Assessing the vulnerability to soil erosion of the Ukai Dam catchments using remote sensing and GIS

Abstract

The investigation of basins for planning soil conservation requires a selective approach to identify smaller hydrological units, which would be suitable for more efficient and targeted conservation management programmes. One criterion, generally used to determine the vulnerability of catchments to erosion, is the sediment yield of a basin. In India, sediment yield data are generally not collected for smaller sub-catchments and it becomes difficult to identify the most vulnerable areas for erosion that can be treated on a priority basis. An index-based approach, based on the surface factors mainly responsible for soil erosion, is suggested in this study. These factors include soil type, vegetation, slope and various catchment properties such as drainage density, form factor, etc. The method is illustrated with a case study of sub-catchments immediately upstream of the Ukai Reservoir located on the River Tapi in Gujarat State, India. The area is divided into 16 watersheds and different soil, vegetation, topography and morphology-related parameters are estimated separately for each watershed. Satellite data are used to evaluate the soil and vegetation indices, while a GIS system is used to evaluate the topography and morphology-related indices. The integrated effect of all the parameters is evaluated to find different areas vulnerable to soil erosion. Two watersheds were identified as being most susceptible to soil erosion. Based on the integrated index, a priority rating of the watersheds for soil conservation planning is recommended.     

分季节的太湖悬浮物遥感估测模型研究

根据1996-2002年无锡太湖监测站的水质资料分析,太湖悬浮物具有季节性特征,因而分季节的悬浮物估测模型比单一的模型可能更加适合用来估测太湖全年的悬浮物浓度.在分析太湖水体光谱特征的基础上,根据太湖悬浮物的季节性分布特征,使用春夏秋冬四季的Landsat TM/ETM图像和准同步的水质采样数据,建立了太湖分季节的悬浮物估算模型.结果表明:估测因子(B2 B3)/(B2/B3)在春、秋、冬三季都能很好地估测出悬浮物的浓度(R2＞0.52).夏季由于叶绿素的干扰性较大,悬浮物的估测效果不理想.冬季的估测效果最好(R2=0.81),模型为lnSS=14.656×(B2 B3)/(B2/B3) 1.661,其中,ln SS表示悬浮物取自然对数后的值,B2、B3为TM/ETM图像经过6S大气校正、3×3低通滤波后第2、3波段的反射率值.     

A distributed TOPMODEL for modelling impacts of land‐cover change on river flow in upland peatland catchments

There is global concern about headwater management and associated impacts on river flow. In many wet temperate zones peatlands can be found covering headwater catchments. In the UK there is major concern about how environmental change, driven by human interventions, has altered the surface cover of headwater blanket peatlands. However, the impact of such land‐cover changes on river flow is poorly understood. In particular, there is poor understanding of the impacts of different spatial configurations of bare peat or well‐vegetated, restored peat on river flow peaks in upland catchments. In this paper, a physically based, distributed and continuous catchment hydrological model was developed to explore such impacts. The original TOPMODEL, with its process representation being suitable for blanket peat catchments, was utilized as a prototype acting as the basis for the new model. The equations were downscaled from the catchment level to the cell level. The runoff produced by each cell is divided into subsurface flow and saturation‐excess overland flow before an overland flow calculation takes place. A new overland flow module with a set of detailed stochastic algorithms representing overland flow routing and re‐infiltration mechanisms was created to simulate saturation‐excess overland flow movement. The new model was tested in the Trout Beck catchment of the North Pennines of England and found to work well in this catchment. The influence of land cover on surface roughness could be explicitly represented in the model and the model was found to be sensitive to land cover. Copyright © 2014 John Wiley & Sons, Ltd.     

Effects of land‐cover changes on the hydrological response of interior Columbia River basin forested catchments

The topographically explicit distributed hydrology–soil–vegetation model (DHSVM) is used to simulate hydrological effects of changes in land cover for four catchments, ranging from 27 to 1033 km², within the Columbia River basin. Surface fluxes (stream flow and evapotranspiration) and state variables (soil moisture and snow water equivalent) corresponding to historical (1900) and current (1990) vegetation are compared. In addition a sensitivity analysis, where the catchments are covered entirely by conifers at different maturity stages, was conducted. In general, lower leaf‐area index (LAI) resulted in higher snow water equivalent, more stream flow and less evapotranspiration. Comparisons with the macroscale variable infiltration capacity (VIC) model, which parameterizes, rather than explicitly represents, topographic effects, show that runoff predicted by DHSVM is more sensitive to land‐cover changes than is runoff predicted by VIC. This is explained by model differences in soil parameters and evapotranspiration calculations, and by the more explicit representation of saturation excess in DHSVM and its higher sensitivity to LAI changes in the calculation of evapotranspiration. Copyright © 2002 John Wiley & Sons, Ltd.     

Land cover classification of remote sensing imagery based on interval-valued data fuzzy c-means algorithm

There is a certain degree of ambiguity associated with remote sensing as a means of performing earth observations.Using interval-valued data to describe clustering prototype features may be more suitable for handling the fuzzy nature of remote sensing data,which is caused by the uncertainty and heterogeneity in the surface spectral reflectance of ground objects.After constructing a multi-spectral interval-valued model of source data and defining a distance measure to achieve the maximum dissimilarity between intervals,an interval-valued fuzzy c-means(FCM)clustering algorithm that considers both the functional characteristics of fuzzy clustering algorithms and the interregional features of ground object spectral reflectance was applied in this study.Such a process can significantly improve the clustering effect;specifically,the process can reduce the synonym spectrum phenomenon and the misclassification caused by the overlap of spectral features between classes of clustering results.Clustering analysis experiments aimed at land cover classification using remote sensing imagery from the SPOT-5 satellite sensor for the Pearl River Delta region,China,and the TM sensor for Yushu,Qinghai,China,were conducted,as well as experiments involving the conventional FCM algorithm,the results of which were used for comparative analysis.Next,a supervised classification method was used to validate the clustering results.The final results indicate that the proposed interval-valued FCM clustering is more effective than the conventional FCM clustering method for land cover classification using multi-spectral remote sensing imagery.     

On the usefulness of remote sensing input data for spatially distributed hydrological modelling: case of the Tarim River basin in China

The ecological situation of the Tarim River basin in China seriously declined since the early 1950s, mainly due to a strong increase in water abstraction for irrigation purposes. To restore the ecological system and support sustainable development of the Tarim River basin region in China, more hydrological studies are demanded to properly understand the processes of the watershed and efficiently manage the water resources. Such studies are, however, complicated due to the limited data availability, especially in the mountainous headwater regions of the Tarim River basin. This study investigated the usefulness of remote sensing (RS) data to overcome that lack of data in the spatially distributed hydrological modelling of the basin. Complementary to the conventional station‐based (SB) data, the RS products that are directly used in this study include precipitation, evapotranspiration and leaf area index. They are derived from raw image data of the Chinese Fengyun meteorological satellite and from the Moderate Resolution Imaging Spectroradiometer (MODIS). The MODIS land surface temperature was used to calculate the atmospheric temperature lapse rate to describe the temperature dependency on topographical variations. Moreover, MODIS‐based snow cover images were used to obtain model initial conditions and as validation reference for the snow model component. Comparison of model results based on RS input versus conventional SB input exhibited similar results in terms of high and low river runoff extremes, cumulative runoff volumes in both runoff and snow melting seasons and spatial and temporal variability of snow cover. During summer time, when the snow cover shrinks in the permanent glacier region, it was found that the model resolution influences the model results dramatically, hence, showing the importance of detailed (RS based) spatially distributed input data. Copyright © 2011 John Wiley & Sons, Ltd.     

A revised soil erosion budget for India: role of reservoir sedimentation and land‐use protection measures

Among the different controls of erosion budget at basin level, the relative impact of dams and land management is yet to be investigated. In this paper, the impact of dams on sediment yield has been assessed by using a conceptual modelling framework which considers the gross erosion and the cascade of dams constructed on a river network. The sediment budget has been estimated based on the gross erosion, deposition of sediment in reservoirs, and sediment yields of 23 mainland river basins of India. The gross erosion of the country is estimated as 5.11 ± 0.4 Gt yr^?1 or 1559 t km^?2 yr^?1, out of which 34.1 ± 12% of the total eroded soil is deposited in the reservoirs, 22.9 ± 29% is discharged outside the country (mainly to oceans), and the remaining 43.0 ± 41% is displaced within the river basins. The river basins of northern India contribute about 81% of the total sediment yield from landmass while the share of southern river basins is 19%. The components of revised sediment budget for India are prominently influenced by the sediment trapped in reservoirs and the treatment of catchment areas by soil and water conservation measures. Analysis of sediment deposition in 4937 reservoirs indicated the average annual percentage capacity loss as 1.04% though it varies from 0.8% to >2% per year in smaller dams (1–50 Mm³ capacity) and from <0.5% to 0.8% per year in larger dams (51 to >1000 Mm³ capacity). Siltation of smaller dams poses a serious threat to their ecosystem services as they cater to a wider population for domestic, agricultural, and industrial purposes. Amongst the environment controls, land use significantly impacts the gross erosion rate and specific sediment yield as compared to climatic and topographic parameters. However, to analyse their integrated effect on the complex processes of sediment fluxes in a basin, further research efforts are needed. Copyright © 2016 John Wiley & Sons, Ltd.     

Modelling artificial channel and land‐use changes and their impact on floods and sediment yield to the Ishikari basin

The potential for flooding and sediment transport is greatly affected by river channel form and changes in land use. Therefore the modelling of channel morphology prior to canalization and of land‐use change is important with respect to the prediction of floods and sediment yield and their consequences. A combination of land‐use transformation maps and soil properties shows certain decision rules for the conversion of forest into arable or vice versa. The model proposed, from this study, was used to simulate possible past and/or future channel and land‐use patterns. Subsequently, the outcome of this simulation was used to assess the risk of flooding, sediment transport and soil‐erosion under different conditions. In this study, channel morphology prior to canalization and land‐use change in the Ishikari basin, Hokkaido, Japan, were analysed by comparing three scenarios using a physical based channel and slope model. The results indicate that pre‐canalization channel morphology has a significant impact on flood peak, but no significant effect on sediment yield. In contrast, land‐use change has a significant effect on soil eroded from hillslopes, but no significant effect on flooding for Ishikari basin. This study also illustrates the challenges that a simple model, such as a physical based channel and slope model, can simulate large‐scale river basin processes using fewer hydrological data resources. Copyright © 2004 John Wiley & Sons, Ltd.     

Estimation of spatially distributed surface energy fluxes using remotely‐sensed data for agricultural fields

Land surface energy fluxes are required in many environmental studies, including hydrology, agronomy and meteorology. Surface energy balance models simulate microscale energy exchange processes between the ground surface and the atmospheric layer near ground level. Spatial variability of energy fluxes limits point measurements to be used for larger areas. Remote sensing provides the basis for spatial mapping of energy fluxes. Remote‐sensing‐based surface energy flux‐mapping was conducted using seven Landsat images from 1997 to 2002 at four contiguous crop fields located in Polk County, northwestern Minnesota. Spatially distributed surface energy fluxes were estimated and mapped at 30 m pixel level from Landsat Thematic Mapper and Enhanced Thematic Mapper images and weather information. Net radiation was determined using the surface energy balance algorithm for land (SEBAL) procedure. Applying the two‐source energy balance (TSEB) model, the surface temperature and the latent and sensible heat fluxes were partitioned into vegetation and soil components and estimated at the pixel level. Yield data for wheat and soybean from 1997 to 2002 were mapped and compared with latent heat (evapotranspiration) for four of the fields at pixel level. The spatial distribution and the relation of latent heat flux and Bowen ratio (ratio of sensible heat to latent heat) to crop yield were studied. The root‐mean‐square error and the mean absolute percentage of error between the observed and predicted energy fluxes were between 7 and 22 W m⁻² and 12 and 24% respectively. Results show that latent heat flux and Bowen ratio were correlated (positive and negative) to the yield data. Wheat and soybean yields were predicted using latent heat flux with mean R² = 0·67 and 0·70 respectively, average residual means of −4·2 bushels/acre and 0·11 bushels/acre respectively, and average residual standard deviations of 16·2 bushels/acre and 16·6 bushels/acre respectively (1 bushel/acre ≈ 0·087 m³ ha⁻¹). The flux estimation procedure from the SEBAL‐TSEB model was useful and applicable to agricultural fields. Copyright © 2005 John Wiley & Sons, Ltd.     

Sediment concentrations in run‐off varying with spatial scale in an agricultural subwatershed of the Chinese Loess Plateau

Information is scarce on the spatial‐scale effect on sediment concentrations in run‐off. This study addressed this issue within an agricultural subwatershed of the Chinese Loess Plateau, using data observed at a hilltop plot, three nested hillslope plots, two entire‐slope plots (a combination of hillslope and valley side slope) and the subwatershed outlet. Dominated by the splash and sheet erosions, the hilltop plot has a minimum C_ae (mean sediment concentration for all recorded events) of 45 kg m^?3. Unexpectedly, the high sediment concentrations at the hilltop do not occur at high rainfall intensities or large run‐off events because of the protection of surface soils by relatively thick sheet flows. Because of the emergence of rills, C_ae is as high as 310 kg m^?3 even on the most upper hillslope. Downslope, both C_ae and ESC (extreme large values of recorded sediment concentrations) increase; such a slope length effect attenuates with increasing slope length and event magnitude as a result of insufficient sediment availability associated with rill development. Active mass wastings ensure sufficient sediment supply and thus a spatially invariant C_ae (approximately 700 kg m^?3) and ESC (approximately 1000 kg m^?3) at the scale of the entire slope and subwatershed. Detailed examination shows that most small events experience a decrease in sediment concentrations when moving from the entire slope to the subwatershed, indicating that the spatially invariant sediment concentration is valid only for large run‐off events. This study highlights the control of the spatial scale, which determines the dominant erosional process, on erosional regime. Copyright © 2015 John Wiley & Sons, Ltd.     

Modelling the impact of recent land‐cover changes on the stream flows in northeastern Puerto Rico

We investigated the influence of recent and future land‐cover changes on stream flow of a watershed northeastern Puerto Rico using hydrological modeling and simulation analysis. Monthly and average annual stream flows were compared between an agricultural period (1973–1980) and an urbanized/reforested period (1988–1995) using the revised Generalized Watershed Loading Function model. Our validated results show that a smaller proportion of rainfall became stream flows in the urbanized/forested period compared with the agricultural period, apparently because of reforestation. Sensitivity analysis of the model showed that evapotranspiration, precipitation, and curve number were the most significant factors influencing stream flow. Simulations of projected land‐cover scenarios indicate that annual stream flows would increase by 9·6% in a total urbanization scenario, decrease by 3·6% in a total reforestation scenario, and decrease by 1·1% if both reforestation and urbanization continue at their current rates to 2020. An imposed hurricane event that was similar in scale to the largest recent event on the three land‐cover scenarios would increase the daily stream flow by 62·1%, 68·4% and 67·1% respectively. Owing to the environmental setting of eastern Puerto Rico, where sea breezes caused by temperature differences between land surface and the ocean dominate the local climate, we suggest that managing local land‐cover changes can have important consequences for water management. Copyright © 2007 John Wiley & Sons, Ltd.     

Automatic mapping of snow cover depletion curves using optical remote sensing data under conditions of frequent cloud cover and temporary snow

Snow cover depletion curves are required for several water management applications of snow hydrology and are often difficult to obtain automatically using optical remote sensing data owing to both frequent cloud cover and temporary snow cover. This study develops a methodology to produce accurate snow cover depletion curves automatically using high temporal resolution optical remote sensing data (e.g. Terra Moderate Resolution Imaging Spectroradiometer (MODIS), Aqua MODIS or National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR)) by snow cover change trajectory analysis. The method consists of four major steps. The first is to reclassify both cloud‐obscured land and snow into more distinct subclasses and to determine their snow cover status (seasonal snow cover or not) based on the snow cover change trajectories over the whole snowmelt season. The second step is to derive rules based on the analysis of snow cover change trajectories. These rules are subsequently used to determine for a given date, the snow cover status of a pixel based on snow cover maps from the beginning of the snowmelt season to that given date. The third step is to apply a decision‐tree‐like processing flow based on these rules to determine the snow cover status of a pixel for a given date and to create daily seasonal snow cover maps. The final step is to produce snow cover depletion curves using these maps. A case study using this method based on Terra MODIS snow cover map products (MOD10A1) was conducted in the lower and middle reaches of the Kaidu River Watershed (19 000 km²) in the Chinese Tien Shan, Xinjiang Uygur Autonomous Region, China. High resolution remote sensing data (charge coupled device (CCD) camera data with 19·5 m resolution of the China and Brazil Environmental and Resources Satellite (CBERS) data (19·5 m resolution), and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data with 15 m resolution of the Terra) were used to validate the results. The study shows that the seasonal snow cover classification was consistent with that determined using a high spatial resolution dataset, with an accuracy of 87–91%. The snow cover depletion curves clearly reflected the impact of the variation of temperature and the appearance of temporary snow cover on seasonal snow cover. The findings from this case study suggest that the approach is successful in generating accurate snow cover depletion curves automatically under conditions of frequent cloud cover and temporary snow cover using high temporal resolution optical remote sensing data. Copyright © 2007 John Wiley & Sons, Ltd.     

Long‐term effects of management practices on water‐driven soil erosion in an intense agricultural sub‐watershed: monitoring and modelling

The overarching objective of this research was to provide an improved understanding of the role of land use and associated management practices on long‐term water‐driven soil erosion in small agricultural watersheds by coupling the established, physically based, distributed parameter Water Erosion Prediction Project (WEPP) model with long‐term hydrologic, land use and soil data. A key step towards achieving this objective was the development of a detailed methodology for model calibration using physical ranges of key governing parameters such as effective hydraulic conductivity, critical hydraulic shear stress and rill/inter‐rill erodibilities. The physical ranges for these governing parameters were obtained based on in situ observations within the South Amana Sub‐Watershed (SASW) (～26 km²) of the Clear Creek, IA watershed where detailed documentation of the different land uses was available for a period of nearly 100 years. A quasi validation of the calibrated model was conducted through long‐term field estimates of water and sediment discharge at the outlet of SASW and also by comparing the results with data reported in the literature for other Iowa watersheds exhibiting similar biogeochemical properties. Once WEPP was verified, ‘thought experiments’ were conducted to test our hypothesis that land use and associated management practices may be the major control of long‐term erosion in small agricultural watersheds such as SASW. Those experiments were performed using the dominant 2‐year crop rotations in the SASW, namely, fall till corn–no till bean (FTC‐NTB), no till bean–spring till corn (NTB‐STC) and no till corn–fall till bean (NTC‐FTB), which comprised approximately 90% of the total acreage in SASW. Results of this study showed that for all crop rotations, a strong correspondence existed between soil erosion rates and high‐magnitude precipitation events during the period of mid‐April and late July, as expected. The magnitude of this correspondence, however, was strongly affected by the crop rotation characteristics, such as canopy/residue cover provided by the crop, and the type and associated timing of tillage. Tillage type (i.e. primary and secondary tillages) affected the roughness of the soil surface and resulted in increases of the rill/inter‐rill erodibilities up to 35% and 300%, respectively. Particularly, the NTC‐FTB crop rotation, being the most intense land use in terms of tillage operations, caused the highest average annual erosion rate within the SASW, yielding quadrupled erosion rates comparatively to NTB‐STC. The impacts of tillage operation were further exacerbated by the timing of the operations in relation to precipitation events. Timing of operations affected the ‘life‐time’ of residue cover and as a result, the degree of protection that residue cover offers against the water action on the soil surface. In the case of NTC‐FTB crop rotation, dense corn residue stayed on the ground for only 40 days, whereas for the other two rotations, corn residue provided a protective layer for nearly 7 months, lessening thus the degree of soil erosion. The cumulative effects of tillage type and timing in conjunction with canopy/residue cover led to the conclusion that land management practices can significantly amplify or deamplify the impact of precipitation on long‐term soil erosion in small agricultural watersheds. Copyright © 2009 John Wiley & Sons, Ltd.     

Lichens and mosses moisture content assessment through high‐spectral resolution remote sensing technology: a case study of the Hudson Bay Lowlands,Canada

Assessing moisture contents of lichens and mosses using ground‐based high‐spectral resolution spectrometers offers immense opportunities for a comprehensive monitoring of peatland moisture status by satellite/airborne imagery. This study investigates the impact of various moisture conditions of common subarctic lichen and moss species upon the spectral signatures obtained. The lichens are Cladina stellaris and Cladina rangiferina, and the mosses are Dicranum elongatum and Tomenthypnum nitens. Reflectance and moisture content measurements of these species were made in a laboratory setting, while maintaining the natural moisture conditions of the samples; once the moisture and spectral measurements were complete, the samples were returned to the field and placed in their natural setting, continuously receiving moisture from precipitation and groundwater and losing water through evaporation and drainage. Changes in reflectance of the visible to shortwave infrared (SWIR) range (400–2500 nm) at various moisture contents were examined, as well as the potential of current spectral reflectance indices to evaluate the plants' moisture contents was examined. Results indicate that the SWIR region is useful in identifying variations in plants moisture conditions, while the unique spectral signatures of the lichens and mosses in the visible and near‐infrared range suggest that these species may be detected by satellite and airborne imagery. Of current spectral indices, the normalized difference infrared index (NDII) was most successful in identifying the above plants' moisture content (details are discussed in the paper). Future study should focus on the development of improved moisture content spectral indices, as well as upscaling reflectance data and spectral indices. Copyright © 2010 John Wiley & Sons, Ltd.     

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.     

基于多源遥感数据的巢湖水华长时序时空变化(20092018年)分析与发生概率预测

湖泊富营养化导致的水华问题严重影响了淡水资源的利用和保护,快速、全面、准确的监测水华信息对于湖泊水环境的治理具有十分重要的意义.本文以巢湖为研究区域,利用多源光学遥感影像和时空融合技术,采用波段融合的方式将NDVI指数波段加入到遥感影像当中,并通过监督分类解译水华信息,以此揭示20092018年10年间巢湖水华的时空变...     

Optical remote sensing of submerged aquatic vegetation: Opportunities for shallow clearwater streams

Remote sensing has rarely been used as a tool to map and monitor submerged aquatic vegetation (SAV) in rivers, due to a combination of insufficient spatial resolution of available image data and strong attenuation of light in water through absorption and scattering. The latter process reduces the possibility to use spectral reflectance information to accurately classify submerged species. However, increasing availability of very high resolution (VHR) image data may enable the use of shape and texture features to help discriminate between species by taking an object based image analysis (OBIA) approach, and overcome some of the present limitations.This study aimed to investigate the possibility of using optical remote sensing for the detection and mapping of SAV. It firstly looked at the possibilities to discriminate submerged macrophyte species based on spectral information only. Reflectance spectra of three macrophyte species were measured in situ across a range of submergence depths. The results showed that water depth will be a limiting factor for the classification of species from remote sensing images. Only Spiked Water Milfoil (Myriophyllum spicatum) was indicated as spectrally distinct through ANOVA analysis, but subsequent Jeffries–Matusita distance analysis did not confirm this. In particular Water Crowfoot (Ranunculus fluitans) and Pondweed (Potamogeton pectinatus) could not be discriminated at 95% significance level. Spectral separability of these two species was also not possible without the effect of an overlying water column.Secondly, the possibility to improve species discrimination, using spatial and textural information was investigated for the same SAV species. VHR image data was acquired with a Near Infrared (NIR) sensitive DSLR camera from four different heights including a telescopic pole and a Helikite UAS. The results show that shape and texture information can improve the detection of the spectrally similar Pondweed and Water Crowfoot from VHR image data. The best performing feature ‘length/width ratio of sub-objects’ was obtained through expert knowledge. All of the shape and texture based features performed better at species differentiation than the spectrally based features.In conclusion this study has shown that there is considerable potential for the combination of VHR data and OBIA to map SAV in shallow stream environments, which can benefit species monitoring and management.     

Estimation of land surface evapotranspiration over complex terrain based on multi‐spectral remote sensing data

Land surface evapotranspiration (ET) plays an important role in energy and water balances. ET can significantly affect the runoff yield of a basin and the available water resources in mountainous areas. The existing models to estimate ET are typically applicable to plains, and excessive data are required to calculate the surface fluxes accurately. This study established a simple and practical model capable of depicting the surface fluxes, while using relatively less parameters. Considering the complex terrain, solar radiation was corrected by importing a series of topographic factors. The water deficit index, a measure of land surface wetness, was calculated by applying the f_c (vegetation fractional cover)‐T_rad (land surface temperature) framework in the two‐source trapezoid model for evapotranspiration model to mountainous areas after corrections of temperature based on altitude variations. The model was successfully applied to the Kaidu River Basin, a basin with few gauges located in the east Tien Shan Mountains of China. Based on the time scale extensions, ET was analyzed at different time scales from 2000 to 2013. The results demonstrated that the corrected solar radiation and water deficit index were reasonably distributed in space and that this model is applicable to ungauged catchments, such as the Kaidu River Basin.     

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.