TEMPORAL MONITORING OF SOIL MOISTURE USING ERS-1 SAR DATA

Multi-temporal synthetic aperture radar (SAR) imagery from the European Remote Sensing Satellite (ERS-1) was evaluated for monitoring soil moisture at the Romney Marsh test site as part of the UK SAR Calibration and Crop Backscatter Experiment. A total of 18 C-band (5.3 GHz) ERS-1 SAR images were acquired during the three day orbit and co-registered. Accurate calibration of the backscatter measurements was achieved using calibration constants derived from an analysis of corner reflector target responses. Mean backscatter measurements were recorded for each field and compared with field data on soil moisture, surface roughness and rainfall patterns. A comparison of daily and hourly rainfall and soil moisture measurements with backscatter for different cover types showed that the observed trends in backscatter are dominated by moisture effects. A high positive correlation between volumetric soil moisture in the range 10–40% was observed for bare soil fields. A much weaker positive relationship between soil moisture and backscatter was observed for grassland fields.     

How well do indicator variograms capture the spatial connectivity of soil moisture?

Indicators are binary transforms of a variable and are 1 or 0, depending on whether the variable is above or below a threshold. Indicator variograms can be used for a similar range of geostatistical estimation techniques as standard variograms. However, they are more flexible as they allow different ranges for small and large values of a hydrological variable. Indicator geostatistics are also sometimes used to represent the connectivity of high values in spatial fields. Examples of connectivity are connected high values of hydraulic conductivity in aquifers, leading to preferential flow, and connected band-shaped saturation zones in catchments. However, to the authors' knowledge the ability of the indicator approach to capture connectivity has never been shown conclusively. Here we analyse indicator variograms of soil moisture in a small south-east Australian catchment and examine how well they can represent connectivity. The indicator variograms are derived from 13 soil moisture patterns, each consisting of 500–2000 point TDR (time domain reflectometry) measurements. Winter patterns are topographically organized with long, thin, highly connected lines of high soil moisture in the drainage lines. In summer the patterns are more random and there is no connectivity of high soil moisture values. The ranges of the 50^th and 90^th percentile indicator semivariograms are approximately 110 and 75 m, respectively, during winter, and 100 and 50 m, respectively, during summer. These ranges indicate that, compared with standard semivariograms, the indicator semivariograms provide additional information about the spatial pattern. However, since the ranges are similar in winter and in summer, the indicator semivariograms were not able to distinguish between connected and unconnected patterns. It is suggested that new statistical measures are needed for capturing connectivity explicitly. © 1998 John Wiley & Sons, Ltd.     

Spatial Variability of Surface Soil Moisture in a Depression Area of Karst Region

An understanding of soil moisture variability is necessary to characterize the linkages between a region's hydrology, ecology, and physiography. In subtropical karst region, the spatial variability of surface soil moisture is still unclear for the rocky ecological environment and intensive land uses. The purpose of this study was to characterize the variation and patterns of soil moisture content at depth of 0–16 cm and to investigate their influencing factors in a karst depression area of southwest China. Soil moisture content was measured at 20 m intervals by intensive sampling on March 11 (dry season) and August 30 (rainy season) in 2005, respectively. Surface soil moisture presented a moderate variability in the depression area at the sampling times. The variability was relatively higher in dry season with lower mean soil moisture, but lower in rainy season with higher mean soil moisture after heavy rain event. Similar results were also obtained from the mosaic patterns of soil moisture generated by ordinary Kriging interpolation with low standard deviations. This suggested that more soil samples might be required and the sampling interval should be shortened in dry season compared with rainy season. The dominant influencing factors on the variability of surface soil moisture were rainfall and land use types. However, altitude, bare‐rock ratio, and soil organic carbon were also important factors, and exerted jointly to control and redistribute the surface soil moisture either in dry or rainy season in the depression area. Such information provided some insights for the study on eco‐hydrological processes of vegetation restoration in the karst degraded ecosystem of southwest China.     

A mathematical model of soil moisture spatial distribution on the hill slopes of the Loess Plateau

Based on important factors that affect soil moisture spatial distribution, such as the slope gradients, land use, vegetation cover, and surface water diffusion characteristics together with field measurements of soil moisture data obtained from the surface soil under different land use structures, a soil moisture spatial distribution model was established. The diffusion degree coefficient of surface water for different vegetations was estimated from soil moisture values obtained from field measurements. The model can be solved using the finite unit method. The soil moisture spatial distribution on the hill slopes in the Loess Plateau were simulated by the model. A comparison of the simulated values with measurement data shows that the model is a good fit.     

Modelling the water balance of a mesoscale catchment basin using remotely sensed land cover data

Hydrological modelling of mesoscale catchments is often adversely affected by a lack of adequate information about specific site conditions. In particular, digital land cover data are available from data sets which were acquired on a European or a national scale. These data sets do not only exhibit a restricted spatial resolution but also a differentiation of crops and impervious areas which is not appropriate to the needs of mesoscale hydrological models. In this paper, the impact of remote sensing data on the reliability of a water balance model is investigated and compared to model results determined on the basis of CORINE (Coordination of Information on the Environment) Land Cover as a reference. The aim is to quantify the improved model performance achieved by an enhanced land cover representation and corresponding model modifications. Making use of medium resolution satellite imagery from SPOT, LANDSAT ETM+ and ASTER, detailed information on land cover, especially agricultural crops and impervious surfaces, was extracted over a 5-year period (2000–2004). Crop-specific evapotranspiration coefficients were derived by using remote sensing data to replace grass reference evapotranspiration necessitated by the use of CORINE land cover for rural areas. For regions classified as settlement or industrial areas, degrees of imperviousness were derived. The data were incorporated into the hydrological model GROWA (large-scale water balance model), which uses an empirical approach combining distributed meteorological data with distributed site parameters to calculate the annual runoff components. Using satellite imagery in combination with runoff data from gauging stations for the years 2000–2004, the actual evapotranspiration calculation in GROWA was methodologically extended by including empirical crop coefficients for actual evapotranspiration calculations. While GROWA originally treated agricultural areas as homogeneous, now a consideration and differentiation of the main crops is possible. The accuracy was determined by runoff measurements from gauging stations. Differences in water balances resulting from the use of remote sensing data as opposed to CORINE were analysed in this study using a representative subcatchment. Resulting Nash–Sutcliff model efficiencies improved from 0.372 to 0.775 and indicate that the enhanced model can produce thematically more accurate and spatially more detailed local water balances. However, the proposed model enhancements by satellite imagery have not exhausted the full potential of water balance modelling, for which a higher temporal resolution is required.     

Spatio-temporal evolution and time-stable characteristics of soil moisture within remote sensing footprints with varying soil, slope, and vegetation

Air-borne passive microwave remote sensors measure soil moisture at the footprint scale, a scale of several hundred square meters or kilometers that encompasses different characteristic combinations of soil, topography, vegetation, and climate. Studies of within-footprint variability of soil moisture are needed to determine the factors governing hydrologic processes and their relative importance, as well as to test the efficacy of remote sensors. Gridded ground-based impedance probe water content data and aircraft-mounted Electronically Scanned Thinned Array Radiometer (ESTAR) pixel-average soil moisture data were used to investigate the spatio-temporal evolution and time-stable characteristics of soil moisture in three selected (LW03, LW13, LW21) footprints from the Southern Great Plains 1997 (SGP97) Hydrology Experiment. Better time-stable features were observed within a footprint containing sandy loam soil than within two pixels containing silty loam soil. Additionally, flat topography with split wheat/grass land cover produced the largest spatio-temporal variability and the least time stability in soil moisture patterns. A comparison of ground-based and remote sensing data showed that ESTAR footprint-average soil moisture was well calibrated for the LW03 pixel with sandy loam soil, rolling topography, and pasture land cover, but improved calibration is warranted for the LW13 (silty loam soil, rolling topography, pasture land) and LW21 (silty loam soil, flat topography, split vegetation of wheat and grass land with tillage practice) pixels. Footprint-scale variability and associated nonlinear soil moisture dynamics may prove to be critical in the regional-scale hydroclimatic models.     

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.     

Using a global reference sample set and a cropland map for area estimation in China

A technically transparent and freely available reference sample set for validation of global land cover mapping was recently established to assess the accuracies of land cover maps with multiple resolutions. This sample set can be used to estimate areas because of its equal-area hexagon-based sampling design. The capabilities of these sample set-based area estimates for cropland were investigated in this paper. A 30-m cropland map for China was consolidated using three thematic maps (cropland, forest and wetland maps) to reduce confusion between cropland and forest/wetland. We compared three area estimation methods using the sample set and the 30 m cropland map. The methods investigated were: (1) pixel counting from a complete coverage map, (2) direct estimation from reference samples, and (3) model-assisted estimation combining the map with samples. Our results indicated that all three methods produced generally consistent estimates which agreed with cropland area measured from an independent national land use dataset. Areas estimated from the reference sample set were less biased by comparing with a National Land Use Dataset of China (NLUD-C). This study indicates that the reference sample set can be used as an alternative source to estimate areas over large regions.     

A cellular model of Holocene upland river basin and alluvial fan evolution

The CAESAR (Cellular Automaton Evolutionary Slope And River) model is used to simulate the Holocene development of a small upland catchment (4·2 km²) and the alluvial fan at its base. The model operates at a 3 m grid scale and simulates every flood over the last 9200 years, using a rainfall record reconstructed from peat bog wetness indices and land cover history derived from palynological sources. Model results show that the simulated catchment sediment discharge above the alluvial fan closely follows the climate signal, but with an increase in the amplitude of response after deforestation. The important effects of sediment storage and remobilization are shown, and findings suggest that soil creep rates may be an important control on long term (>1000 years) temperate catchment sediment yield. The simulated alluvial fan shows a complex and episodic behaviour, with frequent avulsions across the fan surface. However, there appears to be no clear link between fan response and climate or land use changes suggesting that Holocene alluvial fan dynamics may be the result of phases of sediment storage and remobilization, or instabilities and thresholds within the fan itself. Copyright © 2002 John Wiley & Sons, Ltd.     

Which catchment characteristics control the temporal dependence structure of daily river flows?

Hydrological classification systems seek to provide information about the dominant processes in the catchment to enable information to be transferred between catchments. Currently, there is no widely agreed‐upon system for classifying river catchments. This paper develops a novel approach to classifying catchments based on the temporal dependence structure of daily mean river flow time series, applied to 116 near‐natural ‘benchmark’ catchments in the UK. The classification system is validated using 49 independent catchments. Temporal dependence in river flow data is driven by the flow pathways, connectivity and storage within the catchment and can thus be used to assess the influence catchment characteristics have on moderating the precipitation‐to‐flow relationship. Semi‐variograms were computed for the 116 benchmark catchments to provide a robust and efficient way of characterising temporal dependence. Cluster analysis was performed on the semi‐variograms, resulting in four distinct clusters. The influence of a wide range of catchment characteristics on the semi‐variogram shape was investigated, including: elevation, land cover, physiographic characteristics, soil type and geology. Geology, depth to gleyed layer in soils, slope of the catchment and the percentage of arable land were significantly different between the clusters. These characteristics drive the temporal dependence structure by influencing the rate at which water moves through the catchment and/or the storage in the catchment. Quadratic discriminant analysis was used to show that a model with five catchment characteristics is able to predict the temporal dependence structure for un‐gauged catchments. This method could form the basis for future regionalisation strategies, as a way of transferring information on the precipitation‐to‐flow relationship between gauged and un‐gauged catchments. © 2014 The Authors. Hydrological Processes by published by John Wiley & Sons, Ltd.     

Time stability and variability of Electronically Scanned Thinned Array Radiometer soil moisture during Southern Great Plains hydrology experiments

Variability and time‐stability analysis for field‐scale (800 m) Electronically Scanned Thinned Array Radiometer soil moisture within a satellite scale footprint (∼ 50 km) were quantified using observations from the Southern Great Plains Hydrology Experiment 1997 and 1999 (SGP97 and SGP99). The pixels' time‐stability properties were examined with respect to soil, vegetation and topographic parameters in order to determine which physical parameters can be used to identify good candidate observation locations for validating soil moisture from satellite observations and global‐scale model output. The results show that the time‐stability concept remains valid at the satellite scale. The root mean square error values were 1·47, 1·51, 1·93 and 2·32% for the 1st, 2nd, 50th and 100th most stable fields, respectively. The most stable locations had sand and clay percentages consistent with sandy loam soils and moderate to high normalized difference vegetation index values. Neither land cover nor topography properties could be used to identify potentially stable fields in the study region. 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.     

Evapotranspiration models of different complexity for multiple land cover types

A comparison between half‐hourly and daily measured and computed evapotranspiration (ET) using three models of different complexity, namely, the Priestley–Taylor (P‐T), the reference Penman–Monteith (P‐M) and the Common Land Model (CLM), was conducted using three AmeriFlux sites under different land cover and climate conditions (i.e. arid grassland, temperate forest and subhumid cropland). Using the reference P‐M model with a semiempirical soil moisture function to adjust for water‐limiting conditions yielded ET estimates in reasonable agreement with the observations [root mean square error (RMSE) of 64–87 W m^?2 for half‐hourly and RMSE of 0.5–1.9 mm day^?1 for daily] and similar to the complex Common Land Model (RMSE of 60–94 W m^?2 for half‐hourly and RMSE of 0.4–2.1 mm day^?1 for daily) at the grassland and cropland sites. However, the semiempirical soil moisture function was not applicable particularly for the P‐T model at the forest site, suggesting that adjustments to key model variables may be required when applied to diverse land covers. On the other hand, under certain land cover/environmental conditions, the use of microwave‐derived soil moisture information was found to be a reliable metric of regional moisture conditions to adjust simple ET models for water‐limited cases. Further studies are needed to evaluate the utility of the simplified methods for different landscapes. Copyright © 2011 John Wiley & Sons, Ltd.     

Effect of land cover and hydro‐meteorological controls on soil water DOC concentrations in a high‐elevation tropical environment

Páramo soils store high amounts of organic carbon. However, the effects of climate change and changes in land cover and use (LC/LU) in this high‐elevation tropical ecosystem may cause a decrease in their carbon storage capacity. Therefore, better understanding of the factors influencing the Páramo soils' carbon storage and export is urgently needed. To fill this knowledge gap, we investigated the differences in dissolved organic carbon (DOC) content in the soil water of four LC/LU types (tussock grass, natural forest, pine plantations, and pasture) and the factors controlling its variability in the Quinuas Ecohydrological Observatory in south Ecuador. Weekly measurements of soil water DOC concentrations, meteorological variables, soil water content, and temperature from various depths and slope positions were monitored within the soils' organic and mineral horizons between October 2014 and January 2017. These data were used to generate regression trees and random forest statistical models to identify the factors controlling soil water DOC concentrations. From high to low concentrations, natural forest depict the highest DOC concentrations followed by pasture, tussock grass, and pine forest. For all LC/LU types, DOC concentrations increase with decreasing soil moisture. Our results also show that LC/LU is the most important predictor of soil water DOC concentrations, followed by sampling depth and soil moisture. Interestingly, atmospheric variables and antecedent evapotranspiration and precipitation conditions show only little influence on DOC concentrations during the monitoring period. Our findings provide unique information that can help improve the management of soil and water resources in the Páramo and other peat dominated ecosystems elsewhere.     

Declustering experimental variograms by global estimation with fourth order moments

The variogram is a key parameter for geostatistical estimation and simulation. Preferential sampling may bias the spatial structure and often leads to noisy and unreliable variograms. A novel technique is proposed to weight variogram pairs in order to compensate for preferential or clustered sampling . Weighting the variogram pairs by global kriging of the quadratic differences between the tail and head values gives each pair the appropriate weight, removes noise and minimizes artifacts in the experimental variogram. Moreover, variogram uncertainty could be computed by this technique. The required covariance between the pairs going into variogram calculation, is a fourth order covariance that must be calculated by second order moments. This introduces some circularity in the calculation whereby an initial variogram must be assumed before calculating how the pairs should be weighted for the experimental variogram. The methodology is assessed by synthetic and realistic examples. For synthetic example, a comparison between the traditional and declustered variograms shows that the declustered variograms are better estimates of the true underlying variograms. The realistic example also shows that the declustered sample variogram is closer to the true variogram.     

Method development for estimating soil organic carbon content in an alpine region using soil moisture data

The high soil organic carbon(SOC) content in alpine meadow can significantly change soil hydrothermal properties and further affect the soil temperature and moisture as well as the surface water and energy budget. Therefore, this study first introduces a parameterization scheme to describe the effect of SOC content on soil hydraulic and thermal parameters in a land surface model(LSM), and then the SOC content is estimated by minimizing the difference between observed and simulated surface-layer soil moisture. The accuracy of the estimated SOC content was evaluated using in situ observation data at a soil moisture and temperature-measuring network in Naqu, central Tibetan Plateau. Sensitivity experiments show that the optimum time window for stabilizing the estimation results cannot be shorter than three years. In the experimental area, the estimated SOC content can generally reflect the spatial distribution of the measurements, with a root mean square error of 0.099 m^3 m^-3, a mean bias of 0.043 m^3 m^-3, and a correlation coefficient of 0.695. The estimated SOC content is not sensitive to the temporal frequency of the soil moisture data input. Even if the temporal frequency is as low as that of current soil moisture products derived from passive microwave satellites, the estimation result is still stable. Therefore, by combining a high-quality satellite soil moisture product and a parameter optimization method, it is possible to obtain grid-scale effective parameter values, such as SOC content,for an LSM and improve the simulation ability of the LSM.     

Sensitivity of streamflow simulation in the Delaware River Basin to forecasted land‐cover change for 2030 and 2060

In order to simulate the potential effect of forecasted land‐cover change on streamflow and water availability, there has to be confidence that the hydrologic model used is sensitive to small changes in land cover (<10%) and that this land‐cover change exceeds the inherent uncertainty in forecasted conditions. To investigate this, a 26‐year streamflow record was simulated for 33 basins (54–928 km²) in the Delaware River Basin using three dates of land cover: the 2011 National Land‐Cover Dataset (Homer, Fry, & Barnes, 2012 ), 2030 land‐cover conditions representing median values from 101 equally‐likely forecasts, and 2060 land‐cover conditions corresponding to the same iterations used to represent 2030. Streamflow was simulated using a process‐based hydrologic model that includes both pervious and impervious methods as parameterized by three land‐cover‐based hydrologic response units (HRUs)—forested, agricultural, and developed land. Small, but significant differences in streamflow magnitude, variability, and seasonality were seen among the three time periods—2011, 2030, and 2060. Temporal differences were discernible from the range of conditions simulated with 101 equally likely forecasts for 2030. Development was co‐located with the most frequent landscape components, as characterized by topographic wetness index, resulting in a change in hydrology for each HRU, highlighting that knowing the location of disturbance is key to understanding potential streamflow changes. These results show that streamflow simulation using regional calibration that incorporates land‐cover‐based HRUs can be sensitive to relatively small changes in land‐cover and that temporal trends resulting from land‐cover change can be isolated in order to evaluate other changes that might affect water resources.     

Soil erosion at three experimental sites in the ­Mediterranean

The results of erosion studies carried out at three representative sites in the European Mediterranean basin are discussed. The objectives of the study are to clarify the underlying processes affecting soil erosion and to quantify erosion and runoff in the framework of mitigation of land degradation. The study was carried out at three instrumented field stations using similar layouts and experimental set-ups and harmonized field procedures. Runoff and sediment yield from bounded plots were measured for different types of land use for longer periods. The runoff and sediment values were found to be relatively low, and showed average annual values between 2·0 and 8·9 1 m⁻² for runoff, and between 20·2 and 28·1 g m⁻² for sediment yield. The results show that the individual plot response on an event basis shows no relationship between runoff and sediment yield for two of the three sites. On an annual average basis a significant relationship is found between the runoff and sediment yield. Significant differences were observed between different types of land use, especially between semi-natural vegetation, burned and abandoned field cover types on the one hand, and agricultural fields on the other hand. The runoff and erosion values were lowest for the semi-natural fields. It was found that in non-cultivated fields the bounded plots might suffer from depletion of available sediment. It can be concluded that erosion figures are very low for the sites studied, and that the maintenance of semi-natural vegetation may help in the prevention of runoff generation and erosion. It can be concluded that the use of bounded plots may not be as ideal as might be expected from its wide application. In some cases open plots, especially under semi-natural land use, may produce much better results, especially when measuring over longer periods. Copyright © 1999 John Wiley & Sons, Ltd.     

Data assimilation methods in the Earth sciences

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