Iraq's infrastructure's water and environmental security are influenced by its semi-arid to arid climate, marked by erratic variations, including minimal precipitation, increasing air temperatures, and compromised water quality resulting from reduced inflow in the tributary. The transportation sector plays a vital role in improving economic conditions and mitigating the impacts of climate change. However, critical transportation systems, essential for the movement of goods, information, and people, are jeopardized by water scarcity and climate change. This study develops a sensitivity analysis of the priorities among transportation nodes subject to their disruption by water scarcity and other emergent and future stressors. The stressors include social, technological, regulatory, workforce, market, climate, and hydrologic criteria. This analysis describes the nodes of highest importance and quantifies which scenarios are the most and least disruptive to the system order of nodes. This study includes thirteen order criteria, forty-three nodes, and seven risk scenarios. The paper should interest the system owners and operators who are concerned with monitoring their enterprises' resilience and environmental security.

The resilience of vulnerable populations to environmental extremes is a concern for policymaking across environmental justice, economic development, technology innovation, etc. This study models the resilience of system order for a portfolio of investments, focusing on the spatial distributions of environmental stressors (i.e., precipitation, temperature, soil moisture, and elevation), social vulnerability, and risk exposure. The methodology quantifies risk as a disruption of baseline order under each of several scenarios that combine social and environmental factors, with attention to vulnerable populations. A realistic example is described with features of a southeastern region of the USA. The results and methodology are a rationale for the allocation of investments for economic development and system resilience, balancing among several criteria of social vulnerability and environmental justice.

Space-borne remote sensing provides great potential for soil moisture (SM) retrieval and emerged as a significant data source for research in land surface dynamics and associated applications. This study compared the error characteristics of SM estimates retrieved from the Advanced Microwave Scanning Radiometer 2 (AMSR2) instrument on board NASA’s Aqua satellite across different vegetation gradients and land cover conditions, at different overpass times and frequencies, to demonstrate their strengths and limitations. Results demonstrate that AMSR2 C-band products outperform AMSR2 X-band products over moderately and densely vegetated conditions due to lower attenuation by the vegetation canopy. Conversely, X-band products performed better than C-band products in barren lands possibly due to uneven sensing depth and microwave emissions from subsurface in C-band. The daytime products have a higher signal-to-noise ratio (SNR) in sparsely and moderately vegetated areas, whereas nighttime products have a higher SNR in densely vegetated areas. When these products are used selectively based on their error characteristics, the probability of obtaining SM with stronger signal than noise can be significantly improved (95%) at the expense of impaired spatial coverage (70%-pixel loss).

We studied the temporal and spatial changes in flood water elevation and variation in the surface extent due to flooding resulting from Hurricane Florence (September 2018) using the L-band observation from an unmanned aerial vehicle synthetic aperture radar (UAVSAR) and C-band synthetic aperture radar (SAR) sensors on Sentinel-1. The novelty of this study lies in the estimation of the changes in the flood depth during the hurricane and investigating the best method. Overall, flood depths from SAR were observed to be well-correlated with the spatially distributed ground-based observations ($R^{2} = 0.79$–0.96). The corresponding change in water level ($\partial \text{h}/\partial \text{t}$) also compared well between the remote sensing approach and the ground observations ($R^{2} = 0.90$). This study highlights the potential use of SAR remote sensing for inundated landscapes (and locations with scarce ground observations), and it emphasizes the need for more frequent SAR observations during flood inundation to provide spatially distributed and high temporal repeat observations of inundation to characterize flood dynamics.

Groundwater plays a critical role in the water- food-energy nexus and extensively supports global drinking water and food production. Despite the pressing demands for groundwater resources, groundwater withdrawals are not actively monitored in most regions. Thus, reliable methods are required to estimate withdrawals at local scales suitable for implementing sustainable groundwater management practices. Here, we combine publicly available remote sensing datasets into a deep learning framework for estimating groundwater withdrawals at high resolution (5 km) over the states of Arizona and Kansas in the USA. We compare ensemble machine learning and deep learning algorithms using groundwater pumping data from 2002–2019. Our research shows promising results in sub-humid and semi-arid (Kansas) and arid (Arizona) regions, which demonstrates the robustness and extensibility of this integrated approach. The success of this method indicates that we can effectively and accurately estimate local-scale groundwater withdrawals under different climatic conditions and aquifer properties.

Prediction of large-scale water-related natural disasters such as droughts, floods, wildfires, landslides, and dust outbreaks can benefit from the high spatial resolution soil moisture (SM) data of satellite and modeled products because antecedent SM conditions in the topsoil layer govern the partitioning of precipitation into infiltration and runoff. SM data retrieved from Soil Moisture Active Passive (SMAP) have proved to be an effective method of monitoring SM content at different spatial resolutions: 1) radiometer-based product gridded at 36 km; 2) radiometer-only enhanced posting product gridded at 9 km; and 3) SMAP/Sentinel-1A/B products at 3 and 1 km. In this article, we focused on 9-, 3-, and 1-km SM products: three products were validated against in situ data using conventional and triple collocation analysis (TCA) statistics and were then merged with a Noah-Multiparameterization version-3.6 (NoahMP36) land surface model (LSM). An exponential filter and a cumulative density function (CDF) were applied for further evaluation of the three SM products, and the maximize-R method was applied to combine SMAP and NoahMP36 SM data. CDF-matched 9-, 3-, and 1-km SMAP SM data showed reliable performance: R and ubRMSD values of the CDF-matched SMAP products were 0.658, 0.626, and 0.570 and 0.049, 0.053, and 0.055 m3/m3, respectively. When SMAP and NoahMP36 were combined, the R-values for the 9-, 3-, and 1-km SMAP SM data were greatly improved: R-values were 0.825, 0.804, and 0.795, and ubRMSDs were 0.034, 0.036, and 0.037 m3/m3, respectively. These results indicate the potential uses of SMAP/Sentinel data for improving regional-scale SM estimates and for creating further applications of LSMs with improved accuracy.

The SMAP (Soil Moisture Active/Passive) satellite provides global soil moisture (SM) estimates that can be used for scientific research and applications (such as the hydrological cycle, agriculture, ecology, and land atmosphere interactions). Currently, SMAP provides the enhanced radiometer-only SM product (L2SMP) at 9 km grid resolution. However, this spatial resolution is still not enough to satisfy the needs of some studies that require a finer spatial resolution SM product, particularly in agricultural and watershed applications. This study applied a downscaling algorithm to the SMAP 9 km SM product to produce a 1 km resolution over the CONUS (Contiguous United States). The downscaling algorithm is based on the relationship between temperature change and SM modulated by Normalized Difference Vegetation Index (NDVI) of a given time period. This relationship was modeled using variables derived from NLDAS (North America Land Data Assimilation System) and NASA's LTDR (Land Long Term Data Record) between 1981 - 2018. The algorithm was implemented uses the 1 km MODIS Aqua LST (Land Surface Temperature) product. The downscaled SMAP 1 km SM was validated using in situ SM measurements from the ISMN (International Soil Moisture Network). The validation metrics show an improved overall accuracy of the downscaled SM.

SMAP (Soil Moisture Active/Passive) and SMOS (Soil Moisture Ocean Salinity) provide soil moisture observations that can be used for studying the global hydrological cycle, agriculture, ecology, and land atmosphere interactions. SMAP provides soil moisture at two grid scales; 36 km (which is close to its native radiometer spatial resolution) and an enhanced grid resolution of 9 km. However, these scales are not compatible with some agricultural and watershed applications that require a higher spatial resolution. This study applied a downscaling algorithm to the SMAP Level-2 radiometer 36 km product and improved the grid resolution to 1 km over the CONUS (Contiguous United States). The downscaling algorithm is built on the thermal inertial relationship between daily temperature change and averaged soil moisture modulated by Normalized Difference Vegetation Index (NDVI). The average soil moisture and thermal inertia model functions were developed by using data from NLDAS (North America Land Data Assimilation System) and LTDR (Land Long Term Data Record) for 1981 - 2016. The algorithm is applied with the 1 km MODIS Aqua LST product and the downscaled SMAP 1 km soil moisture was validated by in situ soil moisture measurements from the ISMN (International Soil Moisture Network). The validation variables show improved accuracy of the downscaled soil moisture.

Watershed management plays a dynamic role in water resource engineering. Divination and determination of surface runoff are the most important processes of hydrology as understanding the basic relationship between rainfall and runoff is effective for sustainable resource management. National Resources Conservation Service- Curve Number method is employed with geospatial tools to compute surface runoff. Temporal land use and soil maps are integrated in GIS environment to observe the temporal variation on the runoff potential. The study interprets, that the urbanization has increased 13.4 percent for 2001 to 2010 and 38.4 percent for 2010 to 2015 for which the weighted CN comes out to be 68.5, 67.4 and 68.6 for 2001, 2010 and 2015 respectively. Runoff depends upon the rainfall events which has definitely affected due to climate change. Thus, there is a high need to monitor and design storage tanks or to implement low impact development techniques to store stormwater and to reduce pressure from freshwater assets.

Soil moisture at high spatial resolution is required for various land processes related studies. However, currently the resolution of passive microwave retrieved soil moisture is low. To solve this problem, a soil moisture disaggregation algorithm based on thermal inertia relationship between daily temperature change and average soil moisture modulated by vegetation conditions has been formulated. This algorithm was applied to the SMAP (Soil Moisture Active/Passive) to produce the 1 km downscaled soil moisture over the SMAPVEX15 (SMAP Validation Experiment 2015). The disaggregated soil moisture has been compared to in situ observations and the results of this approach are very encouraging.

Soil moisture derived from the SMAP passive microwave radiometers has a spatial resolution of 36km. In the case of applications of weather, catchment hydrology and agriculture there is a requirement of high spatial resolution. In this paper we present an innovative method to downscale passive soil moisture retrievals using vegetation index and surface temperature.

Soil moisture derived from passive microwave radiometers have low spatial resolution due to limitation of antenna size. In the case of many geophysical applications high spatial resolution is desired, examples of these include weather, catchment hydrology and agriculture. In this paper we present two innovative methods to downscale passive soil moisture retrievals using (a) radar backscatter and (b) vegetation index and surface temperature.

The Advanced Synthetic Aperture Radar (ASAR) Global Monitoring (GM) mode offers an opportunity for global soil moisture (SM) monitoring at much finer spatial resolution than that provided by the currently operational Advanced Microwave Scanning Radiometer for the Earth Observing System and future planned missions such as Soil Moisture and Ocean Salinity and Soil Moisture Active Passive. Considering the difficulties in modeling the complex soil-vegetation scattering mechanisms and the great need of ancillary data for microwave backscatter SM inversion, algorithms based on temporal change are currently the best method to examine SM variability. This paper evaluates the spatial sensitivity of the ASAR GM surface SM product derived using the temporal change detection methodology developed by the Vienna University of Technology. This evaluation is made for an area in southeastern Australia using data from the National Airborne Field Experiment 2005. The spatial evaluation is made using three different types of SM data (station, field, and airborne) across several different scales (1-25 km). Results confirmed the expected better agreement when using point (Rstation= 0.75) data as compared to spatial (RPLMR, 1 km= 0.4) data. While the aircraft-ASAR GM correlation values at 1-km resolution were low, they significantly improved when averaged to 5 km (RPLMR, 5 km= 0.67) or coarser. Consequently, this assessment shows the ASAR GM potential for monitoring SM when averaged to a spatial resolution of at least 5 km.

The QuikSCAT enhanced (2.225-km) backscattering product is investigated for sensitivity to changes in soil moisture and its potential for spatial disaggregation of Advanced Microwave Scanning Radiometer (AMSR-E) soil moisture. Specifically, an active-passive methodology based on temporal change detection is tested using data from the 2006 National Airborne Field Experiment data set. This campaign was carried out from October 29 to November 20, 2006 in a 60 km times 40 km area of the Murrumbidgee catchment, southeast Australia. Temporal change detection analysis and accuracy in terms of spatial pattern distribution throughout the domain were assessed using a passive microwave airborne product derived from the Polarimetric L-band Multibeam Radiometer at 1-km spatial resolution. QuikSCAT-AMSR-E intercomparisons indicated higher correlations when using C-band observations. The greatest sensitivity to soil moisture was observed when using V-polarized backscatter measurement. While backscattering data showed adequate temporal sensitivity to changes in soil moisture due to precipitation events, the spatial agreement was complicated by the presence of irrigation and standing water (rice fields). This resulted in low Cramer's Phi values (less than 0.06), which were used as a measure of spatial correspondence in terms of change in soil moisture and backscatter. In addition, the high QuikSCAT sensor frequency and existence of noise in the observed data contributed to the observed discrepancies.

Soil moisture (SM) is an important variable in determining streamflow, agricultural productivity, weather, and climate. An effective way to map SM over large areas on a regular basis is by using active microwave observations. This paper examines the influence of topography on radar backscatter measurements for a range of vegetation conditions and on the development of a normalization technique for the correction of topography-induced variability. Radar backscatter observations derived from the QuikSCAT sensor were analyzed to investigate the effect of sloping terrain over the North American Monsoon Experiment region that is characterized by heterogeneous surface conditions and complex topographic terrain. A digital elevation model, along with local incidence angle and slope, was used to investigate the backscatter dependence on topography variation for eight main vegetation classes. Pearson product-moment correlation (R) analysis showed strong backscatter dependence on the local incidence angle caused by changes in slope. The overall average reduction in variances after correction for August 2004 depended on vegetation type and ranged between -16% to -42% and -18% to -37% for horizontal and vertical polarizations, respectively. The corrected sigma-0 was also evaluated usinginsituSM observations obtained during the Soil Moisture Experiment 2004 field campaign. The computed percent change inRbetween sigma-0 and SM demonstrated significant improvement after correction when using vertically observed sigma-0. The standard errors of estimate for these two vegetation classes were lowered by about 12% and 5%, respectively, after applying the proposed topographic normalization technique to the QuikSCAT observations.

The combination of radiometer and radar observations is a very promising technique for spatial disaggregation of soil moisture. The enhanced QuikSCAT sigma-0 product (2.225 km) offers a possibility for overcoming the temporal and spatial limitations of the available radar systems. The current study investigates QuikSCAT sensitivity to soil moisture and its capability to accurately monitor and capture change in soil moisture. The research was undertaken for the National Airborne Field Experiment area located in the Murrumbidgee catchment, SE Australia. Validation of the temporal change detection analysis was undertaken using an airborne soil moisture product derived from the Polarimetric L-band Multibeam Radiometer (PLMR). The main propose of the PLMR use was to assess accuracy in terms of spatial patterns distribution. The results reveal expected temporal variability and adequate response of the active sensor to change in meteorological conditions. The presence of irrigation and standing water (rice fields) in the region challenges the spatial agreement throughout the study area.

Validation is a challenging task for passive microwave remote sensing of soil moisture from Earth orbit. The key issue is spatial scale; conventional measurements of soil moisture are made at a point, whereas satellite sensors provide an integrated area/volume value for a much larger spatial extent. A robust validation program should include as many types of comparisons as possible and must attempt to provide actual spatially representative ground based soil moisture. A ground based validation program also requires a wide range of conditions, long temporal coverage and continuous observations. As part of the AMSR-E validation activity an augmented network of dedicated validation sites at actively monitored watersheds has been developed. These provide estimates of the average soil moisture over watersheds and surrounding areas that approximate the size of the AMSR-E footprint. This is done on a continuous basis, partially in real time. A public database of the watershed data for all sites is being developed and made available. To implement this network, additional surface soil moisture and temperature sensors (0-5 cm depth) were installed at and around existing instrument locations in four watersheds located in different climate regions of the U.S. Through short term and extended field campaigns the calibration of these instruments has been established. Methods for scaling from the point measurements to the integrated watershed/footprint average have also been developed as part of the validation effort. These efforts will be of value in alternate algorithm comparisons and will benefit future missions including SMOS.

Ecological and hydrological cycles play a key role in global climate dynamics. Remote sensing data are important in climate studies as they provide a global coverage of important hydrological variables at a variety of spatial and temporal scales. In this paper we study the long term relationships between NDVI derived from AVHRR and microwave brightness temperature from SSM/I sensor for different climate regimes in the world.

The soil moisture experiments held during June-July 2002 (SMEX02) at Iowa demonstrated the potential of the L-band radiometer (PALS) in estimation of near surface soil moisture under dense vegetation canopy conditions. The L-band radar was also shown to be sensitive to near surface soil moisture. However, the spatial resolution of a typical satellite L-band radiometer is of the order of tens of kilometers, which is not sufficient to serve the full range of science needs for land surface hydrology and weather modeling applications. Disaggregation schemes for deriving subpixel estimates of soil moisture from radiometer data using higher resolution radar observations may provide the means for making available global soil moisture observations at a much finer scale. This paper presents a simple approach for estimation of change in soil moisture at a higher (radar) spatial resolution by combining L-band copolarized radar backscattering coefficients and L-band radiometric brightness temperatures. Sensitivity of AIRSAR L-band copolarized channels has been demonstrated by comparison with in situ soil moisture measurements as well as PALS brightness temperatures. The change estimation algorithm has been applied to coincident PALS and AIRSAR datasets acquired during the SMEX02 campaign. Using AIRSAR data aggregated to a 100-m resolution, PALS radiometer estimates of soil moisture change at a 400-m resolution have been disaggregated to 100-m resolution. The effect of surface roughness variability on the change estimation algorithm has been explained using integral equation model (IEM) simulations. A simulation experiment using synthetic data has been performed to analyze the performance of the algorithm over a region undergoing gradual wetting and dry down.

Vegetation is important factor in global climate variability and hence plays a key role in the complex interaction between land surface and atmosphere. In this study, we analyzed vegetation water content (VWC), leaf area index (LAI), and normalized difference vegetation index (NDVI) and land surface temperature (Ts) from AMSR-E (Advanced Microwave Scanning Radiometer for EOS) and MODIS (Moderate Imaging Sepectrodiometer). For spatial analysis of the relationship between the four variables we selected three regions which have climatically differing characteristics: NAMS (North America Monsoon System) region, SGP (South Great Plains) region, and Little River Watershed in Tifton, GA. Also temporal analyses were performed by comparing of 2003 and 2004. From the introduction of normalized vegetation water content (NVWC) derived from satellite-derived VWC and LAI data, amount of water in individual leaves has been estimated and yielded significant correlation with NDVI and Ts. The analysis of three regions in NVWC and NDVI relationship shows their negative exponential relation, and Ts and NDVI relationship (TvX relationship) is inversely proportional. This correlation between these variables are higher in the more arid areas such as NAMS regions, and becomes less correlated in the more humid and more vegetated regions such as east areas of Georgia. Moreover, land cover map is used to exam how the different vegetation types are related to the land and atmospheric variables, and it is identified that the regional distribution of each vegetation type reflects its biological characteristics related to water and its growing environment.

The SMEX02 experiments held in June-July 2002, at Iowa demonstrated the potential of the L band radiometer (PALS) in estimation of near surface soil moisture under dense vegetation canopy conditions. The L band radar was also shown to be sensitive to near surface soil moisture. However, the spatial resolution of a typical satellite L band radiometer is of the order of tens of kilometers, which is not sufficient to serve the full range of science needs for land surface hydrology and weather modeling applications. Disaggregation schemes for deriving sub pixel estimates of soil moisture from radiometer data using higher resolution radar observations may provide the means for making available global soil moisture observations at much finer scale. This paper presents a simple approach for disaggregation of coarser resolution radiometer estimates of soil moisture using higher resolution radar backscatter and vegetation water content measurements. The algorithm has been applied to coincident PALS radiometer and Airsar datasets of 400 m and 30 m spatial resolutions respectively acquired during the SMEX02 campaign. PALS radiometer estimates of soil moisture at a 400 m resolution have been disaggregated to 100 m resolution.

We discuss the assimilation of remotely sensed soil brightness temperature into a runoff prediction model. Data used in this study was acquired during the 2002 Soil Moisture Experiments (SMEX02) near Ames, IA. The Passive and Active L- and S-band (PALS) instrument was flown for six days of the study before and after a major rain event in the region. We combine a radiative transfer model and observed PALS brightness temperatures to estimate soil moisture within the top five centimeters over watershed. These estimates are assimilated into the active soil layer in a distributed runoff model. Runoff estimates are compared to observed stream gauge measurements within the watershed

An automated soil moisture network for continuous measurement of soil moisture in the top 30 cm of the soil over an 8000 km2region has been established. The network consists of 32 stations encompassing a diversity of soil types. The measurements are being used to improve drought, flood, and agronomic production forecasts. In addition, the data are being used to examine the accuracy of remotely sensed measurements of soil moisture and the degree to which they represent natural variability across the landscape. The data were used to evaluate soil moisture conditions during the SMEX03 experiment. The data are being used to support testing of AMSR, AMSR-E, PSR, and synthetic aperture radar (SAR) observations. Gravimetric samples were collected for the period from June 23, 2003 to July 2, 2003 for comparison to both the in situ network and the remotely sensed data. During the experiment, daily in situ soil moisture measurements were taken and plant and soil samples collected for oven drying and determination of moisture content. The automated network provided continuous in situ soil moisture measurements throughout the coverage area. A wide variation in soil moisture was observed both over the time period and from site to site

There have been numerous field experiments which have tested the effectiveness of microwave remote sensing, both active and passive under varied land surface conditions. The Southern Great Plains Experiment 1999 (SGP99) was held in Chickasha Oklahoma where winter wheat and rangeland was the predominant land surface type whereas at the Soil Moisture Experiment 2002 (SMEX02) in Walnut River watershed in Ames Iowa it was a mixture of corn, and soyabeans. In the SMEX03 (Soil Moisture Experiment in 2003) in Little River Watershed, the land surface was a mixture of peanuts, vegetables, cotton and pasture and for SMEX04 (Soil Moisture Experiment in 2004) in Walnut Gulch, Arizona, the land surface cover is primarily brush and grass covered rangeland vegetation. Given that microwaves have low sensitivity to soil moisture in the presence of vegetation, these field experiments offer an opportunity to examine observations of sensitivity in the presence of varied (and varying with time) vegetation densities. In addition, in each of these experiments, there were different instruments on aircrafts and satellite sensors that were deployed. In SGP99, we had observations from the PALS (Passive Active L and S band Radar and Radiometer), PSR (Polarimetric Scanning Radiometer) from the C130 aircraft and the TMI (TRMM Microwave Imager) and SSM/I (Special Sensor Microwave Imager) from space. In SMEX02, we had PALS, PSR, AIRSAR (Airborne Synthetic Aperture Radar) from the aircraft platforms and in SMEX03 PSR only. Satellite sensors in SMEX02 and SMEX03 included AMSR (Advanced Microwave Scanning Radiometer), TMI, and SSM/I. In the recently concluded SMEX04, PSR was used from the aircraft and AMSR, TMI and SSM/I satellite observations were available. We will use these sensors and observations in the microwave channel in conjunction with ground observations of vegetation characteristics and soil moisture to study the sensitivity of microwaves to soil moisture under varied land surface conditions.

The SMEX02 experiments held in June-July 2002, at Iowa demonstrated the potential of an L band radiometer (PALS) in estimation of near surface soil moisture under dense vegetation canopy conditions. The L band radar was also shown to be sufficiently sensitive to near surface soil moisture. However, the spatial resolution of a typical satellite mounted L band radiometer is of the order of 10's of kilometers which is not sufficient to serve the science needs of land surface hydrology and weather modeling applications. Disaggregation schemes for deriving sub pixel estimates of soil moisture from radiometer data using higher resolution radar observations hold the promise of making global soil moisture observations at much finer scale available. The HYDROS instrument is proposed to have an L band radiometer and L band radar onboard. The passive instrument has spatial resolution of the order of tens of kilometers and operates along with the active instrument that takes observations at a resolution of tens of meters. This paper presents a simple approach for disaggregation of coarser resolution radiometer estimates of soil moisture using higher resolution radar backscatter measurements. The algorithm has been applied to a coincident PALS radar/radiometer and AIRSAR dataset acquired during the SMEX02 campaign

The 6.6-, 10.7-, and 18-GHz data from the Scanning Multichannel Microwave Radiometer (SMMR) for 1979, 1980, and 1982 have been used to derive soil moisture and surface temperature for the south central United States. The 1979 data have been used to calibrate the radiative transfer model parameters, and the 1980 and 1982 data were used to derive soil moisture and surface temperature that have been compared with the corresponding values from the National Centers for Environmental Prediction (NCEP) reanalyses model outputs. These comparisons have shown that SMMR is able to qualitatively predict the seasonal cycle of land surface hydrological variability, and this information can be used for studies involving land-atmosphere interaction and hydrology. This study is of particular importance with the presence of both the Aqua satellite and the Advanced Earth Observing Satellite II that carry onboard the Advanced Microwave Scanning Radiometer (AMSR), which has channels similar to the SMMR, but with better spatial resolution. The results of this study will help us to plan for AMSR retrievals of soil moisture and surface temperature.

Soil moisture is an important variable controlling biogeochemical cycles, heat exchange and infiltration rates at land/atmosphere boundary. The microwave portion of the electromagnetic spectrum have been used to monitor the moisture content of soils due to the sensitivity of microwave brightness temperatures to land surface variables. In this paper, the simulation of the C-band brightness temperatures are carried out for the SMEX02 (Soil Moisture Experiments 2002) regions in Ames, Iowa for the time period between June 25 to July 31, 2002. The simulated brightness temperatures have been compared with the corresponding observations using the Advanced Microwave Scanning Radiometer (AMSR).

In the present study, remote sensing of soil moisture is carried out using the Passive and Active L- and S-band airborne sensor (PALS). The data in this paper were taken from five days of overflights near Chickasha, OK during the 1999 Southern Great Plains (SGP99) experiment. Presently, we analyze the collected data to understand the relationships between the observed signals (radiometer brightness temperature and radar backscatter) and surface parameters (surface soil moisture, temperature, vegetation water content, and roughness). In addition, a radiative transfer model and two radar backscatter models are used to simulate the PALS observations. An integration of observations, regression retrievals, and forward modeling is used to derive the best estimates of soil moisture under varying surface conditions.

The Advanced Microwave Scanning Radiometer (AMSR-E) on the Earth Observing System (EOS) Aqua satellite was launched on May 4, 2002. The AMSR-E instrument provides a potentially improved soil moisture sensing capability over previous spaceborne radiometers such as the Scanning Multichannel Microwave Radiometer and Special Sensor Microwave/Imager due to its combination of low frequency and higher spatial resolution (approximately 60 km at 6.9 GHz). The AMSR-E soil moisture retrieval approach and its implementation are described in this paper. A postlaunch validation program is in progress that will provide evaluations of the retrieved soil moisture and enable improved hydrologic applications of the data. Key aspects of the validation program include assessments of the effects on retrieved soil moisture of variability in vegetation water content, surface temperature, and spatial heterogeneity. Examples of AMSR-E brightness temperature observations over land are shown from the first few months of instrument operation, indicating general features of global vegetation and soil moisture variability. The AMSR-E sensor calibration and extent of radio frequency interference are currently being assessed, to be followed by quantitative assessments of the soil moisture retrievals.

Soil moisture is one of the most important hydrological variables that characterizes the land surface water and energy balance. Measurements from space suffer from the problem of subpixel heterogeneity, i.e., soil moisture has spatial variability at all scales; therefore, it is important to realize the exact physical implication of the single value of the satellite measurements. In this paper, we study the sensitivity of C-band passive microwave brightness temperatures to various land surface variables. The issue of heterogeneity and its role in interpretation of single spatially averaged value of satellite brightness temperature is investigated. Finally, we use the brightness temperatures from the Scanning Multichannel Microwave Radiometer to characterize spatial variability and to understand the variation of this variability with scale.

The Southern Great Plains 1999 (SGP99) Experiment, conducted in Oklahoma, July 8-21, 1999, provided an opportunity to observe spatial and temporal variations in surface temperature. During the experiment, aircraft (Passive/Active L/S-band airborne sensor) and satellite [Advanced Very High Resolution Radiometer (AVHRR) and TIROS Operational Vertical Sounder (TOVS)] sensors collected surface temperature that was compared to in situ observations over the same time period to determine the accuracy and consistency of surface temperature measurements at different spatial resolutions using remotely sensed data. In addition, in situ surface temperature was observed in a 400/spl times/400 m field at various spatial grid spacing: 50 m, 10 m, and 1 m in order to quantify the variability of the spatially distributed behavior of surface temperature during a drydown period. Average differences between the in situ surface temperature observations and the aircraft and satellite sensors utilized during this study ranged from 0.7/spl deg/C (AVHRR High Resolution Picture Transmission) to more than 20/spl deg/C (AVHRR Global Area Coverage (GAC), TOVS). We have shown that the temporal adjustments of the remotely sensed surface temperatures (from aircraft and satellite sensors) shows a better comparison to in situ ground data. A ratio was set up using information derived from a mosaic land surface model to temporally locate the various estimates of surface temperature. The corrected surface temperature comparisons decreased the average differences (with in situ) to as much as 78% [AVHRR (GAC)] and as little as 6% (TOVS). The average difference between remotely sensed and in situ observations was around 48%.

Data were acquired by the Passive and Active L- and S-band airborne sensor (PALS) during the 1999 Southern Great Plains (SGP99) experiment in Oklahoma to study remote sensing of soil moisture in vegetated terrain using low-frequency microwave radiometer and radar measurements. The PALS instrument measures radiometric brightness temperature and radar backscatter at L- and S-band frequencies with multiple polarizations and approximately equal spatial resolutions. The data acquired during SGP99 provide information on the sensitivities of multichannel low-frequency passive and active measurements to soil moisture for vegetation conditions including bare, pasture, and crop surface cover with field-averaged vegetation water contents mainly in the 0-2.5 kg m/sup -2/ range. Precipitation occurring during the experiment provided an opportunity to observe wetting and drying surface conditions. Good correlations with soil moisture were observed in the radiometric channels. The 1.41-GHz horizontal-polarization channel showed the greatest sensitivity to soil moisture over the range of vegetation observed. For the fields sampled, a radiometric soil moisture retrieval accuracy of 2.3% volumetric was obtained. The radar channels showed significant correlation with soil moisture for some individual fields, with greatest sensitivity at 1.26-GHz vertical copolarized channel. However, variability in vegetation cover degraded the radar correlations for the combined field data. Images generated from data collected on a sequence of flight lines over the watershed region showed similar patterns of soil moisture change in the radiometer and radar responses. This indicates that under vegetated conditions for which soil moisture estimates may not be feasible using current radar algorithms, the radar measurements nevertheless show a response to soil moisture change, and they can provide useful information on the spatial and temporal variability of soil moisture. An illustration of the change detection approach is given.

ASSIVE microwave remote sensing of soil moisture has been a focus of research for several decades. Only in recent years has this work begun to address the key issues related to implementing this approach as part of large scale and global applications such as climate analysis and prediction. The papers collected in this Special Issue on Large Scale Passive Microwave Remote Sensing of Soil Moisture address one of three important aspects that will eventually contribute to operational studies. These are • theoretical and experimental investigations to develop and refine robust retrieval algorithms; • demonstration of retrieval techniques over regional scales using aircraft and satellite observations; • integration of remotely sensed soil moisture measurements in hydrologic applications. One additional paper is included that describes a planned L-band satellite mission. The impending launches of the ad

The 1993 summer flood event in the midwestern United States was one of the most devastating floods of modern times. Record amounts of rain fell throughout the midwest causing extensive damage. The precipitation events can be attributed to anomalies in atmospheric circulation patterns and jet stream flows. These factors coupled with the above normal soil moisture beginning in the end of May 1993 set the stage for a massive flood event with the advent of considerable precipitation. The authors attempt to relate this increased soil moisture to the afternoon minus morning surface temperature differences as observed by the high resolution infrared sounder (HIRS2) on the NOAA-11 and NOAA-12 satellites. It is seen using satellite data that increased rainfall decreases this diurnal surface temperature difference. This is related to the discharge values at gauging stations along the Mississippi River at McGregor, IA (upstream) and St. Louis, MO (downstream).