Satellite-based analysis of the role of land use/land cover and vegetation density on surface temperature regime of Delhi,India

The knowledge of the surface temperature is important to a range of issues and themes in earth sciences central to urban climatology, global environmental change and human-environment interactions. The study analyses land surface temperature (LST) estimation using temporal ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) datasets (day time and night time) over National Capital Territory Delhi using the surface emissivity information at pixel level. The spatial variations of LST over different land use/land cover (LU/LC) at day time and night time were analysed and relationship between the spatial distribution of LU/LC and vegetation density with LST was developed. Minimum noise fraction (MNF) was used for LU/LC classification which gave better accuracy than classification with original bands. The satellite derived emissivity values were found to be in good agreement with literature and field measured values. It was observed that fallow land, waste land/bare soil, commercial/industrial and high dense built-up area have high surface temperature values during day time, compared to those over water bodies, agricultural cropland, and dense vegetation. During night time high surface temperature values are found over high dense built-up, water bodies, commercial/industrial and low dense built-up than over fallow land, dense vegetation and agricultural cropland. It was found that there is a strong negative correlation between surface temperature and NDVI over dense vegetation, sparse vegetation and low dense built-up area while with fraction vegetation cover, it indicates a moderate negative correlation. The results suggest that the methodology is feasible to estimate NDVI, surface emissivity and surface temperature with reasonable accuracy over heterogeneous urban area. The analysis also indicates that the relationship between the spatial distribution of LU/LC and vegetation density is closely related to the development of urban heat islands (UHI).     

连续植被的热辐射方向性

正确认识非同温混合像元热辐射方向性规律是利用多角度遥感数据反演像元组分温度的前提。论文基于局地热平衡条件和组分有效发射率概念探讨了连续植被的热辐射方向性模型。模型表明同温下的热辐射方向性只决定于连续植被体系总有效发射率的方向性，它是各组分有效发射率的和，决定于植被叶面和土壤表面的发射率、冠层结构参数。在非同温状况下，组分温度通过组分有效发射率调节体系的辐射亮度方向变化。模型解释了热辐射中孔穴效应问题。并通过蒙特卡罗逆向模拟从微观探讨了热辐射方向性与植被叶面和土壤表面的发射率、冠层结构参数的关系，并对孔穴效应引起体系发射率的增量和辐射亮度的增量进行了模拟分析。结果表明，对于球面型连续植被，叶面和土壤表面发射率值分别取0.98和0.94时，垂直方向上孔穴效应使体系的总有效发射率有0.01-0.025幅度的增值。当连续植被处于20℃同温状况时，孔穴效应引起的辐射亮度增量基本上都在0.8℃以上，最高可达到1.3℃。     

基于ASTER GED产品的地表发射率估算

地表发射率是地表温度反演的重要输入参数,为了解决现有地表发射率估算方法在裸露地表精度较差的问题,本文基于最新的ASTER全球地表发射率产品(ASTER GED)和基于植被覆盖度的方法(VCM),提出了一个改进的地表发射率估算方法。首先,利用ASTER GED产品求解裸土发射率,然后,利用ASTER波谱库中的植被发射率和植被覆盖度结合VCM方法计算地表发射率。利用张掖地区2012年11景ASTER TES算法反演的地表发射率产品和实测地表发射率数据进行了验证,同时利用一景Landsat 8 TIRS数据分析了对地表温度反演精度的影响。结果表明该方法估算的地表发射率整体精度较高,可以有效改进裸露地表的发射率估算精度,用于支持利用多种热红外传感器数据生产高精度的地表温度产品。     

Land Surface Emissivity and Temperature Determination Using NOAA‐AVHRR Data

Abstract

Studies on land surface processes using remote sensing data gains importance in the context of Geosphere Biosphere Programme. Present study addresses the applicability of split‐window method, in a tropical environment for mapping of surface temperature over heterogeneous surface from satellite data. The accuracy of the method is about +2.2°K, which is reasonable value taking into account the atmospheric attenuation in tropical environment. An attempt has been made to derive emissivity from normalized difference vegetation index (NDVI) by taking into account the fraction of vegetation cover of each pixel, which is determined by satellite data. The emissivity values estimated from satellite data found to be in reasonable agreement with an estimated error of less than 1%. The results of the study indicate the potential use of NDVI as a modulating parameter in the land surface temperature estimation from satellite data.     

Retrieval of land surface temperature (LST) from landsat TM6 and TIRS data by single channel radiative transfer algorithm using satellite and ground-based inputs

The present study proposes land surface temperature (LST) retrieval from satellite-based thermal IR data by single channel radiative transfer algorithm using atmospheric correction parameters derived from satellite-based and in-situ data and land surface emissivity (LSE) derived by a hybrid LSE model. For example, atmospheric transmittance (τ) was derived from Terra MODIS spectral radiance in atmospheric window and absorption bands, whereas the atmospheric path radiance and sky radiance were estimated using satellite- and ground-based in-situ solar radiation, geographic location and observation conditions. The hybrid LSE model which is coupled with ground-based emissivity measurements is more versatile than the previous LSE models and yields improved emissivity values by knowledge-based approach. It uses NDVI-based and NDVI Threshold method (NDVI^THM) based algorithms and field-measured emissivity values. The model is applicable for dense vegetation cover, mixed vegetation cover, bare earth including coal mining related land surface classes. The study was conducted in a coalfield of India badly affected by coal fire for decades. In a coal fire affected coalfield, LST would provide precise temperature difference between thermally anomalous coal fire pixels and background pixels to facilitate coal fire detection and monitoring. The derived LST products of the present study were compared with radiant temperature images across some of the prominent coal fire locations in the study area by graphical means and by some standard mathematical dispersion coefficients such as coefficient of variation, coefficient of quartile deviation, coefficient of quartile deviation for 3rd quartile vs. maximum temperature, coefficient of mean deviation (about median) indicating significant increase in the temperature difference among the pixels. The average temperature slope between adjacent pixels, which increases the potential of coal fire pixel detection from background pixels, is significantly larger in the derived LST products than the corresponding radiant temperature images.     

Land surface temperature retrieval and its validation using NOAA AVHRR thermal data

The retrieval of land (soil-vegetation complex) surface temperature (LST) was carried out over semi-arid mixed agriculture landscape of Gujarat using thermal bands (channel 4 and 5) and ground emissivity from atmospherically corrected NDVI of NOAA AVHRR LAC images. The atmospheric correction of Visible and NIR band reflectance was done using SMAC model. The LST computed from split-window method and subsequently corrected with fractional vegetation cover were then compared with near synchronous ground observations of soil and air temperatures made during 13–17 January and April, 1997 at five Land Surface Processes Experiment (LASPEX) sites of Anand, Sanand, Derol, Arnej and Khandha covering 100 km x 100 km. The fractional vegetation cover corrected LST at noon hrs. varied from 301.6 – 311.9K in January and from 315.8 – 325.6K in April. The LST_corr were found to lie in the mid way between AT and ST during January. But in April, LST were found to be more close to ST which may be due to relatively poor vegetation growth as indicated by lower NDVI values in April indicating more contribution to LST from exposed soil surface.     

SSM/I的云南思茅微波发射率的稳定性分析

利用装载于美国国防卫星上的特种微波成像仪SSM/I资料和相应的气象台站观测地面和探空资料, 在微波辐射传输模拟分析的基础上, 对云南思茅的微波发射率特性的稳定性进行定量分析。分析结果表明, 中国云南思茅地区地表植被覆盖度较好, 地表微波发射率稳定。地表微波发射率有弱的正弦季节变化(变化幅度很小, 最大不超过0.02), 在1—4月有很小的年际变化(发射率最大差异约0.008), 这些在有地面和探空观测的情况下, 可以通过模式模拟得到跟踪订正。     

atmospheric corrections to thermal IR data for estimation of land surface temperature

Satellite data provides important inputs far estimating regional surface emisslviiy and surface temperature. The methodology for estimation of emissivity over heterogeneous areas is based on the calculation of fraction vegetation cover per pixel taking NDVI, reflectances of pure pixels as input. The surface temperature is calculated using a sptit-window equation, which depends on atmospheric water vapour, viewing angle and channel surface emissivities. In the present study model coefficients for atmospheric corrections to NOAA AVHRR thermal data Fqr tropical atmospheres have been derived with a view to operationally use the methodolpgy for generating land surface temperature information from satellite data. The results of the study show that the estimated temperature values are comparable with the ctimatological values over the region Suggesting the possible use of the methodology.     

利用ATSR—2数据提取地表组分温度

发展了一种迭代算法，能够利用ATSR－2双角观测同时进行大气校正和反演地表的组分（植被和土壤）温度。在算法中，全球通用二次方（QUAD）算法用于进行大气校正，LSF模型用于计算等效方向发射率，通过迭代的方法，同时反演地表组分温度和进行大气校正。结果表明，在可接受的范围内，土壤温度和植被温度可以被分离开来，而且，反演出的两个方向发射率的差和经过大气校正后的两个方向亮温的差有很好的相关性。更进一步的敏感性和不确定性分析表明，如果利用USM进行分阶段反演，可以得到更好的结果。     

用遗传算法反演连续植被的组分温度

由于热红外多波段数据间具有高度的相关性和混合像元的大量存在，使得多波段陆面温度反演精度难以提高，并且难以得到组分温度信息。在连续植被热辐射方向性规律上的基础上，以喜直型连续植被为例，进行了大量的Monte-Carlo模拟，建立了组分有效比辐射率与土壤表面比辐射率和植被叶面积指数之间的经验函数关系，并以此构造目标函数，采用遗传算法，从热红外多角度数据中，同时反演混合像元组分温度和土壤比辐射率以及叶面积指数。通过对模拟的观测数据进行遗传算法反演的大量试验，结果表明，遗传算法反演组分温度非常稳健，在宽松的先验知识条件下，遗传算法可以解决不确定性反演问题。遗传算法反演结果和野外实测数据作了比较，证实了反演原理的正确性，为基于热红外方向性辐射模型反演组分温度，提供了新方法。     

联合热红外与微波的作物辐射方向性模型研究

热红外遥感提供地表表层辐射信息为主,被动微波遥感可更好地提供植被和土壤背景垂直结构的辐射信息。结合热红外与被动微波遥感的优势协同反演植被和土壤组分温度是提高组分温度反演精度的一种思路。本文在对热红外辐射传输模型和微波辐射模型进行比较的基础上,构建均匀作物的统一场景,将统一场景的参数分为直接参数和间接参数。基于统一场景,修改微波辐射模型的场景结构及叶倾角分布,并增加组分温度参数以计算辐射亮温,最终构建热红外与微波辐射联合模拟模型(UEasmmes模型)。针对均匀玉米作物,利用UEasmmes模型进行联合模拟,分析了组分温度、组分发射率、叶面积指数LAI及叶倾角分布LAD对热红外与微波的方向性亮温DBT的敏感性响应差异。分析结果表明:协同热红外与被动微波遥感反演植被和土壤组分温度是可行的,但对于如何克服组分发射率、LAI及LAD对植被有效发射率的影响而导致的微波辐射亮温变化以及实现热红外表皮温度与微波等效温度之间的转化仍是需要深入研究和探讨的问题。     

Contribution of Landsat 8 data for the estimation of land surface temperature in Batna city,Eastern Algeria

In this study, we presented a mono-window (MW) algorithm for land surface temperature retrieval from Landsat 8 TIRS. MW needs spectral radiance and emissivity of thermal infrared bands as input for deriving LST. The spectral radiance was estimated using band 10, and the surface emissivity value was derived with the help of NDVI and vegetation proportion parameters for which OLI bands 5 and 4 were used. The results in comparison with MODIS (MOD11A1) products indicated that the proposed algorithm is capable of retrieving accurate LST values, with a correlation coefficient of 0.850. The industrial area, public facilities and military area show higher surface temperature (more than 37 °C) in comparison with adjoining areas, while the green spaces in urban areas (34 °C) and forests (29 °C) were the cooler part of the city. These successful results obtained in the study could be used as an efficient method for the environmental impact assessment.     

Calculation of microwave land surface emissivity from satellite observations: validity of the specular approximation over snow-free surfaces?

To determine land surface emissivity from satellite microwave measurements, the surface is usually assumed to be specular. Questions about the validity of this approximation to estimate emissivity from nadir viewing radiometers were raised. This work aims to examine the validity of the specular assumption by evaluating errors induced when deriving emissivities from near-nadir measurements over snow-free areas. Brightness temperature simulations near nadir above both a specular and a Lambertian surface are compared. Errors on the retrieved emissivity introduced by the specular assumption are also quantified. The results show that the impact of the specular assumption when the surface is Lambertian is limited: less than 1% error in most atmospheric situations over natural snow-free surfaces.     

Influence of surface and vegetation characteristics on C-band radar measurements for soil moisture content

Soil moisture estimation using microwave remote sensing faces challenges of the segregation of influences mainly from roughness and vegetation. Under static surface conditions, it was found that Radarsat C-band SAR shows reasonably good correlation and sensitivity with changing soil moisture. Dynamic surface and vegetation conditions are supposed to result in a substantial reduction in radar sensitivity to soil moisture. A C-band scatterometer system (5.2 GHz) with a multi-polarization and multi-angular configuration was used 12 times to sense the soil moisture over a tall vegetated grass field. A score of vegetation and soil parameters were recorded on every occasion of the experiment. Three radar backscattering models Viz., Integral Equation Model (IEM), an empirical model and a volume scattering model, have been used to predict the backscattering phenomena. The volume scattering model, using the Distorted Born Approximation, is found to predict the backscattering phenomena reasonably well. But the surface scattering models are expectedly found to be inadequate for the purpose. The temporal variation of soil moisture does show good empirical relationship with the observed radar backscattering. But as the vegetation biomass increases, the radar shows higher sensitivity to the vegetation parameters compared to surface characteristics. A sensitivity analysis of the volume scattering model for all the parameters also reveals that the radar is more sensitive to plant parameters under high biomass conditions, particularly vegetation water content, but the sensitivity to surface characteristics, particularly to soil moisture, is also appreciable.     

SSM／I微波辐射计数据中国陆地覆盖特征季节变化分析

利用星载微波辐射计SSM／I多通道、多时相亮温数据开展了中国陆地覆盖特征的季节变化研究。通过对国内外星载微波辐射应用研究分析，提出了归一化极化指数(NDPI)的概念。处理了1997年4月、7月、10月和1998年1月的(每月的20、24日各两天)多通道SSM／I数据，在此基础上计算形成了第一幅中国陆地区域的归一化微波极化指数图，开展了中国陆地区域覆盖特征随季节变化的研究。研究结果表明，不同的陆地覆盖特征有特征的NDPI值，NDPI随季节而变化，植被、水分是引起NDPI变化的主要因子。     

基于大气温度和水汽含量改进HJ-1B地表温度反演

为了提高地面气象站稀少地区地表温度遥感反演的精度,本文基于多源遥感数据的优势,首先利用MODIS影像获取研究区像元尺度上平均大气水汽含量;然后利用同时相的HJ-1B影像估算区域地表比辐射率,再采用温度-植被指数法获取近地表大气温度;最后将以上3个参数输入单窗体算法,改进其地表温度反演的精度。研究结果表明,改进单窗体算法反演地表温度与地面实测温度的偏差小于1 K,为地面气象站点稀少的植被覆盖区域提供了一种可行的精确遥感反演地表温度方法。     

Estimating global land surface broadband thermal-infrared emissivity using advanced very high resolution radiometer optical data

An algorithm for retrieving global eight-day 5 km broadband emissivity (BBE) from advanced very high resolution radiometer (AVHRR) visible and near-infrared data from 1981 through 1999 was presented. Land surface was divided into three types according to its normalized difference vegetation index (NDVI) values: bare soil, vegetated area, and transition zone. For each type, BBE at 8–13.5 µm was formulated as a nonlinear function of AVHRR reflectance for Channels 1 and 2. Given difficulties in validating coarse emissivity products with ground measurements, the algorithm was cross-validated by comparing retrieved BBE with BBE derived through different methods. Retrieved BBE was initially compared with BBE derived from moderate-resolution imaging spectroradiometer (MODIS) albedos. Respective absolute bias and root-mean-square error were less than 0.003 and 0.014 for bare soil, less than 0.002 and 0.011 for transition zones, and ?0.002 and 0.005 for vegetated areas. Retrieved BBE was also compared with BBE obtained through the NDVI threshold method. The proposed algorithm was better than the NDVI threshold method, particularly for bare soil. Finally, retrieved BBE and BBE derived from MODIS data were consistent, as were the two BBE values.     

Angular effect of MODIS emissivity products and its application to the split-window algorithm

The angular effects of emissivity are ignored in current land surface temperature (LST) products. As a result, the directionality of these LST products limits their further application in many fields. Accurate correction of the angular problem of LST products requires explicit understanding of the angular effects of emissivity at the pixel scale. Currently, nearly ten years of global emissivity products of MODIS are available. However, the pixel-scale directionality of emissivity has never been analyzed. By performing a statistical analysis of 5-year MODIS emissivity products over most of East Asia, we generated the empirical relationships between the directional emissivity, land cover, and seasonal variations. Two look-up tables (LUTs) of directional emissivity were created for typical land cover types and applied to the generalized split-window algorithm to modify the MODIS LST. The results showed that the angular effect of emissivity could introduce a significant bias of −1-3 K to the 1 km resolution LST. Finally, the spatial scale effects of emissivity were analyzed, and it was found that the temperature differences caused by scale effects fell within +/−0.5 K for most pixels if 5 km emissivity was used in 1 km LST retrieval. Therefore, wide use of the LUTs can be expected.     

Mapping burns and natural reforestation using thematic Mapper data

Remote sensing techniques are specially suitable to detect and to map areas affected by forest fires. In this work, Landsat 5 Thematic Mapper (TM) data has been used to study a number of forest fires that occurred in the province of Valencia (Spain) and to monitor the vegetation regeneration over burnt areas.

A reference area (non‐burnt forest) was established to assess the change produced by fire. The radiance in the thermal band (10.4–12.5 μm) and the normalized difference in reflectance between near 1R (0.76–0.90 μm) and middle IR (2.08–2.35 μm) were the most suitable parameters to map burnt areas. This index can also be used for monitoring vegetation regeneration in burnt areas. About a month after the fire, the burns show temperatures of 5–6 °C higher than those found in the reference area, and the vegetation index shows negative values whereas the reference area values remain positive. The differences between the burns and the reference area for the vegetation index decrease with time as vegetation regenerates.     

Monitoring grass nutrients and biomass as indicators of rangeland quality and quantity using random forest modelling and WorldView-2 data

Land use and climate change could have huge impacts on food security and the health of various ecosystems. Leaf nitrogen (N) and above-ground biomass are some of the key factors limiting agricultural production and ecosystem functioning. Leaf N and biomass can be used as indicators of rangeland quality and quantity. Conventional methods for assessing these vegetation parameters at landscape scale level are time consuming and tedious. Remote sensing provides a bird-eye view of the landscape, which creates an opportunity to assess these vegetation parameters over wider rangeland areas. Estimation of leaf N has been successful during peak productivity or high biomass and limited studies estimated leaf N in dry season. The estimation of above-ground biomass has been hindered by the signal saturation problems using conventional vegetation indices. The objective of this study is to monitor leaf N and above-ground biomass as an indicator of rangeland quality and quantity using WorldView-2 satellite images and random forest technique in the north-eastern part of South Africa. Series of field work to collect samples for leaf N and biomass were undertaken in March 2013, April or May 2012 (end of wet season) and July 2012 (dry season). Several conventional and red edge based vegetation indices were computed. Overall results indicate that random forest and vegetation indices explained over 89% of leaf N concentrations for grass and trees, and less than 89% for all the years of assessment. The red edge based vegetation indices were among the important variables for predicting leaf N. For the biomass, random forest model explained over 84% of biomass variation in all years, and visible bands including red edge based vegetation indices were found to be important. The study demonstrated that leaf N could be monitored using high spatial resolution with the red edge band capability, and is important for rangeland assessment and monitoring.