综合主动和被动微波数据监测土壤水分变化

微波遥感测量土壤水分的方法主要分主动和被动两种，它们都是基于干燥土壤和水体之间介电常数的巨大差异。估算植被覆盖土壤表面土壤水分必须要考虑地表粗糙度和植被覆盖影响的问题。植被覆盖土壤表面的后向散射包括来自植被的体散射，来自地表的面散射和植被与地表间的交互作用散射项。本研究建立了一个半经验公式模型，用来计算体散射项，综合时间序列的主动和被动微波数据，消除植被覆盖的影响，估算地表土壤水分的变化状况。并应用1997年美国SGP‘97综合实验中的机载800m分辨辐射计ESTAR数据计算表面反射系数，综合Radarsat的SCAN-SAR数据得到体散射项，然后，由NOAA/AVHRR和TM计算得到的NDVI值加权分配50m分辨率的体散射项，最后计算50m分辨率的表面反射系数的变化值，从而得到土壤水分的变化情况，验证数据表明该计算结果与实测值一致。     

Concurrent use of active and passive microwave remote sensing data for monitoring of rice crop

Estimation of crop area, growth and phenological information is very important for monitoring of agricultural crops. However, judicious combination of spatial and temporal data from different spectral regions is necessary to meet the requirement. This study highlights the use of active microwave QuikSCAT Ku-band scatterometer and Special Sensor Microwave/Imager (SSM/I) passive microwave radiometer data to derive information on important phenological phases of rice crop. The wetness index, a weekly composite product derived using brightness temperatures from 19, 37 and 85 GHz channels of SSM/I, was used to identify the puddling period. Ku-band scatterometer data provided the signal of transplanted rice seedlings since they acts as scatterers and increases the backscattering. Dual peak nature of temporal backscatter curve around the heading stage of rice crop was observed in Ku-band. The decrease of backscatter after first peak was associated with the threshold value of 60% crop canopy cover. The symmetric (Gaussian) and asymmetric (lognormal) curve fits were attempted to derive the date of initiation of the heading phase. The temporal signature from each of these sensors was found to complement each other in crop growth monitoring. Image showing pixel-wise timings of heading stage revealed the differences exists in various parts of the study area.     

目标分解技术在植被覆盖条件下土壤水分计算中的应用

目标分解技术利用协方差距阵的特征值和特征矢量，将极化雷达后向散射测量值分解为单向散射，双向散射和交叉极化散射三个分量，并建立了植被覆盖地表的一阶物理离散散射模型。通过分解的各分量与该模型的比较，建立重轨极化雷达测量数据估算土壤水分的方法，采用Washita‘92实验区多时相全极化L波段JPL/AIRSAR图像雷达测量数据，利用分解的散射测量值，我们评估了在同一入射角，单频（L波段），多路条件下，分解理论在进行土壤水分估计时减少植被影响的能力。结果表明利用目标分解理论和重轨极化雷达数据可以估算植被覆盖区域土壤水分的变化情况。     

Using Passive Microwave Response to Soil Moisture Change for Soil Mapping: A Case Study for the Livingstone Creek Catchment

The 46-$hbox{km}^{2}$ Livingstone Creek Catchment in southeastern Australia was flown with a passive microwave airborne remote sensor four times throughout the three-week National Airborne Field Experiment in 2006, with a spatial resolution of $sim$200 m. Both continuous and discrete measurements of soil moisture were taken to help with interpretation of results. The catchment was experiencing extreme drought conditions leading up to the experiment, and as a result, ground cover in the catchment was minimal with many paddocks consisting of sparse dry stubble and grass. During the experiment period of November 2006, 30 mm of rainfall occurred, with the catchment going from parched dry conditions to surface wet conditions and back to dry conditions again in a short period of time. Changes in moisture responses observed by the airborne passive microwave sensor were field verified to reflect the different geology, soil, and landform elements of the catchment. Consequently, this study suggests that passive microwave remote sensing has potential as a tool to assist with soil mapping, through detecting changes in soil moisture spatial and temporal patterns.      

Assessing the SMOS Soil Moisture Retrieval Parameters With High-Resolution NAFE'06 Data

The spatial and temporal invariance of Soil Moisture and Ocean Salinity (SMOS) forward model parameters for soil moisture retrieval was assessed at 1-km resolution on a diurnal basis with data from the National Airborne Field Experiment 2006. The approach used was to apply the SMOS default parameters uniformly over 27 1-km validation pixels, retrieve soil moisture from the airborne observations, and then to interpret the differences between airborne and ground estimates in terms of land use, parameter variability, and sensing depth. For pastures (17 pixels) and nonirrigated crops (5 pixels), the root mean square error (rmse) was 0.03 volumetric (vol./vol.) soil moisture with a bias of 0.004 vol./vol. For pixels dominated by irrigated crops (5 pixels), the rmse was 0.10 vol./vol., and the bias was $-$0.09 vol./vol. The correlation coefficient between bias in irrigated areas and the 1-km field soil moisture variability was found to be 0.73, which suggests either 1) an increase of the soil dielectric roughness (up to about one) associated with small-scale heterogeneity of soil moisture or/and 2) a difference in sensing depth between an L-band radiometer and the in situ measurements, combined with a strong vertical gradient of soil moisture in the top 6 cm of the soil.      

The contribution of multitemporal SAR data in assessing hydrological parameters

The sensitivity of radar backscattering to the principal hydrological parameters, such as vegetation biomass, soil moisture, and surface roughness, is discussed. Results obtained by using multifrequency synthetic aperture radar (SAR) data measured by the Jet Propulsion Laboratory Airborne Synthetic Aperture Radar, Spaceborne Imaging Radar-C, and European Remote Sensing 1/2 sensors are summarized. The sensitivity of L- and C-bands to spatial variations of plant and soil parameters is masked by the presence of surface roughness, which in turn affects the radar signal. However, from the observation of data collected at different dates and averaged over a relatively wide area that includes several fields, the correlation to soil moisture and vegetation biomass is found to be significant, since the effects of spatial variations are smoothed. On the other hand, the sensitivity to surface roughness becomes appreciable when multitemporal data are averaged in time, thus reducing the effects of temporal moisture variations.     

ALOS PALSAR双极化数据水稻制图

以江苏省海安县为研究区,使用2008年获取的日本ALOS卫星PALSAR双极化模式数据,分析水稻在L波段SAR图像上的后向散射特征,并提出相应的水稻制图方法。水稻在L波段上表现出了和C波段相同的时相变化特征。HH极化后向散射依赖于水稻植株的空间分布结构,某些机械插秧区域的布拉格共振现象引起水稻后向散射严重增强,给利用PALSAR数据水稻制图带来了困难。而HV极化不存在布拉格共振现象。在考虑布拉格共振影响的条件下,提出了联合PALSAR双极化模式HH和HV极化数据、基于时相变化特征进行水稻制图的方法,获得了88.4%的制图精度。     

中国西部地区基于经验模型的土壤水分反演

地表土壤水分含量的时空分布信息是十分重要的,常常作为水文模型、气候模型、生态模型的输入参数,同时,也是干旱预报、农作物估产等工作的重要指标。被动微波遥感是监测土壤含水量最有效的手段之一。相比红外与可见光,它具有波长长,穿透能力强的优势。相比主动微波雷达,被动微波辐射计具有监测面积大、周期短,受粗糙度影响小,对土壤水分更为敏感,算法更为成熟的优势。目前,已研究出许多反演土壤水分的方法.本课题的主要内容是借助AMSR-E土壤水分影像数据、MODIS归一化植被指数(NDVI)影像数据和MODIS分类影像数据,利用ENVI软件进行遥感图像数据处理,运用统计分析方法建立NDVI与土壤水分的经验模型,研究中国西部地区稀疏植被覆盖区土壤水分的反演。     

AMSR微波遥感数据进行土壤湿度的计算机反演

为了有效解决大尺度区域土壤水分时、空间变化监测的问题,在总结了被动微波遥感反演土壤湿度规律的基础上,基于先进的AMSR星载被动微波遥感数据,提出了利用双谱模型计算土壤表面发射率的计算机算法。首先需要由双站散射系数计算反射率和发射率,然后应用人工神经网络反演土壤湿度,实现了在随机粗糙面状况下基于被动微波遥感的土壤表面水分反演,并在实验区进行了成功的应用。     

Influence of surface and vegetation characteristics on C-band radar measurements for soil moisture content

Soil moisture estimation using microwave remote sensing faces challenges of the segregation of influences mainly from roughness and vegetation. Under static surface conditions, it was found that Radarsat C-band SAR shows reasonably good correlation and sensitivity with changing soil moisture. Dynamic surface and vegetation conditions are supposed to result in a substantial reduction in radar sensitivity to soil moisture. A C-band scatterometer system (5.2 GHz) with a multi-polarization and multi-angular configuration was used 12 times to sense the soil moisture over a tall vegetated grass field. A score of vegetation and soil parameters were recorded on every occasion of the experiment. Three radar backscattering models Viz., Integral Equation Model (IEM), an empirical model and a volume scattering model, have been used to predict the backscattering phenomena. The volume scattering model, using the Distorted Born Approximation, is found to predict the backscattering phenomena reasonably well. But the surface scattering models are expectedly found to be inadequate for the purpose. The temporal variation of soil moisture does show good empirical relationship with the observed radar backscattering. But as the vegetation biomass increases, the radar shows higher sensitivity to the vegetation parameters compared to surface characteristics. A sensitivity analysis of the volume scattering model for all the parameters also reveals that the radar is more sensitive to plant parameters under high biomass conditions, particularly vegetation water content, but the sensitivity to surface characteristics, particularly to soil moisture, is also appreciable.     

DisPATCh as a tool to evaluate coarse-scale remotely sensed soil moisture using localized in situ measurements: Application to SMOS and AMSR-E data in Southeastern Australia

Validating coarse-scale satellite soil moisture data still represents a big challenge, notably due to the large mismatch existing between the spatial resolution (> 10 km) of microwave radiometers and the representativeness scale (several m) of localized in situ measurements. This study aims to examine the potential of DisPATCh (Disaggregation based on Physical and Theoretical scale Change) for validating SMOS (Soil Moisture and Ocean Salinity) and AMSR-E (Advanced Microwave Scanning Radiometer-Earth observation system) level-3 soil moisture products. The ∽40–50 km resolution SMOS and AMSR-E data are disaggregated at 1 km resolution over the Murrumbidgee catchment in Southeastern Australia during a one year period in 2010–2011, and the satellite products are compared with the in situ measurements of 38 stations distributed within the study area. It is found that disaggregation improves the mean difference, correlation coefficient and slope of the linear regression between satellite and in situ data in 77%, 92% and 94% of cases, respectively. Nevertheless, the downscaling efficiency is lower in winter than during the hotter months when DisPATCh performance is optimal. Consistently, better results are obtained in the semi-arid than in a temperate zone of the catchment. In the semi-arid Yanco region, disaggregation in summer increases the correlation coefficient from 0.63 to 0.78 and from 0.42 to 0.71 for SMOS and AMSR-E in morning overpasses and from 0.37 to 0.63 and from 0.47 to 0.73 for SMOS and AMSR-E in afternoon overpasses, respectively. DisPATCh has strong potential in low vegetated semi-arid areas where it can be used as a tool to evaluate coarse-scale remotely sensed soil moisture by explicitly representing the sub-pixel variability.     

Earth observations during space shuttle mission STS‐34: 18–23 October 1989

Abstract

Although high‐resolution microwave synthetic aperture radar (SAR) sensors possess all‐weather capability for mapping soil moisture from spaceborne platforms, continuous temporal and spatial monitoring of this important hydrological parameter has been relatively limited. However, the recent launch of operational SAR sensors aboard various satellites have made possible synoptic soil moisture monitoring a reality. Such systems operate over a wide range of frequencies, look angles, and polarization combinations, and thus show synergistic advantages when combined for estimating soil moisture patterns. Two soil moisture inversion algorithms have been developed using as inputs radar backscattering data at L, S, and C bands in the microwave frequency range. These models have been tested using radar image simulation with speckle added. It is observed that the neural network algorithm yields superior results in mapping actual soil moisture patterns over the linear statistical inversion technique, although both models show comparable errors in soil moisture estimation. We infer that using statistical estimation errors alone for comparison purposes may lead to erroneous conclusions regarding the advantages of one soil moisture inversion algorithm over another.     

中国城市微波发射率提取与特征分析

城市是陆表重要的覆盖类型之一,精确提取城市微波发射率、研究其辐射特征和影响因素有利于提高城市密集区被动微波遥感反演土壤水分的精度.利用2008 年AMSR-E(Advanced Microwave Scanning Radiometer for Earth Observing System)6.925 GHz 和10.65 GHz 双极化的时间序列亮温数据和其他多种辅助数据,在分析并去除无线电干扰的前提下,提取中国区域相对纯净城市像元的发射率,并分析其辐射特征的季节变化和影响因素.结果表明,根据本文方法提取得到的中国区域纯净城市像元有两个,分别位于北京和上海.城市亮温受到无线电干扰影响,一年内不同城市、波段、过境时间的卫星观测亮温受到的干扰强度不同.去除无线电干扰后,各通道的城市发射率在一年中变化不大,且卫星降轨观测时刻的发射率比升轨时刻更加稳定.降雨是影响城市发射率的重要因子,当降雨量达到一定程度后,城市发射率与降雨量呈现明显的负线性相关.     

Snowmelt detection from QuikSCAT and ASCAT satellite radar scatterometer data across the Alaskan North Slope

The timing of seasonal snowmelt in high-latitude tundra has implications ranging from local biological productivity to global atmospheric circulation, yet remains difficult to quantify, particularly at large spatial scales. Snowmelt detection in such remote polar environments is possible using satellite-based microwave scatterometers, such as NASA’s QuikSCAT. QuikSCAT measured scattering in Ku-band, which is sensitive to snowmelt signals, from 1999 until the antenna failed in 2009. The Advanced Scatterometer (ASCAT) (2006–2021 (projected) operational), which operates at C-band, may be able to extend the QuikSCAT record, but existing techniques fail to adequately monitor tundra environments. Here, we designed a departure threshold algorithm to produce a consistent 15-year time series of melt onset for the tundra of the Alaskan North Slope, using the overlap period for the enhanced resolution datasets to calibrate the ASCAT melt detection record against QuikSCAT. We produced a time series of day of year of melt onset for 4.45 km x 4.45 km grid cells on the Alaskan North Slope from 2000–2014. Time series validation with in situ mean daily air temperature produced mean R² values of 0.75 (QuikSCAT) and 0.72 (ASCAT). We qualitatively observed a difference between early-season melt, which occurred rapidly and was driven by strong wind events, and more typical melt, which occurred gradually along a latitudinal gradient. We speculate that future melt timing will have greater frequency of early-season onset as climate change destabilizes the high-latitude atmosphere.     

An approach to estimate the evapotranspiration using NOAA/AVHRR DATA

Evapotranspiration (ET) is continued process wherein moisture from soil and vegetated surface is transferred to the atmosphere. Changes in evapotranspiration are likely to have large impacts on terrestrial vegetation. Evapotranspiration is a seasonally varying property at a given place; changes in it reflect the status of soil moisture and terrestrial vegetation. Through water balance, ET can include major shifts in vegetative patterns and or its condition leading to climate change. Therefore, in this paper, it is attempted to estimate the evapotranspiration over various land cover using National Oceanic and Atmospheric Administration (NOAA)/ Advanced Very High Resolution Radiometer (AVHRR) data at coarse spatial resolution of 1.1 km. For this purpose, a semi-empirical model has been proposed to estimate the ET. Regression analysis has been carried out to develop an empirical relation between individual land cover surface temperature and ET, which will be helpful to know the effect of each land cover surface temperature on ET. In which, it is observed that surface temperature over grassland is more effective on ET in comparison to other land cover in March 1999 on the Mupfure, Zimbabwe catchment area. This type of estimation will be helpful for climate modeler, climatologists, ecosystem modeler and regional planner.     

Intercomparison of two trapezoid-based soil moisture downscaling methods using three scaling factors

This paper compared two soil moisture downscaling methods using three scaling factors. Level 3 soil moisture product of advanced microwave scanning radiometer for EOS (AMSR-E) is downscaled from 25 to 1?km. The downscaled results are compared with the soil moisture observations from polarimetric scanning radiometer (PSR) microwave radiometer and field sampling. The results show that (1) the scaling factor of normalized soil thermal inertia (NSTIs) and vegetation temperature condition index (VTCI) are better than soil evaporative efficiency in reflecting soil moisture; (2) for method 1, NSTIS is the best in the downscaling of soil moisture. For method 2, VTCI is the best; (3) no significant differences of the correlation coefficients (R²) and the biases were found between the two methods for the same scaling factors. However, method 2 shows a better potential than method 1 in the time-series applications of the downscaling of soil moisture; (4) compared with the relationship between the area-averaged soil moisture of AMSR-E and that of PSR, R² of the 6 sets of the downscaled soil moisture almost do not decrease, which suggests the validity of the downscaling of soil moisture with the two downscaling methods using the three scaling factors.     

全极化SAR图像的山地低矮植被区域土壤含水量估计

山区土壤含水量对山区植被生长监测、滑坡预测等工作具有重要意义,因此针对山地低矮植被区域,提出了全极化SAR图像的土壤含水量估计方法。为解决山地区域SAR图像几何形变和极化旋转问题,根据入射角、坡度、坡向信息定义了可测区域与不可测区域,并对可测区域后向散射系数进行校正。其次以密西根模型为基础,发展了低矮植被的散射模型。在假定植被和土壤特征不变的情况下,基于此散射模型并结合校正数据建立了山区土壤含水量反演方法。结果表明,模型反演的土壤含水量和实验点实测值基本一致,两个实验点反演值分别为14%和15%,实测值为11.45%和15.80%,能够满足一般应用的需求。     

资源型城市长时间序列土壤含水量变化分析——以锡林浩特市为例

大规模的煤炭开采活动将对生态环境产生扰动,而土壤含水量是受扰动的生态参数之一,且具有重要意义。现有的土壤含水量产品分辨率较低,不适用于区域尺度的研究,而高精度的微波反演由于数据的局限性无法进行长时间序列的研究。本文以我国的重要产煤基地锡林浩特市为研究区,以2004—2020年的AMSR-E与AMSR-2土壤含水量产品及同期的Landsat遥感影像为主要数据源,采用随机森林方法对AMSR-E/2土壤含水量产品进行降尺度处理,通过标准差椭圆等方法对研究区土壤含水量的变化特征进行分析。结果表明：①被动微波土壤含水量降尺度方法可实现对资源型城市的土壤含水量进行长时间序列、高空间分辨率的监测;②无论在矿区还是非矿区,降水均是影响土壤含水量变化的主导因素;③研究区土壤含水量的整体分布在空间上由西北向东南呈现逐渐升高的变化特征,且此分布格局在长时间尺度上保持稳定;④煤炭开采活动对土壤含水量产生扰动,且不同开采阶段的影响具有不同特征。研究结果可为资源型城市生态环境的评价与保护提供科学依据。     

基于参数不确定性分析的SAR土壤水分反演精度控制方法

参数不确定性是SAR反演土壤水分的重要不确定性来源,为控制土壤水分反演精度,提出一种基于参数不确定性的有效控制土壤水分反演精度的方法,使用该方法可以控制参数的误差范围。首先使用全局敏感性分析方法,确定后向影响散射系数输出的主要参数;在不同量级高斯噪声随机扰动下,将大量各参数采值输入AIEM模型中,得到带噪声的后向散射系数集合;再使用LUT法反演土壤水分,计算反演结果满足误差量级控制范围。以此为基础,利用ENVISAT ASAR双极化数据(VV、VH)和实测土壤水分数据进行验证,利用LUT法反演得到带噪声的土壤水分,计算ASAR影像中采样点土壤水分反演值RMSE0.04cm3/cm3。结果表明各影响参数误差量级控制范围可有效控制土壤水分反演精度,在较大的入射角范围内都适用。     

Rice monitoring with multi-temporal and dual-polarimetric TerraSAR-X data

This study assesses the use of TerraSAR-X data for monitoring rice cultivation in the Sanjiang Plain in Heilongjiang Province, Northeast China. The main objective is the understanding of the coherent co-polarized X-band backscattering signature of rice at different phenological stages in order to retrieve growth status.For this, multi-temporal dual polarimetric TerraSAR-X High Resolution SpotLight data (HH/VV) as well as single polarized StripMap (VV) data were acquired over the test site. In conjunction with the satellite data acquisition, a ground truth field campaign was carried out.The backscattering coefficients at HH and VV of the observed fields were extracted on the different dates and analysed as a function of rice phenology to provide a physical interpretation for the co-polarized backscatter response in a temporal and spatial manner. Then, a correlation analysis was carried out between TerraSAR-X backscattering signal and rice biomass of stem, leaf and head to evaluate the relationship with different vertical layers within the rice vegetation.HH and VV signatures show two phases of backscatter increase, one at the beginning up to 46 days after transplanting and a second one from 80 days after transplanting onwards. The first increase is related to increasing double bounce reflection from the surface–stem interaction. Then, a decreasing trend of both polarizations can be observed due to signal attenuation by increasing leaf density. A second slight increase is observed during senescence. Correlation analysis showed a significant relationship with different vertical layers at different phenological stages which prove the physical interpretation of X-band backscatter of rice. The seasonal backscatter coefficient showed that X-band is highly sensitive to changes in size, orientation and density of the dominant elements in the upper canopy.