Land Surface Temperature Inversion Research Articles

Inversion of Land Surface Temperature (LST) Using Terra ASTER Data: A Comparison of Three Algorithms

Land Surface Temperature (LST) is an important measurement in studies related to the Earth surface’s processes. The Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) instrument onboard the Terra spacecraft is the currently available Thermal Infrared (TIR) imaging sensor with the highest spatial resolution. This study involves the comparison of LSTs inverted from the sensor using the Split Window Algorithm (SWA), the Single Channel Algorithm (SCA) and the Planck function. This study has used the National Oceanic and Atmospheric Administration’s (NOAA) data to model and compare the results from the three algorithms. The data from the sensor have been processed by the Python programming language in a free and open source software package (QGIS) to enable users to make use of the algorithms. The study revealed that the three algorithms are suitable for LST inversion, whereby the Planck function showed the highest level of accuracy, the SWA had moderate level of accuracy and the SCA had the least accuracy. The algorithms produced results with Root Mean Square Errors (RMSE) of 2.29 K, 3.77 K and 2.88 K for the Planck function, the SCA and SWA respectively.

Non-Contact Measurement of the Spectral Emissivity through Active/Passive Synergy of CO₂ Laser at 10.6 µm and 102F FTIR (Fourier Transform Infrared) Spectrometer.

In the inversion of land surface temperature (LST) from satellite data, obtaining the information on land surface emissivity is most challenging. How to solve both the emissivity and the LST from the underdetermined equations for thermal infrared radiation is a hot research topic related to quantitative thermal infrared remote sensing. The academic research and practical applications based on the temperature-emissivity retrieval algorithms show that directly measuring the emissivity of objects at a fixed thermal infrared waveband is an important way to close the underdetermined equations for thermal infrared radiation. Based on the prior research results of both the authors and others, this paper proposes a new approach of obtaining the spectral emissivity of the object at 8–14 µm with a single-band CO2 laser at 10.6 µm and a 102F FTIR spectrometer. Through experiments, the spectral emissivity of several key samples, including aluminum plate, iron plate, copper plate, marble plate, rubber sheet, and paper board, at 8–14 µm is obtained, and the measured data are basically consistent with the hemispherical emissivity measurement by a Nicolet iS10 FTIR spectrometer for the same objects. For the rough surface of materials, such as marble and rusty iron, the RMSE of emissivity is below 0.05. The differences in the field of view angle and in the measuring direction between the Nicolet FTIR method and the method proposed in the paper, and the heterogeneity in the degree of oxidation, polishing and composition of the samples, are the main reasons for the differences of the emissivities between the two methods.

热红外与被动微波遥感协同反演地表温度研究进展

作为地表与大气界面的关键参量，地表温度是全球与区域气候变化的指示因子。本文首先分别阐述了热红外与被动微波的优劣势，指出将两者协同反演地表温度的必要性。在此基础上着重归纳阐述热红外与被动微波遥感协同反演地表温度的研究进展。随后讲述了热红外与被动微波遥感的三种协同机制即空间尺度匹配、有效温度与肤面温度转换和空间分辨率提升。其中空间尺度匹配问题可利用几何光学模型加以解决。根据热传导方程计算出地表一定深度下地表辐射对有效温度的贡献量，得到地表有效温度，可提高地表温度的反演精确，使地表温度的反演研究更具有实际意义。同时对低分辨率被动微波遥感数据的空间分辨率提升提出了构想。最后归纳总结了目前的进展和面临的问题，并就目前形势提出了未来研究的方向。 As a key parameter of surface and atmosphere, the land surface temperature (LST) is the indicator of global and regional climate change. Firstly, the advantages and disadvantages of thermal infrared and passive microwave remote sensing are expounded in this paper, and the necessity of combining passive microwave with thermal infrared remote sensing to retrieve LST is pointed out. On this basis, the research progress of combining thermal infrared with passive microwave to retrieve LST is focused on. Then this paper describes three kinds of coordination mechanisms of thermal infrared and passive microwave remote sensing, including spatial scale matching, effective temperature and LST conversion and spatial resolution enhancing. Among these, the spatial scale matching problem can be solved by using the geometrical optics model. Calculating the contribution of surface radiation at a certain depth to the effective temperature based on the heat conduction equation to obtain effective temperature of the surface can improve the inversion accuracy of LST, so that the LST inversion research will make more practical significance. At the same time, the conception to improve the spatial resolution of passive microwave is put forward. In the end, the author summarizes the current progress and problems, and puts forward the direction of future research.

Land Surface Temperature Retrieval from Landsat 8 TIRS—Comparison between Radiative Transfer Equation-Based Method, Split Window Algorithm and Single Channel Method

Accurate inversion of land surface geo/biophysical variables from remote sensing data for earth observation applications is an essential and challenging topic for the global change research. Land surface temperature (LST) is one of the key parameters in the physics of earth surface processes from local to global scales. The importance of LST is being increasingly recognized and there is a strong interest in developing methodologies to measure LST from the space. Landsat 8 Thermal Infrared Sensor (TIRS) is the newest thermal infrared sensor for the Landsat project, providing two adjacent thermal bands, which has a great benefit for the LST inversion. In this paper, we compared three different approaches for LST inversion from TIRS, including the radiative transfer equation-based method, the split-window algorithm and the single channel method. Four selected energy balance monitoring sites from the Surface Radiation Budget Network (SURFRAD) were used for validation, combining with the MODIS 8 day emissivity product. For the investigated sites and scenes, results show that the LST inverted from the radiative transfer equation-based method using band 10 has the highest accuracy with RMSE lower than 1 K, while the SW algorithm has moderate accuracy and the SC method has the lowest accuracy.

A temporal-spatial variation analysis of land surface temperature in Beijing

Land surface temperature( LST) is an important parameter in such fields as meteorology,hydrology and ecology,and the inversion of land surface temperature by use of remote sensing data is a simple and effective method. In this paper,with the support of the 3S technology,the authors took Beijing City as the study area to study the land surface temperature inversion and analyze spatial and temporal characteristics. Using TM data obtained from 1989 to 2010 within the 5th Ring Rroad of Beijing,the authors adapted the single- window algorithm in 20 a to the inversion of land surface temperature,with a comparative study of the verification of synchronous MODIS temperature products. On such a basis,the anomaly analysis and hatches analysis were conducted to reveal the overall characteristics of temporal and spatial variation of Beijing surface temperature in more than 20 a. The characteristics of temporal and spatial variation of NDMI and NDBI index were analyzed based on 20 years' land surface temperature of the study area,and the relationship between land surface temperature and NDMI as well as between land surface temperature and NDBI was quantitatively revealed and expounded. Research results show that the single- window algorithm method can obtain the reasonable land surface temperature and can reflect the spatial difference of surface heat condition. Beijing land surface temperature variation characteristics are obvious,surface temperature difference of different land cover types in the same period is evident,and heat island effect is also significant. A significant positive correlation between surface temperature and NDBI exists,and there is a significant negative correlation with NDMI,which means that both NDMI and NDBI are good indicators for marking surface thermal characteristics,and they can be used to provide an effective basis for monitoring surface temperature changes and its spatial and temporal characteristics.

Comparison of two split-window methods for retrieving land surface temperature from MODIS data

Land surface temperature (LST) is a key parameter in environment and earth science study, especially for monitoring drought. The objective of this work is a comparison of two split-window methods: Mao method and Sobrino method, for retrieving LST using MODIS (Moderate-resolution Imaging Spectroradiometer) data in North China Plain. The results show that the max, min and mean errors of Mao method are 1.33K, 1.54K and 0.13K lower than the standard LST product respectively; while those of Sobrino method are 0.73K, 1.46K and 1.50K higher than the standard respectively. Validation of the two methods using LST product based on weather stations shows a good agreement between the standard and Sobrino method, with RMSE of 1.17K, whereas RMSE of Mao method is 1.85K. Finally, the study introduces the Sobmao method, which is based on Sobrino method but simplifies the estimation of atmospheric water vapour content using Mao method. The Sobmao method has almost the same accuracy with Sobrino method. With high accuracy and simplification of water vapour content estimation, the Sobmao method is recommendable in LST inversion for good application in Ningxia region, the northwest China, with mean error of 0.33K and the RMSE value of 0.91K.

A modified land surface temperature split window retrieval algorithm and its applications over China

Due to the difficulties in correcting the influences of the atmosphere absorbability and the Earth surface emissivity diversification, the retrieval of LST (land surface temperature) from satellite data is a challenging task. In this paper, a modified Becker's split window LST inversion algorithm is developed for retrieving LST from the NOAA-16/17 AVHRR data. A new set of parameters for Becker's LST algorithm is proposed. The algorithm is developed from a surface brightness temperature dataset generated from the MODTRAN program, which uses a range of surface parameters and atmospheric quantities as inputs. The 10-day composites of the channels 4 and 5 brightness temperature data of NOAA-17 AVHRR (1-km resolution) are used to generate the clear-sky LST. As a validation of the algorithm, the retrieved LST is compared with MODIS LST of same period and area. The two LST products are found to be consistent, with the absolute difference being about 2.5 K for most areas. The NOAA retrieved LST is also compared with in-situ ground surface 0-cm measurements taken from 257 meteorological stations, which cover overall China area for the three periods of satellite observations. The comparison shows that the correlation between the retrieved LST and in-situ measurements is over 0.90 and the RMSE (root mean square error) is about 3.4 K.

Definition of component effective emissivity for heterogeneous and non-isothermal surfaces and its approximate calculation

Measurement of land surface temperature faces many problems. For example, each pixel over land surface is likely to be heterogeneous and non-isothermal with both vegetation canopy and background soil, and the three-dimensional structure of canopy often makes the canopy radiation angular dependent. It is difficult to define an overall land surface temperature and emissivity at pixel scale for heterogeneous and non-isothermal surfaces. After recalling several definitions of effective emissivities, component effective emissivity is defined in this paper under the conditions of local thermal equilibrium and a constant material emissivity for surface temperature variation in the normal Earth environment. Component effective emissivities make it possible to retrieve the component temperature based on multi-directional measurements. The sum of component effective emissivities is equal to the overall effective emissivity, which can be used to inverse pixel-averaged effective temperature. Taking the continuous plant/soil system as an example, the Monte Carlo method is used to simulate the effective emissivities, and an analytical expression equation (AEE) of the effective emissivities including direct-line emission and single scattering contribution is developed. Monte Carlo simulated results show that the sum of direct-line emission and single scattering effective emissivity is close to overall effective emissivity when soil and leaf are set to 0.94 and 0.98 respectively. Then component and overall effective emissivities calculated by Monte Carlo method and AEE are compared, and their differences are analysed for different soil and leaf emissivities. It is shown that when soil and leaf emissivities are set respectively to 0.94 and 0.98, the differences are less than 0.006 within a 50° view zenith angle. When soil or leaf emissivity is set to 0.9, the difference reaches 0.025 or 0.016, which is large enough to introduce a 1 K error in land surface temperature inversion when this effective emissivity is used. The paper finally proposes that the linear relationship of difference with soil and leaf emissivity can be used to compensate the errors.

斜面温暖帯形成要因分析へのＬＡＮＤＳＡＴ／ＴＭ夜間熱赤外画像の利用 赤城山南斜面における事例

In order to examine the usefulness of thermal images in analysis of thermal belt formation, eleven nocturnal LANDSAT/ TM thermal images of the southern slope of Mt. Akagi at the northern edge of Kanto Plain in central Japan were analyzed. Nine images have the higher LST (land surface temperature) on the mid-slope, which is always accompanied with LST difference due to land cover difference (forest dominating the mid-slope and the higher part of the slope has surface temperature higher than other land use categories), but appears with LST inversion in only two images. Because only the LST inversion may reflect thermal belt, these two elements must be separated before the image is used in analysis of thermal belt formation. The two images with LST inversion were acquired under similar meteorological conditions which should be considered as one of the possible factors of thermal belt formation.