Verification of land surface evapotranspiration estimation from remote sensing spatial contextual information

Estimation of evapotranspiration (ET) is of great significance in modeling the water and energy interactions between land and atmosphere. Negative correlation of surface temperature (T_s) versus vegetation index (VI) from remote sensing data provides diagnosis on the spatial pattern of surface soil moisture and ET. This study further examined the applicability of T_s–VI triangle method with a newly developed edges determination technique in estimating regional evaporative fraction (EF) and ET at MODIS pixel scale through comparison with large aperture scintillometer (LAS) and high‐level eddy covariance measurements collected at Changwu agro‐ecological experiment station from late June to late October, 2009. An algorithm with merely land and atmosphere products from MODIS onboard Terra satellite was used to estimate the surface net radiation (R_n) and soil heat flux. In most cases, the estimated instantaneous R_n was in good agreement with surface measurement with slight overestimation by 12 W/m². Validation results from LAS measurement showed that the root mean square error is 0.097 for instantaneous EF, 48 W/m² for instantaneous sensible heat flux, and 30 W/m² for daily latent heat flux. This paper successfully presents a miniature of the overall capability of T_s–VI triangle in estimating regional EF and ET from limited number of data. For a thorough interpretation, further comprehensive investigation needs to be done with more integration of remote sensing data and in‐situ surface measurements. Copyright © 2011 John Wiley & Sons, Ltd.     

Integrating a model with remote sensing observations by a data assimilation approach to improve the model simulation accuracy of carbon flux and evapotranspiration at two flux sites

Model simulation and in situ observations are often used to research water and carbon cycles in terrestrial ecosystems, but each of these methods has its own advantages and limitations. Combining these two methods could improve the accuracy of quantifying the dynamics of the water and carbon fluxes of an ecosystem. Data assimilation is an effective means of integrating modeling with in situ observation. In this study, the ensemble Kalman filter(En KF) and the unscented Kalman filter(UKF) algorithms were used to assimilate remotely sensed leaf area index(LAI) data with the Biome-BGC model to simulate water and carbon fluxes at the Harvard Forest Environmental Monitoring Site(EMS) and the Dinghushan site. After MODIS LAI data from 2000–2004 were assimilated into the improved Biome-BGC model using the En KF algorithm at the Harvard Forest site, the R2 between the simulated and observed results for NEE and evapotranspiration increased by 7.8% and 4.7%, respectively. In addition, the sum of the absolute error(SAE) and the root mean square error(RMSE) of NEE decreased by an average of 21.9% and 26.3%, and the SAE and RMSE of evapotranspiration decreased by 24.5% and 25.5%, respectively. MODIS LAI data of 2003 were assimilated into the Biome-BGC model for the Dinghushan site, and the R2 values between the simulated and observed results for NEE and evapotranspiration were increased by 6.7% and 17.3%, respectively. In addition, the SAE values of NEE and ET were decreased by 11.3% and 30.7%, respectively, and the RMSE values of NEE and ET decreased by 10.1% and 30.9%, respectively. These results demonstrate that the accuracy of carbon and water flux simulations can be effectively improved when remotely sensed LAI data are properly integrated with ecosystem models through a data assimilation approach.     

An intercomparison of remotely sensed soil moisture products at various spatial scales over the Iberian Peninsula

Soil moisture (SM) can be retrieved from active microwave (AM), passive microwave (PM) and thermal infrared (TIR) observations, each having unique spatial and temporal coverages. A limitation of TIR‐based retrievals is a dependence on cloud‐free conditions, whereas microwave retrievals are almost all weather proof. A downside of SM retrievals from PM is the coarse spatial resolution. Although SM retrievals at coarse spatial resolution proved to be valuable for global‐scale and continental‐scale studies, their value for regional‐scale studies remains limited. To increase the use of SM retrievals from PM observations, an existing method to enhance their spatial resolution was applied. We present an intercomparison study over the Iberian Peninsula for three SM products on two different spatial sampling grids. The remotely sensed SM products were also compared with in situ observations from the Remedhus network. Variations between ground data and satellite‐based SM are observed; all three remotely sensed SM products show good agreement to the ground observations. The comparison shows that these ground observations and satellite data are consistent, based on the correlation coefficient (R) and root mean square error (RMSE). The remotely sensed products were intercompared after sampling at 25 × 25 km² and after applying the smoothing filter‐based intensity modulation (SFIM) downscaling technique at 10 × 10 km² grids. After the application of the SFIM technique, the SM retrievals from PM observations show better agreement with the other remotely sensed SM products for approximately 40% of the study area. For another 40% of the study area, we found a similar agreement between these product combinations, whereas in extreme environments, both arid and densely vegetated regions, the agreement decreases after the application of the SFIM technique. Agreement between retrievals of absolute SM content from PM and TIR observations is generally high (R = 0.77 for semi‐arid areas). This study enhances our understanding of the remotely sensed SM products for improvements of SM retrieval and merging strategies. Copyright © 2013 John Wiley & Sons, Ltd.     

基于HJ-CCD和MODIS的吉林省中西部湖泊透明度反演对比

水体透明度能够反映光在水体中的穿透程度,影响水生植被及以光为依赖条件的水生生物的分布,获取透明度的传统方法是采用透明度盘进行测量,但也可以通过遥感方法获得.环境减灾卫星是专门用于环境与灾害监测预报的小卫星星座,影像覆盖范围广,空间、时相分辨率较高,可以为水环境遥感提供较好的数据源.MODIS数据在近岸水体和内陆大型湖泊水环境监测中也有广泛应用,它的时相分辨率也很高,但空间分辨率低.利用HJ-1A卫星CCD数据和MODIS日反射率产品(MOD09GA),以2012年9月吉林省石头口门水库、二龙湖、查干湖、月亮泡等地的实测透明度为基础(实测点数74个,最小值为0.134 m,最大值为1.410 m,平均值为0.488 m),根据灰色关联度选取构建模型的波段组合,建立水体透明度反演模型.HJ1A-CCD数据与MOD09GA数据建立的模型R2分别为0.639和0.894,均方根误差(RMSE)分别为0.248和0.135,模型验证的平均相对误差(MRE)分别为17.1%和9.5%,RMSE分别为0.207和0.089.MODIS数据以其较高的辐射分辨率使模型精度较高,但是HJ数据在应用于透明度小于1 m的水体时精度也较高(MRE=13.5%,RMSE=0.066).HJ-CCD数据在空间分辨率上的优势使其能够获得透明度空间分布的细节信息.比较两者反演得到的湖泊平均透明度,结果较为一致.     

Improvement of TOPLATS‐based discharge predictions through assimilation of ERS‐based remotely sensed soil moisture values

In this paper, we investigate the possibility to improve discharge predictions from a lumped hydrological model through assimilation of remotely sensed soil moisture values. Therefore, an algorithm to estimate surface soil moisture values through active microwave remote sensing is developed, bypassing the need to collect in situ ground parameters. The algorithm to estimate soil moisture by use of radar data combines a physically based and an empirical back‐scatter model. This method estimates effective soil roughness parameters, and good estimates of surface soil moisture are provided for bare soils. These remotely sensed soil moisture values over bare soils are then assimilated into a hydrological model using the statistical correction method. The results suggest that it is possible to determine soil moisture values over bare soils from remote sensing observations without the need to collect ground truth data, and that there is potential to improve model‐based discharge predictions through assimilation of these remotely sensed soil moisture values. Copyright © 2002 John Wiley & Sons, Ltd.     

Integration of remotely sensed C factor into SWAT for modelling sediment yield

The C factor, representing the impact of plant and ground cover on soil loss, is one of the important factors of the Modified Universal Soil Loss Equation (MUSLE) in the Soil and Water Assessment Tool (SWAT) to model sediment yield. The daily update of C factors in SWAT was originally determined by land use types and plant growth cycles. This does not reflect the spatial variation of C values that exists within a large land use area. We present a new approach to integrate remotely sensed C factors into SWAT for highlighting the effect of detailed vegetative cover data on soil erosion and sediment yield. First, the C factor was estimated using the abundance of ground components extracted from remote sensing images. Then, the gridding data of the C factor were aggregated to hydrological response units (HRUs), instead of to land use units of SWAT. In the end, the C factor values in HRUs were integrated into SWAT to predict sediment yield by modifying the ysed subroutine. This substitution work not only increases the spatial variation of the C factor in SWAT, but also makes it possible to utilize other sources of C databases rather than those from the United States. The demonstration in the Dage basin shows that the modified SWAT produces reasonable results in water flow simulation and sediment yield prediction using remotely sensed C values. The Nash–Sutcliffe efficiency coefficient (E_NS) and R² for surface runoff range from 0·69 to 0·77 and 0·73 to 0·87, respectively. The coefficients E_NS and R² for sediment yield were generally above 0·70 and 0·60, respectively. The soil erosion risk map based on sediment yield prediction at the HRU level illustrates instructive details on spatial distribution of soil loss. Copyright © 2011 John Wiley & Sons, Ltd.     

Evaluation of multiple surface soil moisture for Korean regional flux monitoring network sites: Advanced Microwave Scanning Radiometer E,land surface model,and ground measurements

In the past few decades, there have been great developments in remotely sensed soil moisture, with validation efforts using land surface models (LSMs) and ground‐based measurements, because soil moisture information is essential to understanding complex land surface–atmosphere interactions. However, the validation of remotely sensed soil moisture has been very limited because of the scarcity of the ground measurements in Korea. This study validated Advanced Microwave Scanning Radiometer E (AMSR‐E) soil moisture data with the Common Land Model (CLM), one of the most widely used LSMs, and ground‐based measurements at two Korean regional flux monitoring network sites. There was reasonable agreement regarding the different soil moisture products for monitoring temporal trends except National Snow and Ice Data Centre (NSIDC) AMSR‐E soil moisture, albeit there were essential comparison limitations by different spatial scales and soil depths. The AMSR‐E soil moisture data published by the National Aeronautics and Space Administration and Vrije Universiteit Amsterdam (VUA) showed potential to replicate temporal variability patterns (root‐mean‐square errors = 0·10–0·14 m³ m^?3 and wet BIAS = 0·09 ? 0·04 m³ m^?3) with the CLM and ground‐based measurements. However, the NSIDC AMSR‐E soil moisture was problematic because of the extremely low temporal variability and the VUA AMSR‐E soil moisture was relatively inaccurate in Gwangneung site characterized by complex geophysical conditions. Additional evaluations should be required to facilitate the use of recent and forthcoming remotely sensed soil moisture data from Soil Moisture and Ocean Salinity and Soil Moisture Active and Passive missions at representative future validation sites. Copyright © 2011 John Wiley & Sons, Ltd.     

Relationships between riparian evapotranspiration and groundwater depth along a semiarid irrigated river valley

Evapotranspiration (ET) from riparian vegetation can be difficult to estimate due to relatively abundant water supply, spatial vegetation heterogeneity, and interactions with anthropogenic influences such as shallower groundwater tables, increased salinity, and nonpoint source pollution induced by irrigation. In semiarid south-eastern Colorado, reliable ET estimates are scarce for the riparian corridor that borders the Arkansas River. This work investigates relationships between the riparian ecosystem along the Arkansas River and an underlying alluvial aquifer using ET estimates from remotely sensed data and modelled water table depths. Results from a calibrated, finite-difference groundwater model are used to estimate weekly water table fluctuations in the riparian ecosystem from 1999 to 2009, and estimates of ET are calculated using the Operational Simplified Surface Energy Balance (SSEBop) model with over 200 Landsat scenes covering over 30 km² of riparian ecosystem along a 70-km stretch of the river. Comparison of calculated monthly SSEBop ET to estimated alfalfa reference ET from local micrometeorological station data indicated statistically significant high linear correspondence (R² = .87). Daily calculated SSEBop ET showed statistically significant moderate linear correspondence with data from a local weighing lysimeter (R² = .59). Simulated monthly SSEBop ET values were larger in drier years compared with wetter years, and ET variability was also larger in drier years. Peak ET most commonly occurred during the month of June for all 11 years of analysis. Relationships between ET and water table depth showed that peak monthly ET was highest when groundwater depths were less than about 3 m, and ET values were significantly lower for groundwater depths greater than 3 m. Negative sample Spearman correlation highlighted riparian corridor locations where ET increased as a result of decreased groundwater depths across years with different hydroclimatic conditions. This study shows how a combination of remotely sensed riparian ET estimates and a regional groundwater model can improve our understanding of linkages between riparian consumptive use and near-river groundwater conditions influenced by irrigation return flow and different climatic drivers.     

Uncertainty of snow water equivalent retrieved from AMSR‐E brightness temperature in northeast Asia

Accurate estimation of snow water equivalent (SWE) has been significantly recognized to improve management and analyses of water resource in specific regions. Although several studies have focused on developing SWE values based on remotely sensed brightness temperatures obtained by microwave sensor systems, it is known that there are still a number of uncertainties in SWE values retrieved from microwave radiometers. Therefore, further research for improving remotely sensed SWE values including global validation should be conducted in unexplored regions such as Northeast Asia. In this regard, we evaluated SWE through comparison of values produced by the Advanced Microwave Scanning Radiometer Earth Observing System (AMSR‐E) from December 2002 to February 2011 with in situ SWE values converted from snow‐depth observation data from four regions in the South Korea. The results from three areas showed similarities which indicated that the AMSR‐E SWE values were overestimated when compared with in situ SWE values, and their Mean Absolute Errors (MAE) by month were relatively small (1.1 to 6.5 mm). Contrariwise, the AMSR‐E SWE values of one area were significantly underestimated when compared with in situ SWE values and the MAE were much greater (4.9 to 35.2 mm). These results were closely related to AMSR‐E algorithm‐related error sources, which we analyzed with respect to topographic characteristics and snow properties. In particular, we found that snow density data used in the AMSR‐E SWE algorithm should be based on reliable in situ data as the current AMSR‐E SWE algorithm cannot reflect the spatio‐temporal variability of snow density values. Additionally, we derived better results considering saturation effect of AMSR‐E SWE. Despite the demise of AMSR‐E, this study's analysis is significant for providing a baseline for the new sensor and suggests parameters important for obtaining more reliable SWE. Copyright © 2013 John Wiley & Sons, Ltd.     

An assessment of water reserve changes in Salt Lake,Turkey, through multi‐temporal Landsat imagery and real‐time ground surveys

This paper focuses mainly on the investigation of water reserve changes in Salt Lake, Turkey, using remote‐sensing data. The study is performed in two stages: (1) correlation analysis for real‐time ground and satellite data and (2) assessment of water reserve changes using multi‐temporal Landsat imagery. First, correlation analysis is conducted to investigate the relationship between digital data from Landsat‐5 TM and spectral (in situ) measurements collected using a field spectroradiometer on the same day and time. A radiometric correction procedure, including conversions from digital numbers to radiance and from radiance to at‐satellite reflectance, is executed to make satellite data comparable to in situ measurements. This procedure show that simultaneous ground and satellite remote‐sensing data are highly correlated (0·84 > R² > 97) and the near‐infrared region (for this study TM4‐Landsat‐5 TM, band 4) is the best spectral range to distinguish salt and water on the satellite data for the multi‐temporal analysis of the water reserve in Salt Lake. It also shows that the use of shortwave infrared band(s) will result in confusion for the determination of the water reserve in this water‐covered study area. In a second and last phase, the water reserve change in the lake is examined using multi‐temporal Landsat imagery collected in 1990, 2001 and 2005. The remotely sensed, sampled and treated data show that the water reserve in the lake has decreased markedly between 1990 and 2005 due to drought and uncontrolled water usage. It is suggested that the use of water supplies around Salt Lake should be controlled and that the lake should regularly be monitored by up‐to‐date remote‐sensing data (at least annually) for better management of water resources. Copyright © 2007 John Wiley & Sons, Ltd.     

Simulation of snowmelt runoff in ungauged basins based on MODIS: a case study in the Lhasa River basin

It is theoretically and practically significant to conduct snowmelt runoff simulations and hydrological research for high-elevation regions. The Lhasa River basin, an ungauged basin, is a typical alpine headwater region where snowmelt runoff contributes significantly to its stream flow. In this study, the snowmelt period, defined by the snow cover curves obtained at different altitudinal zones based on Moderate-Resolution Imaging Spectroradiometer (MODIS) and Digital Elevation Model data, occurred from March 6 to July 12 in the basin. The snowmelt processes were simulated with the Snowmelt Runoff Model (SRM) in 2002 and 2003 for calibration and validation, respectively. The coefficients of determination (R ² ) were 0.86 and 0.87 for calibration and validation, respectively, and the Nash–Sutcliffe coefficients were both 0.80, which indicate reasonable performances in simulating hydrological processes in the Lhasa River basin. The simulated snowmelt at altitudes below 5,000 m accounts for most of the snowmelt. And the simulated snowmelt runoff contributed 3–6 % to the total runoff. The sensitivity of individual parameters was analysed and ranked as follows: α and γ > C _S  > C _R  > T _crit . In short, the SRM based on MODIS remotely sensed data performed well for the ungauged Lhasa River basin.     

Combining MODIS and AMSR‐E observations to improve MCD43A3 short‐time snow‐covered Albedo estimation

Land surface albedo plays an important role in the radiation budget and global climate models. NASA's Moderate Resolution Imaging Spectroradiometer (MODIS) provide 16‐day albedo product with 500‐m resolution every 8 days (MCD43A3). Some in‐situ albedo measurements were used as the true surface albedo values to validate the MCD43A3 product. As the 16‐day MODIS albedo retrievals do not include snow observations when there is ephemeral snow on the ground surface in a 16‐day period, comparisons between MCD43A3 and 16 day averages of field data do not agree well. Another reason is that the MODIS cannot detect the snow when the area is covered by clouds. The Advanced Microwave Scanning Radiometer for EOS (AMSR‐E) data are not affected by weather conditions and are a good supplement for optical remote sensing in cloudy weather. When the surface is covered by ephemeral snow, the AMSR‐E data can be used as the additional information to retrieve the snow albedo. In this study, we developed an improved method by using the MODIS products and the AMSR‐E snow water equivalent (SWE) product to improve the MCD43A3 short‐time snow‐covered albedo estimation. The MODIS daily snow products MOD10A1 and MYD10A1 both provide snow and cloud information from observations. In our study region, we updated the MODIS daily snow product by combining MOD10A1 and MYD10A1. Then, the product was combined with the AMSR‐E SWE product to generate new daily snow‐cover and SWE products at a spatial resolution of 500 m. New SWE datasets were integrated into the Noah Land Surface Model snow model to calculate the albedo above a snow surface, and these values were then utilized to improve the MODIS 16‐day albedo product. After comparison of the results with in‐situ albedo measurements, we found that the new corrected 16‐day albedo can show the albedo changes during the short snowfall season. For example, from January 25 to March 14, 2007 at the BJ site, the albedo retrieved from snow‐free observations does not indicate the albedo changes affected by snow; the improved albedo conforms well to the in‐situ measurements. The correlation coefficient of the original MODIS albedo and the in‐situ albedo is 0.42 during the ephemeral snow season, but the correlation coefficient of the improved MODIS albedo and the in‐situ albedo is 0.64. It is concluded that the new method is capable of capturing the snow information from AMSR‐E SWE to improve the short‐time snow‐covered albedo estimation. Copyright © 2012 John Wiley & Sons, Ltd.     

Remote sensing of volumetric storage changes in lakes

Three‐dimensional remote sensing promises a giant leap forward for surface‐water hydrology in much the same way that radar altimetry transformed physical oceanography. However, the complex geometries of small terrestrial water bodies introduce difficulties, particularly with respect to trade‐offs between changing water depth and inundation area. We use in situ measurements of water‐surface stage (ΔH/dt) and remotely‐sensed area (A) to compute time varying storage changes (ΔS) in nine lakes of the Peace‐Athabasca Delta, Canada. Despite their identical geomorphic setting, regression slopes between ΔH and A vary significantly between lakes, primarily from a predictable ‘area‐effect’ but also small bathymetric variations between basins. On average, lateral contraction/expansion (versus stage adjustment) contributes as little as 7% (versus 93%) to as much as 76% (versus 24%) of overall storage change ΔS. We conclude that both surface‐area and ΔH/dt, rather than just either alone, must be measured to confidently estimate ΔS from space. Copyright © 2009 John Wiley & Sons, Ltd.     

INCORPORATING RADIATION INPUTS INTO THE SNOWMELT RUNOFF MODEL

Process-based, distributed-area snowmelt runoff models operating at small scales are essential to understand subtle effects of climate change, but require data not commonly available. Temperature index models operating over large areas provide realistic simulations of basin runoff with operationally available data, but lack rigorous physically based algorithms. A compromise between the two types of models is required to provide realistic evaluations of basin response to environmental changes in cold regions. One adaptation that is uniformly required for snowmelt models is the use of remotely sensed data, either as input or in model validation. At a minimum, snowmelt forecasting models need to incorporate snowcover extent information, which is currently obtained operationally. As more remote sensing capabilities come on line, models should accept upgraded information on snow water equivalent; additional remotely sensed information on landcover, frozen soil, soil moisture, cloudiness and albedo would also be useful. Adaptations to the semi-distributed snowmelt runoff model (SRM) are underway to make it more physically based for use in large area studies. A net radiation index has been added to the model, which formerly used only a temperature (degree–day) index to melt snow from a basin's elevation zones. The addition of radiation to the SRM allows the basin to be subdivided into hydrological response units by general aspect (orientation) as well as elevation. Testing of the new radiation-based SRM with measured radiation from a small research basin is the first step towards large scale simulations. Results from the W-3 research basin in Vermont, USA are promising. In the radiation version, the factor that multiplies the degree–day index is estimated independently of model output and is held constant throughout the season, in contrast with the degree–day version, where the corresponding factor is allowed to increase throughout the season. Without calibrating or optimizing on this important parameter, the goodness-of-fit measure R² is improved in two out of six test years when the radiation version of the SRM is used in place of the degree–day version in melt season simulations. When the accumulation of error is eliminated with periodic updating of streamflow, more significant improvement is noted with radiation included.     

Measurement of evapotranspiration in a winter wheat field

Daily evapotranspiration from a winter wheat field on the North China Plain measured by large‐scale weighing lysimeter was linearly related to that measured by the Bowen ratio energy balance (BREB) technique. Soil evaporation averaged about 23·6% of evapotranspiration from the post‐winter dormancy revival stage to the grain ripening stage in 1999. On clear days during winter dormancy, about half of the net radiation flux R_n was used to warm soil. During the revival stage, conductive heat flux G also used most of the incoming R_n, but the ratio of latent heat flux λE to R_n increased. During the stem‐extension stage, λE was about 50% of R_n; thereafter, λE/R_n increased continually, but G remained less than 10% of R_n. During the ripening stage, λE was almost 90% of R_n. Evaporative fraction (EF) can be expressed as a function of plant status and atmospheric boundary layer conditions. The relationship between EF and available energy under moderate air temperature and vapour pressure deficit conditions was examined for five combinations of aerodynamic and canopy conductance. Although the theoretical relationship indicates that EF should be highly correlated to soil water content, the correlation has been difficult to identify under field conditions. However, we observed that there exists a threshold value of R_n ? G, above which EF is less than 1·0, and that the threshold value is lower under soil‐water deficit conditions than under abundant soil‐water conditions. Copyright © 2002 John Wiley & Sons, Ltd.     

Aerosol retrieval over land by exploiting the synergy of TERRA and AQUA MODIS DATA

Aerosol particles over land mainly come from man- made source such as biomass burning, industrial de-bris and natural source such as soil dust, sea salt parti-cles, etc. More and more research results show that, aerosols impact global and regional energy radiative budget; aerosol particles also modify the cloud mi-crophysics, as a result, aerosol particles may change the cloud radiative properties. Aerosol particles also play an important role in many biogeochemical cycles. All the above-menti…     

Further evaluation of the Sim-ReSET model for ET estimation driven by only satellite inputs

Abstract

A simple remote sensing evapotranspiration (ET) model (Sim-ReSET) has been proposed but only tested using field measurements at a site with a semi-arid climate. Its performance for mapping ET using only satellite data remained unknown. In this study, the Sim-ReSET model was further evaluated for ET estimation driven by only MODIS data products. The estimated ET rates were compared with ground-based observational data from a variety of ecosystems and climates across China. The results show that MODIS-based ET estimates are consistent with both the ET measurements from eddy covariance flux towers and those from the Penman-Monteith method combined with micrometeorological data. Evaporation fraction (EF) is indicative of land surface moisture. The derivative EF maps demonstrate that the proposed ET data set obtained from the Sim-ReSET model and MODIS data is capable of capturing the spatio-temporal pattern of land surface moisture for different land covers with different climates.

Editor Z.W. Kundzewicz

Citation Sun, Z.G., Wang, Q.X., Matsushita, B., Fukushima, T., Ouyang, Z., Watanabe, M., and Gebremichael, M., 2013. Further evaluation of the Sim-ReSET model for ET estimation driven by only satellite inputs. Hydrological Sciences Journal, 58 (5), 994–1012.     

Characterization of field scale soil variability using remotely and proximally sensed data and response surface method

Soil salinization of the reclaimed tidelands is problematic. Therefore, there is a need to characterize the spatial variability of soil salinity associated with soil moisture and other soil properties across the reclaimed tidelands. One approach is the use of easily-acquired ancillary data as surrogates for the arduous conventional soil sampling. In a reclaimed coastal tideland in the south of Hangzhou Gulf, backscattering coefficient (σ⁰) from remotely sensed ALOS/PALSAR radar imagery (HH polarization mode) and apparent soil electrical conductivity (ECa) from a proximally sensed EM38 were used to indicate the spatial distribution of soil moisture and salinity, respectively. After that, response surface methodology (RSM) was employed to determine an optimal set of 12 soil samples using spatially referenced σ⁰ and ECa data. Spatial distributions of three soil chemical properties [i.e. soil organic matter (SOM), available nitrogen (AN), and available potassium (AK)] were predicted using inverse distance weighted method based on the 12 samples and were then compared with the predictions generated using 42 samples obtained from a conventional grid sampling scheme. It was concluded that combination of radar imagery and EM induction data can delineate the spatial variability of two key soil properties (i.e. moisture and salinity) across the study area. Besides, RSM-based sampling using radar imagery and EM induction data was highly effective in characterizing the spatial variability of SOM, AN and AK, compared with the conventional grid sampling. This new approach may be used to assist site specific management in precision agriculture.     

Remotely sensed estimation of water discharge into the rapidly dwindling Dead Sea

Abstract

The Dead Sea is shrinking as its water level drops at the alarming rate of about 1 m year^-1. The Dead Sea is important to the economies of Israel and Jordan due to the extracted minerals (primarily potassium, also magnesium and bromide). It is also central to regional tourism. It is the lowest place on Earth and its endorheic, saline basin attracts international research in various disciplines. Additional to the Lower Jordan River, the discharge of which has been decreased to a small fraction of its original value, fresh to brackish springs are the main source of water to the rapidly dwindling Dead Sea. Although the existence of these springs has been known for decades, until recently estimates of spring water discharge into the Dead Sea were scarce. In this study, we developed a methodology of water discharge estimation for channels incising into a lacustrine bed using remotely sensed data and a single hydraulic geometry variable, water-surface width. Based on calibration of over 400 in situ measurements and simultaneous hydrometric data from aerial images, width–discharge rating curves were established, tested, and found suitable (r² = 0.92, p = 0.001) for the estimation of water discharge, with ±5% uncertainty. Furthermore, we used these relationships to estimate retrospectively the temporal changes in water discharge of seven main channels traceable in historical aerial images. The reconstructed trend reveals a major (63%) reduction in average freshwater inflow between 1990 and 2006. Our results maximize the use of water-surface width information from aerial imagery, and suggest applicability to areas experiencing rapid exposure of sea/lake bed and consequent access difficulties in in situ discharge monitoring.

Editor Z.W. Kundzewicz     

适用于多种卫星数据的太湖水体漫衰减系数估算算法

下行漫衰减系数(K_d)是描述水下光场的重要参数,决定水体真光层深度,影响着浮游藻类初级生产力及其分布特征.基于2008—2013年太湖4次大规模野外试验数据,分析太湖水体漫衰减系数特征及其影响因素,建立适用于多种卫星数据且较高精度的太湖水体490 nm处下行漫衰减系数估算模型.结果表明:无机悬浮物是太湖水体漫衰减系数的主要影响因素;红绿波段比值(674 nm/555 nm)最适合于太湖K_d(490)估算,模型反演精度较高(N=72,R~2=0.72,RMSE=0.89 m~(-1),MAPE=21.58%);利用实测光谱数据,模拟得到MODIS/EOS、OLCI/Sentinel-3、GOCI/COMS和MSI/Sentinel-2等主要传感器波段的信号,构建适用于多种卫星传感器K_d(490)估算的红绿波段模型,建模精度较高(N=72,R~20.7,RMSE0.9 m~(-1),MAPE22.0%),且进行了验证(N=37,R~20.7,RMSE0.9 m~(-1),MAPE22.0%).