Thomas R. H. Holmes

Comparing ERA-40-Based L-Band Brightness Temperatures with Skylab Observations: A Calibration/Validation Study Using the Community Microwave Emission Model

Abstract The Community Microwave Emission Model (CMEM) has been used to compute global L-band brightness temperatures at the top of the atmosphere. The input data comprise surface fields from the 40-yr ECMWF Re-Analysis (ERA-40), vegetation data from the ECOCLIMAP dataset, and the Food and Agriculture Organization’s (FAO) soil database. Modeled brightness temperatures have been compared against (historic) observations from the S-194 passive microwave radiometer onboard the Skylab space station. Different parameterizations for surface roughness and the vegetation optical depth have been used to calibrate the model. The best results have been obtained for rather simple approaches proposed by Wigneron et al. and Kirdyashev et al. The rms errors after calibration are 10.7 and 9.8 K for North and South America, respectively. Comparing the ERA-40 soil moisture product against the corresponding in situ observations suggests that the uncertainty in the modeled soil moisture is the predominant contributor to these...

Parameterization of the Land Parameter Retrieval Model for L-Band Observations Using the NAFE'05 Data Set

The Land Parameter Retrieval Model (LPRM) has been successfully applied to retrieve soil moisture from space-borne passive microwave observations at C-, X-, or Ku-band and high incidence angles (50deg-55deg). However, LPRM had never been applied to lower angles or to L-band observations. This letter describes the parameterization and performance of LPRM using aircraft and ground data from the National Airborne Field Experiment 2005. This experiment was undertaken in November 2005 in the Goulburn River catchment, which is located in southeastern Australia. It was found that model convergence could only be achieved with a temporally dynamic roughness. The roughness was parameterized according to incidence angle and soil moisture. These findings were integrated in LPRM, resulting in one uniform parameterization for all sites. The parameterized LPRM correlated well with field observations at 5-cm depth (r = 0.93 based on all sites) with a negligible bias and an accuracy of 0.06 m3middotm-3. These results demonstrate comparable retrieval accuracies as the official SMOS soil-moisture retrieval algorithm (L-MEB), but without the need for the ancillary data that are required by L-MEB. However, care should be taken when using the proposed dynamic roughness model as it is based on a limited data set, and a more thorough evaluation is necessary to test the validity of this new approach to a wider range of conditions.

A Methodology to Determine Radio-Frequency Interference in AMSR2 Observations

A study to determine radio-frequency interference (RFI) in low-frequency passive microwave observations of the Advanced Microwave Scanning Radiometer-2 (AMSR2) is performed. RFI detection methods, such as the spectral difference method, have already been applied on microwave satellite sensors. However, these methods may result in false RFI detection, particularly in zones with extreme environmental conditions. To overcome this problem, this paper proposes an approach that uses the additional 7.3-GHz channel of the AMSR2 sensor in a new RFI detection method. This method uses calculated standard errors of estimate to detect RFI contamination in 6.9- and 7.3-GHz observations. It was found that 6.9-GHz observations are mainly contaminated in the USA, India, Japan, and parts of Europe. The 7.3-GHz observations are contaminated in South America, Ukraine, the Middle East, Southeast Asia, and Russia. The fact that these channels are not affected by RFI in exactly the same regions is useful for studies that prefer C-band brightness temperature observations (e.g., soil moisture retrieval algorithms). Therefore, a decision tree approach was set up to determine RFI and to select reliable brightness temperature observations in the lowest frequency free of any man-made contamination. The result is a reduction of the total contaminated pixels in the 6.9-GHz observations of 66% for horizontal observations and even 85% for vertical observations when 7.3 and 10.7 GHz are used. By linking RFI maps with civilization maps, this paper further shows that RFI sources at the C-band frequency are mainly located in urbanized areas.

Improving long-term, retrospective precipitation datasets using satellite-based surface soil moisture retrievals and the Soil Moisture Analysis Rainfall Tool

Abstract. Using historical satellite surface soil moisture products, the Soil Moisture Analysis Rainfall Tool (SMART) is applied to improve the submonthly scale accuracy of a multi-decadal global daily rainfall product that has been bias-corrected to match the monthly totals of available rain gauge observations. In order to adapt to the irregular retrieval frequency of heritage soil moisture products, a new variable correction window method is developed that allows for better efficiency in leveraging temporally sparse satellite soil moisture retrievals. Results confirm the advantage of using this variable window method relative to an existing fixed-window version of SMART over a range of one- to 30-day accumulation periods. Using this modified version of SMART and heritage satellite surface soil moisture products, a 1.0-deg, 20-year (1979 to 1998) global rainfall dataset over land is corrected and validated. Relative to the original precipitation product, the corrected dataset demonstrates improved correlation with a global gauge-based daily rainfall product, lower root-mean-square-error ( − 13 % ) on a 10-day scale and provides a higher probability of detection ( + 5 % ) and lower false alarm rates ( − 3.4 % ) for five-day rainfall accumulation estimates. This corrected rainfall dataset is expected to provide improved rainfall forcing data for the land surface modeling community.

Benchmarking a Soil Moisture Data Assimilation System for Agricultural Drought Monitoring

AbstractDespite considerable interest in the application of land surface data assimilation systems (LDASs) for agricultural drought applications, relatively little is known about the large-scale performance of such systems and, thus, the optimal methodological approach for implementing them. To address this need, this paper evaluates an LDAS for agricultural drought monitoring by benchmarking individual components of the system (i.e., a satellite soil moisture retrieval algorithm, a soil water balance model, and a sequential data assimilation filter) against a series of linear models that perform the same function (i.e., have the same basic input/output structure) as the full system component. Benchmarking is based on the calculation of the lagged rank cross correlation between the normalized difference vegetation index (NDVI) and soil moisture estimates acquired for various components of the system. Lagged soil moisture/NDVI correlations obtained using individual LDAS components versus their linear analo...

Deriving land surface parameters from three different vegetated sites with the ELBARA 1.4-GHz passive microwave radiometer

A methodology was recently developed to estimate the land surface parameters soil moisture, soil temperature and vegetation optical depth on a global scale by using passive microwave remote sensing. This methodology is general, in a way that it does not require any field observations of soil moisture or canopy biophysical properties for calibration purposes, and can be used with microwave observations at different wavelengths. However, several algorithms in this approach are somewhat empirical, and the vegetation component in this methodology is still difficult to understand and interpret. A follow up field experiment was planned for April 2003 to address some of these issues. The experiment was conducted at a controlled meteorological field site in Wageningen (The Netherlands). Three different plots, a bare soil, a soil with short grass (reference site), and a site with growing grass vegetation were selected. Several hydro-meteorological parameters were monitored extensively at each site, including the radiobrightness temperatures from the ELBARA 1.4 GHz passive microwave radiometer. This paper gives a description of this field experiment and will demonstrate several effects of vegetation on the radiobrightness temperature.

Attribution of Flux Partitioning Variations between Land Surface Models over the Continental U.S.

Accurate quantification of the terrestrial evapotranspiration ( E T ) components of plant transpiration (T), soil evaporation (E) and evaporation of the intercepted water (I) is necessary for improving our understanding of the links between the carbon and water cycles. Recent studies have noted that, among the modeled estimates, large disagreements exist in the relative contributions of T, E and I to the total E T . As these models are often used in data assimilation environments for incorporating and extending E T relevant remote sensing measurements, understanding the sources of inter-model differences in E T components is also necessary for improving the utilization of such remote sensing measurements. This study quantifies the contributions of two key factors explaining inter-model disagreements to the uncertainty in total E T : (1) contribution of the local partitioning and (2) regional distribution of E T . The analysis is conducted by using outputs from a suite of land surface models in the North American Land Data Assimilation System (NLDAS) configuration. For most of these models, transpiration is the dominant component of the E T partition. The results indicate that the uncertainty in local partitioning dominates the inter-model spread in modeled soil evaporation E. The inter-model differences in T are dominated by the uncertainty in the distribution of E T over the Eastern U.S. and the local partitioning uncertainty in the Western U.S. The results also indicate that uncertainty in the T estimates is the primary driver of total E T errors. Over the majority of the U.S., the contribution of the two factors of uncertainty to the overall uncertainty is non-trivial.

Mapping evapotranspiration at multiple scales using multi-sensor data fusion

Thermal-infrared remote sensing of land surface temperature (LST) provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition. This paper describes a multi-scale LST-based energy balance model built using a Two-Source Energy Balance (TSEB) algorithm, which solves for the soil/substrate and canopy temperatures and flux partitioning. A regional modeling system applies the TSEB to time-differential LST measurements from geostationary satellites, providing coarse ET estimates which can be downscaled to finer spatial resolutions using data from polar orbiting satellites. This modeling system, along with strategies for fusing information from multiple satellite platforms and wavebands, has been used to generate ET maps from field to global scales. We describe applications for high spatiotemporal resolution ET retrievals in assessing impacts of human activities and climate change on water resources and agricultural production.

Leveraging microwave polarization information for the calibration of a land data assimilation system

This letter contributes a new approach to calibrating a tau-omega radiative transfer model coupled to land surface model output with low-frequency (<10 GHz) microwave brightness temperature (TB) observations. The problem of calibrating this system is generally poorly posed because various parameter combinations may yield indistinguishable (least squares error) results. This is theoretically important for a land data assimilation system since alternative parameter combinations have different impacts on the sensitivity of TB to soil moisture and misattribution of systematic error may therefore disrupt data assimilation system performance. Via synthetic experiments we demonstrate that using TB polarization difference to parameterize vegetation opacity can improve the stability of calibrated soil moisture/TB sensitivities relative to the more typical approach of utilizing ancillary information to estimate vegetation opacity. The proposed approach fully follows from the radiative transfer model, implemented according to commonly adopted assumptions, and reduces by one the number of calibration parameters.

Passive microwave retrieval of land surface properties

A methodology for retrieving land surface properties from passive microwave observations is presented. Dual polarization microwave brightness temperature data, together with a simple radiative transfer model are used to derive surface soil moisture and vegetation optical depth simultaneously, in a non linear optimization procedure using a forward modeling approach. Soil temperature is derived off-line with a common heat flow model, driven by high frequency vertical polarization microwave data and remotely sensed observations of net radiation. The methodology does not require any field observations of soil moisture or canopy biophysical properties for calibration purposes and is independent of wavelength. Remote sensing provides an excellent opportunity to monitor and gather environmental data in regions that have little or no instrumentation. Moreover, microwave technology provides a more all-weather capability than is typically afforded with visible and near infrared wavelengths. The model was developed for regional- to global-scale monitoring and related environmental applications such as surface energy balance modelling, numerical weather prediction, flood and drought forecasting, and climate change studies. However, at higher spatial resolutions, which would be possible with aircraft, especially unmanned vehicles, tactical applications may be realized as well. Retrieval results compare well with field observations of soil moisture and satellite-derived vegetation index data from optical sensors.