Patrick J. Starks

Correcting eddy-covariance flux underestimates over a grassland

Independent measurements of the major energy balance flux components are not often consistent with the principle of conservation of energy. This is referred to as a lack of closure of the surface energy balance. Most results in the literature have shown the sum of sensible and latent heat fluxes measured by eddy covariance to be less than the difference between net radiation and soil heat fluxes. This under-measurement of sensible and latent heat fluxes by eddy-covariance instruments has occurred in numerous field experiments and among many different manufacturers of instruments. Four eddy-covariance systems consisting of the same models of instruments were set up side-by-side during the Southern Great Plains 1997 Hydrology Experiment and all systems under-measured fluxes by similar amounts. One of these eddy-covariance systems was collocated with three other types of eddy-covariance systems at different sites; all of these systems under-measured the sensible and latent-heat fluxes. The net radiometers and soil heat flux plates used in conjunction with the eddy-covariance systems were calibrated independently and measurements of net radiation and soil heat flux showed little scatter for various sites. The 10% absolute uncertainty in available energy measurements was considerably smaller than the systematic closure problem in the surface energy budget, which varied from 10 to 30%. When available-energy measurement errors are known and modest, eddy-covariance measurements of sensible and latent heat fluxes should be adjusted for closure. Although the preferred method of energy balance closure is to maintain the Bowen‐ratio, the method for obtaining closure appears to be less important than assuring that eddy-covariance measurements are consistent with conservation of energy. Based on numerous measurements over a sorghum canopy, carbon dioxide fluxes, which are measured by eddy covariance, are underestimated by the same factor as eddy covariance evaporation measurements when energy balance closure is not achieved. Published by Elsevier Science B.V.

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.

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.

Soil moisture estimates from TRMM Microwave Imager observations over the Southern United States

Abstract The lack of continuous soil moisture fields at large spatial scales, based on observations, has hampered hydrologists from understanding its role in weather and climate. The most readily available observations from which a surface wetness state could be derived is the Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) observations at 10.65 GHz. This paper describes the first attempt to map daily soil moisture from space over an extended period of time. Methods to adjust for diurnal changes associated with this temporal variability and how to mosaic these orbits are presented. The algorithm for deriving soil moisture and temperature from TMI observations is based on a physical model of microwave emission from a layered soil–vegetation–atmosphere medium. An iterative, least-squares minimization method, which uses dual polarization observations at 10.65 GHz, is employed in the retrieval algorithm. Soil moisture estimates were compared with ground measurements over the U.S. Southern Great Plains (SGP) in Oklahoma and the Little River Watershed, Georgia. The soil moisture experiment in Oklahoma was conducted in July 1999 and Little River in June 2000. During both the experiments, the region was dry at the onset of the experiment, and experienced moderate rainfall during the course of the experiment. The regions experienced a quick dry-down before the end of the experiment. The estimated soil moisture compared well with the ground observations for these experiments (standard error of 2.5%). The TMI-estimated soil moisture during 6–22 July over Southern U.S. was analyzed and found to be consistent with the observed meteorological conditions.

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.

Assimilation of surface soil moisture to estimate profile soil water content

Abstract The use of surface soil water content data as additional input for the Root Zone Water Quality Model in modeling profile soil water content was investigated at four field sites in the Little Washita River Experimental Watershed in south central Oklahoma, coincident with the Southern Great Plains 1997 (SGP97) Hydrology Experiment. Modeled soil water profile estimates were compared to field measurements made periodically during the same time period using a field calibrated time-domain reflectometry (TDR) system. The model was first run in the normal mode with inputs of initial conditions and upper boundary conditions of measured rainfall intensities and daily mean meteorological variables that determined evapotranspiration (ET). Soil hydraulic properties used in the model were estimated from limited soils data information, since in practical terms this is usually the case. Moreover, in our earlier study even the complete description of hydraulic properties based on laboratory and field measurements did not improve the results over average profile estimates using only limited input data. The model runs were then repeated with the daily simulated soil water content in the surface 0–5 cm layer being replaced by 0–5 cm measured soil water content. This process of forcing measured surface water content as additional model input is called direct insertion data assimilation. The simulated profile soil water contents, with and without data assimilation, were compared with TDR-measured profiles to a depth of 60 cm. Gravimetric surface soil water content was measured during SGP97 from June 18 to July 16, 1997 and used as a surrogate for remotely sensed surface moisture data. Data assimilation of surface soil moisture improved model estimates to a depth of 30 cm at all sites. Of particular significance, with data assimilation, model estimates more closely matched the measured dynamic fluctuations of soil moisture in the top 30 cm in response to rainfall events. There was no significant improvement in soil water estimates below the 30 cm depth. This may indicate that data assimilation of surface soil moisture tends to compensate for any errors in model simulations emanating from: (1) errors in the measurement of rainfall intensities or in using 5-min averaged rainfall intensities as done here; (2) errors in using daily average values of meteorological variables that determine ET in a daily ET model; (3) errors in determining hydraulic properties of the surface soil by either laboratory methods or more simple techniques; (4) errors due to the spatial variability of soil hydraulic properties not properly represented in the model.

Polarimetric scanning radiometer C and X band microwave observations during SMEX03

Soil Moisture Experiments 2003 (SMEX03) was the second in a series of field campaigns using the NOAA Polarimetric Scanning Radiometer (PSR/CX) designed to validate brightness temperature data and soil moisture retrieval algorithms for the Advanced Microwave Scanning Radiometer on the Aqua satellite. Data from the TRMM Microwave Imager were also used for X-band comparisons. The study was conducted in different climate/vegetation regions of the US (Alabama, Georgia, Oklahoma). In the current investigation, more than one hundred flightlines of PSR/CX data were extensively processed to produce gridded brightness temperature products for the four study regions. Variations associated with soil moisture were not as large as hoped for due to the lack of significant rainfall in Oklahoma. Observations obtained over Alabama include a wide range of soil moisture and vegetation conditions. Comparisons were made between the PSR and AMSR for all sites

Estimation of shortwave hemispherical reflectance (albedo) from bidirectionally reflected radiance data

Abstract Albedo is the ratio of reflected solar radiation from a surface to that incident upon it. Due to the spatial and temporal resolution of satellite remote sensing instruments, many formulations have been developed to convert remotely sensed data into estmates of albedo. Most of these equations depend upon the assumption of isotropic reflection and, therefore, use only nadir measurements; only in recent years have investigators attempted to model the anisotropic nature of terrestrial surfaces. A Barnes Modular Multiband Radiometer (MMR) was used to collect remotely sensed data from prairie vegetation at seven view zenith angles in the solar principal plane. An equation to estimate albedo from bidirectional reflectance data is proposed and evaluated in this paper. The estimates of albedo were greater than values obtained with simultaneous pyranometer measurements: a more conventional approach. The overestimation was systematic. Potential sources of error are discussed and include: 1) extrapolation of the bidirectional reflectance data out to a view zenith angle of 90°; 2) use of inappropriate weighting coefficients in the numerator of the albedo equation; 3) surface shadowing caused by the A-frame intrumentation used to measure the incoming and outgoing radiation fluxes; 4) errors in estimates of the denominator of the proposed albedo equation (i.e., incoming shortwave radiation); and 5) a “hot spot” contribution in bidirectional data measured by the MMR.

Estimation of Surface Heat Fluxes at Field Scale Using Surface Layer Versus Mixed-Layer Atmospheric Variables with Radiometric Temperature Observations

Radiometric surface temperature observations TR(f), near-surface meteorological/surface energy flux (METFLUX), and atmospheric boundary layer (ABL) data were collected during the Washita ’94 Experiment conducted in the Little Washita Experimental Watershed near Chickasha, Oklahoma. The TR(f) measurements were made from ground and aircraft platforms near the METFLUX stations located over vegetated surfaces of varying amounts of cover and over bare soil. Continuous, half-hourly averaged ground-based TR(f) measurements essentially at the point scale were calibrated with periodic ground transect and aircraft-based TR(f) observations at coarser resolutions so that the continuous TR(f) measurements would be representative of surface temperatures at the field scale (i.e., on the order of 104 m2). The METFLUX data were collected nominally at 2 m above the surface, while ABL measurements were made in the lower 8‐10 km of the atmosphere. The ‘‘local’’ wind speed, u, and air temperature, TA, from the METFLUX stations, as well as the mixed-layer wind speed, UM, and potential temperature, QM, were used in a two-source energy balance model for computing fluxes with continuous TR(f) measurements from the various surfaces. Standard Monin‐ Obukhov surface layer similarity was used with the ‘‘local’’ u and TA data from the METFLUX stations. Bulk similarity approaches were used with the UM and QM data referenced either to ABL height or the top of the surface layer. This latter approach of using mixed-layer data to drive model computations for the different sites is similar to the so-called flux-aggregation schemes or methods proposed to account for subgrid variability in atmospheric models, such as the ‘‘tile’’ or ‘‘mosaic’’ approach. There was less agreement between modeled and measured fluxes when using mixedlayer versus local meteorological variables data for driving the model, and the type of bulk formulation used (i.e., whether local or regional surface roughness was used) also had a significant impact on the results. Differences between the flux observations and model predictions using surface layer similarity with local u and TA data were about 25% on average, while using the bulk formulations with UM and QM differences averaged about 30%. This larger difference was caused by an increase in biases and scatter between modeled and measured fluxes for some sites. Therefore, computing spatially distributed local-scale fluxes with ABL observations of mixed-layer properties will probably yield less reliable flux predictions than using local meteorological data, if available. Given the uncertainty in flux observations is about 20%, these estimates are still considered reasonable and moreover permit the mapping of spatially distributed surface fluxes at regional scales using a single observation of UM and QM with high resolution TR(f) data. Such TR(f) observations with a 90-m pixel resolution will be available from the Advanced Spaceborne Thermal Emission and Reflection Radiometer to be launched on NASA’s Earth Observing System.