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1.
Soil moisture (SM) content is one of the most important environmental variables in relation to land surface climatology, hydrology, and ecology. Long-term SM data-sets on a regional scale provide reasonable information about climate change and global warming specific regions. The aim of this research work is to develop an integrated methodology for SM of kastanozems soils using multispectral satellite data. The study area is Tuv (48°40′30″N and 106°15′55″E) province in the forest steppe zones in Mongolia. In addition to this, land surface temperature (LST) and normalized difference vegetation index (NDVI) from Landsat satellite images were integrated for the assessment. Furthermore, we used a digital elevation model (DEM) from ASTER satellite image with 30-m resolution. Aspect and slope maps were derived from this DEM. The soil moisture index (SMI) was obtained using spectral information from Landsat satellite data. We used regression analysis to develop the model. The model shows how SMI from satellite depends on LST, NDVI, DEM, Slope, and Aspect in the agricultural area. The results of the model were correlated with the ground SM data in Tuv province. The results indicate that there is a good agreement between output SM and SM of ground truth for agricultural area. Further research is focused on moisture mapping for different natural zones in Mongolia. The innovative part of this research is to estimate SM using drivers which are vegetation, land surface temperature, elevation, aspect, and slope in the forested steppe area. This integrative methodology can be applied for different regions with forest and desert steppe zones.  相似文献   

2.
Surface moisture is important to link land surface temperature (LST) to people’s thermal comfort. In urban areas, the surface roughness from buildings and urban trees impacts wind speed, and consequently surface moisture. To find the role of surface roughness in surface moisture estimation, we developed methods to estimate daily and hourly evapotranspiration (ET) and soil moisture, based on a case study of Indianapolis, Indiana, USA. In order to capture the spatial and temporal variations of LST, hourly and daily LST was produced by downscaling techniques. Given the heterogeneity in urban areas, fractions of vegetation, soil, and impervious surfaces were calculated. To describe the urban morphology, surface roughness parameters were calculated from digital elevation model (DEM), digital surface model (DSM), and Terrestrial Light Detection and Ranging (LiDAR). Two source energy balance (TSEB) model was employed to generate ET, and the temperature vegetation index (TVX) method was used to calculate soil moisture. Stable hourly soil moisture fluctuated from 15% to 20%, and daily soil moisture increased due to precipitation and decreased due to seasonal temperature change. ET over soil, vegetation, and impervious surface in the urban areas yielded different patterns in response to precipitation. The surface roughness from high-rise has bigger influence on ET in central urban areas.  相似文献   

3.
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.  相似文献   

4.
A new approach to estimate soil moisture (SM) based on evaporative fraction (EF) retrieved from optical/thermal infrared MODIS data is presented for Canadian Prairies in parts of Saskatchewan and Alberta. An EF model using the remotely sensed land surface temperature (Ts)/vegetation index concept was modified by incorporating North American Regional Reanalysis (NAAR) Ta data and used for SM estimation. Two different combinations of temperature and vegetation fraction using the difference between Ts from MODIS Aqua and Terra images and Ta from NARR data (Ts−Ta Aqua-day and Ts−Ta Terra-day, respectively) were proposed and the results were compared with those obtained from a previously improved model (ΔTs Aqua-DayNight) as a reference. For the estimation of SM from EF, two empirical models were tested and discussed to find the most appropriate model for converting MODIS-derived EF data to SM values. Estimated SM values were then correlated with in situ SM measurements and their relationships were statistically analyzed. Results indicated statistically significant correlations between SM estimated from all three EF estimation approaches and field measured SM values (R2 = 0.42–0.77, p values < 0.04) exhibiting the possibility to estimate SM from remotely sensed EF models. The proposed Ts−Ta MODIS Aqua-day and Terra-day approaches resulted in better estimations of SM (on average higher R2 values and similar RMSEs) as compared with the ΔTs reference approach indicating that the concept of incorporating NARR Ta data into Ts/Vegetation index model improved soil moisture estimation accuracy based on evaporative fraction. The accuracies of the predictions were found to be considerably better for intermediate SM values (from 12 to 22 vol/vol%) with square errors averaging below 11 (vol/vol%)2. This indicates that the model needs further improvements to account for extreme soil moisture conditions. The findings of this research can be potentially used to downscale SM estimations obtained from passive microwave remote sensing techniques.  相似文献   

5.
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   

6.
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7.
综合主动和被动微波数据监测土壤水分变化   总被引：12，自引：1，他引：12  
李震  郭东华  施建成 《遥感学报》2002,6(6):481-484
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。  相似文献   

8.
目标分解技术在植被覆盖条件下土壤水分计算中的应用   总被引：6，自引：0，他引：6  
施建成  李震  李新武 《遥感学报》2002,6(6):412-415
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。  相似文献   

9.
基于梯度结构相似度的矿区土壤湿度空间分析   总被引：1，自引：0，他引：1       下载免费PDF全文
基于中国蒙、陕、晋、三省区的神东矿区2000-2015年成像光谱仪数据,双抛物线型归一化植被指数（normalized difference vegetation index,NDVI）和地表辐射温度（land surface temperature,Ts）（记为NDVI-Ts）特征空间的温度植被干旱指数法计量地表土壤湿度,采用梯度结构相似度法定量分析研究区土壤湿度的时空分布特征。结果表明:神东矿区土壤湿度变化具有明显时空分布异质性,空间上,矿区土壤湿度表现出从西北部向东南部逐渐增加的规律,干旱区域由2000年的96.03%下降到2015年的59.59%;矿区60.05%的区域的土壤湿度发生了突变,其中49.87%区域地表植被覆盖得到明显改善,土壤湿度得到明显提高;35.18%的区域的土壤湿度发生了变化,其中28.13%区域地表植被覆盖有所改善,土壤湿度有所增加;仅有4.77%的区域的土壤湿度没有发生改变。进一步分析表明,地表土壤湿度的时空分布特征受区域地貌类型和下垫面覆盖影响较大。  相似文献   

10.
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   

11.
The susceptibility of a catchment to flooding is affected by its soil moisture prior to an extreme rainfall event. While soil moisture is routinely observed by satellite instruments, results from previous work on the assimilation of remotely sensed soil moisture into hydrologic models have been mixed. This may have been due in part to the low spatial resolution of the observations used. In this study, the remote sensing aspects of a project attempting to improve flow predictions from a distributed hydrologic model by assimilating soil moisture measurements are described. Advanced Synthetic Aperture Radar (ASAR) Wide Swath data were used to measure soil moisture as, unlike low resolution microwave data, they have sufficient resolution to allow soil moisture variations due to local topography to be detected, which may help to take into account the spatial heterogeneity of hydrological processes. Surface soil moisture content (SSMC) was measured over the catchments of the Severn and Avon rivers in the South West UK. To reduce the influence of vegetation, measurements were made only over homogeneous pixels of improved grassland determined from a land cover map. Radar backscatter was corrected for terrain variations and normalized to a common incidence angle. SSMC was calculated using change detection.To search for evidence of a topographic signal, the mean SSMC from improved grassland pixels on low slopes near rivers was compared to that on higher slopes. When the mean SSMC on low slopes was 30–90%, the higher slopes were slightly drier than the low slopes. The effect was reversed for lower SSMC values. It was also more pronounced during a drying event. These findings contribute to the scant information in the literature on the use of high resolution SAR soil moisture measurement to improve hydrologic models.  相似文献   

12.
In this study, the NIR-red spectral space of Landsat-8 images, which is manifested by a triangle shape, is deployed for developing two new Soil Moisture (SM) indices. First, ten parameters consisting of six distances and four angles were extracted using the position of a random pixel in this triangle. Then, some correlation assessments were made to derive those parameters that were useful for SM estimation, which were five parameters. To build a soil moisture index, all combinations of these five parameters, which were in total 31 different regression equations, were considered, and the best model was named the Triangle Soil Moisture Index (TSMI). The TSMI consists of three parameters. It showed a RMSE of 0.08 and correlation coefficient (R) of 0.67. Since the TSMI does not consider vegetation interface in SM estimation, the Modified TSMI (MTSMI), which takes into account the fraction of soil cover in each pixel, beside those parameters which were used in the TSMI, was developed (MTSMI: RMSE = 0.07, R = 0.74). The results of the TSMI and MTSMI were compared with each other, and with another soil moisture index (SMMRS introduced by Zhan et al. (2007)). It was concluded that the TSMI and MTSMI provide similar results for bare soil or sparsely vegetated surfaces. However, the MTSMI demonstrated a much better performance in densely vegetated surfaces. The accuracy of both the TSMI and MTSMI were significantly higher than the SMMRS. Moreover, the TSMI and MTSMI were validated by comparison with field measured SM data at five different depths. The results showed that satellite estimated SM by these two indices was more correlated with in situ data at 5 cm soil depth compared to other depths. Also, to show the high applicability of the proposed approach for SM estimation, we selected another set of field SM data collected in Australia. The results proved the effectiveness of the method in different study areas.  相似文献   

13.
Penman–Monteith (PM) theory has been successfully applied to calculate land surface evapotranspiration (ET) for regional and global scales. However, soil surface resistance, related to soil moisture, is always difficult to determine over a large region, especially in arid or semiarid areas. In this study, we developed an ET estimation algorithm by incorporating soil moisture control, a soil moisture index (SMI) derived from the surface temperature and vegetation index space. We denoted this ET algorithm as the PM-SMI. The PM-SMI algorithm was compared with several other algorithms that calculated soil evaporation using relative humidity, and validated with Bowen ratio measurements at seven sites in the Southern Great Plain (SGP) that were covered by grassland and cropland with low vegetation cover, as well as at three eddy covariance sites from AmeriFlux covered by forest with high vegetation cover. The results show that in comparison with the other methods examined, the PM-SMI algorithm significantly improved the daily ET estimates at SGP sites with a root mean square error (RMSE) of 0.91 mm/d, bias of 0.33 mm/d, and R2 of 0.77. For three forest sites, the PM-SMI ET estimates are closer to the ET measurements during the non-growing season when compared with the other three algorithms. At all the 10 validation sites, the PM-SMI algorithm performed the best. PM-SMI 8-day ET estimates were also compared with MODIS 8-day ET products (MOD16A2), and the latter showed negligible bias at SGP sites. In contrast, most of the PM-SMI 8-day ET estimates are around the 1:1 line.  相似文献   

14.
罗时雨  童玲  陈彦 《遥感学报》2017,21(6):907-916
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。  相似文献   

15.
Knowledge of sub-pixel heterogeneity, particularly at the passive microwave scale, can improve the brightness temperature (and ultimately the soil moisture) estimation. However, the impact of surface heterogeneity (in terms of soil moisture, soil temperature and vegetation water content) on brightness temperature in an agricultural setting is relatively unknown. The Soil Moisture Active Passive Validation Experiment 2012 (SMAPVEX12) provided an opportunity to evaluate sub-pixel heterogeneity at the scale of a Soil Moisture Ocean Salinity (SMOS) or the Soil Moisture Active Passive (SMAP) radiometer footprint using field measured data. The first objective of this study was to determine if accounting for surface heterogeneity reduced the error between estimated brightness temperature (Tb) and Tb measured by SMOS. It was found that when accounting for variation in surface soil moisture, temperature and vegetation water content within the pixel footprint, the error between the modelled Tb and the measured Tb was less than if a homogeneous pixel were modelled. The correlation between the surface parameters and the error associated with not accounting for surface heterogeneity were investigated. It was found that there was low to moderate correlation between the error and the coefficient of variance associated with the measured soil moisture, soil temperature and vegetation volumetric water content during the field campaign. However, it was found that the correlations changed depending on the stage of vegetation growth and the amount of time following a precipitation event. At the start of the field campaign (following a precipitation event), there was strong correlation between the error and all three surface parameters (r ≥ 0.75). Following a precipitation event close to the middle of the field campaign (during which there was rapid growth in vegetation), there was strong correlation between the error and the variability in vegetation water content (r = 0.89), moderate correlation with soil moisture (r = 0.61) and low correlation with soil temperature (r = 0.26).  相似文献   

16.
For the soil moisture retrieval from passive microwave sensors, such as ESA’s Soil Moisture and Ocean Salinity (SMOS) and the NASA Soil Moisture Active and Passive (SMAP) mission, a good knowledge about the vegetation characteristics is indispensable. Vegetation cover is a principal factor in the attenuation, scattering and absorption of the microwave emissions from the soil; and has a direct impact on the brightness temperature by way of its canopy emissions. Here, brightness temperatures were measured at three altitudes across the TERENO (Terrestrial Environmental Observatories) Rur catchment site in Germany to achieve a range of spatial resolutions using the airborne Polarimetric L-band Multibeam Radiometer 2 (PLMR2). The L-band Microwave Emission of the Biosphere (L-MEB) model which simulates microwave emissions from the soil–vegetation layer at L-band was used to retrieve surface soil moisture for all resolutions. A Monte Carlo approach was developed to simultaneously estimate soil moisture and the vegetation parameter b’ describing the relationship between the optical thickness τ and the Leaf Area Index (LAI). LAI was retrieved from multispectral RapidEye imagery and the plant specific vegetation parameter b′ was estimated from the lowest flight altitude data for crop, grass, coniferous forest, and deciduous forest. Mean values of b’ were found to be 0.18, 0.07, 0.26 and 0.23, respectively. By assigning the estimated b′ to higher flight altitude data sets, a high accuracy soil moisture retrieval was achieved with a Root Mean Square Difference (RMSD) of 0.035 m3 m−3 when compared to ground-based measurements.  相似文献   

17.
An important research direction in advancing higher spatial resolution and better accuracy in soil moisture remote sensing is the integration of active and passive microwave observations. In an effort to address this objective, an airborne instrument, the passive/active L-band sensor (PALS), was flown over two watersheds as part of the cloud and land surface interaction campaign (CLASIC) conducted in Oklahoma in 2007. Eleven flights were conducted over each watershed during the field campaign. Extensive ground observations (soil moisture, soil temperature, and vegetation) were made concurrent with the PALS measurements. Extremely wet conditions were encountered. As expected from previous research, the radiometer-based retrievals were better than the radar retrievals. The standard error of estimates (SEEs) of the retrieved soil moisture using only the PALS radiometer data were 0.048 m3/m3 for Fort Cobb (FC) and 0.067 m3/m3 for the Little Washita (LW) watershed. These errors were higher than typically observed, which is likely the result of the unusually high soil moisture and standing water conditions. The radar-only-based retrieval SEEs were 0.092 m3/m3 for FC and 0.079 m3/ m3 for LW. Radar retrievals in the FC domain were particularly poor due to the high vegetation water content of the agricultural fields. These results indicate the potential for estimating soil moisture for low-vegetation water content domains from radar observations using a simple vegetation model. Results also showed the compatibility between passive and active microwave observations and the potential for combining the two approaches.  相似文献   

18.
Since soil moisture and vegetation index are direct and important indicators for surface drought status, a new drought monitoring method (MPDI1) is developed in NIR-Red reflectance space. It is a combination of two satellite-derived variables—a soil moisture component using the Perpendicular Drought Index (PDI), and a vegetation component using the Perpendicular Vegetation Index (PVI). Enhanced Thematic Mapper Plus (ETM+) image and in-situ ground observation are introduced to validate the accuracy of the proposed method. Results indicate that MPDI1 is highly consistent to the in-situ ground observation with the coefficient of determination (R2?=?0.49) between MPDI1 and 5–20 cm mean soil moisture, which is slightly higher than the coefficient of determination (R2?=?0.42) between MPDI1 and 10 cm soil moisture. Compared with drought indices such as PDI and the Modified Perpendicular Drought Index (MPDI), MPDI1 provides quite similar trends for bare soil or lower vegetated surface, but it demonstrates a better performance in measuring densely vegetated surface. This paper concludes that MPDI1 provides correct and sufficient information on surface drought status in soil-plant continuum, which appears to have robust available and great potential for surface drought estimation in China and other countries.  相似文献   

19.
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   

20.
We propose a simple, spatially invariant and probabilistic year-round Empirical Standardized Soil Moisture Index (ESSMI) that is designed to classify soil moisture anomalies from harmonized multi-satellite surface data into categories of agricultural drought intensity. The ESSMI is computed by fitting a nonparametric empirical probability density function (ePDF) to historical time-series of soil moisture observations and then transforming it into a normal distribution with a mean of zero and standard deviation of one. Negative standard normal values indicate dry soil conditions, whereas positive values indicate wet soil conditions. Drought intensity is defined as the number of negative standard deviations between the observed soil moisture value and the respective normal climatological conditions. To evaluate the performance of the ESSMI, we fitted the ePDF to the Essential Climate Variable Soil Moisture (ECV SM) v02.0 data values collected in the period between January 1981 and December 2010 at South–Central America, and compared the root-mean-square-errors (RMSE) of residuals with those of beta and normal probability density functions (bPDF and nPDF, respectively). Goodness-of-fit results attained with time-series of ECV SM values averaged at monthly, seasonal, half-yearly and yearly timescales suggest that the ePDF provides triggers of agricultural drought onset and intensity that are more accurate and precise than the bPDF and nPDF. Furthermore, by accurately mapping the occurrence of major drought events over the last three decades, the ESSMI proved to be spatio-temporal consistent and the ECV SM data to provide a well calibrated and homogenized soil moisture climatology for the region. Maize, soybean and wheat crop yields in the region are highly correlated (r > 0.82) with cumulative ESSMI values computed during the months of critical crop growing, indicating that the nonparametric index of soil moisture anomalies can be used for agricultural drought assessment.  相似文献   

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