Synchronous retrieval of land surface temperature and emissivity

This is an old topic for more than ten years to retrieve land surface temperature (LST) from satellite data, but it has not been solved yet. At first, people tried to transplant traditional split window method of sea surface temperature (SST) to the retrieval of LST, but it was found that the emissivities of land surface (ε_i) must be involved in atmospheric correction. Then many different formulas appeared with assumption of emissivities known. In fact, emissivities of land surface with pixel size cannot be known beforehand because of various reasons, so in recent years the focus of attention has been transferred to retrieving emissivities (ε_i) and LST at the same time. Therefore, we have to solve missing equations problem. For this some people try to introduce middle infrared information, but new problems will be brought in which means that it is very difficult to describe middle infrared BRDF of targets with high accuracy and the scattering of atmospheric aerosol cannot be ignored. Therefore a different way is offered to solve this problem only using two thermo-infrared bands data based on three assumptions, constant emissivities in two measurements, and the same atmospheric parameters for neighbouring pixels and the difference of emissivity (Δε) of two channels can be known beforehand. Results of digital simulations show that it is possible to retrieve LST with its root mean square (RMS) of errors less than 1 K and RMS of relative error of ground radiance at 7% if the error of atmospheric temperature at ± 2°C and the relative error of atmospheric water vapor at ± 10% can be satisfied. Results have been confirmed by initial field test. Project supported by the National Natural Science Foundation of China (Grant No. 49471056) and China’s National Key Basic Research Plan.     

A land surface data assimilation framework using the land information system: Description and applications

The Land Information System (LIS) is an established land surface modeling framework that integrates various community land surface models, ground measurements, satellite-based observations, high performance computing and data management tools. The use of advanced software engineering principles in LIS allows interoperability of individual system components and thus enables assessment and prediction of hydrologic conditions at various spatial and temporal scales. In this work, we describe a sequential data assimilation extension of LIS that incorporates multiple observational sources, land surface models and assimilation algorithms. These capabilities are demonstrated here in a suite of experiments that use the ensemble Kalman filter (EnKF) and assimilation through direct insertion. In a soil moisture experiment, we discuss the impact of differences in modeling approaches on assimilation performance. Provided careful choice of model error parameters, we find that two entirely different hydrological modeling approaches offer comparable assimilation results. In a snow assimilation experiment, we investigate the relative merits of assimilating different types of observations (snow cover area and snow water equivalent). The experiments show that data assimilation enhancements in LIS are uniquely suited to compare the assimilation of various data types into different land surface models within a single framework. The high performance infrastructure provides adequate support for efficient data assimilation integrations of high computational granularity.     

Determination of bare surface soil moisture from combined temporal evolution of land surface temperature and net surface shortwave radiation

Land surface soil moisture (SSM) is an important variable for hydrological, ecological, and meteorological applications. A multi‐linear model has recently been proposed to determine the SSM content from the combined diurnal evolution of both land surface temperature (LST) and net surface shortwave radiation (NSSR) with the parameters TN (the LST mid‐morning rising rate divided by the NSSR rising rate during the same period) and t_d (the time of daily maximum temperature). However, in addition to the problem that all the coefficients of the multi‐linear model depend on the atmospheric conditions, the model also suffers from the problems of the nonlinearity of TN as a function of the SSM content and the uncertainty of determining the t_d from the diurnal evolution of the LST. To address these problems, a modified multi‐linear model was developed using the logarithm of TN and normalizing t_d by the mid‐morning temperature difference instead of using the TN and t_d. Except for the constant term, the coefficients of all other variables in the modified multi‐linear model proved to be independent of the atmospheric conditions. Using the relevant simulation data, results from the modified multi‐linear model show that the SSM content can be determined with a root mean square error (RMSE) of 0.030m³/m³, provided that the constant term is known or estimated day to day. The validation of the model was conducted using the field measurements at the Langfang site in 2008 in China. A higher correlation is achieved (coefficient of determination: R² = 0.624, RMSE = 0.107m³/m³) between the measured SSM content and the SSM content estimated using the modified multi‐linear model with the coefficients determined from the simulation data. Another experiment is also conducted to estimate the SSM content using the modified model with the constant term calibrated each day by one‐spot measurements at the site. The estimation result has a relatively larger error (RMSE = 0.125m³/m³). Additionally, the uncertainty of the determination of the coefficients is analysed using the field measurements, and the results indicate that the SSM content obtained using the modified model accurately characterizes the surface soil moisture condition. Copyright © 2012 John Wiley & Sons, Ltd.     

Intercomparison across scales between remotely-sensed land surface temperature and representative equilibrium temperature from a distributed energy water balance model

Abstract

Quantifying the reliability of distributed hydrological models is an important task in hydrology to understand their ability to estimate energy and water fluxes at the agricultural district scale as well the basin scale for water resources management in drought monitoring and flood forecasting. In this context, the paper presents an intercomparison of simulated representative equilibrium temperature (RET) derived from a distributed energy water balance model and remotely-sensed land surface temperature (LST) at spatial scales from the agricultural field to the river basin. The main objective of the study is to evaluate the use of LST retrieved from operational remote sensing data at different spatial and temporal resolutions for the internal validation of a distributed hydrological model to control its mass balance accuracy as a complementary method to traditional calibration with discharge measurements at control river cross-sections. Modelled and observed LST from different radiometric sensors located on the ground surface, on an aeroplane and a satellite are compared for a maize field in Landriano (Italy), the agricultural district of Barrax (Spain) and the Upper Po River basin (Italy). A good ability of the model in reproducing the observed LST values in terms of mean bias error, root mean square error, relative error and Nash-Sutcliffe index is shown.

Editor Z.W. Kundzewicz; Associate editor D. Gerten     

Numerical investigation of the impact of uncertainties in satellite rainfall estimation and land surface model parameters on simulation of soil moisture

This study aims at evaluating the uncertainty in the prediction of soil moisture (1D, vertical column) from an offline land surface model (LSM) forced by hydro-meteorological and radiation data. We focus on two types of uncertainty: an input error due to satellite rainfall retrieval uncertainty, and, LSM soil-parametric error. The study is facilitated by in situ and remotely sensed data-driven (precipitation, radiation, soil moisture) simulation experiments comprising a LSM and stochastic models for error characterization. The parametric uncertainty is represented by the generalized likelihood uncertainty estimation (GLUE) technique, which models the parameter non-uniqueness against direct observations. Half-hourly infra-red (IR) sensor retrievals were used as satellite rainfall estimates. The IR rain retrieval uncertainty is characterized on the basis of a satellite rainfall error model (SREM). The combined uncertainty (i.e., SREM + GLUE) is compared with the partial assessment of uncertainty. It is found that precipitation (IR) error alone may explain moderate to low proportion of the soil moisture simulation uncertainty, depending on the level of model accuracy—50–60% for high model accuracy, and 20–30% for low model accuracy. Comparisons on the basis of two different sites also yielded an increase (50–100%) in soil moisture prediction uncertainty for the more vegetated site. This study exemplified the need for detailed investigations of the rainfall retrieval-modeling parameter error interaction within a comprehensive space-time stochastic framework for achieving optimal integration of satellite rain retrievals in land data assimilation systems.     

The value of remotely sensed surface soil moisture for model calibration using SWAT

Remotely sensed (RS) data can add value to a hydrological model calibration. Among this, RS soil moisture (SM) data have mostly been assimilated into conceptual hydrological models using various transformed variable or indices. In this study, raw RS surface SM is used as a calibration variable in the Soil and Water Assessment Tool model. This means the SM values were not transformed into another variable (e.g., soil water index and root zone SM index). Using a nested catchment, calibration based only on RS SM and optimizing model parameters sensitive to SM using particle swarm optimization improved variations in streamflow predictions at some of the gauging stations compared to the uncalibrated model. This highlighted part of the catchments where the SM signal directly influenced the flow distribution. Additionally, highlighted high and low flow signals were mostly influenced. The seasonal breakdown indicates that the SM signal is more useful for calibrating in wetter seasons and in areas with higher variations in elevation. The results identified that calibration only on RS SM improved the general rainfall–runoff response simulation by introducing delays but cannot correct the overall routing effect. Furthermore, catchment characteristics (e.g., land use, elevation, soil types, and precipitation) regulating SM variation in different seasons highlighted by the model calibration are identified. This provides further opportunities to improve model parameterization.     

Inferring land surface parameters from the diurnal variability of microwave and infrared temperatures

This study investigates the properties of the diurnal cycle of microwave brightness temperatures (TB), namely the phase and the amplitude, and their variability in time and space over the globe to infer information on key land surface parameters like changes in soil texture spatial distribution, soil moisture conditions, and vegetation density. The phase corresponds to the lag between Land Surface Temperature (LST) and TB diurnal cycles. The amplitude is determined as the difference between the maximum and the minimum of TB diurnal cycle. The diurnal cycle of TB was constructed using observations from the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) and the Special Sensor Microwave/Imager (SSM/I). The latter offer a series of sensors, namely, F13, F14, and F15 that were used in this study for a higher temporal coverage and more accurate diurnal cycle determination. LST estimates, which are available every 3 h from the International Satellite Cloud Climatology Project (ISCCP) database were used to build the LST diurnal cycle. ISCCP LST data is an infrared-based temperature with almost no penetration and is the representative of top skin temperature.The analyses of the diurnal cycles showed that the diurnal amplitude of TB decreases as the vegetation density increases, especially in the case of low frequencies which penetrate deeper into the canopy which makes them more sensitive to changes in vegetation density. The interannual variations of TB diurnal amplitudes were also in agreement with the seasonality of the vegetation cover. Over desert and rain forest regions where surface conditions do not vary significantly throughout the year, the changes in diurnal amplitudes were the lowest. A relationship between phase and amplitude values was established. It was found that the amplitude of TB diurnal cycle decreases when the phase lag increases. The spatial distribution of the determined diurnal properties, namely, phase and amplitude of TB, showed an agreement with lithology maps in desert areas. Lower TB amplitudes were observed over regions with loose siliceous rocks. Phase lag values between 1.5 and 3 h corresponded to 83% of the class “loose siliceous rocks” in the Sahara Desert, which corroborates the potential of using the diurnal properties of TB as an indicator of land surface parameters.     

山东省地表温度变化与地震活动关系探讨

使用遥感数据探讨山东省地表温度变化与地震活动的关系。选取2000—2016年MODIS地表温度数据,计算山东省地表温度变异系数(CV)并绘图,与活动断裂、地震震中等图层进行空间分析。结果表明,活动断裂与震中附近地表温度的变异系数明显升高,空间标准差降低,说明地震活动较强区域地表温度变化幅度与普遍性均高于其他地区,地表温度变化与地震活动存在一定相关关系。胶东半岛地区的地表温度变化高于鲁西鲁中地区,可能预示着未来该地区地震活动性增强。     

Comparison of soil moisture products from microwave remote sensing,land model,and reanalysis using global ground observations

High-quality soil moisture (SM) datasets are in great demand for climate, hydrology, and other fields, but detailed evaluation of SM products from various sources is scarce. Thus, using 670 SM stations worldwide, we evaluated and compared SM products from microwave remote sensing [Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) (C- and X-bands) and European Space Agency's Climate Change Initiative (ESA CCI)], land surface model [Global Land Data Assimilation System (GLDAS)], and reanalysis data [ECMWF Re-Analysis-Interim (ERA-Interim) and National Centers for Environmental Prediction (NCEP)] under different time scales and various climates and land covers. We find that: (a) ESA CCI and GLDAS have the closest values to the in situ SM on the annual scale, whereas others overestimate the SM; ERA-Interim (averaged R = 0.58) and ESA CCI (averaged R = 0.54) correlate best with the in situ data, while GLDAS performs worst. (b) Overall, the deviations of each product vary in seasons. ESA CCI and ERA-Interim products are closer to the in situ SM at seasonal scales, and AMSR-E and NCEP perform worst in December–February and June–August, respectively. (c) Except for NCEP and ERA-Interim, others can well reflect the intermonthly variation of the in situ SM. (d) Under various climates and land covers, AMSR-E products are less effective in cold climates, whereas GLDAS and NCEP products perform poorly in arid or temperate and dry climates. Moreover, the Bias and R of each SM product differ obviously under different forest types, especially the AMSR-E products. In summary, SM from ESA CCI is the best, followed by ERA-Interim product, and precipitation is an important auxiliary data for selecting high-quality SM stations and improving the accuracy of SM from GLDAS. These results can provide a reference for improving the accuracy of the above SM products.     

Use of multi‐platform,multi‐temporal remote‐sensing data for calibration of a distributed hydrological model: an application in the Arno basin,Italy

Images from satellite platforms are a valid aid in order to obtain distributed information about hydrological surface states and parameters needed in calibration and validation of the water balance and flood forecasting. Remotely sensed data are easily available on large areas and with a frequency compatible with land cover changes. In this paper, remotely sensed images from different types of sensor have been utilized as a support to the calibration of the distributed hydrological model MOBIDIC, currently used in the experimental system of flood forecasting of the Arno River Basin Authority. Six radar images from ERS‐2 synthetic aperture radar (SAR) sensors (three for summer 2002 and three for spring–summer 2003) have been utilized and a relationship between soil saturation indexes and backscatter coefficient from SAR images has been investigated. Analysis has been performed only on pixels with meagre or no vegetation cover, in order to legitimize the assumption that water content of the soil is the main variable that influences the backscatter coefficient. Such pixels have been obtained by considering vegetation indexes (NDVI) and land cover maps produced by optical sensors (Landsat‐ETM). In order to calibrate the soil moisture model based on information provided by SAR images, an optimization algorithm has been utilized to minimize the regression error between saturation indexes from model and SAR data and error between measured and modelled discharge flows. Utilizing this procedure, model parameters that rule soil moisture fluxes have been calibrated, obtaining not only a good match with remotely sensed data, but also an enhancement of model performance in flow prediction with respect to a previous calibration with river discharge data only. Copyright © 2006 John Wiley & Sons, Ltd.     

近地层能量闭合度对陆面过程模式影响

大量近地层观测试验表明,利用涡动相关法观测的湍流通量小于近地层可利用能量,即近地层能量是不闭合的,这种不闭合度一般为20%甚至更高.而陆面过程模式是基于地气间能量平衡建立,并且模式中的湍流边界层参数化方案通常根据实际观测的湍流通量来确定,因此能量不闭合必将对陆面过程模式造成一定的影响.本文利用2007年春季SACOL站的近地层观测资料,依据能量守恒将能量不闭合中的残余能量通过波文比分配到观测的湍流通量中,即修正涡动相关法观测的湍流通量使得近地层能量达到平衡;之后分别利用观测和修正的湍流通量,建立了能量不闭合和闭合情形下的湍流参数化方案,借助陆面过程模式SHAW,通过数值模拟和对比分析方法考察近地层能量闭合度对陆面过程模式的影响.研究结果表明近地层能量闭合对陆面过程模式有显著的影响:在陆面过程数值模拟中,当应用近地层能量不闭合的湍流通量形成的湍流参数化方案时,陆面过程模式会明显高估地表长波辐射及土壤温度;但当应用修正湍流通量使得近地层能量达到闭合形成的湍流参数化方案后,在不改变任何地表土壤物理生化属性的情况下,陆面过程模式能较好地模拟地表长波辐射和土壤温度.     

近地层能量闭合度对陆面过程模式影响

大量近地层观测试验表明,利用涡动相关法观测的湍流通量小于近地层可利用能量,即近地层能量是不闭合的,这种不闭合度一般为20%甚至更高.而陆面过程模式是基于地气间能量平衡建立,并且模式中的湍流边界层参数化方案通常根据实际观测的湍流通量来确定,因此能量不闭合必将对陆面过程模式造成一定的影响.本文利用2007年春季SACOL站的近地层观测资料,依据能量守恒将能量不闭合中的残余能量通过波文比分配到观测的湍流通量中,即修正涡动相关法观测的湍流通量使得近地层能量达到平衡;之后分别利用观测和修正的湍流通量,建立了能量不闭合和闭合情形下的湍流参数化方案,借助陆面过程模式SHAW,通过数值模拟和对比分析方法考察近地层能量闭合度对陆面过程模式的影响.研究结果表明近地层能量闭合对陆面过程模式有显著的影响:在陆面过程数值模拟中,当应用近地层能量不闭合的湍流通量形成的湍流参数化方案时,陆面过程模式会明显高估地表长波辐射及土壤温度;但当应用修正湍流通量使得近地层能量达到闭合形成的湍流参数化方案后,在不改变任何地表土壤物理生化属性的情况下,陆面过程模式能较好地模拟地表长波辐射和土壤温度.     

新型Landsat 8卫星影像的反射率和地表温度反演

Landsat 8卫星自2013年2月发射以来,其影像的定标参数经过了不断调整和完善,针对Landsat 8开发的各种算法也相继问世.本文采用最新的参数、算法和引入COST算法建立的大气校正模型,对Landsat 8多光谱和热红外波段进行了处理,反演出它们的反射率和地表温度,并与同日的Landsat 7数据和实测地表温度数据进行了对比.结果表明,现有Landsat 8多光谱数据的定标参数和大气顶部反射率反演算法已有很高的精度,本文引入COST算法建立的Landsat 8大气校正模型也与Landsat 7的COST模型所获得的结果几乎相同,相关系数可高达0.99.但是现有针对Landsat 8提出的地表温度反演算法仍不理想,已提出的劈窗算法误差都较大.鉴于TIRS 11热红外波段的定标参数仍不理想,因此在现阶段建议采用单通道算法单独反演TIRS 10波段来求算地表温度,但要注意根据大气水汽含量的情况选用正确的大气参数计算公式.     

A land surface model incorporated with soil freeze/thaw and its application in GAME/Tibet

Land surface process is of great importance in global climate change, moisture and heat exchange in the interface of the earth and atmosphere, human impacts on the environment and eco- system, etc. Soil freeze/thaw plays an important role in cold land surface processes. In this work the diurnal freeze/thaw effects on energy partition in the context of GAME/Tibet are studied. A sophisti- cated land surface model is developed, the particular aspect of which is its physical consideration of soil freeze/thaw and vapor flux. The simultaneous water and heat transfer soil sub-model not only reflects the water flow from unfrozen zone to frozen fringe in freezing/thawing soil, but also demon- strates the change of moisture and temperature field induced by vapor flux from high temperature zone to low temperature zone, which makes the model applicable for various circumstances. The modified Picard numerical method is employed to help with the water balance and convergence of the numerical scheme. Finally, the model is applied to analyze the diurnal energy and water cycle char- acteristics over the Tibetan Plateau using the Game/Tibet datasets observed in May and July of 1998. Heat and energy transfer simulation shows that: (i) There exists a negative feedback mechanism between soil freeze/thaw and soil temperature/ground heat flux; (ii) during freezing period all three heat fluxes do not vary apparently, in spite of the fact that the negative soil temperature is higher than that not considering soil freeze; (iii) during thawing period, ground heat flux increases, and sensible heat flux decreases, but latent heat flux does not change much; and (iv) during freezing period, soil temperature decreases, though ground heat flux increases.     

Comparison of land surface scheme simulations with field observations versus atmospheric model output as forcing

ABSTRACT

The low density of meteorological stations in parts of Canada necessitates using numerical weather prediction (NWP)/assimilation output for hydrological modelling. In this study, comparisons are made of simulated land surface variables when using field observations versus NWP output as forcing for two well-instrumented sites: the mountainous and forested Marmot Creek Basin (MCRB) in the Canadian Rocky Mountains, and a prairie cropland/grassland site (Kenaston). The Canadian Land Surface Scheme 3.6 (CLASS) was used for modelling. The Global Environmental Multiscale (GEM) model with Canadian Precipitation Analysis (CaPA) was also used as forcing. There was good agreement between observed meteorology and GEM/CaPA, though some deficiencies in GEM/CaPA were identified: the effects of sub-grid topography on incoming radiation and wind speed were not accounted for at MCRB, and CaPA did not capture some convective rainfall events at Kenaston. CLASS simulations using both sets of forcing showed difficulties in simulating snow depth, soil moisture and evapotranspiration; certain difficulties were linked to GEM/CaPA deficiencies and/or CLASS. Both sets of forcing tended to overestimate the duration of snow cover at MCRB, but during different years. With GEM/CaPA as forcing, CLASS overestimated the duration of frozen soils. The GEM/CaPA precipitation difficulties at Kenaston degraded soil moisture simulations.

EDITOR A. Castellarin; ASSOCIATE EDITOR E. Volpi     

An operational two-layer remote sensing model to estimate surface flux in regional scale: Physical background

Based on the improved interaction mechanism of two-layer model, this paper proposed Pixel Component Arranging and Comparing Algorithm (PCACA) and theoretically positioning algorithm, estimated the true temperature of mixed pixel in four extreme points in combination with the measurements of dry and wet points in calibration fields and improved the reliability of positioning dry and wet line. A new two-layer energy-separation algorithm was proposed,which was simple and direct without resistance network parameters for each pixel. We also proposed a new thought about the effect of advection. The albedo of mixed pixel was also separated with PCACA. In combination with two-layer energy-separation algorithm, the net radiation of mixed pixel was separated to overcome the uncertainty of conventional energy-separation algorithm using Beer's Law. Through the validation of retrieval result, this method is proved to be feasible and operational. At the same time, the uncertainty of this algorithm was objectively analyzed.     

北京大学陆面过程模式PKULM(Peking University Land Model)介绍及检验

陆面过程模式是气候模式和天气模式的核心组成部分之一.在土壤-植被-大气耦合模式(Soil-Plant-Atmosphere Model, SPAM)的基础上,发展了新一代北京大学陆面过程模式PKULM(Peking University Land Model).本文首先介绍了PKULM的辐射传输、湍流输送、光合作用、土壤水热输送等过程的参数化方案;采用隐式迭代计算框架,发展并应用了一个快速的线性方程组求解算法,提高了模式计算稳定性;提出并使用了二分搜索算法计算气孔阻抗,避免了CLM(Community Land Model)等使用的迭代方法在干旱区不稳定的情况,提高了模式的适用性;采用水势为基础的土壤水分扩散方程,使模式能够模拟土壤饱和区的水分输送过程,为进一步与水文过程模式耦合奠定了基础;还发展了一个地表积水与径流过程的机理模型,提高了模式对地表水分平衡过程的模拟能力;最后,使用"中国西北干旱区陆-气相互作用观测试验"平凉站的资料对模式进行了检验并与NOAH(National Center for Environmental Prediction, Oregon State University, Air Force, and Hydrology Lab model)陆面过程模式的模拟结果进行了比较,结果表明PKULM能够较好地模拟西北半干旱区农田下垫面地气交换过程.     

Impacts of model initialization on an integrated surface water–groundwater model

Integrated hydrologic models characterize catchment responses by coupling the subsurface flow with land surface processes. One of the major areas of uncertainty in such models is the specification of the initial condition and its influence on subsequent simulations. A key challenge in model initialization is that it requires spatially distributed information on model states, groundwater levels and soil moisture, even when such data are not routinely available. Here, the impact of uncertainty in initial condition was explored across a 208 km² catchment in Denmark using the ParFlow.CLM model. The initialization impact was assessed under two meteorological conditions (wet vs dry) using five depth to water table and soil moisture distributions obtained from various equilibrium states (thermal, root zone, discharge, saturated and unsaturated zone equilibrium) during the model spin‐up. Each of these equilibrium states correspond to varying computation times to achieve stability in a particular aspect of the system state. Results identified particular sensitivity in modelled recharge and stream flow to the different initializations, but reduced sensitivity in modelled energy fluxes. Analysis also suggests that to simulate a year that is wetter than the spin‐up period, an initialization based on discharge equilibrium is adequate to capture the direction and magnitude of surface water–groundwater exchanges. For a drier or hydrologically similar year to the spin‐up period, an initialization based on groundwater equilibrium is required. Variability of monthly subsurface storage changes and discharge bias at the scale of a hydrological event show that the initialization impacts do not diminish as the simulations progress, highlighting the importance of robust and accurate initialization in capturing surface water–groundwater dynamics. Copyright © 2015 John Wiley & Sons, Ltd.