren surfaces show a significant NDVI decrease with increased temperature. These trends are consistent with results reported in plot‐warming experiments in the north, which have shown that certain vegetation communities increase, while others decrease in cover fraction and biomass in response to warming. When land cover is grouped into increasing and decreasing NDVI with temperature and stratified by dry and humid regions of Canada, much of the dry region of northern Canada does not exhibit significant NDVI response to preceding temperature anomalies. It is expected that in the absence of disturbance or other limiting factors, an increased frequency of elevated temperature anomalies may eventually contribute to changes in vegetation biomass. A map of land‐cover types that have the potential to increase in biomass with climate warming and those that are vulnerable to decline is presented.     

Variability of fire‐induced changes in MODIS surface reflectance by land‐cover type in Borneo

This study investigates fire‐induced spectral changes detected by the Moderate Resolution Imaging Spectroradiometer (MODIS) in different land‐cover types in Borneo. Linear discriminant analysis is used to determine the most powerful band combinations among the MODIS reflective bands for discrimination between burnt and unburnt areas in each land‐cover type. The results show that the nature of fire‐induced changes is dependent on pre‐fire vegetation characteristics in this region. Bands 1 (0.64 µm), 2 (0.86 µm), and 7 (2.14 µm) are found to be the most sensitive bands in land‐cover types dominated by green vegetation, and consequently indices or combinations of indices using these three bands are potentially effective for burnt‐area detection in the majority of areas. In land‐cover types dominated by dry vegetation and soil, MODIS band 5 (1.24 µm) alone showed the greatest statistical separability and could not be significantly improved by any multiband index.     

Stability of surface reflectance scaling properties explored by using SPOT‐VGT data

VGT reflectance data acquired in four spectral bands (B0, B2, B3 and MIR) from 1998 to 2005 by SPOT‐VGT were investigated. VGT data are characterized by global coverage and consistent image quality and are particularly suitable for time series investigations. The analysis was carried out for a test area located in the island of Sardinia. The study area is covered by shrubs, mainly Mediterranean maquis, which is the typical vegetation cover of the Mediterranean basin. Deviations from uniform power‐law scaling were quantified by using detrended fluctuation analysis (DFA). The results indicate a larger non‐uniform scaling behaviour in the B0 channel. This can be applied to understanding radiation interactions at the surface as required for advancing weather forecasting, climate studies and environmental monitoring.     

Estimating surface solar radiation over complex terrain using moderate‐resolution satellite sensor data

It is well known that taking into account the influence of complex terrain is essential when using high‐resolution satellite remotely sensed data to estimate surface net solar radiation. This paper investigates whether this is also the case when using moderate‐resolution satellite remotely sensed data, such as Moderate Resolution Imaging Spectroradiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). Firstly, topographic data from a gridded digital elevation model, field measurements from the Tibetan Plateau, and results from the atmospheric 6S model are used to estimate surface incoming solar radiation over complex terrain. The associated error caused by not taking into account terrain complexity is then calculated, and the relative radiation error is estimated by standardizing the error. The results show that the standard deviation of the relative radiation error depends on the solar zenith angle, standard deviation of the height, and resolution of the digital elevation model (or resolution of the satellite sensor data). A single regression equation describes the change in the standard deviation of the relative radiation error with solar zenith angle, standard deviation of height, and resolution of the digital elevation model. This demonstrates that it is necessary to consider terrain complexity when using moderate‐resolution remotely sensed data.     

Retrieval of land and sea surface temperature using NOAA-11 AVHRR· data in north-eastern Brazil

The split-window method is investigated and a simple airborne atmospheric correction model for thermal data is proposed. Analysis shows that the atmospheric transmittance has a quadratic behaviour with the water vapour content in the first few kilometres of a wet atmosphere. The effect of the emissivity is evaluated for the retrieval of surface temperature using data from NOAA-11 AVHRR channels 4 and 5 for two extreme atmospheres. The results indicate that a spectral emissivity variation (Δε) in channels 4 and 5 of ±0.01 is not as important for a wet atmosphere as for a dry atmosphere. The split-window algorithm developed in this work has its parameters dependent on the atmospheric state and the values of these parameters are determined by using radiosonde profiles. Data from eighty-five radiosondes have been used to determine and check the local seasonal equatorial split-window parameters. The results of surface temperature retrieval show that the local seasonal equatorial and daily split-window parameters (given by daily radiosonde profiles) for NOAA-11 AVHRR data exhibit good agreement between their surface temperature results and the results of in situ measurements for two days. Comparisons with in situ measurements show that the maximum difference from retrieving a vegetated surface temperature using AVHRR data is less than 1.0°C in a wet atmosphere. Although the seasonal parameters have demonstrated a good performance when applied for two particular days, it does not indicate that they can be used successfully for other times when the atmospheric state differs from the average seasonal profile determined in this work. Evidence of this fact is shown through the variation of water vapour amount in the eighty-five radiosonde atmospheric profiles that have been analysed. This variation presents a wide range in water vapour from 2.8g cm^-2, to 4.92g cm^-2, which can significantly modify the retrieval of surface temperature using remote infrared sensors. Discussion of this problem is given in this paper.     

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.     

Covariation in subskin‐bulk temperature difference with environmental parameters in the north Indian Ocean

This study aims to explore the dependence of the ocean subskin–bulk temperature difference ΔT (T _subskin?T _2.5m) on environmental parameters over the north Indian Ocean. This was possible because of the fortuitous concurrence of two parallel programmes, viz., the Tropical Rainfall Measuring Mission (TRMM) carrying a Microwave Imager (TMI) and the Indian moored buoy programme, over 6 years (1998–2003). The environmental parameters considered in this study are total water vapour (TWV), cloud liquid water content (CLW), sea surface wind speed, bulk temperature (T _2.5m) and T _2.5m?T _air temperature difference, consisting of a composite data matrix of more than 3000 sets. The study revealed absence of any perceptible dependence on TWV and CLW. For T _2.5m between 24°C and 28°C, the mean Δ T is a decreasing function of T _2.5m. Data classification indicates that most of the cases of T _2.5m<28°C belong to Bay of Bengal during December to February. For the T _2.5m>28°C, Δ T is very small. Our results on the variation of Δ T with T _2.5m?T _air are linear and in opposite phase. The daytime Δ T variation over the north Indian Ocean displays a decreasing trend with increasing wind. The night‐time Δ T pattern, especially over the Bay of Bengal, is found to be very unusual: it remains insensitive to the variations in wind speed, and it is predominantly positive. Diurnal variation of Δ T under wind speed below 6 m s^?1 is studied for overall data set as well as for the individual season data set. The low wind cases reproduce onset of warming at 9 h local time and a peak around 15 h local time. The average maximum amplitude of Δ T is more than 0.34°C with a standard deviation of greater than 0.7°C. Diurnal warming during pre‐monsoon (post‐monsoon) is highest (lowest).     

Review Article. The sea surface thermal boundary layer and its relevance to the measurement of sea surface temperature by airborne and spaceborne radiometers†

This review examines the importance of the thermal boundary layer of the ocean to the measurement of sea surface temperature (SST) by satcllitc-bornc infrared (IR) radiometers. Attention is focused on the difference between the temperature of the top 01mm which is observed by radiometers, and the temperature found at depths between a few centimetres and one metre, which is that recorded as bulk SST by ship measurement techniques. The question addressed is whether corrections for this effect are necessary in the light of the accuracy of IR radiometers, and the demands made by the application of SST data.

A brief preview is made of the accuracy of the best presently available sensor for space observations of SST, the AVHRR, the associated atmospheric corrections and other sources of error. Current knowledge of the thermal boundary layer of the ocean is then presented in a thorough review of the scientific literature. The evidence of field observations suggests that the surface skin can be typically 0.1-0.5 degK cooler than the water a few centimetres deeper. Theoretical models of the effect are available and there is some understanding of the dependence of spatial and temporal variability of the skin effect on other environmental parameters, but there is a need for more systematic observation before prediction of the effect can confidently be achieved.

The existing and potential applications of SST in oceanography and meteorology are summarized, with emphasis on the required accuracy, in certain areas 0 2 degK being desirable. Following a survey of the likely capabilities of the next generation of spaceborne IR radiometers (notably the ATSR) it is concluded that while the inaccuracies of present systems tend to be greater than the skin effect, if future systems meet their specification then the skin temperature deviation will become a significant factor in the calibration procedure. In the light of this, the review concludes with some remarks regarding the value of further studies of the thermal skin effect.     

Two‐scale surface deformation analysis using the SBAS‐DInSAR technique: a case study of the city of Rome,Italy

We have exploited the capability of the differential synthetic aperture radar (SAR) interferometry (DInSAR) technique, referred to as Small BAseline Subset (SBAS) approach, to analyse surface deformation at two distinct spatial scales: a low resolution, large scale, and a fine resolution, local scale. At the large scale, the technique investigates DInSAR data with a ground resolution of the order of 100 m×100 m and leads to generate mean deformation velocity maps and associated time series for areas extending to some thousands of square kilometres. At the local scale, the technique exploits the SAR images at full spatial resolution (typically of the order of 5 m×20 m), detecting and analysing localized deformation phenomena. The study is focused on the city of Rome, Italy, and we used the ERS‐1/2 satellite radar data relevant to the 1995–2000 time period. The presented results demonstrate the capability of the SBAS approach to retrieve, from the low‐resolution DInSAR data, large‐scale deformation information leading to identify several sites affected by significant displacements. Our analysis permitted us to conclude that a major contribution to the detected displacements is due to the consolidation of the alluvial soils present in the area, mostly enforced by the buildings' overload. Furthermore, in a selected area, a detailed analysis was carried out by exploiting the full resolution DInSAR data. In this case we investigated deformation phenomena at the scale of single buildings. As key result we showed that differential displacements of few mm a^?1, affecting single man‐made structures or building complexes, could be detected, thus allowing to identify sites that may potentially be involved in critical situations.     

Near‐surface soil moisture retrieval from ASAR Wide Swath imagery using a Principal Component Analysis

The new ENVISAT Advanced Synthetic Aperture Radar (ASAR) Wide Swath Mode (WSM) data provide good potential for routinely monitoring near surface soil moisture due to the increased temporal revisit capability compared with ERS2 SAR. In this paper, we propose a Principal Components Analysis (PCA) to retrieve near‐surface soil moisture from ASAR WSM data. The results demonstrate that PCA transformation can at least partially separate or group the effect factors, such as surface roughness, land cover, vegetation, and topography within the limitations of our datasets. The second principal component which was consistent with soil moisture and rainfall‐runoff dynamics was representative of the soil moisture Saturation Potential Index over a large area. Validation against field measurements and against the UK Met Office Surface Exchange Scheme shows the retrieval performs with reasonable accuracy.