A remote sensing model for monitoring soil evaporation based on differential thermal inertia and its validation

To develop geosciences quantification and multi-dimensional researches will be an inevitable trend in the 21st century. The interaction between the land surface and the atmosphere not only serves as an important component in geosciences quantification, bu…     

The potential information in the temperature difference between shadow and sunlit of surfaces and a new way of retrieving the soil moisture

The thermal inertia and plant water stress index are often adopted to estimate soil moisture available for crops or plants. However, it is not very easy to obtain two temporal temperatures for thermal inertia model and air temperature for the plant water stress mode. Shadows of ground objects are often referred to noise on visible and near infrared remote sensing. But the difference of temperature between shadows and sunlit contains rich information concerning with heat-water status for soil. This paper presented a new way to excavate just by temperature difference usually between shadow and sunlit surface. Experiments validated the ideal. We can adopt thermal camera to measure the differences in the field measurements. However, we must use inversion based on multianglar thermal infrared remote sensing data in airborne and spaceborne. An inverting model was also presented by using Monte-Carlo and the least square method. Results show that this way is feasible.     

利用卫星热红外遥感技术监测长白山天池火山活动性

介绍了卫星热红外遥感技术用于火山监测的国内外研究现状,阐述了热红外遥感技术的原理,分析了卫星热红外遥感技术用于火山活动性监测的可行性。以长白山天池火山为例,基于Landsat TM/ETM影像和ASTER影像反演获得了1999—2008年的温度场,并选取了其中的3种地面覆盖类型(森林植被、土壤和植被(矮草)以及裸露岩石),从而去除了地表环境因素的影响;从每种地面覆盖类型中扣除了当日天池气象站的平均气温,去除了气象因素的影响,得到了由火山热活动可能导致的温度热异常。结果显示,从1999—2005年,由火山活动导致的温度热异常伴随着扰动发生了明显的上升,自2005年以后逐渐下降,2006—2008年趋于稳定。这些结果与测震、GPS形变以及He同位素比值变化趋势保持了较好的一致性,表明卫星热红外遥感技术用于火山活动性监测的巨大潜力和优势,可以作为一种常规的监测手段尝试性地纳入日常的火山监测工作中     

Towards the estimation root-zone soil moisture via the simultaneous assimilation of thermal and microwave soil moisture retrievals

The upcoming deployment of satellite-based microwave sensors designed specifically to retrieve surface soil moisture represents an important milestone in efforts to develop hydrologic applications for remote sensing observations. However, typical measurement depths of microwave-based soil moisture retrievals are generally considered too shallow (top 2–5 cm of the soil column) for many important water cycle and agricultural applications. Recent work has demonstrated that thermal remote sensing estimates of surface radiometric temperature provide a complementary source of land surface information that can be used to define a robust proxy for root-zone (top 1 m of the soil column) soil moisture availability. In this analysis, we examine the potential benefits of simultaneously assimilating both microwave-based surface soil moisture retrievals and thermal infrared-based root-zone soil moisture estimates into a soil water balance model using a series of synthetic twin data assimilation experiments conducted at the USDA Optimizing Production Inputs for Economic and Environmental Enhancements (OPE³) site. Results from these experiments illustrate that, relative to a baseline case of assimilating only surface soil moisture retrievals, the assimilation of both root- and surface-zone soil moisture estimates reduces the root-mean-square difference between estimated and true root-zone soil moisture by 50% to 35% (assuming instantaneous root-zone soil moisture retrievals are obtained at an accuracy of between 0.020 and 0.030 m³ m⁻³). Most significantly, improvements in root-zone soil moisture accuracy are seen even for cases in which root-zone soil moisture retrievals are assumed to be relatively inaccurate (i.e. retrievals errors of up to 0.070 m³ m⁻³) or limited to only very sparse sampling (i.e. one instantaneous measurement every eight days). Preliminary real data results demonstrate a clear increase in the R² correlation coefficient with ground-based root-zone observations (from 0.51 to 0.73) upon assimilation of actual surface soil moisture and tower-based thermal infrared temperature observations made at the OPE³ study site.     

Use of remote sensing for evapotranspiration monitoring over land surfaces

Abstract

Monitoring evapotranspiration (ET) at large scales is important for assessing climate and anthropogenic effects on natural and agricultural ecosystems. This paper describes techniques used in evaluating ET with remote sensing, which is the only technology that can efficiently and economically provide regional and global coverage. Some of the empirical/statistical techniques have been used operationally with satellite data for computing daily ET at regional scales. The more complex numerical simulation models require detailed input parameters that may limit their application to regions containing a large database of soils and vegetation properties. Current efforts are being directed towards simplifying the parameter requirements of these models. Essentially all energy balance models rely on an estimate of the available energy (net radiation less soil heat flux). Net radiation is not easily determined from space, although progress is being made. Simplified approaches for estimating soil heat flux appear promising for operational applications. In addition, most ET models utilize remote sensing data in the shortwave and thermal wavelengths to measure key boundary conditions. Differences between the radiometric surface temperature and aerodynamic temperature can be significant and progress in incorporating this effect is evident. Atmospheric effects on optical data are significant, and optical sensors cannot see through clouds. This has led some to use microwave observations as a surrogate for optical data to provide estimates of surface moisture and surface temperature; preliminary results are encouraging. The approaches that appear most promising use surface temperature and vegetation indices or a time rate of change in surface temperature coupled to an atmospheric boundary layer model. For many of these models, differences with ET observations can be as low as 20% from hourly to daily time scales, approaching the level of uncertainty in the measurement of ET and contradicting some recent pessimistic conclusions concerning the utility of remotely sensed radiometric surface temperature for determining the surface energy balance.     

A temperature prediction-correction method for estimating surface soil heat flux from soil temperature and moisture data

Surface soil heat flux is a component of surface energy budget and its estimation is needed in land-atmosphere interaction studies. This paper develops a new simple method to estimate soil heat flux from soil temperature and moisture observations. It gives soil temperature profile with the thermal diffusion equation and, then, adjusts the temperature profile with differences between observed and computed soil temperatures. The soil flux is obtained through integrating the soil temperature profile. Compared with previous methods, the new method does not require accurate thermal conductivity. Case studies based on observations, synthetic data, and sensitivity analyses show that the new method is preferable and the results obtained with it are not sensitive to the availability of temperature data in the topsoil. In addition, we pointed out that the soil heat flux measured with a heat-plate can be quite erroneous in magnitude though its phase is accurate.     

基于简化参数方法的蒙古干旱区土壤湿度被动微波遥感

卫星被动微波遥感土壤湿度,是准确分析大空间尺度上陆表水分变化信息的有效手段.美国航天局(NASA)发布的基于AMSR-E观测亮温资料的全球土壤湿度反演产品,在蒙古干旱区的实际精度并不令人满意.本文基于对地表微波辐射传输中地表粗糙度和植被层影响的简化处理方法,采用AMSR-E的6.9 GHz,10.7 GHz和18.7 GHz之V极化亮温资料,应用多频率反演算法,并以国际能量和水循环协同观测计划(The Coordinated Energy and Water Cycle Observations Project)即CEOP实验在蒙古国东部荒漠地区的地面实验资料作为先验知识,获取被动微波遥感模型的优化参数,以期获得蒙古干旱区精度更高的土壤湿度遥感估算结果.分析表明,本文方法反演的白天和夜间土壤湿度结果与地面验证值之间的均方根误差(RMSE)接近0.030 cm³/cm³, 证明所用方法在不需要其他辅助资料或参数帮助下,可较精确地反演干旱区表层土壤湿度信息,能够全天候、动态监测大空间尺度的土壤湿度变化,可为干旱区气候变化研究及陆面过程模拟和数据同化研究提供高精度的表层土壤湿度初始场资料.     

Determination of regional distribution of crop transpiration and soil water use efficiency using quantitative remote sensing data through inversion

As is widely known, there is a severe shortage of water resources in North China. There have been frequent droughts in recent years. Developing water saving measures, especially in agricul-ture, has become an urgent task. In water-saving agriculture, one …     

The effect of roughness in simultaneously retrieval of land surface parameters

Using remotely-sensed data, various soil moisture estimation models have been developed for bare soil areas. Previous studies have shown that the brightness temperature (BT) measured by passive microwave sensors were affected by characteristics of the land surface parameters including soil moisture, vegetation cover and soil roughness. Therefore knowledge of vegetation cover and soil roughness is important for obtaining frequent and global estimations of land surface parameters especially soil moisture.In this study, a model called Simultaneous Land Parameters Retrieval Model (SLPRM) that is an iterative least-squares minimization method is proposed. The algorithm estimates surface soil moisture, land surface temperature and canopy temperature simultaneously in vegetated areas using AMSR-E (Advance Microwave Scanning Radiometer-EOS) brightness temperature data. The simultaneous estimations of the three parameters are based on a multi-parameter inversion algorithm which includes model construction, calibration and validation using observations carried out for the SMEX03 (Soil Moisture Experiment, 2003) region in the South and North of Oklahoma.Roughness parameter has also been included in the algorithm to increase the soil parameters retrieval accuracy. Unlike other methods, the SLPRM method works efficiently in all land covers types.The study focuses on soil parameters estimation by comparing three different scenarios with the inclusion of roughness data and selects the most appropriate one. The difference between the resulted accuracies of scenarios is due to the roughness calculation approach.The analysis on the retrieval model shows a meaningful and acceptable accuracy on soil moisture estimation according to the three scenarios.The SLPRM method has shown better performance when the SAR (Synthetic Aperture RADAR) data are used for roughness calculation.     

Assessing Ground Water Development Potential Using Landsat Imagery

Seven villages in southeastern Kenya surround Mt. Kasigau and depend on the mountain's cloud forest for their water supply. Five of these villages have regularly experienced water shortages, and all village water supplies were contaminated with Escherichia coli bacteria. There is a need to economically find new sources of fresh ground water. Remote sensing offers a relatively quick and cost-effective way of identifying areas with high potential for ground water development. This study used spectral properties of features on Landsat remote sensing imagery to map linear features, soil types, surface moisture, and vegetation. Linear features represented geologic or geomorphologic features indicating either shallow ground water or areas of increased subsurface hydraulic conductivity. Regarding soil type, black soils were identified as potential indicators of shallow aquifers based on their relatively lower elevation and association with river valleys. A vegetation map was created using unsupervised classification, and three of the resulting vegetation classes were observed to be commonly associated with wet areas and/or ground water discharge. A wetness map, created using tasseled cap analysis, was used to identify all areas of high ground moisture, including those that corresponded to vegetated areas. The linear features, soil type, vegetation, and wetness maps were overlaid to produce a composite that highlighted areas with the highest potential for ground water development. Electrical resistivity surveys confirmed that areas highlighted by the composite image had relatively shallow depths to the water table. Some figures in this paper are available in color in the online version of the paper.     

Numerical simulation and data assimilation of the water-energy cycle over semiarid northeastern China

The default fractional vegetation cover and terrain height were replaced by the estimated fractional vegetation cover, which was calculated by the Normalized Difference Vegetation Index(NDVI) of Earth Observing System Moderate-Resolution Imaging Spectroradiometer(EOS-MODIS) and the Digital Elevation Model of the Shuttle Radar Topography Mission(SRTM) system. The near-surface meteorological elements over northeastern China were assimilated into the three-dimensional variational data assimilation system(3DVar) module in the Weather Research and Forecasting(WRF) model. The structure and daily variations of air temperature, humidity, wind and energy fields over northeastern China were simulated using the WRF model. Four groups of numerical experiments were performed, and the simulation results were analyzed of latent heat flux, sensible heat flux, and their relationships with changes in the surface energy flux due to soil moisture and precipitation over different surfaces. The simulations were compared with observations of the stations Tongyu, Naiman, Jinzhou, and Miyun from June to August, 2009. The results showed that the WRF model achieves high-quality simulations of the diurnal characteristics of the surface layer temperature, wind direction, net radiation, sensible heat flux, and latent heat flux over semiarid northeastern China in the summer. The simulated near-surface temperature, relative humidity, and wind speed were improved in the data assimilation case(Case 2) compared with control case(Case 1). The simulated sensible heat fluxes and surface heat fluxes were improved by the land surface parameterization case(Case 3) and the combined case(Case 4). The simulated temporal variations in soil moisture over the northeastern arid areas agree well with observations in Case 4, but the simulated precipitation should be improved in the WRF model. This study could improve the land surface parameters by utilizing remote sensing data and could further improve atmospheric elements with a data assimilation system. This work provides an effective attempt at combining multi-source data with different spatial and temporal scales into numerical simulations. The assimilation datasets generated by this work can be applied to research on climate change and environmental monitoring of arid lands, as well as research on the formation and stability of climate over semiarid areas.     

Genetic inverse algorithm for retrieval of component temperature of mixed pixel by multi-angle thermal infrared remote sensing data

After carefully studying the results of retrieval of land surface temperature(LST) by multi-channel thermal infrared remote sensing data, the authors of this paper point out that its accuracy and significance for applications are seriously damaged by the high correlation coefficient among multi-channel information and its disablement of direct retrieval of component temperature. Based on the model of directional radiation of non-isothermal mixed pixel, the authors point out that multi-angle thermal infrared remote sensing can offer the possibility to directly retrieve component temperature, but it is also a multi-parameter synchronous inverse problem. The results of digital simulation and field experiments show that the genetic inverse algorithm (GIA) is an effective method to fulfill multi-parameter synchronous retrieval. So it is possible to realize retrieval of component temperature with error less than 1K by multi-angle thermal infrared remote sensing data and GIA.     

Improvement of multi-layer soil moisture prediction using support vector machines and ensemble Kalman filter coupled with remote sensing soil moisture datasets over an agriculture dominant basin in China

Soil moisture prediction is of great importance in crop yield forecasting and drought monitoring. In this study, the multi-layer root zone soil moisture (0-5, 0-10, 10-40 and 40-100 cm) prediction is conducted over an agriculture dominant basin, namely the Xiang River Basin, in southern China. The support vector machines (SVM) coupled with dual ensemble Kalman filter (EnKF) technique (SVM-EnKF) is compared with SVM for its potential capability to improve the efficiency of soil moisture prediction. Three remote sensing soil moisture products, namely SMAP, ASCAT and AMSR2, are evaluated for their performance in multi-layer soil moisture prediction with SVM and SVM-EnKF, respectively. Multiple cases are designed to investigate the performance of SVM, the effectiveness of coupling dual EnKF technique and the applicability of the remote sensing products in soil moisture prediction. The main results are as follows: (a) The efficiency of soil moisture prediction with SVM using meteorological variables as inputs is satisfactory for the surface layers (0-5 and 0-10 cm), while poor for the root zone layers (10-40 and 40-100 cm). Adding SMAP as input to SVM can improve its performance in soil moisture prediction, with more than 47% increase in the R-value and at least 11% reduction in RMSE for all layers. However, adding ASCAT or AMSR2 has no improvement for its performance. (b) Coupling dual EnKF can significantly improve the performance of SVM in the soil moisture prediction of both surface and the root zone layers. The increase in R-value is above 80%, while the reduction in BIAS and RMSE is respectively more than 90% and 63%. However, adding remote sensing soil moisture products as inputs can no further improve the performance of SVM-EnKF. (c) The SVM-EnKF can eliminate the influence of remote sensing soil moisture extreme values in soil moisture prediction, therefore, improve its accuracy.     

The retrieval of two-dimensional distribution of the earth''''s surface aerodynamic roughness using SAR image and TM thermal infrared image

The research for the land surface fluxes has madea quiet great progress for its breakthroughs in the fieldof regional or global interactions between land surfaceand atmosphere. However, many remote sensing mod-els for estimating the land surface fluxes need the pa-rameters of surface momentum, heat, resistance ofwater vapor at a referenced height, which are the func-tion of aerodynamic surface roughness zad. It hasbeen validated that the retrieval of the land surfacefluxes is very sensitive to…     

On the validation of a coupled water and energy balance model at small catchment scales

Catchment runoff is the most widely used catchment scale measurement in modelling studies, and we have a reasonable degree of confidence in its accuracy. The advent of satellites gives access to a new suite of measurements taken over a defined spatial range. These measurements, principally reflected or emitted radiation, provide hydrologists with new possibilities for quantifying the state of a catchment. Surface temperatures can be readily measured by a satellite on a scale comparable to the size of a small catchment.

In this paper we show that satellite sensed temperatures can provide an important measure of catchment status, which can complement runoff measurements in water balance studies. A one-dimensional model, which couples the land surface energy balance with the soil and surface water balance is tested by comparison with runoff and with remotely sensed surface temperature measurements. Simulations have been run over four years for two small catchments which have a fairly homogeneous vegetation, one being forest and its neighbour pasture. Satellite “surface” temperatures have been interpreted in terms of the energy balance, and used as a test of modelling accuracy. An “effective” surface temperature is calculated as a weighted mean of temperatures of the separate soil and leaf surfaces. This modelled “effective” temperature correlates well with Landsat TM surface temperatures.

When pasture replaces forest, the model predicts a reduction in evapotranspiration of around 30%, a three-fold increase in runoff, and an increase in mean soil moisture status. The change to pasture also results in a rise in mean effective surface temperature of about 4°C, and an increase in summer diurnal temperature range from 10 to 22°C. The winter diurnal temperature range is similar for both vegetation systems.

Inclusion of soil moisture variability in thermal properties results in an increase in mean daily maximum temperature of about 2°C in summer and winter, without much change in daily minima. The daily mean temperature is not significantly affected.     

Data assimilation methods in the Earth sciences

Although remote sensing data are often plentiful, they do not usually satisfy the users’ needs directly. Data assimilation is required to extract information about geophysical fields of interest from the remote sensing observations and to make the data more accessible to users. Remote sensing may provide, for example, measurements of surface soil moisture, snow water equivalent, snow cover, or land surface (skin) temperature. Data assimilation can then be used to estimate variables that are not directly observed from space but are needed for applications, for instance root zone soil moisture or land surface fluxes. The paper provides a brief introduction to modern data assimilation methods in the Earth sciences, their applications, and pertinent research questions. Our general overview is readily accessible to hydrologic remote sensing scientists. Within the general context of Earth science data assimilation, we point to examples of the assimilation of remotely sensed observations in land surface hydrology.     

Evaluation of remote sensing‐based evapotranspiration estimates using a water transfer numerical simulation under different vegetation conditions in an arid area

Daily actual evapotranspiration (AET) and seasonal AET values are of great practical importance in the management of regional water resources and hydrological modelling. Remotely sensed AET models and Landsat satellite images have been used widely in producing AET estimates at the field scale. However, the lack of validation at a high spatial frequency under different soil water conditions and vegetation coverages limits their operational applications. To assess the accuracies of remote sensing‐based AET in an oasis‐desert region, a total of 59 local‐scale daily AET time series, simulated using HYDRUS‐1D calibrated with soil moisture profiles, were used as ground truth values. Of 59 sampling sites, 31 sites were located in the oasis subarea and 28 sites were located in the desert subarea. Additionally, the locally validated mapping evapotranspiration at high resolution with internalized calibration surface energy balance model was employed to estimate instantaneous AET values in the area containing all 59 of the sampling sites using seven Landsat subimages acquired from June 5 to August 24 in 2011. Daily AET was obtained using extrapolation and interpolation methods with the instantaneous AET maps. Compared against HYDRUS‐1D, the remote sensing‐based method produced reasonably similar daily AET values for the oasis sites, while no correlation was observed for daily AET estimated using these two methods for the desert sites. Nevertheless, a reasonable monthly AET could be estimated. The correlation analysis between HYDRUS‐1D‐simulated and remote sensing‐estimated monthly AET values showed relative root‐mean‐square error values of 15.1%, 12.1%, and 12.3% for June, July, and August, respectively. The root mean square error of the summer AET was 10.0%. Overall, remotely sensed models can provide reasonable monthly and seasonal AET estimates based on periodic snapshots from Landsat images in this arid oasis‐desert region.     

基于Lasso函数的分层混合模糊-神经网络及其在遥感影像分类中的应用

本文提出一种新的分层混合模糊-神经网络(HHFNN)算法.在模糊系统中使用Takagi-Sugeno模型和三角波隶属函数.同时,为降低离散输入变量中可能存在的强交瓦作用,采用了系数收缩机制中的Lasso函数.最后,以福建的漳平洛阳—安溪潘田地区LANDSAT ETM+遥感影像数据地物分类为例,应用本文的改进算法与其他神...     

Calibration of an integrated land surface process and radiobrightness (LSP/R) model during summertime

In this study, a soil vegetation and atmosphere transfer (SVAT) model was linked with a microwave emission model to simulate microwave signatures for different terrain during summertime, when the energy and moisture fluxes at the land surface are strong. The integrated model, land surface process/radiobrightness (LSP/R), was forced with weather and initial conditions observed during a field experiment. It simulated the fluxes and brightness temperatures for bare soil and brome grass in the Northern Great Plains. The model estimates of soil temperature and moisture profiles and terrain brightness temperatures were compared with the observed values. Overall, the LSP model provides realistic estimates of soil moisture and temperature profiles to be used with a microwave model. The maximum mean differences and standard deviations between the modeled and the observed temperatures (canopy and soil) were 2.6 K and 6.8 K, respectively; those for the volumetric soil moisture were 0.9% and 1.5%, respectively. Brightness temperatures at 19 GHz matched well with the observations for bare soil, when a rough surface model was incorporated indicating reduced dielectric sensitivity to soil moisture by surface roughness. The brightness temperatures of the brome grass matched well with the observations indicating that a simple emission model was sufficient to simulate accurate brightness temperatures for grass typical of that region and surface roughness was not a significant issue for grass-covered soil at 19 GHz. Such integrated SVAT-microwave models allow for direct assimilation of microwave observations and can also be used to understand sensitivity of microwave signatures to changes in weather forcings and soil conditions for different terrain types.