首页 | 本学科首页   官方微博 | 高级检索  
相似文献
 共查询到20条相似文献，搜索用时 78 毫秒
1.
利用GPS-IR监测土壤含水量的反演模型   总被引：1，自引：0，他引：1       下载免费PDF全文
GPS-IR（GPS-interferometric reflectometry）本质上是一种基于GPS辐射源的双基地雷达技术,利用大地测量型接收机记录的信噪比（signal-to-noise ratio,SNR）数据可用于反演土壤含水量。针对GPS-IR获取土壤含水量的参数估计问题,提出了一种改进的反射信号参数估计方法,并研究了土壤含水量反演模型的建立过程。实验结果表明,利用改进的反射信号参数估计方法可获得更加准确可靠的结果,反射信号相位与土壤含水量间存在显著的线性相关,可建立土壤含水量的线性反演模型,但在连续降雨条件下会存在较大误差。  相似文献   

2.
全球定位系统干涉反射测量（GPS-IR）是一种新型的遥感技术,可用于估算近地表土壤水分含量。本文从多卫星融合角度出发,提出了一种基于多星融合的地表土壤湿度估算方法。首先通过低阶多项式拟合分离出卫星反射信号;然后建立反射信号正弦拟合模型,获取相对延迟相位;最后基于多卫星相对延迟相位建立多元线性回归模型。利用美国板块边界观测计划（PBO）提供的监测数据,对比分析不同建模序列长度的反演效果,从而确定最佳的建模长度。试验结果表明,采用多元线性回归模型可实现多颗卫星的有效融合,运用于土壤湿度估算是可行的。  相似文献   

3.
全球定位系统干涉反射测量(GPS-IR)是一种新的遥感技术,可用于估算近地表土壤水分含量。考虑到多卫星融合的优势和土壤湿度的时空尺度性,提出一种基于多星融合的土壤湿度最小二乘支持向量机(LS-SVM)滚动式估算模型。首先通过低阶多项式拟合分离GPS卫星直射和反射信号,进而建立反射信号正弦拟合模型,获取相对延迟相位。最后,通过线性回归模型有效分析和选取多卫星相对延迟相位,并建立基于多星融合的最小二乘支持向量机模型进行滚动式估算土壤湿度。以美国板块边界观测计划PBO提供的监测数据为例,对比分析利用单颗、多颗GPS卫星进行土壤湿度滚动式估算的可行性和有效性。经理论分析和两个测站实验表明:该模型充分发挥了LS-SVM的优势,有效综合了各卫星的性能,改善了采用单颗卫星进行土壤湿度估算时,其结果极易出现异常跳变的现象;模型只需较少的建模数据,采用滚动式能实现较长时间的估算,估算误差较为稳定;模型所估算的结果与土壤湿度实测值之间的相关系数R2以及均方根误差分别为0.942和0.962、0.072和0.032,相对于部分单一卫星至少提高了18.18%。因此,土壤湿度问题可作为非线性事件处理,采用多卫星融合估算是可行和有效的。  相似文献   

4.
Estimation of vegetation covered soil moisture with satellite images is still a challenging task. Several models are available for soil moisture retrieval in which water cloud model (WCM) is most common. But, it requires an estimation of accurate vegetation parameterization. Thus, there is a need to develop such an approach for soil moisture retrieval which minimize these limitations. Therefore, this paper deals with the soil moisture retrieval using fully polarimetric SAR data by fusing the information from different bands. Various polarimetric indices and observables were critically analysed, and found that the index; SPAN (total scattered power) gives better information of vegetation cover as compared to other indices/observables. Based on this, WCM model has been modified using SPAN as parameter and soil moisture content were retrieved.  相似文献   

5.
Soil moisture is a geophysical key observable for predicting floods and droughts, modeling weather and climate and optimizing agricultural management. Currently available in situ observations are limited to small sampling volumes and restricted number of sites, whereas measurements from satellites lack spatial resolution. Global navigation satellite system (GNSS) receivers can be used to estimate soil moisture time series at an intermediate scale of about 1000 m2. In this study, GNSS signal-to-noise ratio (SNR) data at the station Sutherland, South Africa, are used to estimate soil moisture variations during 2008–2014. The results capture the wetting and drying cycles in response to rainfall. The GNSS Volumetric Water Content (VWC) is highly correlated (r 2 = 0.8) with in situ observations by time-domain reflectometry sensors and is accurate to 0.05 m3/m3. The soil moisture estimates derived from the SNR of the L1 and L2P signals compared to the L2C show small differences with a RMSE of 0.03 m3/m3. A reduction in the SNR sampling rate from 1 to 30 s has very little impact on the accuracy of the soil moisture estimates (RMSE of the VWC difference 1–30 s is 0.01 m3/m3). The results show that the existing data of the global tracking network with continuous observations of the L1 and L2P signals with a 30-s sampling rate over the last two decades can provide valuable complementary soil moisture observations worldwide.  相似文献   

6.
Using GPS multipath to measure soil moisture fluctuations: initial results   总被引：13，自引：2，他引：11  
Measurements of soil moisture are important for studies of climate and weather forecasting, flood prediction, and aquifer recharge studies. Although soil moisture measurement networks exist, most are sparsely distributed and lack standardized instrumentation. Measurements of soil moisture from satellites have extremely large spatial footprints (40–60 km). A methodology is described here that uses existing networks of continuously-operating GPS receivers to measure soil moisture fluctuations. In this technique, incoming signals are reflected off and attenuated by the ground before reception by the GPS receiver. These multipath reflections directly affect signal-to-noise ratio (SNR) data routinely collected by GPS receivers, creating amplitude variations that are a function of ground reflectivity and therefore soil moisture content. After describing this technique, multipath reflection amplitudes at a GPS site in Tashkent, Uzbekistan are compared to estimates of soil moisture from the Noah land surface model. Although the GPS multipath amplitudes and the land surface model are uncalibrated, over the 70-day period studied, they both rise sharply following each rainfall event and slowly decrease over a period of ∼10 days.  相似文献   

7.
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.  相似文献   

8.
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。  相似文献   

9.
To date, triple collocation (TC) analysis is one of the most important methods for the global-scale evaluation of remotely sensed soil moisture data sets. In this study we review existing implementations of soil moisture TC analysis as well as investigations of the assumptions underlying the method. Different notations that are used to formulate the TC problem are shown to be mathematically identical. While many studies have investigated issues related to possible violations of the underlying assumptions, only few TC modifications have been proposed to mitigate the impact of these violations. Moreover, assumptions, which are often understood as a limitation that is unique to TC analysis are shown to be common also to other conventional performance metrics. Noteworthy advances in TC analysis have been made in the way error estimates are being presented by moving from the investigation of absolute error variance estimates to the investigation of signal-to-noise ratio (SNR) metrics. Here we review existing error presentations and propose the combined investigation of the SNR (expressed in logarithmic units), the unscaled error variances, and the soil moisture sensitivities of the data sets as an optimal strategy for the evaluation of remotely-sensed soil moisture data sets.  相似文献   

10.
全球定位系统干涉反射测量(GPS-interferometric reflectometry,GPS-IR)是一种新的遥感技术,利用测量型接收机记录的信噪比(SNR)观测值可实现近地表土壤湿度的监测。考虑到目前利用多星组合反演土壤湿度的研究较少,本文提出一种土壤湿度多星线性回归反演模型。试验表明:①多星组合反演能够更全面地反映测站附近有效监测范围内的土壤湿度信息,有效改善采用单颗卫星反演时反演过程极易出现异常跳变的现象,提高了突发性降雨时段的土壤湿度反演精度。②当组合卫星数达到6颗以上时,其反演结果与土壤湿度参考值之间的相关系数均大于0.9,相对于单颗卫星至少提高了20.8%。  相似文献   

11.
利用SNR观测值进行GPS土壤湿度监测   总被引：5，自引：0，他引：5  
利用GPS信噪比(SNR)观测值监测土壤湿度变化可克服传统手段破坏观测对象、数据难以同化、时空分辨率受限等缺点,但同时也存在测量区域不明确、卫星与波段选择缺少相应依据、多径延迟相位与土壤湿度之间相关性的定量数值描述函数与模型亟待建立等问题。本文通过引入菲涅尔反射区域,结合仿真和实测土壤湿度数据、GPS观测值开展对比实验对上述问题进行研究。实验过程及结果表明,采用SNR观测值能有效跟踪土壤湿度的变化趋势,最大有效测量范围约45m,利用指数函数能较好地描述SNR多径延迟相位与土壤湿度之间的关系。同时,选择高级卫星和记录L2C观测值,有利于获得更准确的结果。  相似文献   

12.
MPDI在微波辐射计植被覆盖区土壤水分反演中的应用   总被引：5，自引：0，他引：5  
王磊  李震  陈权 《遥感学报》2006,10(1):34-38
大尺度上的土壤水分变化监测对于建立全球的水循环模型意义重大，是实现气候变化预测和洪涝监测的基础。星载辐射计为实现大尺度上土壤水分的监测提供了监测途径。但是在星载辐射计观测时，地表植被层的吸收和散射作用会对土壤向上的微波辐射产生衰减影响，这种影响在反演土壤水分的过程中必须予以计算和消除。原有的反演算法中，在计算这部分影响的时候，需要大量的关于地表植被状况的辅助数据，而这些即时的辅助数据往往不易获得。以AMSR—E数据为例，研究证明了微波极化差异指数（MPDI）能够反映地表植被覆盖状况。以中国华北、华东地区为实验区，选择2004年4月8日的AMSR—E亮温数据和MODIS数据为样本数据，建立起MPDI与NDVI之间的负指数关系方程。基于对NDVI的认识，得到植被覆盖度高、中、低三种状况所对应的MPDI域值，以此域值为依据对中等植被覆盖度地区作出自动判断，并用MPDI计算植被层不透明度。  相似文献   

13.
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。  相似文献   

14.
时序双极化SAR开采沉陷区土壤水分估计   总被引：1，自引：0，他引：1  
马威  陈登魁  杨娜  马超 《遥感学报》2018,22(3):521-534
开采沉陷地质灾害诱发矿区生态环境恶化的关键因子是土壤水分变化。研究提出了一种利用Sentinel-1A双极化SAR和OLI地表反射率数据联合反演土壤含水量的方法,即基于归一化水体指数(NDWI)反演植被含水量;采用Water-Cloud Model(WCM)模型消除植被对Sentinel-1A后向散射系数产生的影响,将其转化为裸土区的后向散射系数;利用基于AIEM模型和Oh模型建立的经验模型反演研究区地表参数,并用OLI光学反演结果进行验证;最后比较了开采沉陷区内外土壤水分含量。研究表明:(1)与基于OLI的土壤水分监测指数(SMMI)的土壤水分含量反演结果相比,两种极化方式中VH极化反演的水分结果具有更好的一致性,且两种极化方式反演结果也表明荒漠化草原区比黄土丘陵沟壑区反演效果更好,说明地形对后向散射的影响不可忽略。(2)在2016年内72期数据中,VH极化反演结果对比区土壤水分含量大于沉陷区的有41期,所占比例为57%;VV极化反演结果对比区土壤水分含量大于沉陷区的有36期,所占比例为50%,且不同矿区内的沉陷区受到的影响不同。说明开采沉陷造成的地表粗糙度的增加会对地表土壤水分产生负面影响,但不同矿区之间又有差异。  相似文献   

15.
The fractional vegetation cover (FVC), crop residue cover (CRC), and bare soil (BS) are three important parameters in vegetation–soil ecosystems, and their correct and timely estimation can improve crop monitoring and environmental monitoring. The triangular space method uses one CRC index and one vegetation index to create a triangular space in which the three vertices represent pure vegetation, crop residue, and bare soil. Subsequently, the CRC, FVC, and BS of mixed remote sensing pixels can be distinguished by their spatial locations in the triangular space. However, soil moisture and crop-residue moisture (SM-CRM) significantly reduce the performance of broadband remote sensing CRC indices and can thus decrease the accuracy of the remote estimation and mapping of CRC, FVC, and BS. This study evaluated the use of broadband remote sensing, the triangular space method, and the random forest (RF) technique to estimate and map the FVC, CRC, and BS of cropland in which SM-CRM changes dramatically. A spectral dataset was obtained using: (1) from a field-based experiment with a field spectrometer; and (2) from a laboratory-based simulation that included four distinct soil types, three types of crop residue (winter-wheat, maize, and rice), one crop (winter wheat), and varying SM-CRM. We trained an RF model [designated the broadband crop-residue index from random forest (CRRF)] that can magnify spectral features of crop residue and soil by using the broadband remote sensing angle indices as input, and uses a moisture-resistant hyperspectral index as the target. The effects of moisture on crop residue and soil were minimized by using the broadband CRRF. Then, the CRRF-NDVI triangular space method was used to estimate and map CRC, FVC, and BS. Our method was validated by using both laboratory- and field-based experiments and Sentinel-2 broadband remote-sensing images. Our results indicate that the CRRF-NDVI triangular space method can reduce the effect of moisture on the broadband remote-sensing of CRC, and may also help to obtain laboratory and field CRC, FVC, and BS. Thus, the proposed method has great potential for application to croplands in which the SM-CRM content changes dramatically.  相似文献   

16.
GNSS信号土壤衰减模型的试验验证方法   总被引：1，自引：0，他引：1  
汉牟田  杨毅  张波 《测绘学报》1957,49(9):1202-1212
GNSS（global navigation satellite system）信号在土壤中的衰减情况对于研究GNSS反射信号的有效遥感深度具有重要意义。本文通过试验研究了北斗信号与GPS（global positioning system）信号在土壤中的衰减情况。在试验设计上将GNSS天线置入土壤中并不断改变天线上方的土壤厚度与湿度以采集GNSS信号的功率衰减数据,最后利用这些数据反演土壤湿度以对GNSS信号土壤衰减模型进行验证。试验结果表明,土壤能够使GNSS信号发生明显的衰减。土壤的湿度值与厚度值越大,GNSS信号功率衰减越严重。在黏土土质,土壤湿度为0.15~0.30 cm3/cm3的情况下,当土壤厚度达到21 cm时,GNSS信号功率已衰减至无法被GNSS接收机测出。进一步根据GNSS信号衰减模型反演土壤湿度,结果显示,模型在土壤厚度大于等于10 cm、卫星仰角高于50°的情况下较为精确,此时利用北斗B1信号与GPS L1信号反演土壤湿度的均方根误差分别小于0.04 cm3/cm3与0.09 cm3/cm3。  相似文献   

17.
本文提出了一种基于CYGNSS数据的星载GNSS-R土壤湿度反演方法。首先,基于CYGNSS数据提取地表反射率参数,联合SMAP数据中提取的植被光学厚度、地表粗糙度和温度等辅助信息,初步构建了土壤湿度反演理论模型,并利用神经网络模型确定了土壤湿度反演的精细数学模型;然后,将该模型处理获得的土壤湿度以35%为分界点,利用本文提出的阶段函数模型提高反演精度,并使用2018年10月—2019年5月的CYGNSS数据,获得了全球范围内星载GNSS-R土壤湿度;最后,通过与SMAP提供的土壤湿度数据进行对比,评估了本文提出的星载GNSS-R土壤湿度反演方法的有效性,并对获取的星载GNSS-R土壤湿度进行了时间序列分析。结果表明,本文提出的土壤湿度反演方法的结果与SMAP土壤湿度具有良好的一致性,且随时间变化的趋势也相符合,为高精度土壤湿度反演提供了一种思路。  相似文献   

18.
积雪是全球水循环中的重要组成部分,积雪深度与雪水当量的精确监测对全球气候变化研究极其重要。随着GNSS研究与应用的不断深入,基于多路径效应的GNSS-MR（GNSS multipath reflectometry）技术用于地表环境监测（植被、土壤湿度、雪深、海平面等）已成为一种新兴的遥感手段。分析了SNR（signal-to-noise ratio）信噪比值的变化特性,详细给出了基于SNR观测值的GNSS-MR技术探测雪深的基本原理及其计算流程图。为了验证算法的有效性,利用科罗拉多州17 d连续跟踪站NWOT的GPS数据反演了降雪厚度,其结果与实测的雪深记录数据吻合较好,误差均值为0.07 m。初步研究结果验证了GNSS-MR技术用于积雪深度探测的可行性,并为后续充分利用现有的全球密集GNSS跟踪站数据开展地表环境监测提供重要参考。  相似文献   

19.
The assessment and quantification of spatio-temporal soil characteristics and moisture patterns are important parameters in the monitoring and modeling of soil landscapes. Remote-sensing techniques can be applied to characterize and quantify soil moisture patterns, but only when dealing with bare soil. For soils with vegetation, it is only possible to quantify soil-moisture characteristics through indirect vegetation indicators, i.e. the “vitality” of plants. The “vitality” of vegetation is a sum of many indicators, whereby different stress factors can induce similar changes to the biochemical and physiological characteristics of plants. Analysis of the cause and effect of soil-moisture properties, patterns and stress factors can therefore only be carried out using an experimental approach that specifically separates the causes. The study describes an experimental approach and the results from using an imaging hyperspectral sensor AISA-EAGLE (400–970 nm) and a non-imaging spectral sensor ASD (400–2,500 nm) under controlled and comparable conditions in a laboratory to study the spectral response compared to biochemical and biophysical vegetation parameters (“vitality”) as a function of soil moisture characteristics over the entire blooming period of Ash trees. At the same time that measurements were taken from the hyperspectral sensors, the following vegetation variables were also recorded: leaf area index (LAI), chlorophyll meter value — SPAD-205, vegetation height, C/N content and leaf water content as indicators of the “vitality” and the state of the vegetation. The spectrum of each hyperspectral image was used to calculate a range of vegetation indices (VI’s) with relationships for soil moisture characteristics and stress factors. The relationship between vegetation indices and plant “vitality” indicators was analysed using a Generalized Additive Model (GAM). The results show that leaf water content is the most appropriate vegetation indicator for assessing the “vitality” of vegetation. With the Water Index (WI) it was possible to differentiate between the moisture treatments of the control, moisture drought stress and the moisture flooding treatment over the entire growing season of the plants (R 2?=?0.94). There is a correlation between the “vitality” vegetation parameters (LAI, C/N content and vegetation height) and the indicators NDVI, WI, PRI and Vog2. In our study with Ash trees the vegetation parameter chlorophyll was found not to be a suitable indicator for detecting the “vitality” of plants using the spectral indicators. There is a possibility that the sensitivity of the indicators selected was too low compared to changes in the chlorophyll content of Ash trees. Adding the co-variable ‘time’ strengthens the correlation, whereas incorporating time and moisture treatment only improves the model very slightly. This shows that changes to the biochemical and biophysical characteristics caused by phenology, overlay a differentiation of the moisture treatments.  相似文献   

20.
Accurate estimation of soil moisture through remote sensing technique has been a challenge till date. In optical and thermal remote sensing, there is no index developed to detect the changes in soil moisture levels. In microwave region, soil roughness and other target parameters equally affect the technique for soil moisture estimation. Therefore, a computational technique in C language based on Shannon’s Information Theory (Shannon, 1948) has been developed to calculate total information content from multispectral radiometer data. The total information content is a compressed single value, which quantifies the information content of soil spectral reflectance in the electromagnetic spectrum range (400–1100 nm) under study. This technique was tested over a wide range of soil moisture levels. The study revealed that as compared to other techniques total information content index is very sensitive to change in moisture content of soil. This technique could not only quantify the soil moisture content in optical and near infra red region, but also led to a simplified one dimensional separability and clustering analysis.  相似文献   

设为首页 | 免责声明 | 关于勤云 | 加入收藏

Copyright©北京勤云科技发展有限公司  京ICP备09084417号