Spatio-temporal variation of land surface temperature and temperature lapse rate over mountainous Kashmir Himalaya

In this study, Land Surface Temperature(LST) and its lapse rate over the mountainous Kashmir Himalaya was estimated using MODIS data and correlated with the observed in-situ air temperature(Tair) data. Comparison between the MODIS LST and Tair showed a close agreement with the maximum error of the estimate ±1°C and the correlation coefficient 0.90. Analysis of the LST data from 2002-2012 showed an increasing trend at all the selected locations except at a site located in the southeastern part of Kashmir valley. Using the GTOPO30 DEM, MODIS LST data was used to estimate the actual temperature lapse rate(ATLR) along various transects across Kashmir Himalaya, which showed significant variations in space and time ranging from 0.3°C to 1.2°C per 100 m altitude change. This observation is at variance with the standard temperature lapse rate(STLR) of 0.65°C used universally in most of the hydrological and other land surface models. Snowmelt Runoff Model(SRM) was used to determine the efficacy of using the ATLR for simulating the stream flows in one of the glaciated and snow-covered watersheds in Kashmir. The use of ATLR in the SRM model improved the R2 between the observed and predicted streamflows from 0.92 to 0.97.It is hoped that the operational use of satellite-derived LST and ATLR shall improve the understanding and quantification of various processes related to climate, hydrology and ecosystem in the mountainous and data-scarce Himalaya where the use of temperature and ATLR are critical parameters for understanding various land surface and climate processes.     

New methods for calculating bare soil land surface temperature over mountainous terrain

Land surface temperature(LST) causes the phase change of water, links to the partitioning of surface water and energy budget, and becomes an important parameter to hydrology, meteorology, ecohydrology, and other researches in the high mountain cold regions. Unlike air temperature, which has common altitudinal lapse rates in the mountainous regions, the influence of terrain leads to complicated estimation for soil LST. This study presents two methods that use air temperature and solar position,to estimate bare LST with high temporal resolution over horizontal sites and mountainous terrain with a random slope azimuth. The data from three horizontal meteorological stations and fourteen LST observation fields with different aspects and slopes were used to test the proposed LST methods. The calculated and measured LST were compared in a range of statistical analysis, and the analysis showed that the average RMSE(root mean square error),MAD(mean absolute deviation), and R~2(correlation coefficient) for three horizontal sites were 5.09℃,3.66℃, 0.92, and 5.03℃, 3.52℃, 0.85 for the fourteen complex terrain sites. The proposed methods showed acceptable accuracy, provide a simple way to estimate LST, and will be helpful for simulating the water and energy cycles in alpine mountainous terrain.     

Improvement of mono-window algorithm for retrieving land surface temperature from HJ-1B satellite data

The thermal infrared channel (IRS4) of HJ-1B satellite obtains view zenith angles (VZA) up to ±33°. The view angle should be taken into account when retrieving land surface temperature (LST) from IRS4 data. This study aims at improving the mono-window algorithm for retrieving LST from IRS4 data. Based on atmospheric radiative transfer simulations,a model for correcting the VZA effects on atmospheric transmittance is proposed. In addition,a generalized model for calculating the effective mean atmospheric temperature is developed. Validation with the simulated dataset based on standard atmospheric profiles reveals that the improved mono-window algorithm for IRS4 obtains high accuracy for LST retrieval,with the mean absolute error (MAE) and root mean square error (RMSE) being 1.0 K and 1.1 K,respectively. Numerical experiment with the radiosonde profile acquired in Beijing in winter demonstrates that the improved mono-window algorithm exhibits excellent ability for LST retrieval,with MAE and RMSE being 0.6 K and 0.6 K,respectively. Further application in Qinghai Lake and comparison with the Moderate-Resolution Imaging Spectroradiometer (MODIS) LST product suggest that the improved mono-window algorithm is applicable and feasible in actual conditions.     

改进的DisTrad模型在地形起伏区地表温度空间分辨率提升的应用

DisTrad（Disaggregation procedure for radiometric surface temperature）模型是常用于遥感地表温度空间分辨率提升的主要模型之一。DisTrad模型常面向空间范围有限、地形相对平坦的研究区域,且常选用植被参数（如植被指数或植被覆盖度等）作为关键参数。然而在空间范围较大、地形起伏地区,地表温度的空间变异可能无法完全通过植被参数解释。本研究选取四川盆地及毗邻地区为研究区,通过模拟数据研究DisTrad模型在地形起伏区地表温度空间分辨率提升中的适用性。数字高程模型（Digital Elevation Model,DEM）、归一化差值植被指数（Normalized Difference Vegetation Index,NDVI）等参数,采用滑动窗口逐步回归,将空间分辨率为6km的地表温度提升至空间分辨率为1km。研究结果表明,改进的模型在平原及海拔较低的高原地区提升获得的地表温度空间分辨率具有较高精度,均方根误差（Root Mean Square Error,RMSE）为0.5K左右;在地形起伏较大的地区,RMSE为4K,验证了改进的模型提升的可行性。     

北京2008奥运赛场环境特征的GIS系统分析

本文针对不同环境特征参数的空间和时间变化特征,利用2006年不同空间分辨率和时间分辨率的多源遥感数据和产品,对31个奥运场馆、公路自行车赛场和奥林匹克公园的建筑指数、水体指数、白天/夜间陆表温度以及植被指数特征进行了GIS系统分析,其结果:(1)31个奥运场馆的建筑时间、地理位置以及露天情况对其环境特征有着重要的影响,建筑指数、水体指数、白天/夜间的陆表温度以及植被指数均表现出不同的变化特征,故奥运场馆的绿化和赛事安排应对这些因素进行综合考虑。(2)公路自行车赛场和奥林匹克公园均表现出建筑指数较高,水体指数为负值的特征,而且夏季和秋季白天/夜间陆表温度较高。建议在奥林匹克公园适当增加一些水体以提高其环境质量。     

河谷型城市热岛空间分布格局演变及对策——以西宁市区为例

城市热岛效应直接反映着城市的气候特征,这对于研究由城市化发展与环境改变引起的城市气温的变化及保护城市的生态环境具有重要的现实意义。本文利用LandsatTM影像、气象台站资料,基于GIS的空间分析技术及单窗算法,对河谷型城市西宁市的地表温度进行反演,分析了地表温度与NDVI、NDBI的空间对应关系。结果表明:西宁市存在明显的城市热岛效应,热场分布及延伸与西宁市空间扩展布局相一致,热岛范围呈逐年增长的趋势;低、中温区的热岛面积大幅度减少,高温区的热岛范围显著增加;热岛效应冬季最强,夏季次之,秋季有明显减弱的趋势。在河谷型城市的空间格局上,地表温度与NDVI呈负相关关系、与NDBI呈正相关关系。最后,依据热岛时空演化、成因分析和策略研究的思路,从不同角度提出了缓解城市热岛效应的措施和对策,为未来西宁市热环境的改善提供科学参考和决策支持。     

Spatio-temporal changes of ≥ 10°C accumulated temperature in northeastern China since 1961

The objective of this study was to provide reliable basis for decision making for national food security and layout and structure adjustment of grain production in the northeastern China. The data of mean daily air temperature of 1961-2009 from 106 meteorological stations in the northeastern China were chosen in this study. Using statistical methods and isoline method, the spatio-temporal changes of various decadal ≥ 10℃ accumulated temperature and the climatic means of ≥ 10℃ accumulated temperature were studied in this paper. The results showed that 1) The geo-graphical distribution of ≥ 10℃ accumulated temperature in the northeastern China could be influenced directly by the latitude, longitude and altitude. If latitude moved one degree northward, the average decrease amplitude of the climatic means was 101.9℃ in the study area. 2) The means of decadal ≥ 10℃ accumulated temperature rose since the 1980s, and their increase amplitudes became larger in the 1990s and the 2010s obviously. Compared with those of the 1980s, ≥ 10℃ accumulated temperature increased by about 100℃ in the mountainous and plain areas in the 1990s; compared with those of the 1990s, ≥ 10℃ accumulated temperature increased by about 200℃ in the Hulun Buir High Plain and the Songnen Plain, and 100℃ in the Sanjiang Plain and the Liaohe Plain in the 2010s. 3) The means of the decadal ≥ 10℃ accumulated temperature for 106 meteorological stations in the northeastern China increased with the rate of 145.57℃/10yr in 1961-2009. 4) The climatic means of ≥ 10℃ accumulated temperature increased from 1961-1990 to 1971-2000 and 1981-2009. Compared with the climatic mean of 1971-2000, that of 1981-2009 had increased by above 50℃ in most of the study area, even up to 156℃. Compared with the climatic mean of 1961-1990, that of 1981-2009 increased by above 100℃ in most parts of the study area, even up to 200℃. 5) The maximum northward shift, eastward and westward extension amplitudes of 3100℃, 3300℃ and 3500℃ isolines were larger among all isoli-nes for the climatic means of the three phases. Compared with the positions of the isolines of 1961-1990, those ampli-tudes of 3100℃ isoline of 1981-2009 were 145 km, 109 km and 64 km, respectively; those of 3300℃ isoline were 154 km, 54 km and 64 km, respectively; and the maximum northward shift of 3500℃ isoline was about 100 km.     

基于随机森林算法的地表温度鲁棒降尺度方法

陆地表面温度是描述区域或者全球范围内陆地表面与大气的相互作用和能量平衡最重要的环境参数之一。针对目前尚未有遥感卫星能够同时提供具有高时间和高空间分辨率的地表温度产品的问题,国内外学者发展了多种对低空间分辨率的地表温度进行降尺度的算法。然而,由于对地表温度解释变量和降尺度模型的选择往往具有区域局限性,导致了降尺度模型的泛化能力受到了一定的限制。本文首先评估了地表反射率、遥感光谱指数、地形因子、地表覆盖、经纬度以及基本状态变量6类环境参量与地表温度之间的相关关系,并在此基础上筛选出最佳解释变量;同时,结合在非线性回归问题上表现比较优秀的随机森林算法,建立了一种鲁棒性的基于随机森林算法地表温度降尺度模型（RRF）。本文选取了中国范围内具有代表性的11个地区作为主要研究区,将空间分辨率为1 km的MODIS地表温度产品降尺度至90 m。以北京市2个典型地表类型的子区域为代表研究区,通过与传统的基于归一化植被指数与地表温度相关关系的TsHARP模型,以及基于红波段和近红外波段以及地表高程作为尺度因子建立的简单Basic-RF模型的对比分析可得,RRF模型在2个子研究区降尺度结果均优于TsHARP模型和Basic-RF模型,其均方根误差分别为2.39 K和2.27 K。通过进一步对2个子研究区训练的RRF进行交叉验证,证明在一个研究区训练的RRF应用至另一研究区的降尺度时,RRF模型表现出了较好的鲁棒性,降尺度结果的均方根误差分别为2.56 K和2.44 K,精度误差相差仅为0.17 K。通过将RRF应用于中国范围内的多个研究区,结果表明利用少量训练数据构建的RRF模型适用于大范围的区域,地表温度降尺度结果都能取得较好的精度。     

基于DCT-PLS算法的MODIS LST缺值填补方法研究

受云层、传感器误差等因素影响,中分辨率成像光谱仪（MODIS）获取的地表温度产品（LST）在时间和空间上存在大量缺失数据,严重影响了基于时间序列数据的分析与应用。本文引进一种基于离散余弦变换与惩罚最小二乘的多维数据快速平滑方法（DCT-PLS）,利用数据集自身的时间和空间信息填补缺值。本文在粤港澳大湾区开展实证研究,将DCT-PLS算法用于填补该地区2001年1月—2017年12月的月值MODIS LST数据缺值,并引入人工模拟缺值对算法进行误差分析与精度验证。算法误差分析结果表明,填补误差主要来源于三维算法对数据集中有偏LST时间信息的使用,并因此产生显著高估或低估的填补结果。基于此,本文提出了利用MODIS LST数据集自身时间序列信息自动计算获取有效辅助LST信息的优化策略,从而实现填补算法计算效率和精度的提升：平均计算时间从12.0 s提高至1.7 s,平均R从0.94提高至0.97,平均RMSE从1.94 K提高至0.74 K（相较于三维算法）。在大湾区的填补结果表明（日间结果：R=0.98、RMSE=0.79 K;夜间结果：R=0.99、RMSE=0.56 K）,优化后的DCT-PLS算法可以快速鲁棒地填补MODIS LST月值数据产品中的缺值,并且具有稳定性强、不依赖外部数据集的计算特性,能够适应长时间序列MODIS LST缺值填补。     

Impact of land cover change on land surface temperature: A case study of Spiti Valley

Land surface temperature(LST) is the skin temperature of the earth surface. LST depends on the amount of sunlight received by any geographical area. Apart from sun light, LST is also affected by the land cover, which leads to change in land surface temperature. Impact of land cover change(LCC) on LST has been assessed using Landsat TM5, Landsat 8 TIRS/OLI and Digital Elevation Model(ASTER) for Spiti Valley, Himachal Pradesh, India. In the present study, Spiti valley was divided into three altitudinal zones to check the pattern of changing land cover along different altitudes and LST was calculated for all the four land cover categories extracted from remote sensing data for the years of 1990 and 2015. Matrix table was used as a technique to evaluate the land cover change between two different years. Matrix table shows that as a whole, about 2,151,647 ha(30%) area of Spiti valley experienced change in land cover in the last 25 years. The result also shows vegetation and water bodies increased by 107,560.2 ha(605.87%) and 45 ha(0.98%), respectively. Snow cover and barren land decreased by 19,016.5 ha(23.92%) and 88,589(14.14%), during the study period. A significant increase has been noticed in vegetation amongst all land cover types. Minimum, maximum and mean LST for three altitudinal zones have been calculated. The mean LST recorded was 11℃ in 1990 but it rose by 2℃ and reached to 13℃ in 2015. Changes in LST were obtained for each land cover categories. The mean temperature of different land cover types was calculated by averaging value of all pixels of a given land cover types. The mean LST of vegetation, barren land, snow cover and water body increased by 6℃, 9℃, 1℃, and 7℃, respectively. Further, relationships between LST, Normalized Difference Snow Index(NDSI), and Normalised Difference Vegetation Index(NDVI) were established using Linear Regression.     

面向GF-1 WFV数据和MODIS数据的时空融合算法对比分析

对城市热岛效应、植物覆盖指数、叶面积指数等地表参数的高频次高精度反演,能更好地实现基于遥感手段的地表特征动态监测。然而,目前单一数据源的遥感影像还很难实现高时空分辨率数据的同步获取,时空融合技术是解决这个时空分辨率矛盾的有效方法。根据原理不同,时空融合算法可以分为基于线性模型的融合算法、基于光谱解混的融合算法等。高分卫星产品是近几年中国高分辨率对地观测系统重大专项天基系统中的首发星,对于该类数据的时空融合研究仍然较少。因此,本文拟采用4种常见的时空融合算法（STARFM、FSDAF、STDFA、Fit_FC）实现GF-1 WFV数据与MODIS数据的时空融合,分析这几种方法对GF-1 WFV数据时空融合的有效性和精度,从而为后续的研究提供一定依据。     

Surface heat budget and solar radiation allocation at a melt pond during summer in the central Arctic Ocean

The heat budget of a melt pond surface and the solar radiation allocation at the melt pond are studied using the 2010 Chinese National Arctic Research Expedition data collected in the central Arctic. Temperature at a melt pond surface is proportional to the air temperature above it. However, the linear relationship between the two varies, depending on whether the air temperature is higher or lower than 0℃. The melt pond surface temperature is strongly influenced by the air temperature when the latter is lower than 0℃. Both net longwave radiation and turbulent heat flux can cause energy loss in a melt pond, but the loss by the latter is larger than that by the former. The turbulent heat flux is more than twice the net longwave radiation when the air temperature is lower than 0℃. More than 50% of the radiation energy entering the pond surface is absorbed by pond water. Very thin ice sheet on the pond surface(black ice) appears when the air temperature is lower than 0℃; on the other hand, only a small percentage(5.5%) of net longwave in the solar radiation is absorbed by such a thin ice sheet.     

复杂山区地形条件下ERA5再分析地表气温降尺度方法

高时空分辨率的气温栅格数据是多种地学模型和气候模型的重要输入。山区地形复杂,气温空间异质性强,如何获取高时空分辨率的山区地表气温数据一直是研究热点与难点。本文选择地形复杂的河北省张家口市作为试验区,基于局部薄盘样条函数对ERA5再分析日均近地表气温（2 m高度）进行空间插值,并利用随机森林算法,结合少量气象站观测气温数据、地形地表参数数据构建日均气温订正模型和气温逐时化模型,实现空间分辨率由0.1 °（约11 km）到30 m的逐时气温降尺度,最后将该模型拓展应用于其他时间与区域,检验本文发展的降尺度方法在没有站点观测数据条件下的时空移植性。结果显示,本文降尺度方法得到的高时空分辨率山区气温数据精度较高,1月均方根误差（RMSE）平均值为2.4 ℃,明显优于气象站点插值结果,且气温相对高低的空间分布更为合理、纹理更加丰富;将该方法应用到其他时间与区域的RMSE平均值分别为2.9 ℃与2.5 ℃,均小于再分析资料直接插值所产生的误差。研究结果总体表明,在气象站点较少甚至没有时,可利用本文方法通过ERA5再分析气温准确获取复杂地形条件下的山区高时空分辨率气温数据。     

Retrieval of Land-surface Temperature from AMSR2 Data Using a Deep Dynamic Learning Neural Network

It is more difficult to retrieve land surface temperature(LST) from passive microwave remote sensing data than from thermal remote sensing data, because the emissivities in the passive microwave band can change more easily than those in the thermal infrared band. Thus, it is very difficult to build a stable relationship. Passive microwave band emissivities are greatly influenced by the soil moisture, which varies with time. This makes it difficult to develop a general physical algorithm. This paper proposes a method to utilize multiple-satellite, sensors and resolution coupled with a deep dynamic learning neural network to retrieve the land surface temperature from images acquired by the Advanced Microwave Scanning Radiometer 2(AMSR2), a sensor that is similar to the Advanced Microwave Scanning Radiometer Earth Observing System(AMSR-E). The AMSR-E and MODIS sensors are located aboard the Aqua satellite. The MODIS LST product is used as the ground truth data to overcome the difficulties in obtaining large scale land surface temperature data. The mean and standard deviation of the retrieval error are approximately 1.4° and 1.9° when five frequencies(ten channels, 10.7, 18.7, 23.8, 36.5, 89 V/H GHz) are used. This method can effectively eliminate the influences of the soil moisture, roughness, atmosphere and various other factors. An analysis of the application of this method to the retrieval of land surface temperature from AMSR2 data indicates that the method is feasible. The accuracy is approximately 1.8° through a comparison between the retrieval results with ground measurement data from meteorological stations.     

全球36 km格网土壤水分逐日估算

土壤水分是陆面生态系统和能量循环的核心变量之一,利用微波遥感技术获得的土壤水分产品的时间分辨率一般是2-3 d,因此精确地获得具有较高时间分辨率的土壤水分成了人们关注的焦点。本文尝试将SMAP (the Soil Moisture Passive and Active）土壤水分和MODIS光学数据相结合,利用广义回归神经网络进行全球36 km土壤水分的估算,提升SMAP土壤水分的时间分辨率。结果显示,广义回归神经网络估算土壤水分与SMAP保持了高相关性(r = 0.7528),但其却保留了较高的误差 (rmse = 0.0914 m³/m³)。尽管如此,估算的土壤水分能够很好地保持SMAP土壤水分的整体空间变化,并且提升了土壤水分的时间分辨率（1 d）。此处,本文研究了SMAP土壤水分与MODIS光学数据之间的关系,这对今后利用机器学习进行SMAP土壤水分降尺度研究提供了重要的参考价值。     

Estimation of mass elevation effect and its annual variation based on MODIS and NECP data in the Tibetan Plateau

The lofty and extensive Tibetan Plateau has significant mass elevation effect(MEE). In recent years, a great effort has been made to quantify MEE, with the recognition of intra-mountain basal elevation(MBE) as the main determinant of MEE. In this study, we improved the method of estimating MEE with MODIS and NECP data, by refining temperature laps rate, and dividing MBE plots, and then analyzed the spatio-temporal variation of MEE in the Plateau. The main conclusions include: 1) the highest average annual MEE of the plateau is as high as 11.5488°C in the southwest of the plateau, where exists a high-MEE core and MEE takes on a trend of decreasing from the core to the surrounding areas; 2) in the interior of the plateau, the maximum monthly MEE is 14.1108°C in the highest MBE plot(4934 m) in August; while the minimum monthly MEE appeared primarily in January and February; 3) in the peripheral areas of the plateau, annual mean MEE is relatively low, mostly between 3.0068°C–5.1972°C, where monthly MEE is high in January and December and low in June and July, completely different from the MEE time-series variation in the internal parts of the plateau.     

福建省地表温度与植被覆盖度的相关性分析

地表温度（Land surface Tenperature, LST）和植被覆盖度（Fractional Vegetation Coverage, FVC)是生态环境变化的重要指标因子,研究两者的时空变化及相互关系对评价区域生态环境建设、改善区域生态环境具有重要意义。本文以福建省为研究区域,利用2001-2015年MODIS 11A2 LST和13Q1 NDVI数据,在时序数据重构的基础上对福建省LST时空变化及LST与FVC的相互关系进行分析。结果表明：①2001-2015年福建省LST总体呈轻微下降趋势,尤其是2010年之后其LST明显降低。LST与FVC的空间分布具有较好的负相关一致性：在FVC较高的区域,LST值较低;在FVC较低的区域,LST较高。② LST与FVC、DEM和纬度均成负相关关系,且负相关性在一年之中随着月份的变化而呈规律性增加或降低。夏季FVC对LST的负相关性最大为0.7,冬季FVC对LST的负相关性降低为0.4。③LST随着FVC增加而降低的趋势呈现分段线性关系,存在“FVC拐点”。“FVC拐点”前后随着FVC增加LST的降低速率在夏季 “先慢后快”,而在冬季则“先快后慢”。春秋两季,LST随着FVC增加而降低的速率在“FVC拐点”前后差异变小。在夏季,当FVC大于0.4时,FVC每增加0.1可降低LST约0.77 °C,降温效果大约是FVC小于0.4时的2倍。因此如果要有效地降低夏季地表高温,要使地表植被覆盖大于40%,才能较好的发挥植被的降温的作用。④在1-8月份,FVC对LST的负相关作用存在滞后性,FVC变化对滞后一个月的LST时空分布影响更大。研究成果对福建省生态环境建设与评估具有一定的意义,对于发挥植被对区域高温抑制作用提供了重要的参考依据。     

基于DEM修正的MODIS地表温度产品空间插值

地表温度是资源环境、气候变化、陆地生态系统等科学研究的重要参数之一。MODIS LST（Land Surface Temperature, LST）产品是地表温度相关研究的重要数据源。而现有MODIS LST产品均存在云覆盖区域,因此云覆盖区域地表温度估计已成为热红外遥感的前沿性研究难题。为解决MODIS LST产品云遮挡区域地表温度信息缺失,以秦岭地区为研究区,选用2001-2017年的MOD11A2数据,在传统的反距离权重（IDW）、规则样条函数（SPLINE）、普通克里金（OK）、趋势面（TREND）空间插值方法中引入高程因子,通过反复试验形成基于DEM修正的MODIS LST空间插值方法。分析空间插值结果表明： ① 空间插值精度由高到低为：OK>SPLINE>IDW>TREND,基于DEM修正后精度分别提高了约0.38、0.31、0.32和0.78℃; ② 空间插值结果的精度呈现季节差异,夏季6、7、8月的精度较高,1月的精度最低;③ 插值精度与云区的范围存在一定的关系,当云覆盖区域<1.1 km²时,DEM+OK方法的插值误差<0.55 ℃,当云覆盖区域<3.1 km²,插值误差<1 ℃;DEM+SPLINE方法在云覆盖区域<2.7 km²时,插值误差<0.55 ℃,云覆盖区域<10.4 km²,插值误差<1℃;当云覆盖为1.1~2.7 km²时,DEM+SPLINE方法的插值精度高于DEM+OK方法。     

Effect of altitude and latitude on surface air temperature across the Qinghai-Tibet Plateau

The correlation between mean surface air temperature and altitude is analyzed in this paper based on the annual and monthly mean surface air temperature data from 106 weather stations over the period 1961–2003 across the Qinghai-Tibet Plateau. The results show that temperature variations not only depend on altitude but also latitude, and there is a gradual decrease in temperature with the increasing altitude and latitude. The overall trend for the vertical temperature lapse rate for the whole plateau is approximately linear. Three methods, namely multivariate composite analysis, simple correlation and traditional stepwise regression, were applied to analyze these three correlations. The results assessed with the first method are well matched to those with the latter two methods. The apparent mean annual near-surface lapse rate is −4.8 °C /km and the latitudinal effect is −0.87 °C /^olatitude. In summer, the altitude influences the temperature variations more significantly with a July lapse rate of -4.3°C /km and the effect of latitude is only −0.28°C /^olatitude. In winter, the reverse happens. The temperature decrease is mainly due to the increase in latitude. The mean January lapse rate is −5.0°C /km, while the effect of latitude is −1.51°C /^olatitude. Comparative analysis for pairs of adjacent stations shows that at a small spatial scale the difference in altitude is the dominant factor affecting differences in mean annual near-surface air temperature, aided to some extent by differences of latitude. In contrast, the lapse rate in a small area is greater than the overall mean value for the Qinghai-Tibet Plateau (5 to 13°C /km). An increasing trend has been detected for the surface lapse rate with increases in altitude. The temperature difference has obvious seasonal variations, and the trends for the southern group of stations (south of 33° latitude) and for the more northerly group are opposite, mainly because of the differences in seasonal variation at low altitudes. For yearly changes, the temperature for high-altitude stations occurs earlier clearly. Temperature datasets at high altitude stations are well-correlated, and those in Nanjing were lagged for 1 year but less for contemporaneous correlations. The slope of linear trendline of temperature change for available years is clearly related to altitude, and the amplitude of temperature variation is enlarged by high altitude. The change effect in near-surface lapse rate at the varying altitude is approximately 1.0°C /km on the rate of warming over a hundred-year period.     

中国气候舒适度时空分布特征分析

气候舒适度对人类活动和地区适宜性评价等研究具有重要意义,而温湿指数是气候舒适度评价的一项重要指标。传统的温湿指数计算都是基于站点数据,无法获取大尺度区域舒适度的时空变化特征。本文利用2005—2018年MODIS地表温度、大气可降水量数据,结合地理加权回归方法对经典温湿指数模型进行改进,计算并分析中国年均和月均气候舒适度时空演变特征。结果如下：① 采用GWR方法进行地表温度和气温的拟合,拟合精度（Adjusted R²=0.9~0.98,RMSE=0.14~1.89 ℃）较为理想,说明采用LST、NDVI、DEM作为自变量的地理加权回归分析,能够较精确地拟合地面气温;② 2005—2018年年均温湿指数统计结果表示,云南省累计舒适月数最多,高达167个月,中部省份相对于东南沿海省市舒适时期较多,最高舒适月数差值可达到41个月。中国年均舒适度空间分布规律基本保持一致,除新疆、西藏和东北的部分区域以外,舒适度空间呈现从南到北,舒适度等级由舒适变寒冷。从舒适度等级面积变化情况看,2005—2018年全国舒适度等级呈现由寒冷变舒适的趋势;③ 2018年全年舒适面积最大的月份为5月,其次为10月,不舒适月份集中在1月和7月,全国呈现极冷或极热。春季和秋季空间分布特征较为相似,呈现由东南到西北逐渐递减的趋势;除青藏高原地区外,夏季和冬季呈现由南到北递减趋势。舒适区域主要集中在低纬、中海拔地区。