SMAP卫星后向散射系数与海表面风场关系

海面风是海气互相作用的重要参数之一,如何通过雷达后向散射数据有效提取海表面风场信息,对于海洋动力环境遥感监测具有重要的研究意义。使用SMAP卫星L波段真实孔径雷达数据和国家环境预测中心再分析风场数据进行匹配,利用地球物理模型函数分析了SMAP卫星数据的后向散射系数与海表面风场之间的关系,讨论了不同风速和不同相对风向角时SMAP卫星数据反演海表面风场的潜力。研究显示,水平极化和垂直极化的后向散射系数与风速的关系紧密,适于海表面风场的反演;SMAP卫星数据存在正-侧风不对称现象和逆正-侧风不对称现象;在相对风向角为90°和270°时后向散射系数与风场的关系较为模糊;随着风速的增加,后向散射系数与相对风向角的规律关系也越来越明显,振幅也随风速增大而增大。GMF函数计算的风速偏差为1.19m/s(水平极化)和1.51m/s(垂直极化),均方根误差为1.58m/s(水平极化)和1.67m/s(垂直极化)。     

SMOS卫星海表面亮温数据与海表面盐度数据的相关性研究

海表面亮温是反演海表面盐度的关键。从不同海表面亮温参数与海表面盐度的关系入手,利用2014年7月8日西北太平洋区域SMOS(Soil Moisture and Ocean Salinity)卫星L1C数据和Argo实测盐度数据,使用数据拟合、显著性检验、偏相关分析和广义相加模型等方法,分析了海表面盐度SSS(Sea Surface Salinity)与SMOS卫星不同极化方式和不同入射角亮温参数的相互关系,并得到以下结论:水平极化亮温、垂直极化亮温、第一斯托克斯参数和第二斯托克斯参数4种亮温参数与入射角具有较强的相关性,水平极化亮温、第一斯托克斯参数与海表面盐度相关性较好,其中12.5°第一斯托克斯参数为反演海表面盐度的最佳亮温参数。     

NCEP/QSCAT混合风向用于SAR图像反演高分辨率海面风速

SAR图像反演高分辨率海面风速在微波遥感领域具有重要的意义。利用 SAR图像和NCEP/QSCAT混合风向数据,对NCEP/QSCAT混合风向用于SAR图像反演高分辨率海面风速的方法进行了初步研究。以2005年12月6日一景ENVISat ASAR图像为例,反演了大范围、高分辨率海面风速。海面风速的反演结果与NCEP/QSCAT混合风速、日平均散射计风速的比较结果显示,其均方根误差分别为1.9 m/s、1.6 m/s,二者符合较好,显示了SAR反演高分辨率海面风速的能力与SAR海面风场业务化应用的前景。     

基于星载微波辐射计的海面风场对海表盐度反演影响研究

为了研究海面风场对海表盐度反演结果的影响,需要构建准确的海面风场影响下的海表亮温模型。将不同海面粗糙度模型计算的结果与Aquarius盐度计卫星产品中的海面粗糙度数据进行了比较,结果表明双尺度模型结合海面泡沫模型的计算结果与Aquarius卫星产品粗糙度数据一致性最好。基于此构建了海面风场影响下的海面亮温仿真模型以及双极化通道的盐度反演模型,研究了海面风场对L波段海面微波辐射特性的影响以及风场资料误差对盐度反演精度的影响。仿真结果表明,2m/s的风速误差对盐度反演结果的影响较大。在低温和大风速条件下达到1psu以上,尚不能满足目前盐度遥感的精度要求。20°的风向误差对在中小入射角条件下对盐度反演结果影响较小,对盐度遥感的月平均要求影响不大。     

海面粗糙度引起的SMOS卫星亮温增益研究

海表面亮温是决定海表面盐度反演精度的关键因素。针对海表面粗糙度对海表面亮温增益的影响问题,修正了SMOS(Soil Moisture and Ocean Salinity)卫星亮温粗糙度模型。从SMOS卫星半经验半理论模型入手,利用2014年7月8日西北太平洋区域SMOS卫星L1C数据、L2级盐度产品数据、HY-2A(海洋二号)卫星SWH(有效波高)、U10(海面10m处风矢量)数据、Argo实测海表面盐度和海表面温度数据,使用非线性拟合、P检验和BP神经网络模型等方法,对平静海面亮温和海面粗糙度引起的亮温增益模型进行了算法修正。通过与Argo实测海表面盐度对比,评价了模型修正效果。模型修正后的水平极化亮温和垂直极化亮温反演出的海表面盐度的相对误差分别为0.005和0.004,优于模型修正前。     

基于外部风向初始信息的SAR影像海面风场反演研究

首先利用SeaWinds散射计风向作为初始信息进行SAR(Synthetic Aperture Radar)影像海面风场反演,在对SAR影像进行了噪声剔除、辐射定标、极化转换等处理后获得VV极化下各分辨单元的后向散射系数,结合地球物理模式函数获取风速并显示输出海面风场的分布情况。在此基础上,尝试利用WRF(Weather Research Forecast)数值预报模式风向作为初始场从SAR影像中反演风速信息,将结果与之前以散射计风向作为初始信息得到的反演结果进行对比,验证实验方法的正确性,高分辨率数值预报模式风向结合SAR影像将是未来业务化近岸海面风场反演的发展趋势。     

基于FY-3B微波成像仪的海面风速反演

风云三号B星微波成像仪的10.65、18.7、23.8和36.5 GHz频点对海表面粗糙度和介电特性比较敏感,能够用于海面地球物理参数的反演。为获得一种适用于全球大部分海域的海面风速反演算法,利用快速辐射传输模式和再分析大气廓线库模拟微波成像仪海面微波辐射特性,在此基础上建立了半经验反演算式,并利用浮标现场测量数据及WindSat全极化辐射计风速产品对半经验算法和经验算法分别进行了验证和对比。另外,通过分析风向对风速反演的影响,借助AT BV-TBH模型,尝试利用查找表算法对风向造成的晴空区域风速反演偏差进行初步校正。校正风向误差后,反演风速与浮标风速的均方根误差为0.9775 m/s。     

微波散射计反演海面风场的神经网络方法研究

研究利用神经网络方法处理微波散射计数据,反演海面风场。重点研究海洋二号(HY-2)卫星微波散射计数据反演,特别是中高风速条件下的风场反演。其中风速的反演基于后向传播(Back Propagation,BP)神经网络;多解风向的反演基于混合密度(Mixture Density Network,MDN)神经网络,求解过程中的核函数采用高斯分布;网络训练的目标风场采用欧洲中期天气预报中心(European Centre for Medium-range Weather Foresting,ECMWF)模式风场。通过与ECMWF风场的比较,利用神经网络方法反演的风场可以满足HY-2微波散射计风场反演的精度要求。同时通过与国家卫星海洋应用中心发布的HY-2微波散射计L2B级风场产品相比较,表明该方法反演的风场更接近ECMWF模式风场。     

基于长时间序列ASAR数据的近海风场反演

首先获取覆盖浙江省近海215景长时间序列多模式ASAR Level 1B数据,对获取的ASAR数据进行几何校正、辐射定标、极化比转化、噪声剔除后,将所有的影像统一重采样得到1km×1km分辨单元的后向散射系数影像,尝试利用CCMP风向作为SAR风速反演地球物理模式函数的风向初始场,对比目前国际上最常用的3种模式函数CMOD4、CMOD-IFR2和CMOD5的风速反演效果,将反演得到风速分别与CCMP插值风速和现场观测气象站点的风速进行对比,结果表明CCMP风向数据可作为SAR风速反演的风向初始场,其中结合CMOD4模式函数能够得到较高精度的反演风速,由于CCMP数据有效时间长、覆盖范围广、易于获取,因此联合CCMP风向有利于SAR近海风场反演的业务化应用。     

同极化Radarsat-2数据的海面风速反演研究

基于Radarsat-2雷达数据具有多极化方式、多分辨率等成像模式的特点,以中国东部海域为研究区选取多景Radarsat-2影像进行海面风速反演研究。采用导入ERA-Interim数据构建初始海面风向的方法,针对不同极化方式的Radarsat-2数据,利用GMF模型和极化率模型组合进行海面风速反演,并将反演风速与ERA-Interim风速数据进行比较分析。结果表明:对于VV极化的Radarsat-2数据采用3种GMF模型均可反演出较高精度的海面风速,其中CMOD4模型总体表现好于其他二者,其均方根误差可达到1.5m/s以内;HH极化Radarsat-2数据采用Kirchhoff模型进行极化转换更适用于海面风速的反演,3种GMF模型之间反演效果差异不大,其均方根误差均在2m/s以内。同时,研究发现VV和HH极化的Radarsat-2数据均表现出高分辨率成像模式影像的风速反演效果优于低分辨率成像模式的特点。     

An analytical algorithm for the determination of vegetation Leaf Area Index from TRMM/TMI data

In this paper, an analytical algorithm for the determination of land surface vegetation Leaf Area Index (LAI) with the passive microwave remote sensing data is developed. With the developed algorithm and the Tropical Rainfall Measuring Mission/Microwave Imager (TRMM/TMI) remote sensing data collected during the Global Energy and Water Experiment (GEWEX) Asian Monsoon Experiment in Tibet (GAME/Tibet) Intensive Observation Period (IOP'98), the regional and temporal distributions of the land surface vegetation LAI have been evaluated. To validate the developed algorithm and the retrieval results, the maximum-composite Normalized Difference Vegetation Index (NDVI) data over the same study area and period are used in this study; the cloud contaminated NDVI values have been replaced by the cloud-free values reconstructed by the Harmonic ANnalysis of Time Series (HANTS) technique. The results show that the retrieved LAI is in good agreement with the cloud-free NDVI in regional and temporal distributions and in their statistical characteristics; the vegetation characteristics can be clearly assessed from the regional distribution of the retrieved LAI. As lower frequency microwave radiation can penetrate atmosphere and thin cloud layer, with the application of the passive microwave remote sensing data, the developed algorithm can be used to monitor the land surface vegetation condition more effectively.     

基于MODIS数据的玉米植被参数估算方法的对比分析

基于实测数据建立了FPAR、LAI的植被指数估算模型（NDVI、RVI、NDWI）,并将其应用于MODIS BRDF数据对德惠地区玉米FPAR、LAI进行估算,然后将MODIS 15A2 FPAR/LAI产品值分别与BRDF估算值、地面实测值进行对比分析。主要得出以下结论：植被指数NDVI、RVI都能较好地用于实测数据和MODIS BRDF数据的FPAR、LAI估算;NDWI虽然在实测数据中估算玉米FPAR、LAI的效果优于NDVI、RVI,但其应用于MODIS BRDF数据估算FPAR、LAI时,效果却较差。BRDF数据估算FPAR与MODIS 15A2 FPAR值的关系因生长时期不同而异,在玉米生长前期,前者高于后者,而生长后期两者却较相近;BRDF估算LAI值一直都高于MODIS 15A2 LAI产品值。生长季前期,MOD15A2 FPAR、LAI值接近实测值,而在后期却高于实测值。通过分析也表明,玉米苗期MODIS 15A2 FPAR数值变化范围较小,产品算法对实际FPAR变化尚不够敏感,这可能是影响MODIS FPAR产品精度的一个原因。     

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。     

基于高光谱植被指数的加工番茄生长状况监测研究

利用ASD地物非成像高光谱仪,获取2个加工番茄品种4水平施氮量和3种配置种植方式6个关键生育时期冠层的反射光谱数据,通过计算得到归一化植被指数(NDVI)、比值植被指数(RVI)、修改型二次土壤调节植被指数(MSAVI2)和红边归一化植被指数(RENDVI),并分别与其冠层叶绿素密度(CH.D)、叶面积指数(LAI)、地上鲜生物量(AFBM)和地上干生物量(ADBM)进行相关分析,经检验,相关系数均达到1%的极显著水平。其中RENDVI与CH.D的线性相关模型,RVI与LAI的幂指数函数模型的相关性最好(RRENDVI-CH.D=0.8034**,RRVI-LAI=0.8703**,n=54,α=1%),用上述2个相关模型方程分别估算加工番茄CH.D和LAI,实测值与估测值之间均呈极显著的线性相关关系(R实测CH.D-估测CH.D=0.8113**,R实测LAI-估测LAI=0.8546**,n=54,α=1%),估算精度分别为85.5%和86.3%。试验结果表明,用高光谱植被指数,可以对加工番茄冠层CH.D、LAI、AFBM和ADBM进行遥感估算,实现对加工番茄生长状况的实时、无损、非接触和定量的高光谱监测研究。     

Estimation of forest Leaf Area Index using remote sensing and GIS data for modelling net primary production

Ecosystem models can be used to estimate potential net primary production (pNPP) using GIS data, and remote sensing input of actual forest leaf area to such models can provide estimates of current actual net primary production (aNPP) . Comparisons of pNPP and aNPP for a given site or regional landscape can be used to identify forest stands for different management treatments, and may provide new information on wildlife habitat, forest diversity and growth characteristics. Leaf area estimates may be obtained from satellite imagery through correlation with physiologically-based vegetation indices such as the Normalized Difference Vegetation Index (NDVI). However, in areas with high Leaf Area Index (LAI), vegetation indices usually saturate at leaf areas greater than about 4. In predominantly deciduous (hardwood) and mixedwood stands remote sensing estimates may be influenced by understory and other factors. We examined digital Landsat TM imagery and GIS data in the Fundy Model Forest of southeastern New Brunswick to determine relations to forest leaf area index within different stand structures or covertypes. The image data were stratified using GIS covertype information prior to development of LAI predictive equations using spectral reflectance, and the prediction of LAI from Landsat TM imagery was improved with reference to estimates of stem density which are standard forest inventory information contained in GIS databases. Actual stand LAI was compared to assumed maximum LAI values for several species and sites using an ecosystem process model (BIOME-BGC) which relies on climate, soils and topographic information also obtained from the GIS. Subsequent comparison of pNPP and aNPP revealed that even disturbed sites in this environment can reach close to maximum site potential. Specific sites with suboptimal species composition were identified. A future refinement of this approach is to classify the imagery independently of the GIS, which assumes a homogeneous covertype for each polygon in the system, and thus improve still further the aNPP estimates through higher covertype and LAI estimation accuracy.     

Application of hyperspectral vegetation indices to detect variations in high leaf area index temperate shrub thicket canopies

Accurate measurement of leaf area index (LAI), an important characteristic of plant canopies directly linked to primary production, is essential for monitoring changes in ecosystem C stocks and other ecosystem level fluxes. Direct measurement of LAI is labor intensive, impractical at large scales and does not capture seasonal or annual variations in canopy biomass. The need to monitor canopy related fluxes across landscapes makes remote sensing an attractive technique for estimating LAI. Many vegetation indices, such as Normalized Difference Vegetation Index (NDVI), tend to saturate at LAI levels > 4 although tropical and temperate forested ecosystems often exceed that threshold. Using two monospecific shrub thickets as model systems, we evaluated the potential of a variety of algorithms specifically developed to improve accuracy of LAI estimates in canopies where LAI exceeds saturation levels for other indices. We also tested the potential of indices developed to detect variations in canopy chlorophyll to estimate LAI because of the direct relationship between total canopy chlorophyll content and LAI. Indices were evaluated based on data from direct (litterfall) and indirect measurements (LAI-2000) of LAI. Relationships between results of direct and indirect ground-sampling techniques were also evaluated. For these two canopies, the indices that showed the highest potential to accurately differentiate LAI values > 4 were derivative indices based on red-edge spectral reflectance. Algorithms intended to improve accuracy at high LAI values in agricultural systems were insensitive when LAI exceeded 4 and offered little or no improvement over NDVI. Furthermore, indirect ground-sampling techniques often used to evaluate the potential of vegetation indices also saturate when LAI exceeds 4. Comparisons between hyperspectral vegetation indices and a saturated LAI value from indirect measurement may overestimate accuracy and sensitivity of some vegetation indices in high LAI communities. We recommend verification of indirect measurements of LAI with direct destructive sampling or litterfall collection, particularly in canopies with high LAI.     

Yield prediction and waterlogging assessment for tea plantation land using satellite image‐based techniques

Motivated by the operational use of remote sensing in various agricultural crop studies, this study evaluates the application and utility of remote sensing‐based techniques in yield prediction and waterlogging assessment of tea plantation land in the Assam State of India. The potential of widely used vegetation indices like NDVI and SR (simple ratio) and the recently proposed TVI has been evaluated for the prediction of green leaf tea yield and made tea yield based on image‐derived leaf area index (LAI), along with weather parameters. It was observed that the yield model based on the TVI showed the highest correlation (R² = 0.83) with green leaf tea yield. The NDVI‐ and SR‐based models suffered non‐responsiveness when the yield approached maximum. The NDVI and SR showed saturation when the LAI exceeded a magnitude of 4. However, the TVI responded well, even when the LAI exceeded 5, and thus has potential use in the estimation of the LAI of dense vegetation such as some crops and forest where it generally exceeds the threshold value of 4.

An attempt was made for the innovative application of TCT and NDWI in the mapping of waterlogging in tea plantation land. The NDWI in conjunction with TCT offered fairly good accuracy (87%) in the delineation of tea areas prone to waterlogging. This observation indicates the potential of NDWI and TCT in mapping waterlogged areas where the soil has considerable vegetation cover.     

遥感干旱指数在洛川苹果干旱监测中的适用性分析

目前对苹果干旱研究较少且主要运用站点数据,对空间信息表征有限,遥感干旱指数可用于大范围干旱时空动态监测,但在苹果干旱监测中的适用性还有待研究.基于2014～2018年MODIS反射率、地表温度以及地表覆被数据,结合土壤湿度数据和野外调查资料,分析洛川苹果区温度植被干旱指数(TVDI)、归一化植被水分指数(NDWI)、植...     

On the relationship of NDVI with leaf area index in a deciduous forest site

Numerous studies have reported on the relationship between the normalized difference vegetation index (NDVI) and leaf area index (LAI), but the seasonal and annual variability of this relationship has been less explored. This paper reports a study of the NDVI-LAI relationship through the years from 1996 to 2001 at a deciduous forest site. Six years of LAI patterns from the forest were estimated using a radiative transfer model with input of above and below canopy measurements of global radiation, while NDVI data sets were retrieved from composite NDVI time series of various remote sensing sources, namely NOAA Advanced Very High Resolution Radiometer (AVHRR; 1996, 1997, 1998 and 2000), SPOT VEGETATION (1998-2001), and Terra MODIS (2001). Composite NDVI was first used to remove the residual noise based on an adjusted Fourier transform and to obtain the NDVI time-series for each day during each year.The results suggest that the NDVI-LAI relationship can vary both seasonally and inter-annually in tune with the variations in phenological development of the trees and in response to temporal variations of environmental conditions. Strong linear relationships are obtained during the leaf production and leaf senescence periods for all years, but the relationship is poor during periods of maximum LAI, apparently due to the saturation of NDVI at high values of LAI. The NDVI-LAI relationship was found to be poor (R² varied from 0.39 to 0.46 for different sources of NDVI) when all the data were pooled across the years, apparently due to different leaf area development patterns in the different years. The relationship is also affected by background NDVI, but this could be minimized by applying relative NDVI.Comparisons between AVHRR and VEGETATION NDVI revealed that these two had good linear relationships (R²=0.74 for 1998 and 0.63 for 2000). However, VEGETATION NDVI data series had some unreasonably high values during beginning and end of each year period, which must be discarded before adjusted Fourier transform processing. MODIS NDVI had values greater than 0.62 through the entire year in 2001, however, MODIS NDVI still showed an “M-shaped” pattern as observed for VEGETATION NDVI in 2001. MODIS enhanced vegetation index (EVI) was the only index that exhibited a poor linear relationship with LAI during the leaf senescence period in year 2001. The results suggest that a relationship established between the LAI and NDVI in a particular year may not be applicable in other years, so attention must be paid to the temporal scale when applying NDVI-LAI relationships.     

Evaluate Quantitatively Effects of Understory on Leaf Area Index (LAI) Estimation Combining Active and Passive Remote Sensing Data

Natural forests have the vertical three\|dimensional structure of canopy and understory vegetation (shrubs,grasslands,and bare soil).Accurate and quantitative separation of understory vegetation is of great scientific significance and practicality on improving the precision of inversion of forest canopy leaf area index.value.Due to the limitations of traditional passive optical remote sensing data on directly acquiring 3D information,this study intends to combine active and passive ALS and HyperMap data with the Washington Botanic Garden as the key research area.On the basis of individual tree segmentation,the vertical stratification of the forest (forest canopy and undergrowth vegetation layer) is achieved.On this basis,the forest canopy laser point cloud data was used to remove the understory information from the optical image data.By comparing the results of the forest effective leaf area index obtained from aerial optical images and ground measurements,it was found that：(1) forest canopy density has a significant impact on the penetration of ALS data;(2) removal of understory information can effectively improve the forest crown accuracy of LAIe estimated.The correlation between Normalized Difference Vegetation Index (NDVI) and ground surface measured effective leaf area index increased from 0.087 to 0.591.In addition,the optical remote sensing image based on the removal of understory vegetation information was compared with the Simple Ratio vegetation index (SR) (the correlation increased from 0.209 to 0.559) and the simplified simple Ratio vegetation index (RSR) (the correlation increased from 0.147 to 0.358).The NDVI was most sensitive to changes in canopy leaf area index (correlation increased by 0.5).The method of quantitatively separating understory vegetation with the combined active and passive remote sensing data proposed in this study can effectively improve the accuracy of inversion of forest canopy leaf area index,and provide a solid foundation for quantitative and accurate estimate of forest biophysical parameters and study of carbon and water cycle processes.