Mark S. Seyfried

Validation of Advanced Microwave Scanning Radiometer Soil Moisture Products

Validation is an important and particularly challenging task for remote sensing of soil moisture. A key issue in the validation of soil moisture products is the disparity in spatial scales between satellite and in situ observations. 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. In this paper, four soil moisture networks were developed and used as part of the Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) validation program. Each network is located in a different climatic region of the U.S., and provides estimates of the average soil moisture over highly instrumented experimental watersheds and surrounding areas that approximate the size of the AMSR-E footprint. Soil moisture measurements have been made at these validation sites on a continuous basis since 2002, which provided a seven-year period of record for this analysis. The National Aeronautics and Space Administration (NASA) and Japan Aerospace Exploration Agency (JAXA) standard soil moisture products were compared to the network observations, along with two alternative soil moisture products developed using the single-channel algorithm (SCA) and the land parameter retrieval model (LPRM). The metric used for validation is the root-mean-square error (rmse) of the soil moisture estimate as compared to the in situ data. The mission requirement for accuracy defined by the space agencies is 0.06 m3/m3. The statistical results indicate that each algorithm performs differently at each site. Neither the NASA nor the JAXA standard products provide reliable estimates for all the conditions represented by the four watershed sites. The JAXA algorithm performs better than the NASA algorithm under light-vegetation conditions, but the NASA algorithm is more reliable for moderate vegetation. However, both algorithms have a moderate to large bias in all cases. The SCA had the lowest overall rmse with a small bias. The LPRM had a very large overestimation bias and retrieval errors. When site-specific corrections were applied, all algorithms had approximately the same error level and correlation. These results clearly show that there is much room for improvement in the algorithms currently in use by JAXA and NASA. They also illustrate the potential pitfalls in using the products without a careful evaluation.

Scale and the Nature of Spatial Variability: Field Examples Having Implications for Hydrologic Modeling

In this paper we examine how the nature of spatial variability affects hydrologic response over a range of scales using five field studies as examples. The nature of variability was characterized as either stochastic, when random, or deterministic, when due to known, nonrandom sources. We have emphasized how that characterization may change with the scale of hydrologic model. The five field examples, along with corresponding sources of variability, were (1) infiltration and surface runoff affected by shrub canopy, (2) groundwater recharge affected by soil depth, (3) groundwater recharge and streamflow affected by small-scale topography, (4) frozen soil runoff affected by elevation, and (5) snowfall distribution affected by large-scale topography. In each example there was a scale, the deterministic length scale, over which the hydrologic response was strongly dependent upon the specific, location-dependent ecosystem properties. Smaller-scale variability may be represented as either stochastic or homogeneous with nonspatial data. In addition, changes in scale or location sometimes resulted in the introduction of larger-scale sources of variability that subsume smaller-scale sources. Thus recognition of the nature and sources of variability can reduce data requirements by focusing on important sources of variability and using nonspatial data to characterize variability at scales smaller than the deterministic length scale. All the sources of variability described are present in the same watershed and affect hydrologic response simultaneously. Physically based models should therefore utilize both spatial and stochastic data where scale appropriate. Other implications for physically based modeling are that modeling algorithms should reflect larger-scale variability which generally has greater impact and that model and measurement grids should be consistent with the nature of variability.

Validation of Soil Moisture and Ocean Salinity (SMOS) Soil Moisture Over Watershed Networks in the U.S.

Estimation of soil moisture at large scale has been performed using several satellite-based passive microwave sensors and a variety of retrieval methods over the past two decades. The most recent source of soil moisture is the European Space Agency Soil Moisture and Ocean Salinity (SMOS) mission. A thorough validation must be conducted to insure product quality that will, in turn, support the widespread utilization of the data. This is especially important since SMOS utilizes a new sensor technology and is the first passive L-band system in routine operation. In this paper, we contribute to the validation of SMOS using a set of four in situ soil moisture networks located in the U.S. These ground-based observations are combined with retrievals based on another satellite sensor, the Advanced Microwave Scanning Radiometer (AMSR-E). The watershed sites are highly reliable and address scaling with replicate sampling. Results of the validation analysis indicate that the SMOS soil moisture estimates are approaching the level of performance anticipated, based on comparisons with the in situ data and AMSR-E retrievals. The overall root-mean-square error of the SMOS soil moisture estimates is 0.043 m3/m3 for the watershed networks (ascending). There are bias issues at some sites that need to be addressed, as well as some outlier responses. Additional statistical metrics were also considered. Analyses indicated that active or recent rainfall can contribute to interpretation problems when assessing algorithm performance, which is related to the contributing depth of the satellite sensor. Using a precipitation flag can improve the performance. An investigation of the vegetation optical depth (tau) retrievals provided by the SMOS algorithm indicated that, for the watershed sites, these are not a reliable source of information about the vegetation canopy. The SMOS algorithms will continue to be refined as feedback from validation is evaluated, and it is expected that the SMOS estimates will improve.

Ecosystem resilience despite large-scale altered hydroclimatic conditions

Climate change is predicted to increase both drought frequency and duration, and when coupled with substantial warming, will establish a new hydroclimatological model for many regions. Large-scale, warm droughts have recently occurred in North America, Africa, Europe, Amazonia and Australia, resulting in major effects on terrestrial ecosystems, carbon balance and food security. Here we compare the functional response of above-ground net primary production to contrasting hydroclimatic periods in the late twentieth century (1975–1998), and drier, warmer conditions in the early twenty-first century (2000–2009) in the Northern and Southern Hemispheres. We find a common ecosystem water-use efficiency (WUEe: above-ground net primary production/evapotranspiration) across biomes ranging from grassland to forest that indicates an intrinsic system sensitivity to water availability across rainfall regimes, regardless of hydroclimatic conditions. We found higher WUEe in drier years that increased significantly with drought to a maximum WUEe across all biomes; and a minimum native state in wetter years that was common across hydroclimatic periods. This indicates biome-scale resilience to the interannual variability associated with the early twenty-first century drought—that is, the capacity to tolerate low, annual precipitation and to respond to subsequent periods of favourable water balance. These findings provide a conceptual model of ecosystem properties at the decadal scale applicable to the widespread altered hydroclimatic conditions that are predicted for later this century. Understanding the hydroclimatic threshold that will break down ecosystem resilience and alter maximum WUEe may allow us to predict land-surface consequences as large regions become more arid, starting with water-limited, low-productivity grasslands.

Applications and Research Using Remote Sensing for Rangeland Management

Rangelands are grasslands, shrublands, and savannas used by wildlife for habitat and livestock in order to produce food and fiber. Assessment and monitoring of rangelands are currently based on comparing the plant species present in relation to an expected successional end-state defined by the ecological site. In the future, assessment and monitoring may be based on indicators of ecosystem health, including sustainability of soil, sustainability of plant production, and presence of invasive weed species. USDA Agricultural Research Service (ARS) scientists are actively engaged in developing quantitative, repeatable, and low-cost methods to measure indicators of ecosystem health using remote sensing. Noxious weed infestations can be determined by careful selection of the spatial resolution, spectral bands, and timing of image acquisition. Rangeland productivity can be estimated with either Landsat or Advanced Very High Resolution Radiometer data using models of gross primary production based on radiation use efficiency. Lidar measurements are useful for canopy structure and soil roughness, indicating susceptibility to erosion. The value of remote sensing for rangeland management depends in part on combining the imagery with other spatial data within geographic information systems. Finally, ARS scientists are developing the knowledge on which future range-land assessment and monitoring tools will be developed.

Assessment of the SMAP Passive Soil Moisture Product

The National Aeronautics and Space Administration (NASA) Soil Moisture Active Passive (SMAP) satellite mission was launched on January 31, 2015. The observatory was developed to provide global mapping of high-resolution soil moisture and freeze-thaw state every two to three days using an L-band (active) radar and an L-band (passive) radiometer. After an irrecoverable hardware failure of the radar on July 7, 2015, the radiometer-only soil moisture product became the only operational soil moisture product for SMAP. The product provides soil moisture estimates posted on a 36 km Earth-fixed grid produced using brightness temperature observations from descending passes. Within months after the commissioning of the SMAP radiometer, the product was assessed to have attained preliminary (beta) science quality, and data were released to the public for evaluation in September 2015. The product is available from the NASA Distributed Active Archive Center at the National Snow and Ice Data Center. This paper provides a summary of the Level 2 Passive Soil Moisture Product (L2_SM_P) and its validation against in situ ground measurements collected from different data sources. Initial in situ comparisons conducted between March 31, 2015 and October 26, 2015, at a limited number of core validation sites (CVSs) and several hundred sparse network points, indicate that the V-pol Single Channel Algorithm (SCA-V) currently delivers the best performance among algorithms considered for L2_SM_P, based on several metrics. The accuracy of the soil moisture retrievals averaged over the CVSs was 0.038 m3/m3 unbiased root-mean-square difference (ubRMSD), which approaches the SMAP mission requirement of 0.040 m3/m3.

Geophysical imaging of watershed subsurface patterns and prediction of soil texture and water holding capacity

work we show how EMI can be used to image the subsurface of a � 38 ha watershed. We present an imaging approach using kriging to interpolate and sequential Gaussian simulation to estimate the uncertainty in the maps. We also explore the idea of difference ECa mapping to try to exploit changes in soil moisture to identify more hydrologically active locations. In addition, we use a digital elevation model to identify flow paths and compare these with the ECa measurement as a function of distance. Finally, we perform a more traditional calibration of ECa with clay percentage across the watershed and determine soil water holding capacity (SWHC). The values of SWHC range from 0.07 to 0.22 m 3 m � 3 across the watershed, which contrast with the uniform value of 0.13 derived from the traditional soil survey maps. Additional work is needed to appropriately interpret and incorporate EMI data into hydrological studies; however, we argue that there is considerable merit in identifying subsurface soil patterns from these geophysical images.

Calibration of time domain reflectometry for measurement of liquid water in frozen soils

The amount of liquid water (θL) present in soils at sub-freezing temperatures affects soil infiltrability, soil solution migration, and soil-atmosphere energy exchange. For these reasons, a number of frozen soil simulation models calculate θL. Time domain reflectometry (TDR) is the most practica

Searching for chaotic dynamics in snowmelt runoff

Chaos analysis has altered the way we view natural systems. Complex or random-appearing phenomena may be chaotic and thus deterministic, rather than random. In this study, we used the Grassberger-Procaccia algorithm (GPA) to evaluate a runoff time series from a second-order catchment in southwestern Idaho for chaotic dynamics. GPA can identify the presence of low-dimensional chaotic dynamics for experimental time series. A daily runoff record, 8800 days in length, was examined. We found no evidence of chaotic dynamics in snowmelt runoff. Snowmelt runoff measured at a daily time step has a large number of degrees of freedom, which is characteristic of a random rather than chaotic process. These results suggest that the random-appearing behavior of snowmelt runoff is generated from the complex interactions of many factors, rather than low-dimensional chaotic dynamics.

Geographic Database, Reynolds Creek Experimental Watershed, Idaho, United States

The Reynolds Creek Experimental Watershed (RCEW) exhibits spatial variability typical of the intermountain region. We provide a geographic database to provide continuous spatial coverage of landscape properties that may be useful for distributed hydrological modeling or other kinds of spatial analyses and to provide a spatial context for point measurements that have been part of the long-term monitoring described in companion papers. All data are available as separate geographic information system (GIS) layers which can be selected independently according to need. The base map for all the RCEW GIS layers is a 30 m resolution digital elevation model. Data are available in either vector or raster format where appropriate via the U.S. Department of Agriculture, Agricultural Research Service, Northwest Watershed Research Center anonymous ftp site ftp.nwrc.ars.usda.gov.