Identifying baseflow source areas using remotely sensed and ground-based hydrologic data

Understanding how rainfall and snowmelt influence baseflow, the groundwater-fed component of streamflow, is essential for sound water resources management. Current approaches to understand the spatial couplings between these processes and baseflow are limited. The most commonly used methods include geochemical tracers and hydrologic models. A key limitation of the first is cost, while the second is limited by the need for simplifying assumptions. This study developed a data-driven approach which leverages satellite Earth observation data and ground-based data to assess the degree to which baseflow is influenced by upstream rainfall and snowmelt in California's Sierra Nevada. The procedure involved: (1) separation of baseflow from streamflow time series using a low-pass filtering technique, (2) quantification of aquifer drainage timescales through baseflow recession analysis, (3) application of time series and information theory methods to identify the areas which have the greatest impacts on baseflow through both rainfall and snowmelt, and (4) characterization of the elevation zones which have a prevailing influence on baseflow. Results suggest that areas which have the strongest impact on baseflow through rainfall and snowmelt are not necessarily the areas which experience the highest annual rates of snowmelt or rainfall; snowmelt occurring in the 3000–3700 m elevation range was found to be the most important driver of baseflow.     

Modelling rating curves using remotely sensed LiDAR data

Accurate stream discharge measurements are important for many hydrological studies. In remote locations, however, it is often difficult to obtain stream flow information because of the difficulty in making the discharge measurements necessary to define stage‐discharge relationships (rating curves). This study investigates the feasibility of defining rating curves by using a fluid mechanics‐based model constrained with topographic data from an airborne LiDAR scanning. The study was carried out for an 8m‐wide channel in the boreal landscape of northern Sweden. LiDAR data were used to define channel geometry above a low flow water surface along the 90‐m surveyed reach. The channel topography below the water surface was estimated using the simple assumption of a flat streambed. The roughness for the modelled reach was back calculated from a single measurment of discharge. The topographic and roughness information was then used to model a rating curve. To isolate the potential influence of the flat bed assumption, a ‘hybrid model’ rating curve was developed on the basis of data combined from the LiDAR scan and a detailed ground survey. Whereas this hybrid model rating curve was in agreement with the direct measurements of discharge, the LiDAR model rating curve was equally in agreement with the medium and high flow measurements based on confidence intervals calculated from the direct measurements. The discrepancy between the LiDAR model rating curve and the low flow measurements was likely due to reduced roughness associated with unresolved submerged bed topography. Scanning during periods of low flow can help minimize this deficiency. These results suggest that combined ground surveys and LiDAR scans or multifrequency LiDAR scans that see ‘below’ the water surface (bathymetric LiDAR) could be useful in generating data needed to run such a fluid mechanics‐based model. This opens a realm of possibility to remotely sense and monitor stream flows in channels in remote locations. Copyright © 2012 John Wiley & Sons, Ltd.     

近40年台特玛-康拉克湖泊群水域变化遥感监测

车尔臣河下游自1989年改道以来,河道北边形成若干小湖,使台特玛湖-康拉克地区的水域格局发生了很大变化.干涸30多年的台特玛湖,随着自2000年起塔里木河下游应急生态输水工程的实施开始形成大片水域,且水域面积呈扩大趋势.2002年车尔臣河改道结束后康拉克地区的湖泊格局基本形成,而台特玛湖地区的水域则继续大幅变化.本文在1972-2012年102期遥感影像及其相关辅助数据基础上进行各项定量分析,详细描述台特玛湖-康拉克地区的水域变化过程,总结变化趋势,试图找出变化主导因素.笔者认为台特玛湖-康拉克地区的湖泊水域景观格局变化自1970s-2000年主要受车尔臣河径流量年际变化的控制,而21世纪以来则主要受塔里木河下游应急生态输水工程措施的影响.     

Improvement of dark object method in atmospheric correction of hyperspectral remotely sensed data

The existing methods in atmospheric correction of hyperspectral data usually focus on removing the effects of water vapor and other absorptive gases, while this paper mainly studies the method of re- moving the influence of the aerosol and the water vapor simultaneously. Because the hyperspectral data has a larger number of bands, the conventional dark object method cannot be applied to the at- mospheric correction of the hyperspectral data which can be improved, as described in this paper, by adequately making use of spectral characteristics of the hyperspectral data with an iterative correction during the whole process. The effects of the aerosol and water vapor are eliminated at the same time finally. The improved dark object method is used to do the atomospheric correction of the Hyperion data in Yanzhou, Shandong Province as an example. And the result indicates that it can correct the atmospheric influence of the hyperspectral data quickly and remarkably.     

A ground validation problem of remotely sensed soil moisture data

 As the use of space-based sensors to observe soil moisture is becoming more plausible, it is becoming necessary to validate the remotely sensed soil moisture retrieval algorithms. In this paper, measurements of point gauges on the ground are analyzed as a possible ground-truth source for the comparison with remotely sensed data. The design compares a sequence of measurements taken on the ground and from space. The authors review the mean square error of expected differences between the two systems by Ha and North (1994), which is applied to the Little Washita watershed using the soil moisture dynamics model developed by Entekhabi and Rodriguez-Iturbe (1994). The model parameters estimated by Yoo and Shin (1998) for the Washita `92 (relative) soil moisture data are used in this study. By considering about 20 pairs of ground- and space-based measure-ments (especially, for the same case as the Washita `92 that the space-based sensor visits the FOV once a day), the expected error was able to be reduced to approximately 10 of the standard deviation of the fluctuations of the system alone. This seems to be an acceptable level of tolerance for identifying biases in the retrieval algorithms.     

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

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

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.     

Investigation of meandering in Filyos River by means of satellite sensor data

The most significant morphological property of a river is the meandering process, which is dominated and governed by hydraulic, hydrologic and topographic characteristics of the river and its drainage area. It is possible to obtain reliable data on river morphology in the long term by using remotely sensed data. In this study the Filyos River, located at the Western Black Sea region of Turkey, has been selected as the study area to show the capabilities of remote sensing technology and to define the appropriate techniques for achieving the most reliable information on the river morphology by monitoring the meandering processes. The findings of the study indicate that remotely sensed data can be used successfully in defining some basic characteristics of the meandering process on rivers. Copyright © 2004 John Wiley & Sons, Ltd.     

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.     

Patterns of remotely sensed floodplain saturation and its use in runoff predictions

Principal components analysis (PCA) is applied to a time series of European Remote Sensing (ERS) synthetic aperture radar (SAR) scenes of the Alzette River floodplain (Grand‐Duchy of Luxembourg). These images cover markedly different hydrological conditions during several winter seasons in order to enable the examination of the decrease of the radar backscattering signal during drying‐up phases following important flood events. At the floodplain scale, with homogeneous land use and constant topography, the first principal components (PCs) are mainly dominated by the variance related to the changing areas. The PCs are thus mainly controlled by subsurface and surface water dynamics. The field observations of a densely equipped piezometric network in the floodplain are used to calculate a mean soil saturation index (SSI) continuously. A classification scheme, based on the PCs and k‐means algorithm, leads to the segmentation of the floodplain into several hydrological behaviour classes with distinctive responses versus changing moisture conditions. To validate this classification method with ground‐based estimations, the relation between the mean backscattering values of microplots within each PCA‐derived hydrological class and the water table measurements, expressed by means of the SSI, is evaluated. Results show that each class of microplots is characterized by the slope of the ‘backscattering–SSI’ function and by the SSI threshold value at which groundwater resurgence appears. The water ponding implies very low signal return due to the specular backscattering effect on the water surface. Based on established relationships between measured initial water table depths, runoff coefficients and rainfall‐induced water table rises, these results are used to discuss the potential of SAR‐derived information in flood management applications. Copyright © 2005 John Wiley & Sons, Ltd.     

The use of remotely sensed land cover to derive floodplain friction coefficients for flood inundation modelling

Remotely sensed land cover was used to generate spatially‐distributed friction coefficients for use in a two‐dimensional model of flood inundation. Such models are at the forefront of research into the prediction of river flooding. Standard practice, however, is to use single (static) friction coefficients on both the channel and floodplain, which are varied in a calibration procedure to provide a “best fit” to a known inundation extent. Spatially‐distributed friction provides a physically grounded estimate of friction that does not require fitting to a known inundation extent, but which can be fitted if desired. Remote sensing offers the opportunity to map these friction coefficients relatively straightforwardly and for low cost. Inundation was predicted using the LISFLOOD‐FP model for a reach on the River Nene, UK. Friction coefficients were produced from land cover predicted from Landsat TM imagery using both ML and fuzzy c‐means classifiction. The elevetion data used were from combined contour and differential global positioning system (GPS) elevation data. Predicted inundation using spatially‐distributed and static friction were compared. Spatially‐distributed friction had the greatest effect on the timing of flood inundation, but a small effect on predicted inundation extent. The results indicate that spatially‐distributed friction should be considered where the timing of initial flooding (e.g. for early warning) is important. Copyright © 2007 John Wiley & Sons, Ltd.     

Temporal analysis of Soil and Water Assessment Tool (SWAT) performance based on remotely sensed precipitation products

No study has systematically evaluated streamflow modelling between monthly and daily time scales. This study examines streamflow from seven watersheds across the USA where five different precipitation products were used as primary input into the Soil and Water Assessment Tool (SWAT) to generate simulated streamflow. Time scales examined include monthly, dekad (10 days), pentad (5 days), triad (3 days), and daily. The seven basins studied are the San Pedro (Arizona), Cimarron (north‐central Oklahoma), mid‐Nueces (south Texas), mid‐Rio Grande (south Texas and northern Mexico), Yocano (northern Mississippi), Alapaha (south Georgia), and mid‐St. Francis (eastern Arkansas). The precipitation products used to drive simulations include rain gauge, NWS Multisensor Precipitation Estimator, Tropical Rainfall Measurement Mission (TRMM), Multi‐Satellite Precipitation Analysis, TRMM 3B42‐V6, and Climate Prediction Center Morphing Method (CMORPH). Understanding how streamflow varies at sub‐monthly time scales is important because there are a host of hydrological applications such a flood forecast guidance and reservoir inflow forecasts that reside in a temporal domain between monthly and daily time scales. The major finding of this study is the quantification of a strong positive correlation between performance metrics and time step at which model performance deteriorates. Better performing simulations, with higher Nash–Sutcliffe values of 0.80 and above can support modeling at finer time scales to at least daily and perhaps beyond into the sub‐daily realm. These findings are significant in that they clearly document the ability of SWAT to support modeling at sub‐monthly time steps, which is beyond the capability for which SWAT was initially designed. Copyright © 2012 John Wiley & Sons, Ltd.     

Modelling for registration of remotely sensed imagery when reference control points contain error

1 Introduction The process of remotely sensed data acquisition isaffected by factors such as the rotation of the earth, finite scan rate of some sensors, curvature of the earth, non-ideal sensor, variation in platform altitude, attitude, velocity, etc.[1]. One important procedurewhich should be done prior to analyzing remotely sensed data, is geometric correction (image to map) or registration (image to image) of remotely sensed data. The purpose of geometric correction or registration is to e…     

The 1997 eruption of Okmok Volcano, Alaska: a synthesis of remotely sensed imagery

Okmok Volcano, in the eastern Aleutian Islands, erupted in February and March of 1997 producing a 6-km-long lava flow and low-level ash plumes. This caldera is one of the most active in the Aleutian Arc, and is now the focus of international multidisciplinary studies. A synthesis of remotely sensed data (AirSAR, derived DEMs, Landsat MSS and ETM+ data, AVHRR, ERS, JERS, Radarsat) has given a sequence of events for the virtually unobserved 1997 eruption. Elevation data from the AirSAR sensor acquired in October 2000 over Okmok were used to create a 5-m resolution DEM mosaic of Okmok Volcano. AVHRR nighttime imagery has been analyzed between February 13 and April 11, 1997. Landsat imagery and SAR data recorded prior to and after the eruption allowed us to accurately determine the extent of the new flow. The flow was first observed on February 13 without precursory thermal anomalies. At this time, the flow was a large single lobe flowing north. According to AVHRR Band 3 and 4 radiance data and ground observations, the first lobe continued growing until mid to late March, while a second, smaller lobe began to form sometime between March 11 and 12. This is based on a jump in the thermal and volumetric flux determined from the imagery, and the physical size of the thermal anomalies. Total radiance values waned after March 26, indicating lava effusion had ended and a cooling crust was growing. The total area (8.9 km²), thickness (up to 50 m) and volume (1.54×10⁸ m³) of the new lava flow were determined by combining observations from SAR, Landsat ETM+, and AirSAR DEM data. While the first lobe of the flow ponded in a pre-eruption depression, our data suggest the second lobe was volume-limited. Remote sensing has become an integral part of the Alaska Volcano Observatory’s monitoring and hazard mitigation efforts. Studies like this allow access to remote volcanoes, and provide methods to monitor potentially dangerous ones.     

Perspectives in using a remotely sensed dryness index in distributed hydrological models at the river‐basin scale

In a previous study a spatially distributed hydrological model, based on the MIKE SHE code, was constructed and validated for the 375 000 km² Senegal River basin in West Africa. The model was constructed using spatial data on topography, soil types and vegetation characteristics together with time‐series of precipitation from 112 stations in the basin. The model was calibrated and validated based on river discharge data from nine stations in the basin for 11 years. Calibration and validation results suggested that the spatial resolution of the input data in parts of the area was not sufficient for a satisfactory evaluation of the modelling performance. The study further examined the spatial patterns in the model input and output, and it was found that particularly the spatial resolution of the precipitation input had a major impact on the model response. In an attempt to improve the model performance, this study examines a remotely sensed dryness index for its relationship to simulated soil moisture and evaporation for six days in the wet season 1990. The index is derived from observations of surface temperature and vegetation index as measured by the NOAA Advanced Very High Resolution Radiometer (AVHRR) sensor. The correlation results between the index and the simulation results are of mixed quality. A sensitivity analysis, conducted on both estimates, reveals significant uncertainties in both. The study suggests that the remotely sensed dryness index with its current use of NOAA AVHRR data does not offer information that leads to a better calibration or validation of the simulation model in a spatial sense. The method potentially may become more suitable with the use of the upcoming high‐resolution temporal Meteosat Second Generation data. Copyright © 2002 John Wiley & Sons, Ltd.     

Investigation of the temporal relation of remotely sensed coastal water quality with GIS modelled upstream soil erosion

Hydrological processes at the river basin influence the quality of downstream water bodies by controlling the loads of nutrients and suspended solids. Although their monitoring is important for social, economic and environmental reasons, in‐situ measurements are too expensive and thus too sparse to describe their relations. The aim of this study is to investigate the temporal relations of soil erosion in the upstream part of river basins with water quality characteristics in the downstream coastal zone, using satellite remote sensing and GIS modelling. Data from satellite missions of MODIS, SRTM and TRMM were used to describe the soil erosion factors of the Universal Soil Loss Equation in three river basins, and MERIS satellite data was used to estimate chlorophyll‐a and total suspended matter concentrations in the coastal zone of northwest Aegean Sea in Greece, where the rivers discharge. The resulting time series showed an average correlation of upstream rainfall with downstream water quality, which increased when soil erosion was introduced. Higher correlations were observed with the use of a time lag, revealing a variable delay between the three test sites. Lower correlation coefficients were observed for chlorophyll‐a, due to the sensitivity of algae to environmental conditions. The use of free of charge satellite data and easy to operate GIS models renders the findings of this work useful for coastal zone management bodies, in order to help increase aquaculture productivity, predict algal blooms and predict siltation of ports. Copyright © 2014 John Wiley & Sons, Ltd.     

Impact of flooded rice paddy on remotely sensed evapotranspiration in the Krishna River basin,India

Evapotranspiration (ET) is one of the major water exchange processes between the earth's surface and the atmosphere. ET is a combined process of evaporation from open water bodies, bare soil and plant surfaces, and transpiration from vegetation. Remote sensing-based ET models have been developed to estimate spatially distributed ET over large regions, however, many of them reportedly underestimate ET over semi-arid regions (Jamshidi et al., Journal of Hydrometeorology, 2019, 20, 947–964). In this work, we show that underestimation of ET can occur due to the open water evaporation from flooded rice paddies ignored in the existing ET models. To address the gap in ET estimation, we have developed a novel approach that accounts for the missing ET component over flooded rice paddies. Our method improved ET estimates by a modified Penman-Monteith algorithm that considered the fraction of open water evaporation from flooded rice paddies. Daily ET was calculated using ground based meteorological data and the MODIS satellite data over the Krishna River Basin. Seasonal and annual ET values over the Krishna Basin were compared with two different ET algorithms. ET estimates from these two models were also compared for different crop combinations. Results were validated with flux tower-based measurements from other studies. We have identified a 17 mm/year difference in average annual ET over the Krishna River Basin with this new ET algorithm. This is very critical in basin scale water balance analysis and water productivity studies.     

Reliability of reanalysis and remotely sensed precipitation products for hydrological simulation over the Sefidrood River Basin,Iran

ABSTRACT

Hydrological models require different inputs for the simulation of processes, among which precipitation is essential. For hydrological simulation, four different precipitation products – Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE); European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-Interim); Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) real time (RT); and Precipitation Estimation from Remotely Sensed Information using Arti?cial Neural Networks (PERSIANN) – are compared against ground-based datasets. The variable infiltration capacity (VIC) model was calibrated for the Sefidrood River Basin (SRB), Iran. APHRODITE and ERA-Interim gave better rainfall estimates at daily time scale than other products, with Nash-Sutcliffe efficiency (NSE) values of 0.79 and 0.63, and correlation coefficient (CC) of 0.91 and 0.82, respectively. At the monthly time scale, the CC between all rainfall datasets and ground observations is greater than 0.9, except for TMPA-RT. Hydrological assessment indicates that PERSIANN is the best rainfall dataset for capturing the streamflow and peak flows for the studied area (CC: 0.91, NSE: 0.80).     

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.     

Response of remotely sensed Normalized Difference Water Deviation Index to the 2006 Drought of eastern Sichuan Basin

One of the most serious droughts in last century occurred in eastern Sichuan Basin in the summer of 2006 (hereinafter called the Drought). The response of Moderate Resolution Imaging Spectroradiometer (MODIS, boarding on NASA satellites of Terra and Aqua) to the Drought was analyzed in order to reach one practicable monitoring solution for regional soil moisture. Temporal process and spatial extension of the Drought were firstly estimated with ground meteorological and hydrological observations. Then, for the whole region of Sichuan and Chongqing, the remotely sensed Normalized Difference Water In- dex (NDWI) for the summers of 2001―2006 were calculated based on 8-day composite MODIS products, which were further used to construct a new water index (Normalized Difference Water Deviation Index, NDWDI) to examine the sensitivity of remote sensing in the Drought. The study showed that the NDWDI is more sensitive to regional drought than other absolute-soil-moisture-based indices. With the new index, the study extracted the spatial-temporal characteristics of the 2006 Drought, and explored its developing and withdrawing processes, which agreed with related statistics. Compared with ground method of drought observation, the NDWDI-based remote sensing solution of this paper is more pref- erable and practicable in that the local soil properties of water consumption and supply are implicitly taken into account, and the spatial representativity limit of ground observation is circumvented to a degree as satellite remotely senses the earth surface in a way of two-dimensional pixel matrix. So, the NDWDI-based method can be used to monitor regional soil water stress situation more practically and efficiently.