The Radiative Temperature of the Earth at Microwave Frequencies

Dolman, A.J. [Promotor]Jeu, R.A.M. [Copromotor]de Owe, M. [Copromotor

The European heat wave 2003: early indicators from multisensoral microwave remote sensing?

An extreme heat wave affected large parts of Europe in 2003 with severe socioeconomic impacts. The extreme warm weather conditions lasted over a couple of months with positive temperature anomalies of 5°C for large parts of Europe. Simulations of the event using regional climate models revealed that a pronounced precipitation deficit in the beginning of the year, together with an early onset of the vegetation, resulted in a severe deficit of the soil water content. This amplified the course of the heat wave due to an increasing sensible heat flux from the land surface. The monitoring of temporal and spatial dynamics of soil water content can be accomplished using remote-sensing-based techniques. The present paper addresses the question whether there have been early indicators for the low soil water content using either physically based land surface modeling or remote-sensing-based monitoring techniques. The course of the spring surface soil moisture evolution is investigated using observations from two different microwave remote sensing sensors. An intercomparison of the high-resolution data from the European ENVISAT satellite and coarse resolution data from the AMSR-E mission is made. Remote-sensing-derived soil moisture products are compared against the results from a deterministic land surface model. The model enables to relate the year 2003 anomalies to a long-term (30 years) climatology. The year 2003 remote sensing derived soil moisture dynamics is compared against a multiyear climatology. The results reveal a negative surface soil moisture anomaly in 2003. The results indicate that there was in general potential to monitor the spatial and temporal dimensions of the low surface soil water content early in 2003 using remote sensing techniques. Both remote sensing data sets indicate a consistent soil moisture decrease in early 2003. A good agreement between the observed surface soil moisture and soil moisture simulations from a land surface process model was found. An outlook to the use of remote-sensing-based soil moisture estimates for large-scale monitoring of surface soil moisture trends is given. Copyright 2009 by the American Geophysical Union

Multi-Sensor Historical Climatology of Satellite-Derived Global Land Surface Moisture

A historical climatology of continuous satellite-derived global land surface soil moisture is being developed. The data consist of surface soil moisture retrievals derived from all available historical and active satellite microwave sensors, including Nimbus-7 Scanning Multichannel Microwave Radiometer, Defense Meteorological Satellites Program Special Sensor Microwave Imager, Tropical Rainfall Measuring Mission Microwave Imager, and Aqua Advanced Microwave Scanning Radiometer for EOS, and span the period from November 1978 through the end of 2007. This new data set is a global product and is consistent in its retrieval approach for the entire period of data record. The moisture retrievals are made with a radiative transfer-based land parameter retrieval model. The various sensors have different technical specifications, including primary wavelength, spatial resolution, and temporal frequency of coverage. These sensor specifications and their effect on the data retrievals are discussed. The model is described in detail, and the quality of the data with respect to the different sensors is discussed as well. Examples of the different sensor retrievals illustrating global patterns are presented. Additional validation studies were performed with large-scale observational soil moisture data sets and are also presented. The data will be made available for use by the general science community

The impact of land surface temperature on soil moisture anomaly detection from passive microwave observations

For several years passive microwave observations have been used to retrieve soil moisture from the Earth's surface. Low frequency observations have the most sensitivity to soil moisture, therefore the current Soil Moisture and Ocean Salinity (SMOS) and future Soil Moisture Active and Passive (SMAP) satellite missions observe the Earth's surface in the L-band frequency. In the past, several satellite sensors such as the Advanced Microwave Scanning Radiometer-EOS (AMSR-E) and WindSat have been used to retrieve surface soil moisture using multi-channel observations obtained at higher microwave frequencies. While AMSR-E and WindSat lack an L-band channel, they are able to leverage multi-channel microwave observations to estimate additional land surface parameters. In particular, the availability of Ka-band observations allows AMSR-E and WindSat to obtain coincident surface temperature estimates required for the retrieval of surface soil moisture. In contrast, SMOS and SMAP carry only a single frequency radiometer and therefore lack an instrument suited to estimate the physical temperature of the Earth. Instead, soil moisture algorithms from these new generation satellites rely on ancillary sources of surface temperature (e.g. re-analysis or near real time data from weather prediction centres). A consequence of relying on such ancillary data is the need for temporal and spatial interpolation, which may introduce uncertainties. Here, two newly-developed, large-scale soil moisture evaluation techniques, the triple collocation (TC) approach and the <i>R</i><sub>value</sub> data assimilation approach, are applied to quantify the global-scale impact of replacing Ka-band based surface temperature retrievals with Modern Era Retrospective-analysis for Research and Applications (MERRA) surface temperature output on the accuracy of WindSat and AMSR-E based surface soil moisture retrievals. Results demonstrate that under sparsely vegetated conditions, the use of MERRA land surface temperature instead of Ka-band radiometric land surface temperature leads to a relative decrease in skill (on average 9.7%) of soil moisture anomaly estimates. However the situation is reversed for highly vegetated conditions where soil moisture anomaly estimates show a relative increase in skill (on average 13.7%) when using MERRA land surface temperature. In addition, a pre-processing technique to shift phase of the modelled surface temperature is shown to generally enhance the value of MERRA surface temperature estimates for soil moisture retrieval. Finally, a very high correlation (<i>R</i><sup>2</sup> = 0.95) and consistency between the two evaluation techniques lends further credibility to the obtained results

Estimating the Soil Temperature Profile from a single Depth Observation: A simple Empirical Heatflow Solution

Two field data sets are used to model near-surface soil temperature profiles in a bare soil. It is shown that the commonly used solutions to the heat flow equations by Van Wijk perform well when applied at deeper soil layers, but result in large errors when applied to near surface layers, where more extreme variations in temperature occur. The reason for this is that these approaches do not consider heat sources or sinks below the surface. This paper proposes a new approach for modeling the surface soil temperature profiles from a single observation depth. This approach consists of two parts: 1) modeling an instantaneous ground flux profile based on net radiation and the ground heat flux at 5 cm depth; and 2) use of this ground heat flux profile to extrapolate a single temperature observation to a complete surface temperature profile. The new model is validated under different field and weather conditions showing low RMS errors of 1-3 K for wet to dry conditions. Finally, the proposed model is tested under limitations in input data that are associated with remote sensing applications. It is shown that these limitations result in only small increases in the overall error. This approach may be useful for satellite-based global energy balance applications. Copyright 2008 by the American Geophysical Union

Magnitude and variability of land evaporation and its components at the global scale

A process-based methodology is applied to estimate land-surface evaporation from multi-satellite information. GLEAM (Global Land-surface Evaporation: the Amsterdam Methodology) combines a wide range of remotely-sensed observations to derive daily actual evaporation and its different components. Soil water stress conditions are defined from a root-zone profile of soil moisture and used to estimate transpiration based on a Priestley and Taylor equation. The methodology also derives evaporationfrom bare soil and snow sublimation. Tall vegetation rainfall interception is independently estimated by means of the Gash analytical model. Here, GLEAM is applied daily, at global scale and a quarter degree resolution. Triple collocation is used to calculate the error structure of the evaporation estimates and test the relative merits of two different precipitation inputs. The spatial distribution of evaporation – and its different components – is analysed to understand the relative importance of each component over different ecosystems. Annual land evaporation is estimated as 67.9 × 10<sup>3</sup> km<sup>3</sup>, 80% corresponding to transpiration, 11% to interception loss, 7% to bare soil evaporation and 2% snow sublimation. Results show that rainfall interception plays an important role in the partition of precipitation into evaporation and water available for runoff at a continental scale. This study gives insights into the relative importance of precipitation and net radiation in driving evaporation, and how the seasonal influence of these controls varies over different regions. Precipitation is recognised as an important factor driving evaporation, not only in areas that have limited soil water availability, but also in areas of high rainfall interception and low available energy

Global land-surface evaporation estimated from satellite-based observations

This paper outlines a new strategy to derive evaporation from satellite observations. The approach uses a variety of satellite-sensor products to estimate daily evaporation at a global scale and 0.25 degree spatial resolution. Central to this methodology is the use of the Priestley and Taylor (PT) evaporation model. The minimalistic PT equation combines a small number of inputs, the majority of which can be detected from space. This reduces the number of variables that need to be modelled. Key distinguishing features of the approach are the use of microwave-derived soil moisture, land surface temperature and vegetation density, as well as the detailed estimation of rainfall interception loss. The modelled evaporation is validated against one year of eddy covariance measurements from 43 stations. The estimated annual totals correlate well with the stations' annual cumulative evaporation (<i>R</i>=0.80, <i>N</i>=43) and present a low average bias (−5%). The validation of the daily time series at each individual station shows good model performance in all vegetation types and climate conditions with an average correlation coefficient of <i><span style="text-decoration: overline">R</span></i>=0.83, still lower than the <i><span style="text-decoration: overline">R</span></i>=0.90 found in the validation of the monthly time series. The first global map of annual evaporation developed through this methodology is also presented

A new parameterization of the Effective Temperature for L-band Radiometry.

An accurate value of the effective temperature is critical for soil emissivity retrieval, and hence soil moisture content retrieval, from passive microwave observations. Computation of the effective temperature needs fine profile measurements of soil temperature and soil moisture. The availability of a two year long data set of these surface variables from SMOSREX (Surface Monitoring Of the Soil Reservoir EXperiment) makes it possible to study the effective temperature at the seasonal to interannual scale. This study shows that present parameterizations do not adequately describe the seasonal variations in sensing depth. Therefore, a new parameterization is proposed that is stable at the seasonal to interannual scales while retaining simplicity. Copyright 2006 by the American Geophysical Union

Soil moisture active and passive microwave products: intercomparison and evaluation over a Sahelian site

This paper presents a comparison and an evaluation of five soil moisture products based on satellite-based passive and active microwave measurements. Products are evaluated for 2005–2006 against ground measurements obtained from the soil moisture network deployed in Mali (Sahel) in the framework of the African Monsoon Multidisciplinary Analysis project. It is shown that the accuracy of the soil moisture products is sensitive to the retrieval approach as well as to the sensor type (active or passive) and to the signal frequency (from 5.6 GHz to 18.8 GHz). The spatial patterns of surface soil moisture are compared between the different products at meso-scale (14.5° N–17.5° N and 2° W–1° W). A general good consistency between the different satellite soil moisture products is shown in terms of meso-scale spatial distribution, in particular after convective rainfall occurrences. Comparison to ground measurement shows that although soil moisture products obtained from satellite generally over-estimate soil moisture values during the dry season, most of them capture soil moisture temporal variations in good agreement with ground station measurements