植被覆盖地表主动微波遥感反演土壤水分算法研究

主动微波遥感监测土壤水分具有全天时、全天候并对地物有一定的穿透能力等特点,突破了传统测量方法和光学遥感获取土壤水分的局限。文中在分析植被对微波信号影响的基础上,总结了当前国内外基于主动微波遥感监测植被覆盖地表土壤水分的原理和方法,指出利用，宋朝景“水-云模型”从总的极化雷达后向散射中去除植被影响后,能够改进后向散射系数和土壤含水量之间的关系。最后利用ENVISAT ASAR数据,结合实地采样获得的土壤含水量数据拟合两者之间的关系,结果表明农作物覆盖地表土壤水分变化的估算算法还需要进一步发展和改进以提高反演精度。     

基于Sentinel-1和Sentinel-2协同反演干旱区地表土壤水分--以格尔木河中下游为例

摘要：土壤水分是全球水循环的重要组成成分,对研究土壤水分的空间分布、农作物长势和产量 、气候变化、水资源时空分布等有着重要意义 。本文利用Sentinel（哨兵）系列主动微波雷达卫星SAR（Sentinel-1）结合光学卫星（Sentinel-2）对格尔木中下游低矮植被覆盖下的地表土壤水分进行反演研究, 探讨不同极化组合方式和水云模型前后的土壤水分含量反演方法的适用性。结果表明：其中VV (VV Polarization) 极化对比VH (VH Polarization) 极化更加适用该区域,VV极化结合归一化水指数 (NDWI）反演地表土壤水分精度达到42.6%,拟合精度最高,VH极化仅为22.6%;利用水云模型去除植被覆盖后对地表土壤水分的反演精度有所提升,其中,VV极化精度提高约3.5%,VH极化提高1.5%;Sentinel系列卫星影像对于干旱区的土壤水分的反演具有较好的适用性。本文旨在探索一种适用于该研究区乃至柴达木盆地土壤水分实现大面积实时监测的可靠依据和手段。     

干旱区稀疏植被覆盖条件下地表土壤水分微波遥感估算

当前在干旱区土壤水分研究工作中,开展利用微波遥感技术高精度估算稀疏植被覆盖条件下地表粗糙度参数的研究是一项非常有意义的工作。基于微波遥感数据,结合土壤水分实测资料,以IEM模型为基础,分析雷达后向散射系数与土壤含水量、地表粗糙度参数之间的关系,利用最小二乘法和非线性回归的方法,建立光滑地表条件下的土壤含水量模型。结果表明:模型提取的土壤水分与实测值之间有很好的相关性,决定系数达到0.886,所建立的土壤水分信息提取模型在裸土以及稀疏植被覆盖地区能够得到较好的应用效果。     

基于Sentinel-1和Sentinel-2协同反演干旱区地表土壤水分——以格尔木河中下游为例

土壤水分是全球水循环的重要组成成分,对研究水分的空间分布、农作物长势和产量、气候变化、水资源时空特征等有着重要意义.利用Sentinel(哨兵)系列主动微波雷达卫星SAR(Sentinel-1)结合光学卫星(Sentinel-2)对格尔木中下游低矮植被覆盖下的地表土壤水分进行反演研究,探讨不同极化组合方式和水云模型前后...     

基于AMSR-E被动微波遥感数据的广东省土壤水分变化监测

概述了植被覆盖地表微波辐射传输模型的原理和表示方法,指出在地表粗糙度、植被层和大气等影响相似的情况下,微波极化差异指数的变化突显土壤水分变化.利用AMSR-E星载被动微波遥感亮度温度数据,计算分析地表微波极化差异指数(MPDI)的月平均分布,以广东省为试验区,获得了2004- 2009年10月份广东省10.7 GHz通...     

基于HJ-1B/CCD地表反照率的估算

地表反照率是入射太阳能被地表反射的比分,决定地表吸收太阳辐射能的多少,是影响地表能量收支的关键参数.该文使用6S大气辐射传输模型对环境星的多光谱CCD数据进行大气纠正,并利用其观测角度信息,分别基于统计模型和BRDF模型估算研究区不同地表覆盖类型的窄波段地表反照率,并实现窄波段向宽波段反照率的转换.将宽波段反照率与MODIS反照率产品数据进行对比分析,对不同的地表覆盖类型选择模型的适宜性做出分析评价:对于水体,基于朗伯面假设采用统计模型效果较好,相对误差为9.2％;对于植被及非植被覆盖区,采用二向反射模型较合适,相对误差分别为4.02％和5.6％,都能够满足应用需求.     

微波遥感反演土壤水分中构建粗糙度参数的新方法

地表粗糙度是影响雷达后向散射系数的重要因素。该文在基于SAR影像反演地表土壤水分的过程中,考虑到地表粗糙度的野外测量误差、取值范围和雷达入射角等方面的影响,统计了裸土、农用地和草地等几种典型地表的粗糙度测量数据,以此限定AIEM模型的输入参数范围。首先,利用AIEM模型模拟雷达后向散射系数与粗糙度、土壤水分之间的关系,构建了基于曲面拟合思想的、与入射角相关的组合粗糙度参数,并以此为基础利用Envisat ASAR双极化数据(VV、VH)建立了土壤水分反演模型。经实测数据验证,在不同入射角范围内,基于该文建立的模型得到的土壤水分反演结果与实测值都有良好的相关性。与其他形式的组合粗糙度参数进行对比,该文提出的模型反演精度较高,能够适用于入射角范围在(5°,65°)内的SAR影像的反演。     

热红外遥感图像中云覆盖像元地表温度估算初论

云覆盖是热红外遥感应用和地表温度(Land Surface Temperature,LST)遥感定量反演的重要障碍。如何估算热红外遥感图像中云覆盖像元的地表温度,是热红外遥感的前沿研究难题。以地表热量平衡为基础,根据地表温度的空间分布连续性、植被对地表温度的影响,提出三种解决云覆盖像元地表温度估算方案:空间插值修正法、植被关系修正法和改进型地表热量平衡法,并探讨云覆盖区地表温度空间分布的洼地效应现象、洼地效应强度及计算方法。基于地表热量平衡方程的洼地效应强度因子和影像灰度值之间关系的数值模拟,是三种估算方案切实可行的关键。     

黄河流域植被覆盖度动态变化与降水的关系

利用8km分辨率Pathfinder NOAA-NDVI数据，对黄河流域1982－1999年地表植被覆盖的空间分布及时间序列变化进行了分析，并通过计算不同时段降水量与年最大NDVI之间的相关系数分析了降水对流域植被覆盖的影响。结果发现近20年来黄河流域平均植被覆盖度有增加趋势，但青藏高原上有所减小；汛期降水量的多少对地表植被覆盖度的年际变化起主要作用，其中草原地区影响最显著，而在森林植被区及部分灌溉农作区，降水的年际变化对地表覆盖的影响比较小。     

城市地表热通量遥感反演及与下垫面关系分析

选择日本埼玉县熊谷市为研究区,应用ASTER热红外遥感图像,采用PCACA模型以及理论定位算法,对城市地表热通量的相关参数进行反演,进而分析城市下垫面不同土地覆盖类型对地表热通量以及能量平衡的影响.结果表明,PCACA模型应用于城市区域地表通量估算是可行的.这种算法可以大大减少在下垫面结构复杂的城市区域进行地表热通量估算时所需的参数,并有效降低研究结果的不确定性.研究发现,城乡不同下垫面地表覆盖类型对地表热通量的影响差异显著.不同地表下垫面类型的波文比由大到小顺序依次为:工业用地＞住宅用地＞交通用地＞公共设施用地＞旱田＞公共绿地＞水域.在相同的外部气象条件下,与城市周边的植被覆盖区相比,城市人工建筑用地具有较高的显热通量,较低的潜热通量,以及较高的波文比.由于城市地表显热通量和波文比明显高于周边植被覆盖地表,导致城市地表向低层大气供热的增加,这是城市热岛效应形成的重要机制之一.     

Monitoring soil moisture dynamics using satellite imaging radar in northeastern Jordan

This paper describes how synthetic aperture radar (SAR) data from the European Environmental Remote Sensing (ERS) satellite series were used to derive estimates of near-surface soil moisture for seasonally vegetated and bare soil surfaces in the area of the Jordan Badia Research and Development Programme (BRDP). Data were acquired between March 1995 and April 1998, covering both wet and dry seasons. A surface scattering model was calibrated using field data: first to understand how soil moisture affects the SAR signal and, secondly, to predict the response of the SAR signal to changes in volumetric soil moisture. Good agreement between predicted and observed estimates was obtained. Model inversion allowed soil moisture predictions to be made that were deemed realistic in terms of soil moisture values.     

A method to obtain soil-moisture estimates over bare agricultural fields in arid areas by using multi-angle RADARSAT-2 data

Soil moisture is an important parameter for agriculture, meteorological, and hydrological studies. This paper focuses on soil-moisture estimation methodology based on the multi-angle high-and low-incidence-angle mode RADARSAT-2 data obtained over bare agricultural fields in an arid area. Backscattering of the high-and low-incidence angles is simulated by using AIEM(advanced integral equation model), with the surface-roughness estimation model built based on the simulated data. Combining the surface-roughness estimation model with the backscattering model of the low-incidence-angle mode, a soil-moisture estimation method is put forward. First, the natural logarithm(ln) of soil moisture was obtained and then the soil moisture calculated. Soil moisture of the study area in Dunhuang, Gansu Province, was obtained based on this method; a good agreement was observed between the estimated and measured soil moisture. The coefficient of determination was 0.85, and the estimation precision reached 4.02% in root mean square error(RMSE). The results illustrate the high potential of the approach developed and RADARSAT-2 data to monitor soil moisture.     

Incorporating vegetation into visual exposure modelling in urban environments

Visual exposure modelling establishes the extent to which a nominated feature may be seen from a specified location. The advent of high-resolution light detection and ranging (LiDAR)-sourced elevation models has enabled visual exposure modelling to be applied in urban regions, for example, to calculate the field of view occupied by a landmark building when observed from a nearby street. Currently, visual exposure models access a single surface elevation model to establish the lines of sight (LoSs) between the observer and the landmark feature. This is a cause for concern in vegetated areas where trees are represented as solid protrusions in the surface model totally blocking the LoSs. Additionally, the observer's elevation, as read from the surface model, would be incorrectly set to the tree top height in those regions. The research presented here overcomes these issues by introducing a new visual exposure model, which accesses a bare earth terrain model, to establish the observer's true elevation even when passing through vegetated regions, a surface model for the city profile and an additional vegetation map. Where there is a difference between terrain and surface elevations, the vegetation map is consulted. In vegetated areas the LoS is permitted to continue its journey, either passing under the canopy with clear views or partially through it depending on foliage density, otherwise the LoS is terminated. This approach enables landmark visual exposure to be modelled more realistically, with consideration given to urban trees. The model's improvements are demonstrated through a number of real-world trials and compared to current visual exposure methods.     

Estimation of regional evapotranspiration over Northwest China using remote sensing

It is a very complicated problem to estimate evapotranspiration (ET) over a large area of land surface. In this paper, the evapotranspiration estimation models for dense vegetation and bare soil are presented, based on the information of parameters like vegetation cover-degree and surface albedo. Combined with vegetation cover-degree data, a model for regional evapotranspiration estimation over the heterogeneous landscape is derived. Through a case study using remote sensing data over Northwest China, the accuracy of the model for regional evapotranspiration estimation is checked. The result shows that the accuracy of the model is satisfactory. The features of evapotranspiration over Northwest China are also discussed with the application of the model.     

Estimation of regional evapotranspiration over Northwest China using remote sensing

1 IntroductionThe method for estimating evapotranspiration (ET) was first given by Dalton in 1802, and a number of models for ET estimation have been presented since then. Those models, from the experiential and semi-experiential models[1,2] and physical models[3,4] to the models in terms of the mechanism for energy and water fluxes in soil-vegetation-atmosphere transfer system such as SiB, have improved the precision of ET estimation. It is, however, difficult to calculate regional ET by …     

农业干旱遥感监测模型综述

以农业干旱发生时所引起的若干地表特征变化为依坦,分别从土壤水分、植被水分、冠层温度和作物形态4方面,对现有主要农业干旱遥感监测模型进行归纳总结,分析了各种模型的优缺点,以及各自的适用范围。其中土壤水分变化类指数比较适宜于农业旱情预警及土壤干旱型农业旱情的监测;冠层温度变化类指数不仅适宜于农业旱情预警,更适宜于农业旱情监测;作物形态及绿度变化和植被水分变化类指数,较适宜于农业旱灾的预警以及灾后评估。     

基于ASAR双极化雷达数据的半经验模型反演土壤湿度

基于ENVISAT ASAR-APP数据,利用AIEM(改进积分方程模型)模拟分析粗糙度、土壤水分对ASAR 7个入射角后向散射系数的影响.建立小人射角模式ASAR数据后向散射模型和组合粗糙度计算模型,提出小入射角模式下双极化ASAR数据土壤水分反演半经验模型.基于A1EM模拟数据进行试验,验证了该模型的合理性,且反演的土壤水分和实测土壤水分相关系数为O.92,均方根误差(RMSE)为3.98%.     

TEMPORAL SENSITIVITY OF REGIONAL CLIMATE TO LAND-SURFACE HETEROGENEITY

Increased interest in climate change at local and regional scales has prompted climate simulations for regional areas, but tests of climate models have not specifically examined the impacts of regional heterogeneity, and they have largely overlooked possible temporal sensitivity. In this study I used a coupled surface-atmosphere mesoscale model to evaluate the effects of regional heterogeneity in five land-surface parameters that have the strongest impacts on the surface energy balance: albedo, roughness, canopy resistance, rooting profile, and soil water content. I included temporal variability in climate sensitivity by completing a series of mid-month simulations representative of the June-September growing season. I modeled land surfaces of maize contrasted with bare soil, grass, or coniferous trees. Roughness discontinuities were important factors in determining regional energy balance and surface temperature for all three surface contrasts. The effects varied over the growing season as a function of maize height. Canopy resistance was equally important, especially during the middle of the season when the maize canopy was at its fullest extent. Albedo effects appeared to be secondary, but often were more important in September. Changes in soil water content had little impact because vegetation in these simulations was not stressed by low soil moisture. The importance of roots in these simulations was primarily a function of their presence or absence, rather than of the specific profile assigned to each vegetation type. Roughness and canopy resistance discontinuities appeared to play the largest role in determining the regional average energy balance and surface temperature for growing season dates. [Key words: land-surface heterogeneity, energy balance, climatology.]