基于Sentinel-1/2遥感数据的冬小麦覆盖地表土壤水分协同反演

冬小麦是我国重要粮食作物之一,对冬小麦覆盖地表土壤水分进行监测有助于解决因土壤供水导致的冬小麦歉收和农业用水浪费等问题。为了降低冬小麦覆盖地表土壤水分微波遥感反演过程中冬小麦对雷达后向散射系数的影响,该文基于Sentinel-1携带的合成孔径雷达(SAR)数据和Sentinel-2携带的多光谱成像仪(MSI)数据,结合水云模型,开展冬小麦覆盖地表土壤水分协同反演研究。首先,基于MSI数据,该文定义了一种新的植被指数,即融合植被指数(FVI),用于冬小麦含水量反演;然后,该文发展了一种基于主被动遥感数据的冬小麦覆盖地表土壤水分反演半经验模型,校正冬小麦在土壤水分反演过程中对雷达后向散射系数的影响;最后,以河南省某地冬小麦农田为研究区域,开展归一化水体指数(NDWI)和FVI两种指数与VV, VH, VV/VH 3种极化组合而成的6种反演方式下的土壤水分反演对比实验。结果表明:以FVI为植被指数,能够更好地去除冬小麦在土壤水分反演过程中对雷达后向散射系数的影响;6种反演方式中,FVI与VV/VH组合下的反演效果最优,其决定系数为0.7642,均方根误差为0.0209 cm3/cm3,平均绝对误差为0.0174 cm3/cm3,展示了该文所提土壤水分反演模型的研究价值和应用潜力。     

基于Sentinel-1/2遥感数据的冬小麦覆盖地表土壤水分协同反演

冬小麦是我国重要粮食作物之一,对冬小麦覆盖地表土壤水分进行监测有助于解决因土壤供水导致的冬小麦歉收和农业用水浪费等问题.为了降低冬小麦覆盖地表土壤水分微波遥感反演过程中冬小麦对雷达后向散射系数的影响,该文基于Sentinel-1携带的合成孔径雷达(SAR)数据和Sentinel-2携带的多光谱成像仪(MSI)数据,结合水云模型,开展冬小麦覆盖地表土壤水分协同反演研究.首先,基于MSI数据,该文定义了一种新的植被指数,即融合植被指数(FVI),用于冬小麦含水量反演;然后,该文发展了一种基于主被动遥感数据的冬小麦覆盖地表土壤水分反演半经验模型,校正冬小麦在土壤水分反演过程中对雷达后向散射系数的影响;最后,以河南省某地冬小麦农田为研究区域,开展归一化水体指数(NDWI)和FVI两种指数与VV,VH,VV/VH 3种极化组合而成的6种反演方式下的土壤水分反演对比实验.结果表明:以FVI为植被指数,能够更好地去除冬小麦在土壤水分反演过程中对雷达后向散射系数的影响;6种反演方式中,FVI与VV/VH组合下的反演效果最优,其决定系数为0.7642,均方根误差为0.0209 cm3/cm3,平均绝对误差为0.0174 cm3/cm3,展示了该文所提土壤水分反演模型的研究价值和应用潜力.     

大气校正对几种MODIS植被指数影响的分析

利用NASA—ESE提供的算法和代码对EOS／MODIS进行大气校正计算，得到CHl-CH7的地面反射率．再分别用大气校正前后的表观反射率和地面反射率数据计算三种常见植被指数（RVI、NDVI、NDVIg），分析三种植被指数在大气校正前后的变化以及与季节、地物类型之间的关系。指出大气校正并未完全抵消RVI中气溶胶的影响，但在绿度低值区补偿明显；而NDVI和NDVIg从整体上部分补偿了大气影响，在气溶胶较厚或使用绿通道（NDVIg）时必须进行大气校正。     

基于多源遥感数据的草地生物量反演

面向青海省黄南藏族自治州河南蒙古族自治县(河南县)开展实验。基于2018年5—8月的Landsat8、GF-1、GF-4遥感数据建立时空连续性的NDVI时间序列；将NDVI时间序列与人工采样草地生物量进行时空匹配，用于构建NDVI时间序列与草地生物量的经验模型；基于地理学第一定律，将基于点拟合的经验模型推广到面，实现在稀疏地面观测样本条件下的大区域、高精度草原生物量反演。提出的方法高效地构建了时空匹配的星基NDVI与时空分布稀疏的人工草地样方生物量的时间序列对，解决了当前遥感反演方法过度依赖稀疏的地面观测采样数据的问题，提升了反演的成功率和模型的泛化能力。基于提出的方法在试验区展开实验，决定系数(R²)平均为0.75,优于植被指数法(R²取值0.3～0.5);均方根误差(RMSE)为1.10 kg/km²,优于植被指数法(RMSE取值10～60 kg/km²)。     

基于高光谱数据的叶面积指数遥感反演

文中耦合叶片辐射传输模型（PROSPECT）和冠层辐射传输模型（SAILH）,基于高光谱载荷通道设置,模拟高光谱冠层反射率数据;利用模拟数据深入分析了不同植被指数与叶面积指数之间的敏感性;通过敏感性分析发现改进型叶绿素吸收植被指数（MCARI2）具备抗土壤背景因素的影响能力,而且对叶面积指数较为敏感,因此该研究建立植被指数MCARI2 与叶面积指数之间的经验统计模型,并用于高光谱数据进行叶面积指数反演;最后利用飞行同步测量的叶面积指数对反演模型进行精度分析。结果表明:相比实测叶面积指数,文中建立的反演模型约低估0.42,该反演模型能够较好的反映出地物真实叶面积指数。     

星载合成孔径微波辐射计的反演公式

本文利用相关理论对星载合成孔径微波辐射计（简称SAMR）进行了详细的分析和探讨，给出它对地物辐射响应的一般表达式。当地物辐射源为非相关时，得到窄带SAMR对地物亮温度分布的反演公式。当地物辐射源为相关时，论述了辐射计的输出不含地物亮温度分布有用信息。     

Relief-F筛选波段的小麦白粉病早期诊断研究

为了准确监测小麦白粉病染病早期病情,给喷药防治提供技术指导,论文将染病初期的小麦叶片作为研究对象。首先,利用高光谱图像数据,通过图像特征分割出叶片区域和病斑区域,定量计算病情严重度;其次引入Relief-F算法提取染病早期最敏感波段和波段差,计算出白粉病病害指数PMDI （Powdery mildew disease index）;并通过分析病情指数DI （Disease index）与11种植被指数（含PMDI指数）的相关性及线性模型,得出PMDI模型有最高的决定系数（R2=0.839 9）和最低的均方根误差（RMSE=4.522 0）,效果优于其他病害植被指数的结果（其中,Normalized Difference Vegetation Index,NDVI的模型决定系数最高,R2=0.777 1,RMSE=5.336 4）;最后,选择PMDI和NDVI植被指数分别构建小麦白粉病染病早期病情严重度的支持向量回归模型。结果表明:经敏感波段筛选构建的PMDI指数的预测结果更好,预测模型的R2=0.886 3,RMSE=3.553 2,可以实现小麦白粉病早期无损诊断,这为指导作物病害喷药防治提供重要的技术支撑。     

归一化阴影植被指数NSVI的构建及其应用效果

以ALOS AVNIR-2、CBERS-02B CCD、HJ1A-CCD2、Landsat 7 ETM四幅中分辨率遥感影像为试验数据,分析明亮区植被、阴影区植被与水体区的光谱特征与差异,基于近红外波段与归一化植被指数NDVI,构建归一化阴影植被指数NSVI,并评价其光谱差异增强及分类效果.结果表明,NSVI大幅扩大了明亮区植被、阴影区植被、水体区间的光谱相对差异,降低光谱混淆概率;利用NSVI阈值法对四幅试验影像进行分类,总精度均大于97%,总Kappa在0.96以上,且阴影区植被的检测精度均在94%以上,总Kappa系数亦高于0.96.该指数利用地物在近红外波段的辐射差异,解决NDVI只能部分削弱地形影响的问题,扩大地物间的光谱差异,从而提升地物尤其是阴影检测的有效性,且不存在NDVI“易饱和”问题,可为遥感影像阴影去除提供一种新的解决方案.     

归一化阴影植被指数NSVI的构建及其应用效果（英文）

以ALOS AVNIR-2、CBERS-02B CCD、HJ1A-CCD2、Landsat 7 ETM四幅中分辨率遥感影像为试验数据,分析明亮区植被、阴影区植被与水体区的光谱特征与差异,基于近红外波段与归一化植被指数NDVI,构建归一化阴影植被指数NSVI,并评价其光谱差异增强及分类效果.结果表明,NSVI大幅扩大了明亮区植被、阴影区植被、水体区间的光谱相对差异,降低光谱混淆概率;利用NSVI阈值法对四幅试验影像进行分类,总精度均大于97%,总Kappa在0.96以上,且阴影区植被的检测精度均在94%以上,总Kappa系数亦高于0.96.该指数利用地物在近红外波段的辐射差异,解决NDVI只能部分削弱地形影响的问题,扩大地物间的光谱差异,从而提升地物尤其是阴影检测的有效性,且不存在NDVI"易饱和"问题,可为遥感影像阴影去除提供一种新的解决方案.     

基于光电特性参数的典型地物类型反演方法

对于不同类型的地物背景而言,其光电特性参数属于不变特征,利用其差异可以判别地物背景的类型并为目标检测和识别提供有效依据.基于上述研究目的,提出一种基于光电特性参数的典型地物类型反演研究方法.首先构建了典型地物背景的单层热能交换模型,建立了相应的热平衡方程;其次综合考虑大气衰减因子与背景距离等因素影响,基于黑体定标试验得到红外图像灰度与背景表面温度的关系;然后构建了基于光电特性参数的典型地物热平衡函数,并采用假设检验的方法来反演背景物的类型;最后利用外场实测的不同时刻、不同地物背景的红外图像验证了方法的可行性,并对引起反演误差的主要因素进行了详细分析.     

Thermal Land Surface Emissivity Retrieved From SEVIRI/Meteosat

The methodologies used by the Satellite Application Facility on Land Surface Analysis (Land SAF) for retrieving emissivity are presented here. In the first approach, i.e., the vegetation cover method (VCM), the land surface emissivity (EM) is computed for Spinning Enhanced Visible and Infrared Imager (SEVIRI) infrared channels and for the 3- to 14- range using information on the pixel fraction of vegetation cover (FVC). The VCM uses a lookup table, which takes into account the channel's spectral response function, and laboratory reflectance spectra for different materials. The accuracy of the VCM depends on the reliability of FVC and the land cover classification. The EM for SEVIRI split-window channels is primarily used as an internal product by Land SAF for land surface temperature (LST) estimations. However, sensitivity studies show that LST often fails to meet the required accuracy of 2 K over desert and semiarid regions, where the VCM is unable to model the EM spatial variability, which is mostly associated with soil composition. Moreover, it is also over such areas where the atmosphere is generally dry that the impact of EM uncertainties on LST is largest. A second approach to determine the EM for SEVIRI split-window channels is currently being tested. This methodology allows the simultaneous retrieval of LST and channel EMs with the assumption that the latter remain constant. The channel EMs are then averaged over a 22-day period to filter out the noise in the retrievals. A first analysis of the maps obtained for an area within Northern Africa shows spatial patterns with features also present in the surface albedo.     

Soil moisture and TRMM microwave imager relationships in theSouthern Great Plains 1999 (SGP99) experiment

Satellite data collected by the Tropical Rainfall Measuring Mission (TRMM) microwave imager (TMI) and the special sensor microwave/imager (SSM/I) were compared to soil moisture observations as part of the Southern Great Plains (SGP) 1999 Experiment. SGP99 was conducted to address significant gaps in the knowledge base on the microwave remote sensing of soil moisture. Satellite, aircraft and ground based data collection were conducted between July 8, 1999 and July 20, 1999, during which an excellent sequence of meteorological conditions occurred. Cross calibration of the SSM/I data to the same TMI channels showed nearly identical brightness temperatures, 19 GHz SSM/I data and soil moisture relationships were similar to those observed in previous experiments in this region. Comparison studies of the SSM/I and TMI channels revealed that only sampling areas with adequate spatial domains should be used for soil moisture validation. Analyses of the TMI 10 GHz data provide new information on potential improvements that this channel can provide for soil moisture estimation. Soil moisture maps of the region were derived for dates of coverage     

Influence of near-surface soil moisture on regional scale heatfluxes: model results using microwave remote sensing data fromSGP97

During the 1997 Southern Great Plains Hydrology Experiment (SGP97), passive microwave observations using the L-band electronically scanned thinned array radiometer (ESTAR) were used to extend surface soil moisture retrieval algorithms to coarser resolutions and larger regions with more diverse conditions. This near-surface soil moisture product (W) at 800 m pixel resolution together with land use and fractional vegetation cover (f_c) estimated from normalized difference vegetation index (NDVI) was used for computing spatially distributed sensible (H) and latent (LE) heat fluxes over the SGP97 domain (an area ~40×260 km) using a remote sensing model (called the two-source energy Balance-soil moisture, TSEB_SM, model). With regional maps of W and the heat fluxes, spatial correlations were computed to evaluate the influence of W on H and LE. For the whole SGP97 domain and full range in f_c, correlations (R) between W and LE varied from 0.4 to 0.6 (R~0.5 on average), while correlations between W and H varied from -0.3 to -0.7 (R~-0.6 on average). The W-LE and W-H correlations were dramatically higher when variability due to f_c was considered by using NDVI as a surrogate for f_c and computing R between heat fluxes and corresponding W values under similar fractional vegetation cover conditions. The results showed a steady decline in correlation with increasing NDVI or f_c. Typically, |R|≳0.9 for data sorted by NDVI having values ≲0.5 or f_c ≲0.5, while |R|≲0.5 for the data sorted under high canopy cover where NDVI≳0.6 or f_c≳0.7     

Atmospherically resistant vegetation index (ARVI) for EOS-MODIS

An atmospherically resistant vegetation index (ARVI) is proposed and developed for remote sensing of vegetation from the Earth Observing System (EOS) MODIS sensor. The same index can be used for remote sensing from Landsat TM and the EOS-HIRIS sensor. The index takes advantage of the presence of the blue channel (0.47.±0.01 μm) in the MODIS sensor, in addition to the red (0.66±0.025 μm) and the near-IR (0.865±0.02 μm) channels that compose the present normalized difference vegetation index (NDVI). The resistance of the ARVI to atmospheric effects (in comparison to the NDVI) is accomplished by a self-correction process for the atmospheric effect on the red channel, using the difference in the radiance between the blue and the red channels to correct the radiance in the red channel. Simulations using radiative transfer computations on arithmetic and natural surface spectra, for various atmospheric conditions, show that ARVI has a similar dynamic range to the NDVI, but is, on average, four times less sensitive to atmospheric effects than the NDVI     

Soil Moisture Inferences from Thermal-Infrared Measurements of Vegetation Temperatures

Remote sensing methods for the estimation of soil moisture yield direct information only for the topmost layers of soil. Reflected solar, thermal-infrared (IR), and microwave techniques are sensitive to the surface skin, from the surface to about 5 cm, and from the surface to about 10 cm, respectively. When the growth of vegetation is of major interest, soil moisture needs to be infrared at least to the depth of rooting of the plants. Since remote measurement of soil moisture is depth limited, it has been suggested that plant measurements, specifically plant temperatures, may yield information about soil moisture within the root zone. To examine this possibility, three plots of wheat, initially treated similarly, and later irrigated differently, were monitored for vegetation temperature (by infrazed thermometry) and for soil-water content (thrice weekly neutron moisture meter measurements). Vegetation temperatures were converted to a crop water stress index (CWSI). The CWSI was found to be a nonunique function of extractable water. The nonuniqueness was probably caused by inability to adequately specify the root zone and by the fact that plants require a recovery period (five to six days for this experiment) after being stressed before normal water uptake and transpiration proceeds.     

滨海地区地表温度空间异质性影响因素探究

随着城市化进程不断加快, 地表温度升高引起的城市病日益严重。为深入认识滨海城市地表温度的影响因 素, 进而为改善人居环境和生态健康提供科学数据支撑, 采用单窗算法反演了大连市内甘井子区、西岗区、沙河口区 和中山区四区地表温度, 并综合运用多尺度地理加权回归模型 (Multiscale-GWR) 结合归一化建筑指数 (NDBI)、归一 化植被指数 (NDVI)、改进的归一化水体指数 (MNDWI) 和归一化裸土指数 (NDBAI), 探究了地表温度与下垫面指数 空间异质性关系。研究结果表明: (1) 大连市内四区的地表温度呈现由东向西递减的分布态势, 中山区、沙河口区和 西岗区的北部地表温度较南部高, 甘井子区西南部地表温度较其他区域低。 (2) 大连市内四区地表温度与下垫面指数 关系基本上不存在全局效应, 空间异质性很强, Multiscale-GWR 模型可以较好地拟合下垫面指数与地表温度相关关 系。 (3) 从相关系数来看, 下垫面指数对地表温度的影响作用力表现为: NDBAI > NDVI > MNDWI > NDBI, NDBAI、 NDVI 和 MNDWI 指数总体上呈现负相关效应, NDBI 总体上呈现正相关效应。     

不同土壤类型的粗糙地面电磁散射特性及布儒斯特效应研究

土壤表面散射特性对大地遥感等问题有着重要应用,地面对雷达波束的镜面反射是造成镜像干扰的主要原因,利用粗糙地面的布儒斯特效应将有效削弱镜面反射。采用四成分土壤介电模型计算不同类型土壤的介电常数,以二维高斯粗糙面模拟实际地面,引入锥形入射波来克服粗糙面的边缘衍射;应用基于物理意义的双网格法(PBTG)结合稀疏矩阵规范网格法(SMCG)计算分析土壤类型和湿度对地面散射特性的影响,进一步探究了粗糙地面布儒斯特效应随土壤类型、湿度及入射波频率等的变化关系。分析表明:土壤类型和湿度等因素对地面散射特性及布儒斯特效应均会产生不同程度的影响。研究成果对于不同土壤类型的地面环境遥感探测以及削弱镜像干扰提供了理论支撑。     

One-dimensional variational retrieval algorithm of temperature, water vapor, and cloud water profiles from advanced microwave sounding unit (AMSU)

The measurements from satellite microwave imaging and sounding channels are simultaneously utilized through a one-dimensional (1-D) variation method (1D-var) to retrieve the profiles of atmospheric temperature, water vapor and cloud water. Since the radiative transfer model in this 1D-var procedure includes scattering and emission from the earth's atmosphere, the retrieval can perform well under all weather conditions. The iterative procedure is optimized to minimize computational demands and to achieve better accuracy. At first, the profiles of temperature, water vapor, and cloud liquid water are derived using only the AMSU-A measurements at frequencies less than 60 GHz. The second step is to retrieve rain and ice water using the AMSU-B measurements at 89 and 150 GHz. Finally, all AMSU-A/B sounding channels at 50-60 and 183 GHz are utilized to further refine the profiles of temperature and water vapor while the profiles of cloud, rain, and ice water contents are constrained to those previously derived. It is shown that the radiative transfer model including multiple scattering from clouds and precipitation can significantly improve the accuracy for retrieving temperature, moisture and cloud water. In hurricane conditions, an emission-based radiative transfer model tends to produce unrealistic temperature anomalies throughout the atmosphere. With a scattering-based radiative transfer model, the derived temperature profiles agree well with those observed from aircraft dropsondes.     

Directional effects in a daily AVHRR land surface temperature dataset over Africa

Land surface temperature (LST) is a key indicator of the land surface state and can provide information on surface-atmosphere heat and mass fluxes, vegetation water stress, and soil moisture. Split-window algorithms have been used with National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) data to estimate instantaneous LST for nearly 20 years. However, the low accuracy of LST retrievals associated with intractable variability has often hindered its wide use. In this study, we developed a six-year daily (day and night) NOAA-14 AVHRR LST dataset over continental Africa. By combining vegetation structural data available in the literature and a geometric optics model, we estimated the fractions of sunlit and shaded endmembers observed by AVHRR for each pixel of each overpass. Although our simplistic approach requires many assumptions (e.g., only four endmember types per scene), we demonstrate through correlation that some of the AVHRR LST variability can be attributed to angular effects imposed by AVHRR orbit and sensor characteristics, in combination with vegetation structure. These angular effects lead to systematic LST biases, including "hot spot" effects when no shadows are observed. For example, a woodland case showed that LST measurements within the "hot-spot" geometry were about 9 K higher than those at other geometries. We describe the general patterns of these biases as a function of tree cover fraction, season, and satellite drift (time past launch). In general, effects are most pronounced over relatively sparse canopies (tree cover <60%), at wet season sun-view angle geometries (principal plane viewing) and early in the satellite lifetime. These results suggest that noise in LST time series may be strongly reduced for some locations and years, and that long-term LST climate data records should be normalized to a single sun-view geometry, if possible. However, much work remains before these can be accomplished.