Simulation of land use dynamics and impact on land surface temperature using satellite data

GeoJournal - Increasing of global average surface temperature naturally leading to major problems as global warming, which has typically attracted the interest of multinational organizations, civil...     

Study of land surface temperature and spectral emissivity using multi-sensor satellite data

In this study, an attempt has been made to estimate land surface temperatures (LST) and spectral emissivities over a hard rock terrain using multi-sensor satellite data. The study area, of about 6000 km², is a part of Singhbhum-Orissa craton situated in the eastern part of India. TIR data from ASTER, MODIS and Landsat ETM+ have been used in the present study. Telatemp Model AG-42D Portable Infrared Thermometer was used for ground measurements to validate the results derived from satellite (MODIS/ASTER) data. LSTs derived using Landsat ETM+ data of two different dates have been compared with the satellite data (ASTER and MODIS) of those two dates. Various techniques, viz., temperature and emissivity separation (TES) algorithm, gray body adjustment approach in TES algorithm, Split-Window algorithms and Single Channel algorithm along with NDVI based emissivity approach have been used. LSTs derived from bands 31 and 32 of MODIS data using Split-Window algorithms with higher viewing angle (50°) (LST1 and LST2) are found to have closer agreement with ground temperature measurements (ground LST) over waterbody, Dalma forest and Simlipal forest, than that derived from ASTER data (TES with AST 13). However, over agriculture land, there is some uncertainty and difference between the measured and the estimated LSTs for both validation dates for all the derived LSTs. LST obtained using Single Channel algorithm with NDVI based emissivity method in channel 13 of ASTER data has yielded closer agreement with ground measurements recorded over vegetation and mixed lands of low spectral contrast. LST results obtained with TIR band 6 of Landsat ETM+ using Single Channel algorithm show close agreement over Dalma forest, Simlipal forest and waterbody with LSTs obtained using MODIS and ASTER data for a different date. Comparison of LSTs shows good agreement with ground measurements in thermally homogeneous area. However, results in agriculture area with less homogeneity show difference of LST up to 2°C. The results of the present study indicate that continuous monitoring of LST and emissivity can be undertaken with the aid of multi-sensor satellite data over a thermally homogeneous region.     

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.     

利用ASTER数据反演陆面温度的研究进展

目前利用ASTER数据反演陆面温度的算法可划分为3类：ASTERTES（Temperature／Emissivity Separation）算法，多通道算法，辐射传输算法。对上述3类方法进行了分析比较，提出了今后工作中要：实现遥感的综合反演、地表发射率的精确获取以及反演实用价值更大的组分温度的努力方向。     

Estimation of surface temperature from MONTBLEX data

It is observed that the daily mean temperature of the soil is linear with depth and the variation of the temperature is sinusoidal with a period of a day. Based on these observations the one-dimensional heat conduction equation for the soil can be solved which gives the amplitude and phase variation of the temperature wave with depth. Given the temperature data at three levels below the surface, the amplitude and phase variation and hence the surface temperature variation over the day are estimated. The daily mean temperature of the surface is estimated from linear extrapolation of the daily means at the three levels below the surface. Estimated values of soil thermal diffusivity show a subtantial change after sudden and heavy rains.     

Atmospheric correction for sea surface temperature retrieval from single thermal channel radiometer data onboard Kalpana satellite

An atmospheric correction method has been applied on sea surface temperature (SST) retrieval algorithm using Very High Resolution Radiometer (VHRR) single window channel radiance data onboard Kalpana satellite (K-SAT). The technique makes use of concurrent water vapour fields available from Microwave Imager onboard Tropical Rainfall Measuring Mission (TRMM/TMI) satellite. Total water vapour content and satellite zenith angle dependent SST retrieval algorithm has been developed using Radiative Transfer Model [MODTRAN ver3.0] simulations for Kalpana 10.5–12.5 μm thermal window channel. Retrieval of Kalpana SST (K-SST) has been carried out for every half-hourly acquisition of Kalpana data for the year 2008 to cover whole annual cycle of SST over Indian Ocean (IO). Validation of the retrieved corrected SST has been carried out using near-simultaneous observations of ship and buoys datasets covering Arabian Sea, Bay of Bengal and IO regions. A significant improvement in Root Mean Square Deviation (RMSD) of K-SST with respect to buoy (1.50–1.02 K) and to ship datasets (1.41–1.19 K) is seen with the use of near real-time water vapour fields of TMI. Furthermore, comparison of the retrieved SST has also been carried out using near simultaneous observations of TRMM/TMI SST over IO regions. The analysis shows that K-SST has overall cold bias of 1.17 K and an RMSD of 1.09 K after bias correction.     

整合陆地表面温度与植被指数信息进行地表覆盖变化研究

利用植被指数(NDVI)的多时相或时序资料进行地表覆盖研究已取得了大量成果。随着陆地表面温度(LST)遥感反演精度的不断提高,将LST与NDVI结合起来进行地表植被动态变化的监测已成为可能。选择我国北方草地及其东南部毗邻的农牧交错带(farmingpastoralzone)为研究区。该区位于内蒙古高原东南边缘和黄土高原北部,半干旱气候,经纬度范围为:35°～50°N,100°～125°E。     

基于HJ-1B卫星数据的疏勒河上游流域地表温度反演

地表温度是描述地表和大气之间物质交换和能量平衡的关键参数之一. 利用疏勒河上游流域2010年7月25日过境的HJ-1B卫星数据, 采用修正的Jimenez-Mufloz&Sobrino普适性单通道算法反演了疏勒河上游流域的地表温度. 与MODIS LST产品的对比验证表明, 该算法在研究区有较高的精度. 同时, 对不同土地利用类型的地表温度的分析表明, 土地利用类型不同, 其地表温度存在一定的差异, 其中, 稀疏草地的地表温度最高, 裸岩次之. 研究发现, 在河流和冰川区, 修正的单通道算法可能高估了表面温度. 同时, 对模型反演所需的关键地表参数的敏感性分析发现, 算法在一定范围(0~0.01)内对比辐射率不太敏感, 但超过该范围则变得非常敏感, 而对大气水汽含量比较敏感. 因此, 一定程度上, 地表比辐射率和大气水汽含量的精度对HJ-1B卫星数据反演地表温度的准确性起主要作用.     

Estimation of surface latent heat fluxes from IRS-P4/MSMR satellite data

The brightness temperatures of the Microwave sensor MSMR (Multichannel Scanning Microwave Radiometer) launched in May 1999 onboard Indian Oceansat-1 IRS-P4 are used to develop a direct retrieval method for latent heat flux by multivariate regression technique. The MSMR measures the microwave radiances at 8 channels at frequencies of 6.6, 10.7, 18 and 21 GHz at both vertical and horizontal polarizations. It is found that the surface LHF (Latent Heat Flux) is sensitive to all the channels. The coefficients were derived using the National Centre for Environmental Prediction (NCEP) reanalysis data of three months: July, September, November of 1999. The NCEP daily analyzed latent heat fluxes and brightness temperatures observed by MSMR were used to derive the coefficients. Validity of the derived coefficients was checked within situ observations over the Indian Ocean and with NCEP analyzed LHF for global points. The LHF derived directly from the MSMR brightness temperature (Tb) yielded an accuracy of 35 watt/m². LHF was also computed by applying bulk formula using the geophysical parameters extracted from MSMR. In this case the errors were higher apparently due to the errors involved in derivation of the geophysical parameters.     

A comparison of surface fitting algorithms for geophysical data

Many treatments in geophysics require regular grids of data. Since the data are generally recorded irregularly (e.g. gravity measurements along roads) or even on tracks (e.g. satellite measurements), it is necessary to grid the observed data. We present the result of a comparison of various surface fitting algorithms carried out in order to check their reliability. Two different types of sampling have been verified: (i) clouds of points and (ii) points along tracks. Five algorithms have been extensively tested: (1) polynomial fit, (2) algorithm using a combination of spline-laplace, (3) krigeage (4) least-squares fitting method, and (5) finite element method. The suitability of each method for different sets of data and the limitations (in terms of amplitude and gradient) are discussed.     

Reconstructions of long-term ground surface heat flux changes from deep-borehole temperature data

Based on analysis of geothermal data from the Ural superdeep borehole (SG-4) and Onega parametric borehole, the first reconstructions of ground surface heat flux changes for the last 40 kyr have been made. The increase in heat flux during the Pleistocene-Holocene warming (20-10 ka) proceeded ~ 2 kyr earlier than the growth in surface temperature; reaching the maximum value of 0.08-0.13 W/m² at ~ 13 ka, the heat flux was reduced. The coordinated changes in heat flux and average annual insolation at 60° N at 5-24 ka indicate that the orbital factors were the main cause of climatic changes in this period. The correlations between the changes in heat flux and CO₂ content in the Antarctic ice cores and the temperature changes are analyzed.© 2014, V.S. Sobolev IGM, Siberian Branch of the RAS. Published by Elsevier B.V. All rights reserved.     

Generation of emissivity and land surface temperature maps using MODIS TIR data for lithological mapping over the Singhbhum-Orissa Craton

The present study was undertaken with four fold objectives, namely, (i) to estimate land surface temperature using MODIS TIR data; (ii) to calculate relative emissivities from MODIS TIR data; (iii) to identify various lithologies based on relative emissivity and land surface temperature estimation; and finally, (iv) to carry out comparative assessment analysis between the prepared lithological map and the published lithological map. The land surface temperatures for different pixels were estimated using two methods, viz., Reference Channel and Emissivity Normalization; whereas, relative emissivities were calculated by applying three methods, viz., Reference Channel, Emissivity Normalization and Alpha Residual. Lithological maps were subsequently prepared based on the estimated land surface temperatures and relative emissivity values. The present study shows that the Emissivity Normalization method gives the best results for land surface temperature estimation and also for lithological discrimination based on emissivity estimation. Twenty-four lithounits demarcated by the present study match with those of the published map, while four lithounits of the published map could not be identified in the present study. On the other hand, six additional unclassified lithounits could be demarcated in the present study, which need to be crosschecked by field study.     

多种再分析地表温度资料在中国区域的适用性分析

利用1979-2010年中国805个站点的逐日地表温度观测资料与ERA-Interim、ERA-Interim/Land、JRA-55、NCEP/NCAR和NCEP/DOE五种再分析地表温度资料, 对五种再分析地表温度资料在中国区域的适用性进行了比较与分析. 结果表明: 从空间分布特征来看, JRA-55资料与观测资料最为接近; 五种再分析地表温度资料在东部地区的适用性好于西部地区, 且均不能很好地模拟出青藏高原地区和西北西部地区的地表温度分布. 从地表温度的长期变化来看, 五种再分析地表温度资料虽然可以描述地表温度变化趋势, 但均存在低估现象, 且低估现象随着地表温度的增加变得更加严重. 在地表温度人工观测和自动观测交接的时间段, 可以使用再分析资料作为地表温度均一化的参考序列.     

黑龙江省漠河地区土地覆被与地表温度时空变化特征研究

以我国高纬度多年冻土区漠河县城区及郊区862 km²的区域为研究对象,基于RS和GIS技术对该区地表覆被变化和地表温度进行研究。结果表明：地表类型和地表温度反演结果与现场比对结果一致;从1988年到2015年,经过火灾后重建和城镇化发展,建设用地面积增加了11.33 km²;以2015年为例,冬季由于积雪覆盖,有79.02%的区域无植被覆盖,夏季有80.91%的区域为高植被覆盖。冬季地表温度和高程具有高度相关性,高程每升高100 m,地表温度平均升高2.27℃,表明该区地表温度也存在显著的逆温现象。该区城镇热岛效应全年存在,城镇区域地表温度高于全区平均值,夏季最显著,最高温差可达6.37℃。地表温度与NDVI具有明显的负相关关系;不同土地利用类型地表温度由低到高的顺序为水体-林地-草地-裸地-建筑用地。     

Estimation of land coverage from a land cover classification derived from remotely sensed data

Remotely sensed data are an attractive source of land cover information. In many applications the required information relates to the extent or coverage of land cover class(es) in a region, which is generally derived from a count of the pixels allocated to the class(es) of interest in a classification. A highly accurate classification is not required for the derivation of accurate estimates of class coverage, provided the classification is accompanied by appropriate information on its quality. For instance, the information on classification quality contained in the classification confusion matrix can be used to significantly increase the accuracy of the estimates of land coverage. This is illustrated with reference to a case study focused on the estimation of despoiled land coverage in administratively defined local district in industrial South Wales from Landsat TM data. The accuracy of the investigation was assessed relative to a map of despoiled land cover for this region produced by conventional methods. From an image classification of moderate accuracy, the classification accuracy ranged form 57–83% between the districts investigated, a pixel count provided estimated of despoiled land coverage that were only poorly correlated to the mapped coverage;r = 0.27. Using the information on the pattern of error in the class allocation contained in the classification confusion matrix the estimation accuracy was increased significantly, with a correlation ofr = 0.81 observed between the remote sensing based estimate and the mapped land coverage. Furthermore, the r.m.s. error in despoiled land coverage estimation was reduced by approximately half, to less than 1% district area, when the classification was used in conjunction with information on the pattern of classification error.     

大陆地表温度场的时空变化与现今构造活动

试图利用地表温度场数据获取中国西部的构造活动信息。在建立热与应变关系的基础上,对中国西部MODIS/Terra地表温度产品进行分析处理。研究发现:(1)地表温度在一些地区发生偏离年变现象,这种年变偏离与一些活动构造带的活动有关;(2)在扣除年变基准场等主要气候因素后,年变残差(ΔT)中长周期成分(LSTLOW)更接近构造活动所引起的热信息,能为构造活动提供一定的指示信息。研究发现,一个地震的发生对周围不同构造区的影响不同,有的地区升温,有的地区降温。2004年印尼地震最大的影响是引起青藏高原中部巴颜喀拉—松潘地块的降温;(3)与前者相对应,发生在中国周边地区的不同地震引起的温度变化格局不同,对同一地区的影响也不同,例如2001年东昆仑8.1级地震引起龙门山断裂带升温,而2003年斋桑泊7.9级地震和2004年印尼9级地震却引起该带的降温;(4)不同地区地温变化的时间过程不同。这些现象均对构造变形过程有一定的启示。在上述现象的基础上,笔者结合GPS观测结果,不同深度的地温信息以及地震活动等资料,对地表温度场中包含构造活动信息进行了初步检验,并对地表温度场反映的区域构造变形模型进行了讨论。     

Monitoring of Dead Sea water surface variation using multi-temporal satellite data and GIS

Remote sensing (RS) and geographic information systems (GIS) are very useful for environmental-related studies, particularly in the field of surface water studies such as monitoring of lakes. The Dead Sea is exposed to very high evaporating process with considerable scarcity of water sources, thus leading to a remarkable shrinkage in its water surface area. The lake suffers from dry out due to the negative balance of water cycle during the previous four decades. This paper discusses the application of RS, GIS, and Global Positioning System to estimate the lowering and the shrinkage of Dead Sea water surface over the period 1810–2005. A set of multi-temporal remote sensing images were collected and processed to show the lakes aerial extend shrinkage from 1973 up to 2004. Remote sensing data were used to extract spatial information and to compute the surface areas for Dead Sea for various years. The current study aims at estimating the fluctuation of Dead Sea level over the study period with special emphasis on the environmental impact assessment that includes the degradation level of the Dead Sea. The results indicated that there is a decrease of 20 m in the level of the Dead Sea that has occurred during the study period. Further, the results showed that the water surface area of the Dead Sea has shrunk from 934.26 km² in 1973 to 640.62 km² in 2004.