Abstract. 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 (R = 0.80, N = 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 R = 0.83, still lower than the R = 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.

[1] 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.

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 × 10³ km³, 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

The hydrological cycle is expected to intensify in response to global warming. Yet, little unequivocal evidence of such an acceleration has been found on a global scale. This holds in particular for terrestrial evaporation, the crucial return flow of water from land to atmosphere. Here we use satellite observations to reveal that continental evaporation has increased in northern latitudes, at rates consistent with expectations derived from temperature trends. However, at the global scale, the dynamics of the El Niño/Southern Oscillation (ENSO) have dominated the multi-decadal variability. During El Niño, limitations in terrestrial moisture supply result in vegetation water stress and reduced evaporation in eastern and central Australia, southern Africa and eastern South America. The opposite situation occurs during La Niña. Our results suggest that recent multi-year declines in global average continental evaporation8,9 reflect transitions to El Niño conditions, and are not the consequence of a persistent reorganization of the terrestrial water cycle. Future changes in continental evaporation will be determined by the response of ENSO to changes in global radiative forcing, which still remains highly uncertain

This paper outlines a new methodology to derive evaporation from satellite observations. The approach uses a variety of satellite-sensor products to estimate daily evaporation at a global scale, with a 0.25 degree spatial resolution. Central to this approach is the use of the Priestley and Taylor (PT) evaporation model. Because the PT equation is driven by net radiation, this strategy avoids the need to specify surface fields of variables, such as the surface conductance, which cannot be detected directly from space. Key distinguishing features are the use of microwave-derived soil moisture, land surface temperature and vegetation density, as well as the use of a detailed rainfall interception module. 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> = 0.84, <i>N</i> = 43) and show a negligible bias (−1.5%). 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 <span style="border-top: 1px solid #000; color: #000;"><i>R</i></span> = 0.84, still lower than the <span style="border-top: 1px solid #000; color: #000;"><i>R</i></span> = 0.91 found in the validation of the monthly time series. The first global map of annual evaporation developed through this methodology is also presented

[1] An alternative to thermal infrared satellite sensors for measuring land surface temperature (T s ) is presented. The 37 GHz vertical polarized brightness temperature is used to derive T s because it is considered the most appropriate microwave frequency for temperature retrieval. This channel balances a reduced sensitivity to soil surface characteristics with a relatively high atmospheric transmissivity. It is shown that with a simple linear relationship, accurate values for T s can be obtained from this frequency, with a theoretical bias of within 1 K for 70% of vegetated land areas of the globe. Barren, sparsely vegetated, and open shrublands cannot be accurately described with this single channel approach because variable surface conditions become important. The precision of the retrieved land surface temperature is expected to be better than 2.5 K for forests and 3.5 K for low vegetation. This method can be used to complement existing infrared derived temperature products, especially during clouded conditions. With several microwave radiometers currently in orbit, this method can be used to observe the diurnal temperature cycles with surprising accuracy.

In the last few years, research made significant progress towards operational soil moisture remote sensing which lead to the availability of several global data sets. For an optimal use of these data, an accurate estimation of the error structure is an important condition. To solve for the validation problem we introduce the triple collocation error estimation technique. The triple collocation technique is a powerful tool to estimate the root mean square error while simultaneously solving for systematic differences in the climatologies of a set of three independent data sources. We evaluate the method by applying it to a passive microwave (TRMM radiometer) derived, an active microwave (ERS‐2 scatterometer) derived and a modeled (ERA‐Interim reanalysis) soil moisture data sets. The results suggest that the method provides realistic error estimates.

[1] A new methodology for estimating forest rainfall interception from multisatellite observations is presented. The Climate Prediction Center morphing technique (CMORPH) precipitation product is used as driving data and is applied to Gash's analytical model to derive daily interception rates at global scale. Results compare well with field observations of rainfall interception (R = 0.86, n = 42). Global estimates are presented and spatial differences in the distribution of interception over different ecosystems analyzed. According to our findings, interception loss is responsible for the evaporation of approximately 13% of the total incoming rainfall over broadleaf evergreen forests, 19% in broadleaf deciduous forests, and 22% in needleleaf forests. The product is sensitive to the volume of rainfall, rain intensity, and forest cover. In combination with separate estimates of transpiration it offers the potential to study the impact of climate change and deforestation on the dynamics of the global hydrological cycle.