Zhihao Qin

A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region

Remote sensing of land surface temperature (LST) from the thermal band data of Landsat Thematic Mapper (TM) still remains unused in comparison with the extensive studies of its visible and near-infrared (NIR) bands for various applications. The brightness temperature can be computed from the digital number (DN) of TM6 data using the equation provided by the National Aeronautics and Space Administration (NASA). However, a proper algorithm for retrieving LST from the only one thermal band of the sensor still remains unavailable due to many difficulties in the atmospheric correction. Based on thermal radiance transfer equation, an attempt has been made in the paper to develop a mono-window algorithm for retrieving LST from Landsat TM6 data. Three parameters are required for the algorithm: emissivity, transmittance and effective mean atmospheric temperature. Method about determination of atmospheric transmittance is given in the paper through the simulation of atmospheric conditions with LOWTRAN 7 program. A practicable approach of estimating effective mean atmospheric temperature from local meteorological observation is also proposed in the paper when the in situ atmospheric profile data is unavailable at the satellite pass, which is generally the case in the real world especially for the images in the past. Sensitivity analysis of the algorithm indicates that the possible error of ground emissivity, which is difficult to estimate, has relatively insignificant impact on the probable LST estimation error i T, which is sensible to the possible error of transmittance i 6 and mean atmospheric temperature i T a . Validation of the simulated data for various situations of seven typical atmospheres indicates that the algorithm is able to provide an accurate LST retrieval from TM6 data. The LST difference between the retrieved and the simulated ones is less than 0.4°C for most situations. Application of the algorithm to the sand dunes across the Israel-Egypt border results in a reasonable LST estimation of the region. Based on this LST estimation, spatial variation of the interesting thermal phenomenon has been analysed for comparison of LST difference across the border. The result shows that the Israeli side does have significantly higher surface temperature in spite of its denser vegetation cover than the Egyptian side where bare sand is prevalent.

Derivation of split window algorithm and its sensitivity analysis for retrieving land surface temperature from NOAA-advanced very high resolution radiometer data

Retrieval of land surface temperature (LST) from advanced very high resolution radiometer (AVHRR) data is an important methodology in remote sensing. Several split window algorithms have been proposed in last two decades. In this paper we intend to present a better algorithm with less parameters and high accuacry. The algorithm involves only two essential parameters (transmittance and emissivity). The principle and method for the linearization of Plancks radiance equation, the mathematical derivation process of the algorithm, and the method for determining the atmospheric transmittance are discussed with details. Sensitivity analysis of the algorithm has been performed for evaluation of probable LST estimation error due to the possible errors in transmittance and emissivity. Results from the analysis indicate that the proposed algorithm is able to provide an accurate estimation of LST from AVHRR data. Assuming an error of 0.05 in atmospheric transmittance estimate and 0.01 in ground emissivity for the two AVHRR thermal channels, the average LST error with the algorithm is 1.1°C. Two methods have been used to validate the proposed algortihm. Comparison has also been done with the existing 11 algorithms in literature. Results from validation and comparison using the standard atmospheric simulation for various situations and the ground truth data sets demonstrate the applicability of the algorithm. According to the root mean square (RMS) errors of the retrieved LSTs from the measured or assumed LSTs, the proposed algorithm is among the best three. Considering the insignificant RMS error difference among the three, the proposed algorithm is better than the other two because they require more parameters for LST retrieval. Validation with standard atmospheric simulation indicates that this algorithm can achieve the accuacry of 0.25°C in LST retrieval for the case without error in both transmittance and emissivity estimates. The accuary of this algorithm is 1.75°C for the ground truth data set without precise in situ atmospheric water vapor contents. The accuracy increases to 0.24°C for another ground truth data set with precise in situ atmospheric water vapor contents. The much higher accuracy for this data set confirms the appplicability of the proposed algorithm as an alternative for the accurate LST retrieval from AVHRR data.

Derivation of Land Surface Temperature for Landsat-8 TIRS Using a Split Window Algorithm

Land surface temperature (LST) is one of the most important variables measured by satellite remote sensing. Public domain data are available from the newly operational Landsat-8 Thermal Infrared Sensor (TIRS). This paper presents an adjustment of the split window algorithm (SWA) for TIRS that uses atmospheric transmittance and land surface emissivity (LSE) as inputs. Various alternatives for estimating these SWA inputs are reviewed, and a sensitivity analysis of the SWA to misestimating the input parameters is performed. The accuracy of the current development was assessed using simulated Modtran data. The root mean square error (RMSE) of the simulated LST was calculated as 0.93 °C. This SWA development is leading to progress in the determination of LST by Landsat-8 TIRS.

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.

Progress in the remote sensing of land surface temperature and ground emissivity using NOAA-AVHRR data

The extensive requirement of landsurface temperature (LST) for environmental studies and management activities of the Earths resources has made the remote sensing of LST an important academic topi...

Remote Sensed Spectral Imagery to Detect Late Blight in Field Tomatoes

Late blight, caused by the fungal pathogen Phytophthora infestans, is a disease that quickly spreads in tomato fields under suitable weather conditions and can threaten the sustainability of tomato farming in California, USA. This paper explores the applicability of remotely sensed images to detect disease spectral anomalies for precision disease management. We used the indices approach and generated a 5-index image that we used to identify the disease in tomato fields based on information from field-collected spectra and linear combinations of the spectral indices. Field results indicated that we were able to identify five clusters in the image space with small overlaps of a few clusters. Using the identified 5-cluster scheme to classify the tomato field images, we were able to successfully separate the diseased tomatoes from the healthy ones before economic damage was caused. Hence, the method based on a 5-index image may significantly enhance the capability of multispectral remote sensing for disease discrimination at the field level.

An Improved Mono-Window Algorithm for Land Surface Temperature Retrieval from Landsat 8 Thermal Infrared Sensor Data

The successful launch of the Landsat 8 satellite with two thermal infrared bands on February 11, 2013, for continuous Earth observation provided another opportunity for remote sensing of land surface temperature (LST). However, calibration notices issued by the United States Geological Survey (USGS) indicated that data from the Landsat 8 Thermal Infrared Sensor (TIRS) Band 11 have large uncertainty and suggested using TIRS Band 10 data as a single spectral band for LST estimation. In this study, we presented an improved mono-window (IMW) algorithm for LST retrieval from the Landsat 8 TIRS Band 10 data. Three essential parameters (ground emissivity, atmospheric transmittance and effective mean atmospheric temperature) were required for the IMW algorithm to retrieve LST. A new method was proposed to estimate the parameter of effective mean atmospheric temperature from local meteorological data. The other two essential parameters could be both estimated through the so-called land cover approach. Sensitivity analysis conducted for the IMW algorithm revealed that the possible error in estimating the required atmospheric water vapor content has the most significant impact on the probable LST estimation error. Under moderate errors in both water vapor content and ground emissivity, the algorithm had an accuracy of ~1.4 K for LST retrieval. Validation of the IMW algorithm using the simulated datasets for various situations indicated that the LST difference between the retrieved and the simulated ones was 0.67 K on average, with an RMSE of 0.43 K. Comparison of our IMW algorithm with the single-channel (SC) algorithm for three main atmosphere profiles indicated that the average error and RMSE of the IMW algorithm were −0.05 K and 0.84 K, respectively, which were less than the −2.86 K and 1.05 K of the SC algorithm. Application of the IMW algorithm to Nanjing and its vicinity in east China resulted in a reasonable LST estimation for the region. Spatial variation of the extremely hot weather, a frequently-occurring phenomenon of an abnormal heat flux process in summer along the Yangtze River Basin, had been thoroughly analyzed. This successful application suggested that the IMW algorithm presented in the study could be used as an efficient method for LST retrieval from the Landsat 8 TIRS Band 10 data.

Numerical solution of a complete surface energy balance model for simulation of heat fluxes and surface temperature under bare soil environment

Surface energy balance model is an essential approach for heat flux and evaporation estimation in applied meteorology and hydrology. Due to the complexity of soil-air interface system, the model has been simplified for different purposes in many researches. A complete model with full description of its complex factor relationships and its numerical solution has not been yet implemented in practical use. This paper presents a complete surface energy balance model with its inner relations cited from different researches. The model couples soil temperature change simultaneously with soil moisture movement, which makes the solution of the model uneasy. A detailed methodology of numerical approximation to the complete model is presented in the study for practical use. Soil heat and latent heat fluxes in the model are determined according to both soil temperature change and soil moisture movement, which are described as two differential equations. Crank-Nicolson implicit method is used to expand the differential equations into two sets of simultaneous linear equations, which are then solved by applying Gausss elimination method. Latent heat flux is determined at the balance when evaporation from the surface is equal to the soil water loss. And surface temperature is estimated as the heat fluxes of the surface reaching the status of balance. The iterative computation of Newton-Raphson method is used to approximate latent heat flux and surface temperature from the balances. Based on this complexity of the models relationships, a detailed computation procedure of the model is proposed. The methodology has been validated through application to south Israeli desert for heat flux and surface temperature estimation. A good matching of the simulated soil temperature to the measured one proves the validity of the model and method used for its numerical solution.

Spatio-Temporal Dynamics of Land-Use and Land-Cover in the Mu Us Sandy Land, China, Using the Change Vector Analysis Technique

The spatial extent of desertified vs. rehabilitated areas in the Mu Us Sandy Land, China, was explored. The area is characterized by complex landscape changes that were caused by different drivers, either natural or anthropogenic, interacting with each other, and resulting in multiple consequences. Two biophysical variables, NDVI, positively correlated with vegetation cover, and albedo, positively correlated with cover of exposed sands, were computed from a time series of merged NOAA-AVHRR and MODIS images (1981 to 2010). Generally, throughout the study period, NDVI increased and albedo decreased. Improved understanding of spatial and temporal dynamics of these environmental processes was achieved by using the Change Vector Analysis (CVA) technique applied to NDVI and albedo data extracted from four sets of consecutive Landsat images, several years apart. Changes were detected for each time step, as well as over the entire period (1978 to 2007). Four categories of land cover were created—vegetation, exposed sands, water bodies and wetlands. The CVA’s direction and magnitude enable detecting and quantifying finer changes compared to separate NDVI or albedo difference/ratio images and result in pixel-based maps of the change. Each of the four categories has a biophysical meaning that was validated in selected hot-spots, employing very high spatial resolution images (e.g., Ikonos). Selection of images, taking into account inter and intra annual variability of rainfall, enables differentiating between short-term conservancies (e.g., drought) and long-term alterations. NDVI and albedo, although comparable to tasseled cap’s brightness and greenness indices, have the advantage of being computed using reflectance values extracted from various Landsat platforms since the early 1970s. It is shown that, over the entire study period, the majority of the Mu Us Sandy Land area remained unchanged. Part of the area (6%), mainly in the east, was under human-induced rehabilitation processes, in terms of increasing vegetation cover. In other areas (5.1%), bare sands were found to expand to the central-north and the southwest of the area.

Remote Sensing of Agro-droughts in Guangdong Province of China Using MODIS Satellite Data

A practical approach was developed in the study for drought monitoring in Guangdong province of China on the basis of vegetation supply water index (VSWI) and precipitation distance index (PDI). A comprehensive index for assessment of agro-drought severity (SADI) was then established from the normalized VSWI and PDI. Using MODIS satellite images and precipitation data from ground-observed meteorological stations, we applied the approach to Guangdong for drought monitoring in 2006. The monitoring results showed that the drought severity on average was very low in the province during the main growing season from May to September in 2006. However, seasonal variation of the severity was also obvious in difference counties of the province. Higher severity of drought could be seen in the periods of late-June (In China each month is traditionally divided into 3 periods. Each is with 10 days and has different names. This division system is mainly with consideration of farming seasons hence has been widely used as the basis of drought monitoring periods in China. In order to keep this tradition, we define, for example, for June, the early-June as the period from 1st to 10th of June, the mid-June as the period from 11th to 20th, and the late-June as the period from 21st to 30th. So mid-August denotes the period from 11th to 20th of August, and early-July the period from 1st to 10th of July, and so on.), early-July, mid-August and late-September. Regionally, Leizhou Peninsula in the west had the most serious drought before mid-May. Validation indicated that our monitoring results were generally consistent with the drought statistics data and the results from Chinese National Satellite Meteorological Center (CNSMC), which used only remote sensing data. This consistence confirmed the applicability of our approach for drought monitoring. Our better identification of drought severity in Leizhou Peninsula of western Guangdong than that of CNSMC might suggest that the approach developed in the study was able to provide a better alternative to increase the accuracy of drought monitoring for agricultural administration and farming.