MODIS水汽通量估算方法在华北平原农田的适应性验证

利用遥感手段估算区域水汽通量对研究区域气候变化及生态系统功能评价颇具意义。但是由于估算模式涉及时空差异很大的地表特征参数很难完全通过遥感数据获得,因此MODIS水汽通量数据产品 (MOD16) 至今尚未问世。本研究以中科院禹城综合实验站2002年4~5月份冬小麦田的涡度相关实测水汽通量为标准,验证MOD16算法所估算的农田水汽通量,结果表明直接使用MOD16算法计算的麦田水汽通量比实测水汽通量平均偏大近20%。对其中的作物三基点温度、空气动力学阻抗计算方法和植被覆盖度进行修正,修正后的MOD16计算结果和实测值非常吻合,1:1曲线斜率为0.9706,相关系数R₂为0.8845。这为利用MODIS数据大面积估算农田水汽通量提供了科学依据。     

基于MODIS数据的青藏高原气温与增温效应估算

利用2001-2007 年MODIS地表温度数据、137 个气象观测台站数据和ASTERGDEM数据, 采用普通线性回归分析方法(OLS)及地理加权回归分析方法(GWR), 研究了高原月均地表温度与气温的相关关系, 最终选择精度较高的GWR分析方法, 建立了高原气温与地表温度、海拔高度的回归模型。各月气温GWR回归模型的决定系数(Adjusted R²) 都达到了0.91 以上(0.91~0.95), 标准误差(RMSE) 介于1.16~1.58℃;约70%以上的台站各月残差介于-1.5~1.5℃之间, 80%以上的台站的残差介于-2~2℃之间。根据该模型, 估算了青藏高原气温, 并在此基础上, 将高原及周边地区7 月份月均气温转换到4500 m和5000 m海拔高度上, 对比分析高原内部相对于外围地区的增温效应。研究结果表明:(1) 利用GWR方法, 结合地面台站的观测数据和MODIS Ts、DEM等, 对高原气温估算的精度高于以往普通回归分析模型估算的精度(RMSE=2~3℃), 精度可以提高到1.58℃;(2) 高原夏半年海拔5000 m左右的高山区气温能达到0℃以上, 尤其是7 月份, 海拔4000~5500 m的高山区的气温仍能达到10℃左右, 为山地森林的发育提供了温度条件, 使高原成为北半球林线分布最高的地方;(3) 高原的增温效应非常突出, 初步估算, 在相同的海拔高度上高原内部气温要比外围地区高6~10℃。     

基于MOD16的山西省地表蒸散发时空变化特征分析

基于MOD16全球蒸散发产品和气象站点实测数据,运用变异系数法、Sen趋势法等研究了山西省2000—2014年地表蒸散发ET、潜在蒸散发PET的空间分布特征、变化趋势及影响因素。结果表明：① MOD16蒸散产品与气象站点实测蒸散发之间具有良好的时空相关性(R ²=0.90),其产品精度可以满足山西省蒸散发时空分布研究的要求;②山西省多年平均ET、PET分别为816.77、1608.46 mm,年内变化表现为先增高后下降的“单峰”型分布,二者差值在5月、6月最大,此时山西省最为干旱;③ 全省年平均ET呈现西北低、东南高的分布特征,PET呈西南高、东北低的分布特征,二者差值整体上较大,表现为全省地表水分比较缺乏,其中忻州、吕梁西部最为严重;④ 全省近15 a来ET和PET的年际变化都比较小,整体上全省PET在增加,ET在相对减少,意味着近15 a来干旱情况在加剧;⑤ ET、PET的时空变化与诸多气象因子相关,在空间尺度上与降水、相对湿度密切相关,在时间尺度上与气温、降水关系最为密切。     

基于FTIR和MODIS数据估算新疆沙漠宽波段地表比辐射率

基于FTIR和MODIS数据,建立了新疆沙漠宽波段（8～13.5 μm）地表比辐射率的最优估算模型。首先,利用傅立叶变换热红外光谱仪观测的塔克拉玛干沙漠地表比辐射率光谱数据,结合同期MODIS温度/比辐射率产品MOD11A1的29、31和32波段比辐射率值和MOD09A1的第7波段反射率值,建立宽波段地表比辐射率估算模型,并分别采用观测数据和光谱库数据验证了模型的精度,估算结果的均方根误差分别为0.0041和0.0081。其次,选择最优估算模型,利用MODIS数据,估算了新疆4个沙漠的宽波段地表比辐射率,得到了沙漠地表比辐射率的空间分布特征。结果表明：塔克拉玛干沙漠和库鲁克库姆沙漠气候干燥稳定,地表比辐射率分布较为均匀,范围为0.850～0.915;古尔班通古特沙漠受到植被和地表水分的影响,比辐射率空间分布不均匀,范围为0.890～0.915;库木塔格沙漠的地表比辐射率分布与其羽状地表类似,范围为0.860～0.910。     

夏季晴天沼泽湿地贴地气层气温和相对湿度日变化特征

2008年6～8月在洪河国家级自然保护区沼泽湿地0～15 m贴地气层内,进行了6个高度的气温和相对湿度的野外定位观测.根据实测数据,分析了夏季晴天沼泽湿地贴地气层的气温和相对湿度的日变化特征.结果表明,夏季晴天沼泽湿地贴地气层内,气温日变化曲线为单峰型曲线,各高度的日最高气温和日最低气温分别出现在14∶00和03∶00,日平均气温和气温日较差都随高度递减.气温廓线有夜间辐射型、早上过渡型、白天日射型及傍晚过渡型4种分布类型.夏季晴天,0.5～15 m的日平均气温直减率为0.10 ℃/m,5个梯度的日平均气温直减率分别为1.03 ℃/m(0.5～1.5 m)、0.41 ℃/m(1.5～3 m)、0.03 ℃/m(3～5 m)、-0.01 ℃/m(5～8 m)和-0.03 ℃/m(8～15 m).相对湿度日变化曲线呈U型曲线,各高度的日最大相对湿度和日最小相对湿度分别出现在03∶00和14∶00,日平均相对湿度随高度变化不显著,相对湿度日较差随高度递减.相对湿度廓线有夜间和日间2种分布类型,日间在0～8 m出现逆湿,最强逆湿出现在11∶00～13∶00.日间逆湿为沼泽湿地植物蒸腾作用影响的结果.夏季晴天,0.5～15 m的日平均相对湿度直减率为0.16%/m,5个梯度的日平均相对湿度直减率分别为-2.73%/m(0.5～1.5 m)、1.43%/m(1.5～3 m)、-0.02%/m(3～5 m)、0.06%/m(5～8 m)和0.39%/m(8～15 m).     

基于多源空间信息的缺资料地区地表日均大气温度空间分布数据获取研究

地表大气温度是区域水循环研究与模型模拟中的关键因子,其日尺度的空间分布信息是众多生态、水文模型的重要输入。对于缺资料地区,尤其是在地形复杂地区,地表大气温度空间分布数据往往难以获得。基于多源空间信息,首先利用KLEMEN法反演得到研究区卫星过境时刻瞬时地表气温空间分布信息,然后通过建立的时间尺度转化方程实现研究区日均气温空间分布数据的获取。结果表明:研究中所提取的瞬时气温数据精度较高,RMSE为2.33℃,R2约为0.78;所建立的时间尺度转化方程可信度高,R2约为0.98,RMSE约为2℃;在不依赖于地面观测数据的条件下,研究所提取的日均气温数据总体精度R2为0.90,RMSE为4.63℃,且高温部分模拟精度高于低温部分。研究方法具有很好的可移植性,可应用于其他缺资料地区。     

东北冻土区MODIS地表温度估算

地表温度作为重要的地表参数是驱动土壤热状态的主要因子,对冻土分布和活动层厚度变化的研究具有重要意义。常规方式获取地表温度数据往往来自气象站点监测,范围小且不连续。NASA官网提供的MOD11A1地表温度产品可以提供大范围地表温度数据,但在冬季由于对云与雪的混淆导致大量的数据缺失,影响该产品在东北冻土区的使用。根据对东北冻土区植被、裸土、水体、积雪等常见下垫面状况的遥感分类结果,利用劈窗算法反演2006年四幅少云或无云的MODIS1B卫星影像,并分别以气象站实测数据和MODIS温度产品进行验证和对比分析。结果表明:该方法得到地表温度结果与气象站点实测数据误差较小,平均绝对误差仅为1.24℃。且可根据分类情况较好的得到积雪区域地表温度的空间分布状况,与地表温度产品的一致性较高,弥补地表温度产品因为云和积雪的混淆所导致的数据缺失,得到较为完整的地表温度空间分布数据。     

基于TVDI的藏北地区土壤湿度空间格局

利用2010年DOY 209期的Terra/MODIS 16 d合成的植被指数(EVI)产品数据MOD13A2和8d天合成的地表温度(LST)产品数据MOD11 A2,构建LST-EVI特征空间,从而得到了条件温度植被干旱指数TVDI反映的藏北土壤湿度空间分布图.结合野外同步土壤表层水含量测试数据,二者表现出较好的相关...     

基于台站和MOD16数据的山东省蒸散及潜在蒸散时空变化

蒸散发的时空格局分析对理解气候变化与水资源之间的相互影响具有重要的作用。本文基于Penman-Monteith公式,利用MODIS全球蒸散发产品(MOD16)及气象站点的蒸发皿观测数据,先对数据精度进行评价,再从空间和时间两个尺度上对数据进行统计分析,系统阐释了2000-2014年山东省地表蒸散(ET)及潜在蒸散(PET)的时空分布特征及其与气象因子的相关性。主要结论为：①山东省不同区域蒸散分布差异明显,地表植被对ET的月际变化趋势有重要影响;②山东省ET及PET年际波动不大,全省ET均值为1529 mm,PET均值为2178 mm,年均ET与PET相对较大的差值说明该省整体相对缺水。③ET及PET的时空变化与诸多气象因子相关,其中与降水及温度的关系最为密切。     

近14a新疆南疆绿洲地区地表蒸散与干旱的时空变化特征研究

利用2001—2014年MOD16蒸散产品数据、MOD13植被[WTBX]NDVI[WTBZ]数据以及常规气象资料，基于植被指数、地表净辐射、气温优化改进混合型线性双源遥感蒸散模型，拟合地表蒸散分析实际蒸散（ET）、潜在蒸散（PET）时空动态变化特征，结合气象站实测蒸发皿数据验证MOD16数据在绿洲地区的适用性。进一步定义蒸散干旱指数（EDI）并计算△EDI进行研究区干旱特征分析，为大面积特殊地形蒸散估算研究和干旱监测提供一定依据。结果表明：（1） MOD16产品数据与研究区实测蒸发皿数据的相关性很好，通过0.01显著性检验，基于MOD16数据估算南疆绿洲地区蒸散量检验可行。（2） 2001—2014年均蒸散量总体变化不大，四季差异明显，ET与PET空间变化趋势相反；ET、PET年均差值较大，绿洲地区地表缺水情况严重。（3） EDI指数绿洲地区年均值总体偏大，△EDI对旱情的反映和干旱程度的判断比较可靠。     

MODIS-based estimation of air temperature of the Tibetan Plateau

The immense and towering Tibetan Plateau acts as a heating source and, thus, deeply shapes the climate of the Eurasian continent and even the whole world. However, due to the scarcity of meteorological observation stations and very limited climatic data, little is quantitatively known about the heating effect and temperature pattern of the Tibetan Plateau. This paper collected time series of MODIS land surface temperature （LST） data, together with meteorological data of 137 stations and ASTER GDEM data for 2001-2007, to estimate and map the spatial distribution of monthly mean air temperatures in the Tibetan Plateau and its neighboring areas. Time series analysis and both ordinary linear regression （OLS） and geographical weighted regression （GWR） of monthly mean air temperature （Ta） with monthly mean land surface temperature （Ts） were conducted. Regression analysis shows that recorded Ta is rather closely related to Ts, and that the GWR estimation with MODIS Ts and altitude as independent variables, has a much better result with adjusted R 2 〉 0.91 and RMSE = 1.13-1.53℃ than OLS estimation. For more than 80% of the stations, the Ta thus retrieved from Ts has residuals lower than 2℃. Analysis of the spatio-temporal pattern of retrieved Ta data showed that the mean temperature in July （the warmest month） at altitudes of 4500 m can reach 10℃. This may help explain why the highest timberline in the Northern Hemisphere is on the Tibetan Plateau.     

MODIS-based air temperature estimation in the southeastern Tibetan Plateau and neighboring areas

Climatic conditions are difficult to obtain in high mountain regions due to few meteorological stations and, if any, their poorly representative location designed for convenient operation. Fortunately, it has been shown that remote sensing data could be used to estimate near-surface air temperature (Ta) and other climatic conditions. This paper makes use of recorded meteorological data and MODIS data on land surface temperature (Ts) to estimate monthly mean air temperatures in the southeastern Tibetan Plateau and its neighboring areas. A total of 72 weather stations and 84 MODIS images for seven years (2001 to 2007) are used for analysis. Regression analysis and spatio-temporal analysis of monthly mean Ts vs. monthly mean Ta are carried out, showing that recorded Ta is closely related to MODIS Ts in the study region. The regression analysis of monthly mean Ts vs. Ta for every month of all stations shows that monthly mean Ts can be rather accurately used to estimate monthly mean Ta (R2 ranging from 0.62 to 0.90 and standard error between 2.25℃ and 3.23℃). Thirdly, the retrieved monthly mean Ta for the whole study area varies between 1.62℃ (in January, the coldest month) and 17.29 ℃ (in July, the warmest month), and for the warm season (May-September), it is from 13.1℃ to 17.29℃. Finally, the elevation of isotherms is higher in the central mountain ranges than in the outer margins; the 0℃ isotherm occurs at elevation of about 4500±500 m in October, dropping to 3500±500 m in January, and ascending back to 4500±500 m in May next year. This clearly shows that MODIS Ts data combining with observed data could be used to rather accurately estimate air temperature in mountain regions.     

Mapping Air Temperature in the Lancang River Basin Using the Reconstructed MODIS LST Data

Air temperature is an important climatological variable and is usually measured in meteorological stations. Accurate mapping of its spatial and temporal distribution is of great interest for various scientific disciplines, but low station density and complexity of the terrain usually lead to significant errors and unrepresentative spatial patterns over large areas. Fortunately the current studies have shown that the regression models can help overcome the problem with the help of time series remote sensing data. However, noise induced by cloud contamination and other atmospheric disturbances variability impedes the application of LST data. An improved Savizky-Golay (SG) algorithm based on the LST background library is used in this paper to reconstruct MODIS LST product. Data statistical analysis included 12 meteorological stations and 120 reconstructed MODIS LST images of the period from 2001 to 2010. The coeffificient of correlations (R²) for 80% of the stations was higher than 0.5 (below 0.5 for only 2 stations) which illustrated that there is a considerably close agreement between monthly mean TA (air temperature) and the reconstructed LST in the Lancang River basin. Comparing to the regression model for every month with only LST data, the regression model with LST and NDVI had higher R² and RMSE. Finally, the LST-NDVI regression method was applied as an estimate model to produce distributed maps of air temperature with month intervals and 1 km spatial in the Lancang River basin of 2010.     

基于GPS数据的MODIS近红外水汽线性回归模型

针对地基GPS反演水汽空间不连续以及MODIS近红外水汽产品精度不足的问题,利用2010年香港地区地基GPS水汽数据和MODIS近红外水汽数据,提出了一种基于地基GPS订正MODIS水汽产品继而得到高精度空间连续分布可降水汽的方法。通过比较3种干延迟模型,选取最适合香港地区的Hopfield模型并利用高精度软件GAMIT解算GPS水汽,建立GPS水汽与MODIS水汽之间的线性模型,以实现对MODIS近红外水汽产品的逐月订正。结果表明,经过订正的MODIS水汽产品在各月的MRE、RMSE、PBIAS均有所改善,该方法能够有效融合地基GPS和MODIS的优点,可以得到连续区域的高精度水汽数据,这对天气预报、气候监测等有重要意义。     

Effect of Long-term Experimental Warming on the Nutritional Quality of Alpine Meadows in the Northern Tibet

The nutritional quality of grasslands is closely related to recruitment of young and population dynamics of livestock and wild herbivores. However, the response of nutritional quality to climate warming has not been fully understood in the alpine meadow on the Tibetan Plateau, especially in the Northern Tibet. Here, we investigated the effect of experimental warming ( beginning in 2008) on nutritional quality in three alpine meadows (site A: 4313 m, B: 4513 m and C: 4693 m) in the Northern Tibet. Crude protein (CP), neutral detergent fiber (NDF), acid detergent fiber (ADF), crude ash (Ash), ether extract (EE) and water-soluble carbohydrate (WSC) were examined in 2018-2019. Experimental warming only increased the content of CP by 27.25%, ADF by 89.93% and NDF by 41.20%, but it decreased the content of Ash by 57.76% in 2019 at site B. The contents of CP and WSC both increased with soil moisture (SM). The content of CP decreased with vapor pressure deficit (VPD). The combined effect of SM and VPD was greater than air temperature (T_a) in controlling the variations of the CP content, ADF content and nutritional quality. Compared to T_a, VPD explained more of the variation in NDF and Ash content. All of these findings suggest that warming effects on nutritional quality may vary with site and year, and water availability may have a stronger effect on the nutritional quality than temperature in the alpine meadow of the Northern Tibet.     

辽河三角洲不同干湿气候区参考蒸散发敏感因子时空变化

根据辽河三角洲19个气象台站1961~2010年气象观测资料,采用Penman-Monteith参考蒸散发计算方法,分析辽河三角洲半湿润区、半干旱区以及滨海干湿过渡区3个气候亚区参考蒸散发对平均气温、风速、相对湿度和太阳辐射的敏感性及其时空分异。结果表明：在半干旱区,敏感系数由大到小依次是相对湿度、风速、太阳辐射和平均气温;在半湿润区和滨海干湿过渡区,敏感系数由大到小依次是相对湿度、太阳辐射、平均气温和风速。不同气候亚区参考蒸散发对气象因子的敏感系数具有较大的差异和变化趋势。     

基于Modis地表温度的青藏高原东部及其邻近地区气温估算(英文)

Climatic conditions are difficult to obtain in high mountain regions due to few meteorological stations and, if any, their poorly representative location designed for convenient operation. Fortunately, it has been shown that remote sensing data could be used to estimate near-surface air temperature (Ta) and other climatic conditions. This paper makes use of recorded meteorological data and MODIS data on land surface temperature (Ts) to estimate monthly mean air temperatures in the southeastern Tibetan Plateau and its neighboring areas. A total of 72 weather stations and 84 MODIS images for seven years (2001 to 2007) are used for analysis. Regression analysis and spatio-temporal analysis of monthly mean Ts vs. monthly mean Ta are carried out, showing that recorded Ta is closely related to MODIS Ts in the study region. The regression analysis of monthly mean Ts vs. Ta for every month of all stations shows that monthly mean Ts can be rather accurately used to estimate monthly mean Ta (R2 ranging from 0.62 to 0.90 and standard error between 2.25℃ and 3.23℃). Thirdly, the retrieved monthly mean Ta for the whole study area varies between 1.62℃ (in January, the coldest month) and 17.29℃ (in July, the warmest month), and for the warm season (May-September), it is from 13.1℃ to 17.29℃. Finally, the elevation of isotherms is higher in the central mountain ranges than in the outer margins; the 0℃ isotherm occurs at elevation of about 4500±500 m in October, dropping to 3500±500 m in January, and ascending back to 4500±500 m in May next year. This clearly shows that MODIS Ts data combining with observed data could be used to rather accurately estimate air temperature in mountain regions.     

基于Modis地表温度的横断山区气温估算及其时空规律分析

横断山区气象观测站稀少且多分布在河谷之中,气温资料极度匮乏,严重影响山区地理与生态研究。随着遥感技术的发展,热红外遥感数据,结合地面观测数据,可以用来推测山区气温。本文通过对横断山区2001 年-2007 年间64 个气象台站的多年月平均气温数据(Ta) 与Modis地表温度多年月平均值(Ts) 进行了时序分析和回归分析,并取得如下研究结果：(1) Ts 与Ta 具有非常好的线性相关关系,89%的台站的决定系数高于0.5;95%的台站的标准误差都低于3 oC,84.4%的台站标准误差低于2.5 oC;12 个月份的Ts 与Ta 的决定系数R²在0.63~0.90 之间,标准误差在2.22~3.05 oC之间。(2) 研究区内月均气温的变化范围在-2.25~15.64 oC之间;生长季(5-9 月份) 的月均气温变化范围为：10.44~15.64 oC。(3) 等温线的海拔高度自山体外围向内部逐渐升高,与山体效应的增温效应相吻合;0 oC等温线自10 月份从海拔4700±500 m左右逐渐降低,至1月份降至最低点,约在3500±500 m左右,此后,逐渐回升,至次年5 月份再次达到4700±500 m左右,也就是说横断山区5200 m以下的广大山区全年至少有6~12 个月的气温在0 oC以上。研究表明：可以利用Modis月均地表温度结合地面观测台站的数据较精确的估算山区月均气温。