Localization and validation of an urbanized high-resolution land data assimilation system(u-HRLDAS)

This paper uses an urbanized high-resolution land data assimilation system(u-HRLDAS) to parameterize the urban land surface characteristics.The u-HRLDAS model is localized and developed in order to satisfy the need of the weather forecast in Beijing,China.The remote sensing data used to localize and drive u-HRLDAS include the soil type data and MODIS retrieved leaf area index(LAI) data.The evaporation and water depth for impervious surface in urban area are developed to improve the simulation of u-HRLDAS.The result of the urban weather forecast is used for the comparison based on the rapid update cycle system at Beijing Meteorological Bureau(BJ-RUC) without coupled with u-HRLDAS.The land surface temperature,land surface fluxes,and first layer soil moisture in several single sites and urban Beijing region by BJ-RUC are compared with u-HRLDAS after localization and development.The off-line simulation results indicate that compared with BJ-RUC,after the localization and development,u-HRLDAS can improve the simulation of land surface parameters and fluxes definitely.     

Vegetation physiological parameter setting in the Simple Biosphere model 2(SiB2) for alpine meadows in the upper reaches of Heihe river

Land surface process modeling of high and cold area with vegetation cover has not yielded satisfactory results in previous applications. In this study, land surface energy budget is simulated using a land surface model for the A’rou meadow in the upper-reach area of the Heihe River Basin in the eastern Tibetan Plateau. The model performance is evaluated using the in-situ observations and remotely sensed data. Sensible and soil heat fluxes are overestimated while latent heat flux is underestimated when the default parameter setting is used. By analyzing physical and physiological processes and the sensitivities of key parameters, the inappropriate default setting of optimum growth and inhibition temperatures is identified as an important reason for the bias. The average daytime temperature during the period of fastest vegetation growth(June and July) is adopted as the optimum growth temperature, and the inhibition temperatures were adjusted using the same increment as the optimum temperature based on the temperature acclimation. These adjustments significantly reduced the biases in sensible, latent, and soil heat fluxes.     

A physics-based statistical algorithm for retrieving land surface temperature from AMSR-E passive microwave data

AMSR-E and MODIS are two EOS (Earth Observing System) instruments on board the Aqua satellite. A regression analysis between the brightness of all AMSR-E bands and the MODIS land surface tem-perature product indicated that the 89 GHz vertical polarization is the best single band to retrieve land surface temperature. According to simulation analysis with AIEM,the difference of different frequen-cies can eliminate the influence of water in soil and atmosphere,and also the surface roughness partly. The analysis results indicate that the radiation mechanism of surface covered snow is different from others. In order to retrieve land surface temperature more accurately,the land surface should be at least classified into three types:water covered surface,snow covered surface,and non-water and non-snow covered land surface. In order to improve the practicality and accuracy of the algorithm,we built different equations for different ranges of temperature. The average land surface temperature er-ror is about 2―3℃ relative to the MODIS LST product.     

Statistics of breaking waves and its applications to estimation of air-sea fluxes (Ⅰ)——Theoretical models

Wave breaking statistics, such as the whitecap coverage and average volume of broken seawater, are evaluated in terms of wave parameters by use of wave breaking model (Yuan et al., 1988) taking the fifth order Stokes's wave as the analog of the original wave field. Based on the observed fact that breaking waves play an important role in the exchange of mass, momentum and energy between the atmosphere and the ocean, the influence of wave breaking on air-sea fluxes of heat and moisture is investigated. Theoretical expressions of bubble-volume flux and sea spray spectrum at the sea surface and models for bubble-induced and spray droplet-induced heat and moisture fluxes are established. This work can be taken as the basis for further understanding the mechanism of air-sea coupling and parameterization models.     

Integrative inversion of land surface component temperature

Land surface temperature (LST) was a key parame-ter in the study of energy fluxes and substance ex-change between the atmosphere and the ground. Re-trieving LST accurately was the main task of infrared remote sensing these years. According to the request of environmental monitor and global change study, the target was specified that the sea surface temperature (SST) should be accurate to 0.3 K, and the land sur-face temperature (LST) should be accurate to 1K in the near future[1]. Sea su…     

Modeling gross primary production of a temperate grassland ecosystem in Inner Mongolia,China, using MODIS imagery and climate data

Carbon fluxes in temperate grassland ecosystems are characterized by large inter-annual variations due to fluctuations in precipitation and land water availability. Since an eddy flux tower has been in operation in the Xilin Gol grassland, which belongs to typical temperate grassland in North China, in this study, observed eddy covariance flux data were used to critically evaluate the biophysical per- formance of different remote sensing vegetation indices in relation to carbon fluxes. Furthermore, vegetation photosynthesis model (VPM) was introduced to estimate gross primary production (GPP) of the grassland ecosystem for assessing its dependability. As defined by the input variables of VPM, Moderate Resolution Imaging Spectroradimeter (MODIS) and standard data product MOD09A1 were downloaded for calculating enhanced vegetation index (EVI) and land surface water index (LSWI). Measured air temperature (Ta) and photosynthetically active radiation (PAR) data were also included for model simulating. Field CO2 flux data, during the period from May, 2003 to September, 2005, were used to estimate the "observed" GPP (GPPobs) for validation. The seasonal dynamics of GPP predicted from VPM (GPPVPM) was compared quite well (R2=0.903, N=111, p<0.0001) with the observed GPP. The ag- gregate GPPVPM for the study period was 641.5 g C·m?2, representing a ~6% over-estimation, compared with GPPobs. Additionally, GPP predicted from other two typical production efficiency model (PEM) represents either higher overestimation or lower underestimation to GPPobs. Results of this study demonstrate that VPM has potential for estimating site-level or regional grassland GPP, and might be an effective tool for scaling-up carbon fluxes.     

Study on NDVI-T_s space by combining LAI and evapotranspiration

Vegetation index and land surface temperature (Ts) are important parameters for land surface process modeling. The remotely sensed data in visible/near infrared and thermal infrared wavelengths have proven to be well suited to monitoring vegetation status, soil surface moisture conditions, drought and crop yield. More useful information can be created by integrated analyses of these two kinds of data, which will help us to study main principles of the temporal and spatial variations of land su…     

Development of a daily soil moisture product for the period of2002–2011 in Chinese mainland

Soil moisture is an essential climate variable(ECV) concerned widely. Due to its high spatial variability, it is costly to measure soil moisture at tens of kilometers scale. In this study, a ten-year(2002–2011) daily soil moisture dataset at 0.25° spatial resolution for Chinese mainland was produced through assimilating the Advanced Microwave Scanning Radiometer for Earth Observing System(AMSR-E) brightness temperature(TB) data into a land surface model(LSM). The obtained soil moisture data was evaluated against soil moisture-measuring networks deployed in two wet areas and one dry area of the Tibetan Plateau.The results show that for the wet areas the accuracy of the soil moisture product obtained from the assimilation is considerably higher than that of both AMSR-E official soil moisture products and land surface simulation results, and for the dry area their accuracy is comparable to each other. The spatial pattern of the soil moisture from the new product is consistent with that of soil porosity from an independent survey-based dataset, further confirming the credibility of the new product. According to this product, the transition regions in China show stronger seasonal variation of soil moisture than dry and wet regions, and drier regions have stronger inter-annual variability of soil moisture than wetter regions, particularly during transitional seasons(spring and autumn). The soil moisture product is accessible at the National Tibetan Plateau Data Center.     

Modeling the land surface reflectance for optical remote sensing data in rugged terrain

A model for topographic correction and land surface reflectance estimation for optical remote sensing data in rugged terrian is presented.Considering a directional-directional reflectance that is used for direct solar irradiance correction and a hemispheric-directional reflectance that is used for atmospheric diffuse irradiance and terrain background reflected irradiance correction respectively,the directional reflectance-based model for topographic effects removing and land surface reflectance calculation is developed by deducing the directional reflectance with topographic effects and using a radiative transfer model.A canopy reflectance simulated by GOMS model and Landsat/TM raw data covering Jiangxi rugged area were taken to validate the performance of the model presented in the paper.The validation results show that the model presented here has a remarkable ability to correct topography and estimate land surface reflectance and also provides a technique method for sequently quantitative remote sensing application in terrain area.     

Active microwave remote sensing for soil moisture measurement: a field evaluation using ERS‐2

Active microwave remote sensing observations of backscattering, such as C‐band vertically polarized synthetic aperture radar (SAR) observations from the second European remote sensing (ERS‐2) satellite, have the potential to measure moisture content in a near‐surface layer of soil. However, SAR backscattering observations are highly dependent on topography, soil texture, surface roughness and soil moisture, meaning that soil moisture inversion from single frequency and polarization SAR observations is difficult. In this paper, the potential for measuring near‐surface soil moisture with the ERS‐2 satellite is explored by comparing model estimates of backscattering with ERS‐2 SAR observations. This comparison was made for two ERS‐2 overpasses coincident with near‐surface soil moisture measurements in a 6 ha catchment using 15‐cm time domain reflectometry probes on a 20 m grid. In addition, 1‐cm soil moisture data were obtained from a calibrated soil moisture model. Using state‐of‐the‐art theoretical, semi‐empirical and empirical backscattering models, it was found that using measured soil moisture and roughness data there were root mean square (RMS) errors from 3·5 to 8·5 dB and r² values from 0·00 to 0·25, depending on the backscattering model and degree of filtering. Using model soil moisture in place of measured soil moisture reduced RMS errors slightly (0·5 to 2 dB) but did not improve r² values. Likewise, using the first day of ERS‐2 backscattering and soil moisture data to solve for RMS surface roughness reduced RMS errors in backscattering for the second day to between 0·9 and 2·8 dB, but did not improve r² values. Moreover, RMS differences were as large as 3·7 dB and r² values as low as 0·53 between the various backscattering models, even when using the same data as input. These results suggest that more research is required to improve the agreement between backscattering models, and that ERS‐2 SAR data may be useful for estimating fields‐scale average soil moisture but not variations at the hillslope scale. Copyright © 2004 John Wiley & Sons, Ltd.     

Coupling two radar backscattering models to assess soil roughness and surface water content at farm scale

Abstract

Remote sensing techniques are useful for agro-hydrological monitoring at the farm scale because the availability of spatially and temporally distributed data improves agricultural models for irrigation and crop yield optimization under water scarcity conditions. This research focuses on the surface water content retrieval using active microwave data. Two semi-empirical models were chosen as these showed the best performances in simulating cross and co-polarized backscatter. Thus, these models were coupled to obtain reliable assessments of both soil water content and soil roughness. The use of the coupled model enables one to avoid using roughness measured in situ. Remote sensing images and in situ data were collected between April and July 2006 within the European Space Agency-funded project AgriSAR 2006. The images data set includes L-band in HH, VV and VH polarizations acquired from the airborne E-SAR sensor, operated by the German Aerospace Centre. Results were validated using in situ soil water content and roughness measurements. The results show that reliable assessment of both soil roughness (r ² up to ?0.8) and soil water content (r ² ? 0.9) can be retrieved in fields characterized by low fractional coverage.

Editor D. Koutsoyiannis; Associate editor C. Onof

Citation Capodici, F., Maltese, A., Ciraolo, G., La Loggia, G., and D’Urso, G., 2013. Coupling two radar backscattering models to assess soil roughness and surface water content at the farm scale. Hydrological Sciences Journal, 58 (8), 1677–1689.     

Hydraulics of interrill overland flow on rough,bare soil surfaces

A set of laboratory experiments on bare, rough soil surfaces was carried out to study the relationship between soil surface roughness and its hydraulic resistance. Existing models relating roughness coefficients to a measure of surface roughness did not predict the hydraulic resistance well for these surfaces. Therefore, a new model is developed to predict the hydraulic resistance of the surface, based on detailed surface roughness data. Roughness profiles perpendicular to the flow are used to calculate the wet cross‐sectional area and hydraulic radius given a certain water level. The algorithm of Savat is then applied to calculate the hydraulic resistance. The value for the equivalent roughness, which is used in the algorithm of Savat, could be predicted from the roughness profiles. Here, the tortuosity of the submerged part of the surface was used, which means that the calculated roughness depends on flow depth. The roughness increased with discharge, due to the fact that rougher parts of the surface became submerged at higher discharges. Therefore, a single measure of surface roughness (e.g. random roughness) is not sufficient to predict the hydraulic resistance. The proposed model allows the extension of the flow over the surface with increasing discharge to be taken into account, as well as the roughness within the submerged part of the surface. Therefore, the model is able to predict flow velocities reasonably well from discharge and roughness data only. Copyright © 2000 John Wiley & Sons, Ltd.     

SPATIAL AND TEMPORAL VARIABILITY OF SOIL SURFACE ROUGHNESS AND THE APPLICATION IN HYDROLOGICAL AND SOIL EROSION MODELLING

Accurate estimations of water retention and detention are needed to simulate surface runoff and soil erosion following a rainfall event in a catchment. Several equations to estimate the amount of surface depressional storage, the fraction of the soil surface covered by water and the amount of rainfall excess needed to start surface runoff have been developed by Onstad (1984). The random roughness and slope gradient are needed for those estimations. Surface micro-elevation data have been gathered by a photographic method. The random roughness was determined from those elevation measurements. Several factors which have an impact on the soil surface roughness were taken into account. The main sources of influence are the type of land use, the crop stage within the growing period and tillage direction. Analyses of variance indicated that the variation in the RR-index could be explained mainly by type of land use, orientation and field type. The temporal variation was relatively small. Gradient data have been determined from a digital elevation model, constructed by digitizing contours. Combining the random roughness and the steepness of slope, the amounts of surface water retention and detention could be estimated. Knowledge of water retention and detention will improve the estimations of runoff and soil erosion modelling in catchments, such as those made with the LISEM model. The agricultural systems examined in this study have similar random roughness values in summer. Different soil erosion rates for several types of land use can not therefore be explained by the random roughness.     

光滑地表面毁伤检测方法研究

光滑地面毁伤程度评估非常重要,目前评估方法主要是基于光学图像,可是在恶劣的气候或环境条件下,很难获得光学图像,合成孔径雷达（SAR）克服了这个缺点. SAR图像是地表结构和电特征等地球物理参数的映射,通过SAR图像反演地表结构参数,可以推测出光滑地面毁伤程度. 本文以几何光学模型（GOM）为基础,建立神经网络反演模型,以获得光滑地表面受损后的粗糙度参数：表面均方根高度（σ）和表面相关长度（l）,并进一步评估光滑地表面受损程度. 实验结果表明该方法可行.     

星载SAR遥感图像反演海洋大气边界层高度

本研究将边界层相似理论与对流理论应用到具有海洋大气边界层(Marine Atmospheric Boundary Layer, MABL)对流特征的星载合成孔径雷达(Synthetic Aperture Radar, SAR)遥感图像,探讨了星载SAR遥感图像描述海气应力作用下水平扰动尺度变化的潜在可能性.针对具有三维对流涡旋Cell和二维水平滚轴涡旋Roll特征的星载SAR遥感图像,反演了中国海海域MABL高度,并与同步实验获取的MABL高度结果进行对比.结果表明,利用具有对流特征的星载SAR遥感图像反演MABL高度是可行的,展示了以高分辨率、大面积观测为特点的星载SAR遥感图像探测MABL的广阔前景.     

Lysimetric evaluation of SEBAL using high resolution airborne imagery from BEAREX08

In this study, Surface Energy Balance Algorithm for Land (SEBAL) was evaluated for its ability to derive aerodynamic components and surface energy fluxes from very high resolution airborne remote sensing data acquired during the Bushland Evapotranspiration and Agricultural Remote Sensing Experiment 2008 (BEAREX08) in Texas, USA. Issues related to hot and cold pixel selection and the underlying assumptions of difference between air and surface temperature (dT) being linearly related to the surface temperature were also addressed. Estimated instantaneous evapotranspiration (ET) and other components of the surface energy balance were compared with measured data from four large precision weighing lysimeter fields, two each managed under irrigation and dryland conditions. Instantaneous ET was estimated with overall mean bias error and root mean square error (RMSE) of 0.13 and 0.15 mm h⁻¹ (23.8 and 28.2%) respectively, where relatively large RMSE was contributed by dryland field. Sensitivity analysis of the hot and cold pixel selection indicated that up to 20% of the variability in ET estimates could be attributed to differences in the surface energy balance and roughness properties of the anchor pixels. Adoption of an excess resistance to heat transfer parameter model into SEBAL significantly improved the instantaneous ET estimates.     

Estimation of surface runoff and water‐covered area during filling of surface microrelief depressions

During the filling of surface microrelief depressions the precipitation excess (precipitation minus infiltration and interception) is divided between surface storage and runoff, i.e. runoff starts before the surface depressions are filled. Information on the division of precipitation excess is needed for modelling surface runoff during the filling of surface depressions. Furthermore, information on the surface of the area covered with water is needed for calculating infiltration of water stored in soil surface depressions. Thirty‐two soil surface microreliefs were determined in Danish erosion study plots. The slope was c. 10% for all plots. Data were treated initially by removing the slope, after which 20 ‘artificial’ slopes (1–20%) were introduced producing 640 new data sets. Runoff during filling of the microrelief storage was calculated for each of the 640 data sets using a model developed for calculating surface storage and runoff from grid elevation measurements. Runoff started immediately after the first addition of water for all data sets. On a field scale, however, runoff has to travel some distance as overland flow and storage in smaller and larger depressions below the runoff initiation point must be taken into consideration. The runoff increases by intermittent steps. Whenever a depression starts to overflow to the border of the plot, the runoff jumps accordingly. In spite of the jumps, the distribution between surface storage and runoff was closely related to the quotient between precipitation excess and depression storage capacity. Surface area covered with water was exponentially related to the amount of water stored in surface depressions. Models for calculating surface storage and runoff from grid elevation measurements are cumbersome and require time‐consuming measurements of the soil surface microrelief. Therefore, estimation from roughness indices requiring fewer measurements is desirable. New improved equations for such estimations are suggested. Copyright © 2000 John Wiley & Sons, Ltd.     

The GPS-gravimetry boundary value problem

Boundary value problem (BVP) plays a funda-mental role in physical geodesy that aims at determin-ing the earth’s shape and its external gravity field. TheMolodensky BVP and the Stokes BVP are typical inphysical geodesy, and the gravity anomaly is a kind ofbasic data. With the wide use of GPS, measurementaccuracy of the earth’s surface can reach one centime-ter, while that of the gravity measurement can reachμgals. Hence, it is necessary to establish a new kind ofBVP which can satisfy…     

Analysis of surface roughness in relation to soil loss and runoff at high rainfall intensities

The decay of roughness is an important factor governing surface processes such as infiltration and soil erosion. Thus the decay of surface roughness under different surface conditions was investigated and related to quantitative amounts of soil loss, runoff and sediment concentration in a laboratory experiment. Rainfall with an intensity of 128 mm/h was applied to a bare or mulched surfaces of a sandy loam soil with known surface roughness at specified time intervals. The decay of roughness as expressed by roughness ratio, in this experiment, was better predicted when related to an exponential function of the square root of cumulative kinetic energy of rainfall rather than with the cumulative rainfall. The roughness decay equations in literature did not predict breakdown under mulched surfaces accurately. Thus the exponent parameters of the roughness decay equations were adjusted to reflect the reduced decay occurring under mulched surfaces. In a bare soil, regression equations expressing the dependent variables as a function of initial roughness index were significant, but with low coefficients of determination, being 0·39 for soil loss, 0·12 for runoff and 0·36 for sediment concentration. In addition to initial roughness index, cumulative kinetic energy of rainfall was further included in the regressions. This led to an increase in coefficients of determination, which was 0·81 for soil loss, 0·74 for runoff and 0·49 for sediment concentration. The coefficients of determination (0·87 for soil loss, 0·85 for runoff and 0·51 for sediment concentration) were further increased when the final roughness index was included in addition to initial roughness index and cumulative kinetic energy in the regressions. This work shows that soil loss and runoff could be predicted from bare soil surface provided the initial roughness and the energy of rainfall is known. However, field verifications of these relationships are needed under different tillage tools and under natural rainfall. Copyright © 2002 John Wiley & Sons, Ltd.