C. Jiménez

The WACMOS-ET project - Part 1: Tower-scale evaluation of four remote sensing-based evapotranspiration algorithms

Abstract. The WAter Cycle Multi-mission Observation Strategy – EvapoTranspiration (WACMOS-ET) project has compiled a forcing data set covering the period 2005–2007 that aims to maximize the exploitation of European Earth Observations data sets for evapotranspiration (ET) estimation. The data set was used to run fourxa0established ET algorithms: the Priestley–Taylor Jet Propulsion Laboratory model (PT-JPL), the Penman–Monteith algorithm from the MODerate resolution Imaging Spectroradiometer (MODIS) evaporation productxa0(PM-MOD), the Surface Energy Balance Systemxa0(SEBS) and the Global Land Evaporation Amsterdam Modelxa0(GLEAM). In addition, in situ meteorological data from 24xa0FLUXNET towers were used to force the models, with results from both forcing sets compared to tower-based flux observations. Model performance was assessed on several timescales using both sub-daily and daily forcings. The PT-JPL model and GLEAM provide the best performance for both satellite- and tower-based forcing as well as for the considered temporal resolutions. Simulations using the PM-MOD were mostly underestimated, while the SEBS performance was characterized by a systematic overestimation. In general, all four algorithms produce the best results in wet and moderately wet climate regimes. In dry regimes, the correlation and the absolute agreement with the reference tower ET observations were consistently lower. While ET derived with in situ forcing data agrees best with the tower measurements (R2u202fu2009=u2009u202f0.67), the agreement of the satellite-based ET estimates is only marginally lower (R2u202fu2009=u2009u202f0.58). Results also show similar model performance at daily and sub-daily (3-hourly) resolutions. Overall, our validation experiments against in situ measurements indicate that there is no single best-performing algorithm across all biome and forcing types. An extension of the evaluation to a larger selection of 85xa0towers (model inputs resampled to a common grid to facilitate global estimates) confirmed the original findings.

On Uncertainty in Global Terrestrial Evapotranspiration Estimates from Choice of Input Forcing Datasets

AbstractEvapotranspiration ET is a critical water, energy, and climate variable, and recent work has been published comparing different global products. These comparisons have been difficult to interpret, however, because in most studies the evapotranspiration products were derived from models forced by different input data. Some studies have analyzed the uncertainty in regional evapotranspiration estimates from choice of forcings. Still others have analyzed how multiple models vary with choice of net radiation forcing data. However, no analysis has been conducted to determine the uncertainty in global evapotranspiration estimates attributable to each class of input forcing datasets. Here, one of these models [Priestly–Taylor JPL (PT-JPL)] is run with 19 different combinations of forcing data. These data include three net radiation products (SRB, CERES, and ISCCP), three meteorological datasets [CRU, Atmospheric Infrared Sounder (AIRS) Aqua, and MERRA], and three vegetation index products [MODIS; Global I...

Inversion of AMSR‐E observations for land surface temperature estimation: 1. Methodology and evaluation with station temperature

Inversions of the Earth Observation Satellite (EOS) Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperatures (Tbs) to derive the land surface temperature (Ts) are presented based on building a global transfer function by neural networks trained with AMSR-E Tbs and retrieved microwave Ts*. The only required inputs are the Tbs and monthly climatological emissivities, minimizing the dependence on ancillary data. The inversions are accompanied by a coarse estimation of retrieval uncertainty, an estimate of the quality of the retrieval, and a series of flags to signal difficult inversion situations. For ∼75% of the land surface the root-mean-square difference (RMSD) between the training target Ts* and the neural network retrieved Ts is below 2.8 K. The RMSD when comparing with the Moderate Resolution Imaging Spectroradiometer (MODIS) clear-sky Ts is below 3.9 K for the same conditions. Over 10 ground stations, AMSR-E and MODIS Ts were compared with the in situ data. Overall, MODIS agrees better with the station Ts than AMSR-E (all-station mean RMSD of 2.4 K for MODIS and 4.0 for AMSR-E), but AMSR-E provides a larger number of Ts estimates by being able to measure under cloudy conditions, with an approximated ratio of 3 to 1 over the analyzed stations. At many stations the RMSD of the AMSR-E clear and cloudy sky are comparable, highlighting the ability of the microwave inversions to provide Ts under most atmospheric conditions. Closest agreement with the in situ Ts happens for stations with dense vegetation, where AMSR-E emissivity is less varying.