利用任意时刻AVHRR资料近似估计

根据表征土壤温度日变化的土温方程一阶近似解,提出一种利用一日中任意时刻NOAA-AVHRR图像数据估计地表温度日较差的简易推算方法.该方法尤其可用于反演土壤含水量的计算,当图像数据的时次与热惯量法所要求的最高、最低温度时次配不准的情况下,近似地估计地表温度日较差,进而计算地表热惯量及相应的土壤含水量.用实例验证了该方法的可行性,为进一步推广应用奠定基础.     

土壤质地对遥感监测干旱的影响

传统的表观热惯量法没有考虑土壤质地的影响。由于不同土壤的热性质不同,即使土壤水分相同,其地表温度日较差也不同,因而用表观热惯量法进行监测时存在较大误差。本文在 Ｇ Ｉ Ｓ支持下,分土壤质地建立了土壤水分遥感监测模型,从而提高了监测精度。     

NOAA卫星遥感监测不同作物农田土壤湿度模型

根据对新疆几个主要农作物耕种田土壤含水量和土壤温度观测资料，统计分析了土壤含水量与地温的相互关系。结果表明，同样土质条件下（本试验区均为沙壤土），种植不同作物，其土壤湿度大小所响应的地温要素不同，有的与凌晨地温（T08）相关好，有的对中午地温（T14）反应灵敏；有的和地面日温度相关突出，有的则与浅层地温关系好，总体看，土壤湿度与地面日温差（△T＝T14－T08，近似为日较差）相关最佳，其道理众所周知，是土壤的热惯量差异所致。反言之，不同作物田，即使土壤含水量和其它条件都一样，其△T可能不一样，而且变化最敏感的△T所在深度也不同。本文给出了四种农作物生长田里土壤湿度和地温的响应模式，并推论可以用AVHRR辐射温度（亮温）的日差值Tb代替△T，进而提供一种利用NOAA卫星遥感资料监测不同作物区土壤湿度分布的模型。     

基于遥感与GIS集成的土壤墒情监测服务系统

遥感与GIS集成土壤墒情监测服务系统运行于Windows平台,基于遥感与GIS集成技术,利用热惯量法、植被缺水指数法、植被温度条件指数法和单时相资料回归法等多种模式计算土壤含水量。在遥感监测与墒情分析中考虑背景地理信息的影响,提高了遥感墒情监测的精度和服务水平。该系统具有多种墒情模式计算、图像显示、图像叠加、统计分析、模板处理和输出等功能。应用该系统得出的2005年4月河南省遥感墒情分布图和干旱面积与实测结果基本一致。     

遥感技术在土壤墒情监测中的应用

文章利用2005年3月28~30日和4月26~30日极轨气象卫星NOAA—16白天和夜间两个时次的遥感监测数据,计算出可以反映地表热特性的土壤热惯量,制作出全区各盟市的土壤墒情遥感监测产品图。并对图像进行拼接和增强等技术处理,然后对2005年春季内蒙古土壤墒情的分布状况进行分析。     

高产田小麦千粒重农业气象条件的研究

气象条件与小麦千粒重的关系极为密切。乳熟期为综合气候关键期，土壤含水量和温度日较差为影响千粒重的主导农业气象因子，土壤相对湿度在５５￣７５％、温度日较差〉１２℃为千粒重高度值年；而土壤湿度〉８０％或〈５２％及温度日较差〈１０．５℃时为千粒重的低值年。提出，浇透抽穗，扬花水或早浇灌浆水均有利于千粒重的提高。     

GMS 5反演中国几类典型下垫面晴空地表温度的日变化及季节变化

文中利用单时相双光谱分裂窗算法以GMS 5/VISSR红外资料反演地表温度,揭示了中国几类典型下垫面晴空地表温度的日变化及季节变化特征.塔里木盆地、青藏高原、浑善达克沙地、华北平原北部、华南部分地区因地表反射率、土壤含水量、受太阳辐射影响程度不同等地表温度季节变化差异很大,月平均地表温度日较差一年内基本上呈双峰双谷型.作为比较,东亚部分陆地的地表温度与台湾海峡南部、黄海的海表温度及其日变化、季节变化一并进行了分析.塔里木盆地、浑善达克沙地不仅具有强烈的日变化,而且季节变化也显著.2000年两地月平均地表温度日较差最大值超过30 K,浑善达克沙地的年较差高达58.50 K.青藏高原地表温度季节变化小于东亚部分陆地、塔里木盆地、浑善达克沙地,但该区日变化幅度在所研究几个区中最大,2000年年平均日较差达28.05 K.文中将研究时段扩充到1998～2000年后揭示了连续三年地表温度及其日变化的年际变化特征.所获得这几类地表温度的变化特征与量值对于气候与辐射收支研究以及推测地表状况会有一定参考价值.     

土中和地表温度影响因子的分析

本文用一个土壤和地表温度的计算模式,讨论了土壤物理参数、大气中的云量、大气透明度、土壤模式层厚度以及温度初值给定方法等因子对土壤和地表温度计算结果的影响。表明:土壤密度和比热、土壤导温率越大则地表和土壤内温度日变化越小;但是,土壤内的热量传输越大。云量影响也是显著的,尤其是对土壤内温度的影响,在云量增加时,温度和热量通量均减小。透明度的影响与云量的影响类似。土壤模式层厚度的增加,对地表温度的影响较小,对土壤温度的日振幅影响很大。在夏季,温度初值对地表温度和热通量影响较小,对土壤温度影响较大。     

华北平原中部草面温度变化特征

利用华北平原中部地下水科学与工程试验基地2009～2011年逐日的草面温度、地表温度和气温观测数据,分析讨论了该地区草面温度平均值、极端值、日较差特征以及草面温度与地表温度、气温三者之间的变化关系。     

高寒草甸和高寒湿地土壤水热特征比较

利用青藏高原玛多地区高寒草甸和玉树隆宝地区高寒湿地的观测资料,比较分析了土壤水分、地表反照率和土壤热通量在土壤完全融化期、土壤逐渐冻结期、土壤完全冻结期和土壤逐渐融化期的变化情况,并计算了各月份的感热通量和潜热通量。结果表明:在10~50 cm深处,土壤完全融化期高寒湿地土壤含水量为0.66~0.82 m3·m-3,高寒草甸土壤含水量为0.15~0.18 m3·m-3,土壤完全冻结期高寒湿地土壤含水量为0.13~0.21 m3·m-3,高寒草甸土壤含水量为0.01~0.04 m3·m-3。高寒草甸和高寒湿地地表反照率在土壤冻结期间较高,融化期间较低。高寒草甸土壤热通量年变化幅度小,高寒湿地土壤热通量年变化幅度大。高寒草甸月平均感热通量均高于高寒湿地,高寒湿地月平均潜热通量均高于高寒草甸。     

用遥感资料估算深层土壤水分的方法和模型

该文以热惯量法为基础,在EPPL7地理信息系统（GIS）的支持下,考虑土壤质地的影响,探讨了利用NOAA/AVHRR遥感资料估算深层土壤水分的方法和模型。结果表明:表层土壤水分与深层土壤水分之间有较好的非线性关系,可以用遥感得到的表层土壤水分去估算深层土壤水分,且效果优于直接分层建立统计模型。     

考虑植被的热惯量法反演土壤湿度的一次试验

应用NOAA／AVHRR通道1、通道2和通道4资料计算表观热惯量和植被指数，并结合农业气象试验站观测的土壤湿度，分别建立了传统的和考虑植被指数的表观热惯量估算实验区域土壤湿度的方程。     

Relationships between surface albedo,soil thermal parameters and soil moisture in the semi-arid area of Tongyu,northeastern China

Continuous observation data collected over the whole year of 2004 on a cropland surtace m Tongyu, a senti-arid area of northeastern China （44°25＇N, 122°52＇E）, have been used to investigate the variations of surface albedo and soil thermal parameters, including heat capacity, thermal conductivity and thermal diffusivity, and their relationships to soil moisture. The diurnal variation of surface albedo appears as a U shape curve on sunny days. Surface albedo decreases with the increase of solar elevation angle, and it tends to be a constant when solar elevation angle is larger than 40°. So the daily average surface albedo was computed using the data when solar elevation angle is larger than 40° Mean daily surface albedo is found to decrease with the increase of soil moisture, showing an exponential dependence on soil moisture. The variations of soil heat capacity are small during Julian days 90 300. Compared with the heat capacity, soil thermal conductivity has very gentle variations during this period, but the soil thermal diffusivity has wide variations during the same period. The soil thermal conductivity is found to increase as a power function of soil moisture. The soil thermal diffusivity increases firstly and then decreases with the increase of soil moisture.     

土壤湿度和土壤温度模拟中的参数敏感性分析和优化

使用一种复杂洗牌算法 (SCE-UA, Shuffled Complex Evolution Algorithm) 对Noah陆面模式中的参数进行敏感性分析和优化,其中水文参数采取直接优化和优化土壤成份的形式,侧重于研究两种水文参数给出方法对土壤湿度和土壤温度模拟的敏感性。结果表明:将土壤湿度和土壤温度作为判据,模式中水文参数敏感性最高,水文参数对土壤湿度的敏感性要高于对土壤温度的敏感性。表层土壤湿度作为判据对土壤水文参数优化后,可以改善土壤湿度和土壤温度的模拟,加入深层土壤湿度同时作为判据后,优化使土壤温度的模拟变差。当土壤成份作为优化的参数,表层和深层土壤湿度作为判据,优化能够同时改善土壤湿度和土壤温度的模拟。单独使用土壤温度作为判据不能达到优化水文参数的目的。将土壤成份作为优化的参数后,土壤湿度和土壤温度的多判据优化效果最好,且减少不敏感参数的个数后对优化结果的影响总体不大。基于以上结果,将土壤成份作为优化水文参数的方法能够更好的考虑不同水文参数之间的约束关系,优化后的水文参数具有很好的一致性,优化效果较直接优化水文参数更好。     

古尔班通古特沙漠土壤物理参数时间变化特征及其影响因子研究

以古尔班通古特沙漠为研究靶区,利用2020年全年克拉美丽陆-气相互作用观测试验站连续观测数据,分析了古尔班通古特沙漠土壤温湿度、土壤热通量、土壤盐分及导热率等主要土壤参数变化特征及影响因子。结果表明：（1）古尔班通古特沙漠土壤温度年日均值变化呈倒“U”型,季节变化特征明显,总体表现为夏季>春季>秋季>冬季,浅层土壤温度的变化幅度大于深层,湿度变化特征为春夏高,秋冬低,通常表现为随土壤深度增加土壤湿度逐渐升高;土壤热通量变化总体表现为春夏高,秋冬低,日变化幅度春夏秋冬依次递减。（2）古尔班通古特沙漠土壤导热率年均值为0.832 W·m-1·K-1,导热率与降水呈显著的正相关,土壤温湿度、土壤盐分是影响沙漠区土壤导热率的主要因子。在冻土条件下,土壤导热率平均为0.634 W·m-1·K-1,且其随土壤湿度增加而增加,冻土时导热随湿度增加的速率约为非冻土时的2.5倍;在降水条件下,土壤含水量小于0.06 m3·m-3时土壤导热率呈现缓慢增加趋势,大于0.06 m3·m-3时随湿度上升而迅速增加;在融雪时期,土壤含水量小于0.11 m3·m-3时土壤导热率随湿度上升缓慢增加,大于0.11 m3·m-3时土壤导热率迅速上升。     

基于MODIS数据的作物苗期干旱监测方法

针对苗期低植被覆盖情况,在原有的归一化植被指数基础上,增加了土壤调节植被指数,将其与条件温度指数结合,通过与地面实测土壤相对湿度进行相关分析,分别建立作物苗期的干旱监测模型,并与热惯量方法和供水植被指数方法进行对比分析,初步得出辽宁省范围内作物苗期进行大范围干旱遥感监测的最适宜模型为土壤调节植被指数一温度模型.     

A model of the ground surface temperature for micrometeorological analysis

Micrometeorological models at various scales require ground surface temperature, which may not always be measured in sufficient spatial or temporal detail. There is thus a need for a model that can calculate the surface temperature using only widely available weather data, thermal properties of the ground, and surface properties. The vegetated/permeable surface energy balance (VP-SEB) model introduced here requires no a priori knowledge of soil temperature or moisture at any depth. It combines a two-layer characterization of the soil column following the heat conservation law with a sinusoidal function to estimate deep soil temperature, and a simplified procedure for calculating moisture content. A physically based solution is used for each of the energy balance components allowing VP-SEB to be highly portable. VP-SEB was tested using field data measuring bare loess desert soil in dry weather and following rain events. Modeled hourly surface temperature correlated well with the measured data (r ² = 0.95 for a whole year), with a root-mean-square error of 2.77 K. The model was used to generate input for a pedestrian thermal comfort study using the Index of Thermal Stress (ITS). The simulation shows that the thermal stress on a pedestrian standing in the sun on a fully paved surface, which may be over 500 W on a warm summer day, may be as much as 100 W lower on a grass surface exposed to the same meteorological conditions.     

A Variational Method for Estimating Near-Surface Soil Moisture and Surface Heat Fluxes

A variational data assimilation method is proposed to estimate the near-surface soil moisture and surface sensible and latent heat fluxes.The method merges the five parts into a cost function,i.e.,the differences of wind,potential temperature,anti specific humidity gradient between observations and those computed by the profile method,the difference of latent heat fluxes calculated using the ECMWF land surface evaporation scheme and the profile method,and a weak constraint for surface energy balance.By using an optimal algorithm,the best solutions are found.The method is tested with the data collected at Feixi Station (31.41°N,117.08°E) supported by the China Heavy Rain Experiment and Study (HeRES) during 7-30 June 2001.The results show that estimated near-surface soil moistures can quickly respond to rainfall,and their temporal variation is consistent with that of measurements of average soil moisture over 15-cm top depth with a maximum error less than 0.03 m~3 m~(-3).The surface heat fluxes calculated by this method are consistent with those by the Bowen ratio method,but at the same time it can overcome the instability problem occurring in the Bowen ratio method when the latter is about-1.Meanwhile,the variational method is more accurate than the profile method in terms of satisfying the surface energy balance.The sensitivity tests also show that the variational method is the most stable one among the three methods.     

Trends and scales of observed soil moisture variations in China

A new soil moisture dataset from direct gravimetric measurements within the top 50-cm soil layers at 178 soil moisture stations in China covering the period 1981-1998 are used to study the long-term and seasonal trends of soil moisture variations, as well as estimate the temporal and spatial scales of soil moisture for different soil layers. Additional datasets of precipitation and temperature difference between land surface and air (TDSA) are analyzed to gain further insight into the changes of soil moisture. There are increasing trends for the top 10 cm, but decreasing trends for the top 50 cm of soil layers in most regions. Trends in precipitation appear to dominantly influence trends in soil moisture in both cases. Seasonal variation of soil moisture is mainly controlled by precipitation and evaporation, and in some regions can be affected by snow cover in winter. Timescales of soil moisture variation are roughly 1-3 months and increase with soil depth. Further influences of TDSA and precipitation on soil moisture in surface layers, rather than in deeper layers, cause this phenomenon. Seasonal variations of temporal scales for soil moisture are region-dependent and consistent in both layer depths. Spatial scales of soil moisture range from 200-600 km, with topography also having an affect on these. Spatial scales of soil moisture in plains are larger than in mountainous areas. In the former, the spatial scale of soil moisture follows the spatial patterns of precipitation and evaporation, whereas in the latter, the spatial scale is controlled by topography.     

用NOAA/AVHRR资料遥感土壤水分时风速的影响

以热惯量法为基础,在地理信息系统（ＧＩＳ）的支持下,通过计算地形参数Ｒ与Ｆ,间接考虑了风速对用ＮＯＯＡＡ／ＡＶＨＲＲ资料遥感土壤水分的影响。结果表明：考虑风速后,遥感土壤水分的精度比热惯量法有所提高;风速对遥感土壤水分的影响主要限于土壤浅层,到３０ｃｍ深度以下时可以不考虑其影响;遥感土壤水分的最佳深度并不在土壤表层,而在２０ｃｍ深度左右。