基于宇宙射线观测的喀斯特槽谷区典型流域土壤水分反演研究

宇宙射线中子法是一种百米尺度的土壤水分无损测量方法。基于重庆市青木关槽谷区多个站点的多层土壤水分观测数据,针对宇宙射线土壤水分观测系统（COSMOS）同步测得的中子序列开展了土壤含水量反演研究。在反演算法研究过程中,引入S-G滤波对COSMOS快中子数进行平滑,分析了植被含水量的影响,探索和优化了算法率定和验证阶段不同的数据筛选方案。结果表明：该区域植被含水量对COSMOS反演结果影响较小,且考虑全时段土壤水分水平下发展的算法能得到与实测区域平均更为一致的土壤水分序列。最后应用该反演算法进一步生成了COSMOS观测时段的长时间序列土壤水分产品,并与周边相邻土壤水分观测进行间接验证,揭示了该区域的土壤水分季节变化特征。该研究发展的COSMOS土壤水分反演算法在该区域展现了较强的适用性,可为重庆市青木关喀斯特槽谷区典型流域的区域尺度土壤水分观测与水文气象分析提供支持。     

基于模型模拟的植被NDVI与观测天顶角和LAI的关系

利用PROSPECT和SAIL模型模拟了不同叶绿素含量、不同LAI和不同观测天顶角下的植被冠层反射率,分析了NDVI随LAI、观测天顶角和叶绿素含量的变化规律。结果表明:叶绿素影响冠层反射率主要在可见光波段,冠层反射率随叶绿素含量的增加而下降;冠层反射率随观测天顶角的增加而增加,而LAI较高时,其受观测天顶角的影响较小。观测天顶角相同时,随叶绿素含量的增加NDVI呈上升趋势;叶绿素含量一定时,NDVI随LAI的增加而增加。LAI为1时,在不同叶绿素含量下,随观测天顶角的增加,NDVI呈先下降后上升的趋势,拐点在观测天顶角65°或70°处,而LAI为3、5和7时,NDVI呈现下降趋势。叶绿素含量较高时,NDVI受观测天顶角的影响较小。当LAI较大和叶绿素含量较低时,NDVI随观测天顶角的增加(>70°)下降较快。     

基于地基可见光图像的喀斯特典型植被长势监测

生态观测试验站为喀斯特典型脆弱生态区植被生长监测提供了高通量冠层拍摄图像,但目前鲜见有关喀斯特地区裸岩和植被混杂下垫面植被提取的研究报道。利用石漠化生态观测试验站获取的植被冠层RGB图像,研究喀斯特植被适用分割算法和长势监测模型,为基于地基冠层可见光图像的植被监测提供技术支持。结果表明：(1)基于颜色空间、颜色通道非线性组合、机器学习3种算法对喀斯特地区浅绿色植被的区分度均较高,但对裸岩和深绿色植被区分度有明显差异。晴天强光和阴天弱光条件下3种分割方法对植被分割效果差异明显,机器学习算法分割效果最优,阴天弱光条件准确率超过80%,晴天强光条件下可超过90%。(2)基于RGB图像反演的可见光植被指数GLA、NDYI、NGRDI和VARI所反映的植被长势变化趋势相似,但NDYI对植被长势差异响应更敏感。复合正弦函数可以较好地模拟4种可见光植被指数的逐日动态变化特征,且对NGRDI变化趋势模拟精度最高(R²=0.830)。     

基于数据机理的植被叶面积指数遥感反演研究

定量获取地表植被高精度时序及空间覆盖的叶面积指数（Leaf Area Index, LAI）是生态监测及农业生产应用的重要研究内容。通过使用Moderate Resolution Imaging Spectroradiometer（MODIS）植被冠层多角度观测MOD09GA数据及叶面积指数MOD15A2数据,发展了一种参数化的叶面积指数遥感反演方法并完成了必要的检验分析。研究使用基于辐射传输理论的RossThick LiSparse Reciprocal（RTLSR）核驱动模型及Scattering by Arbitrarily Inclined Leaves with Hotspot（SAILH）模型进行植被冠层辐射特征的提取,使用Anisotropic Index （ANIX）异质性指数作为指示植被冠层二向反射分布Bidirectional Reflectance Distribution Function（BRDF）的辅助特征信息,发展了基于数据机理（Data-Based Mechanistic, DBM）的植被叶面积指数建模和估算方法。通过必要的林地、农作物、草地植被实验区反演及数值分析可得知：①时间序列多角度遥感观测数据结合数据机理的叶面积指数估算方法,可实现模型参数的时序动态更新,改进叶面积指数估算结果的时序完整性及精度。②异质性指数可以用做指示植被冠层二向反射分布特征信息,可降低因观测数据几何条件差异所导致的反演结果不确定情况,同时能够补充植被时序生长过程表现的植被结构变化等动态特征。经研究实践,可将算法应用于时空尺度的叶面积指数估算,并能够为生态、农业应用提供植被的高精度遥感监测指标。     

基于双EnKF的土壤水分与土壤属性参数同时估计

为提高土壤水分数据同化结果的精度,将基于双集合卡尔曼滤波(Dual Ensemble Kalman Filter,DEnKF)的状态-参数估计方案与简单生物圈模型(simple biosphere model 2,SiB2)相结合,同时更新土壤水分和优化模型参数(土壤属性参数)。选用2008年6月1日~10月29日黑河上游阿柔冻融观测站为参考站,开展了同化表层土壤水分观测数据的实验。研究结果表明:DEnKF可同时优化土壤属性参数和改进土壤水分估计,该方法对表层土壤水分估计的精度0.04高于EnKF算法的精度0.05。当观测数据稀少时,DEnKF算法仍然可以得到较高精度的土壤水分估计,3层土壤水分的估计精度在0.02~0.05之间。     

灌区土壤水分空间变异及监测方法研究

根据灌区田间实测资料,利用地统计学法和传统统计学方法,对灌区土壤水分的空间变异性进行了研究,得到典型斗渠土壤水分空间分布的特点,并以此为基础提出了灌区土壤水分监测分区原则、各分区取样数目的确定及取样点分布方法,为建立合理的灌区土壤水分监测系统,获得实时准确的灌区土壤水分状态,进行适宜的灌溉预报提供依据.     

基于SAILH模型的植被冠层NDVI二向性分析

NDVI是植被遥感中最为常用的一种植被指数,建立NDVI与其他冠层参数模型必须考虑其方向性问题.本文基于SAILH模型讨论了连续植被冠层NDVI的二向性特征,并分析了叶面积指数、叶倾角分布、热点参数以及太阳天顶角和相对方位角对NDVI的影响.研究表明冠层NDVI在主平面观测方向存在一个明显的负热点,前向散射方向的NDV...     

玉米冠层最佳水分指数优选

针对基于植被指数反演不同生长期、不同冠层结构特征下玉米冠层含水量的序列性研究较少,冠层含水量反演较低等问题,优选不同生长期玉米冠层含水量反演最佳植被指数,完成玉米冠层含水量高精度提取。初步选择4种可靠性强的水分指数:归一化植被指数、归一化水体指数1、归一化水体指数2、水协迫指数,分别基于PROSAIL辐射传输模型、三期实测冠层含水量及同步Landsat-8OLI数据,模拟分析4种植被指数与冠层含水量的关系,优选不同生长期玉米最佳水分指数,实现玉米冠层含水量快速精确反演。实例验证结果表明,水分指数归一化水体指数1可作为植被冠层含水量反演的最佳指数且反演精度随着植被含水量的增加而降低,在玉米生长初期,中误差为0.13kg/m~2,在生长中后期,中误差达到0.582kg/m~2,满足生长初期玉米冠层含水量快速反演需求。研究结果可为植被冠层含水量反演中水分指数选择提供参考,也可为稀疏植被覆盖区土壤水分反演研究提供借鉴。     

基于Landsat ETM+遥感数据的组分温度反演方法研究

地表温度在干旱监测和模拟地表热通量中有重要作用。在干旱半干旱地区,双源能量平衡模型(TSEB)通常用于计算地表的热通量。以黑河中游典型灌区为研究区域,选取4个时相的Landsat-7 ETM+遥感影像,通过植被指数与TSEB模型结合的方法反演土壤表面温度和植被冠层温度,并重点讨论土壤表面温度和植被冠层温度的分解算法。结果表明:土壤表面温度和植被冠层温度具有较好的时空一致性;土壤表面温度与植被冠层温度的反演精度通过地表净辐射与地表热通量得到了间接验证。地表净辐射与地表热通量的计算值与观测值相关性好,相关系数大于0.92。地表净辐射与地表热通量的线性回归分析表明拟合精度高。通过地表温度分解的方法获得的土壤表面温度和植被冠层温度,对监测典型区域的干旱和模拟地表热通量是可行的。     

羊草草甸草原FPAR时间变化规律分析

FPAR是植被叶子在光合有效辐射(400～700nm)波段有多少太阳光能被吸收的一个度量,表示了植被冠层能量的吸收能力,是描述植被结构以及与之相关的物质与能量交换过程的基本生理变量.FPAR模型是否能真实反映植被冠层吸收光合有效辐射状况,将直接影响遥感估算草地NPP的不确定性程度.本文通过对长生季羊草草甸草原冠层PAR各分量的观测,研究了入射PAR、冠层反射PAR及透过冠层到达地面的PAR的时间变化规律,并以观测的PAR为基础计算分析了羊草草甸草原FPAR的时间交化规律.结果表明,羊草草甸草原入射PAR及透射PAR日交化规律明显,呈较标准的正弦曲线变化;晴天FPAR的日变化呈较标准的余弦曲线交化,FPAR在早晚值较高,最高值约0.81,日平均FPAR值可以用9:30/太阳天顶角为48°时瞬时的FPAR值或14:30/太阳天顶角为30°时瞬时的FPAR值表示.     

土壤背景对冠层NDVI的影响分析

归一化差值植被指数NDVI是植被遥感中应用最为广泛的指数之一, 但它受土壤背景等因素的干扰比较强烈。结合实测的土壤数据以及公式推导、PROSAIL 模型模拟等方法分析了这种影响。首先, 假定与土壤线性混合且叶片呈水平分布的植被冠层, 根据土壤与植被分别在红光、近红外波段处的反射率值、植被覆盖度等参数, 利用公式推导了土壤背景对不同覆盖度下冠层NDVI的影响。其次, 利用PROSAIL冠层光谱模拟模型, 模拟分析了土壤背景对不同LAI下冠层NDVI的影响。分析的结果表明:LAI 越小, 土壤背景的影响越大; 暗土壤背景下的冠层NDVI值大于亮土壤背景下冠层的NDVI值; 并且,暗土壤条件下,NDVI值对土壤亮度的变化更敏感,而亮土壤下,NDVI值则对LAI或覆盖度的变化更敏感。最后利用实测的不同土壤背景下的冬小麦冠层光谱数据, 验证了公式推导和模型模拟的结果。     

Soil and Sun angle interactions on partial canopy spectra

Abstract

The spectral behaviour of an incomplete cotton canopy was analysed in relation to solar zenith angle and soil background variations. Soil and vegetation spectral contributions towards canopy response were separated using a first-order interactive model and consequently used to compare the relative sensitivity of canopy spectra to soil background and solar angle differences. Canopy reflectance behaviour with solar angle increased, decreased or remained invariant depending on the reflectance properties of the underlying soil. Sunlit and shaded soil contributions were found to alter vegetation index behaviour significantly over different Sun angles.     

Analytical parameterization of canopy directional emissivity and directional radiance in the thermal infrared. Application on the retrieval of soil and foliage temperatures using two directional measurements

This work is aimed at deriving canopy component (soil and foliage) temperatures from remote sensing measurements. A simulation study above sparse, partial and dense vegetation canopies has been performed to improve the knowledge of the behaviour of the composite radiative temperature and emissivity. Canopy structural parameters have been introduced in the analytical parameterization of the directional canopy emissivity and directional canopy radiance:namely, the leaf area index (LAI), directional gap fraction and angular cavity effect coefficient. The parameterization has been physically defined allowing its extension to a wide range of Leaf Inclination Distribution Functions (LIDF). When single values are used as leaves and soil temperatures, they prove to be retrieved with insignificant errors from two directional measurements of the canopy radiance (namely at 0 and 55 from nadir), provided that the canopy structure parameters are known. A sensitivity study to the different parameters shows the great importance of the accuracy on LAI estimation (an accuracy of 10 per cent is required to retrieve the leaves temperature with an accuracy better than 0.5 degK, the same requirement being 5 per cent for the retrieval of soil temperature). The radiometric noise is important too, but its effects may be limited by using very different angles for the measurements: for 0 and 55, the effect of a Gaussian noise (NEDeltaT 0.05deg K) is lower than 0.5degK on the retrieved soil and foliage temperatures). Uncertainties on the leaf and soil emissivities (Delta epsilon 0.01) cause little errors in the retrieval (lower than 0.5degK). If the inclination dependence of the leaves temperature is considered, a 1 degK error is observed in the retrieved soil and foliage temperatures. This error is due to the fact that the effective foliage temperature varies with the view angle (a few 10 -1 deg K at 55 ), which implies errors in the inversion scheme. This effect may be corrected for by using an angular corrective term delta depending only on the off-nadir angle used.     

Canopy blockage and scattering effects on apparent soil spectral reflectance and its consequence in spectral mixture analysis of vegetated surfaces

Remote sensing technique has become the most efficient and common approach to estimate surface vegetation cover. Among various remote sensing algorithms, spectral mixture analysis (SMA) is the most common approach to obtain sub‐pixel surface coverage. In the SMA, spectral endmembers (the number of endmembers may vary), with invariant spectral reflectance across the whole image, are needed to conduct the mixture procedure. Although the nonlinear effect in quantifying vegetation spectral reflectance was noticed and sometimes addressed in the SMA analysis, the nonlinear effect in soil spectral reflectance is seldom discussed in the literature. In this paper, we investigate the effects of vegetation canopy on the inter‐canopy soil spectral reflectance via mathematical modelling and field measurements. We identify two mechanisms that lead to the difference between remotely sensed apparent soil spectral reflectance and actual soil spectral reflectance. One is a canopy blockage effect, leading to a reduced apparent soil spectral reflectance. The other is a canopy scattering effect, leading to an increased apparent soil spectral reflectance. Without correction, the first (second) mechanism causes an overestimated (underestimated) areal coverage of the low‐spectral‐reflectance endmember. The overall effect of canopy to soil, however, tends to overestimate fractional vegetation cover due to the relative significance of the canopy blockage effect, even though the two mechanisms vary with spectral wavelengths and spectral difference between different vegetation and soil. For the SMA of vegetated surface using multiple‐spectral remote sensing imagery (e.g., LandSat), it is recommended that infrared bands of low vegetation spectral reflectance (e.g. band 7) be first considered; if both visible and infrared bands are used, combination of bands 3, 4, and 5 is appropriate, while use of all six bands could overestimate fraction vegetation cover.     

The relationships between electrical conductivity of soil and reflectance of canopy,grain, and leaf of rice in northeastern Thailand

Soil salinity is a global environmental problem and the most widespread land degradation problem that reduces crop yields and agricultural productivity. The characteristic of soil salinity is conventionally measured by the electric conductivity (EC) of soil while remote-sensing techniques have been extensively applied to detect the presence of salts indirectly through the vegetation using crop spectral reflectance. This study aims primarily to investigate whether salt stress the rice can be detected by field reflectance or not, and second, to search the significant bands of vegetation indices that can indicate the relationships between the EC of soil and field hyperspectral reflectance of canopy, grain, and leaf of rice, using the normalized difference spectral index (NDSI). Field investigations on various paddy fields in northeastern Thailand were carried out in late November 2010 during the ripening season just before harvest in an attempt to realize the applications of the field hyperspectral technique for monitoring the spread of saline soils and estimation of the effects of soil salinity on rice plants. Jasmine rice and glutinous rice were two different rice species selected for this study. Rice plant investigations were conducted by collecting data on crop length, panicle length, canopy openness, leaf area index, and digital photographs of plant conditions from each site. The statistical analysis revealed that the changes in soil EC were significantly sensitive to the ripening stages of both jasmine rice and glutinous rice planted on different levels of soil salinity. Among reflectance measurements, canopy reflectance was highly correlated with soil EC. However, the estimated accuracies of the relationship between soil EC and reflectance of glutinous rice were relatively lower than those of jasmine rice.     

Simulation and Analysis on the Influence of Soil Background of Wheat Farmland on NDVI under Different Vegetation Coverage

It is quite confusing to effectively monitor and precisely evaluate growing conditions of wheat by using normalized differential vegetation index (NDVI)which is based on pixel scale as they are significantly different when acquired by the same growth status wheat with different background of soil types.This paper selects 9 typical soil types in our country as background with the wheat canopy spectrum is fixed which means the NDVIc is a constant value to study the influence of different soil background types on NDVI of wheat and analyze the sensitivity of NDVI of wheat to the vegetation coverage simulated by diverse liner mixed ratio of wheat canopy and soil background.The results show that：(1)wheat NDVI of farmland increases along with the increase of vegetation coverage under the same of soil background type,and vice versa;(2)wheat NDVI of farmland vary greatly with different soil background types,and the difference decrease while the vegetation coverage exceed 25%;(3)NDVI sensitivity also shows a quite difference to vegetation coverage under the diverse soil background types.With the increase of vegetation coverage,NDVI sensitivity decreases with the lower\|reflectance soil background while it increases monotonously with the higher reflectance soil background.It provides the foundation for the times of calculating the remote sensing’s NDVI information of all wheat growing periods under different types of soil background.     

Modelling surface energy fluxes over maize using a two-source patch model and radiometric soil and canopy temperature observations

Models estimating surface energy fluxes over partial canopy cover with thermal remote sensing must account for significant differences between the radiometric temperatures and turbulent exchange rates associated with the soil and canopy components of the thermal pixel scene. Recent progress in separating soil and canopy temperatures from dual angle composite radiometric temperature measurements has encouraged the development of two-source (soil and canopy) approaches to estimating surface energy fluxes given observations of component soil and canopy temperatures. A Simplified Two-Source Energy Balance (STSEB) model has been developed using a “patch” treatment of the surface flux sources, which does not allow interaction between the soil and vegetation canopy components. A simple algorithm to predict the net radiation partitioning between the soil and vegetation is introduced as part of the STSEB patch modelling scheme. The feasibility of the STSEB approach under a full range in fractional vegetation cover conditions is explored using data collected over a maize (corn) crop in Beltsville Maryland, USA during the 2004 summer growing season. Measurements of soil and canopy component temperatures as well as the effective composite temperature were collected over the course of the growing season from crop emergence to cob development. Comparison with tower flux measurements yielded root-mean-square-difference values between 15 and 50 W m^− 2 for the retrieval of the net radiation, soil, sensible and latent heat fluxes. A detailed sensitivity analysis of the STSEB approach to typical uncertainties in the required inputs was also conducted indicating greatest model sensitivity to soil and canopy temperature uncertainties with relative errors reaching ∼ 30% in latent heat flux estimates. With algorithms proposed to infer component temperatures from bi-angular satellite observations, the STSEB model has the capability of being applied operationally.     

Retrieval of crop canopy properties: a comparison between model inversion from hyperspectral data and image classification

The aim of this work was to investigate different approaches for the estimation of canopy structure properties from multiangular measurements at the field scale. Hyperspectral multiangular data were acquired on potato canopies using a spectroradiometer (GER-1500) and corresponding multiangular images using the VIFIS (Variable Interference Filter Imaging Spectrometer). The data obtained using the spectroradiometer were employed in the inversion of the PROSAIL model. The images obtained from the VIFIS were classified into the component image fractions: shaded and sunlit leaves and soil. These classification results were then used directly in the inversion of a simple ray-tracing canopy model. The inversion technique was based on a look-up table approach using a simple ray-tracing model of a plant canopy. Field sampling was carried out for the direct measurement of leaf area index (LAI) and other canopy properties. The experimental error in the data of both sensors was large since the canopy appeared non-homogeneous at the measurement height used, mainly because of the crop row structure. However LAI values retrieved from both approaches were realistic and allowed the discrimination of potato canopies that had received different nitrogen fertilization treatments. The relative merits and practicalities of the two approaches (multiangular hyperspectral reflectance versus image classification) are discussed.     

Effect of soil background on vegetation discrimination using landsat data

Digital count values were extracted for wet and dry areas within three spectrally different soil types from a 21 March 1977 Landsat-2 scene over the southern Central Valley of California. These values were converted to brightness and greenness using the global Kauth-Thomas coefficients. Greenness was scaled using the Kauth-Thomas soil line as the 0% level and a full cover wheat canopy greenness point as 100%. Individual site specific soil lines were then compared. The total difference in greenness among the three test soils ranged up to 14.5% using the global Kauth-Thomas coefficients. Site-specific soil lines were then calculated using soil specific coefficients. The difference among the three test soils was reduced from 14.5% to 3.8%. These results indicate that soil background effects can be significant in Landsat data but can be reduced using site specific soil information.     

Sensitivity studies of the moisture effects on MODIS SWIR reflectance and vegetation water indices

The effects of soil moisture and leaf water content on canopy reflectance of MODIS shortwave infrared (SWIR) bands 5, 6, and 7 and water‐related indices are studied quantitatively using the coupled soil–leaf–canopy reflectance model. Canopy spectra simulations under various input conditions show that soil moisture has a large effect on each SWIR reflectance at low leaf area index (LAI) values, among which band 5 is the most sensitive to soil moisture variations, while band 7 responds strongest to dry soil conditions. Band 5 is also better suited to measure leaf water content change, since it obtains a higher variation when leaf water content changes from dry to wet. In general, each SWIR band responds to soil moisture and leaf water content differently. By using the normalized calculation between the water absorption‐sensitive band and insensitive band, the Normalized Difference Water Index shows the most capability to remove the soil background effect and enhance the sensitivity to leaf water content. These two moisture variables may be separated by combining multiple rather than one SWIR band with a near‐infrared band considering that each SWIR band has a different response to soil moisture and leaf water content.