The differences between the four models, averaged during 1982~2010 at a spatial resolution of 0.05° from the algorithms, driven by MERRA meteorology data.

<p>The differences between the four models, averaged during 1982~2010 at a spatial resolution of 0.05° from the algorithms, driven by MERRA meteorology data.</p

Characteristics of validation data at the EC sites.

<p>Characteristics of validation data at the EC sites.</p

Long-term spatial distributions and trends of the latent heat fluxes over the global cropland ecosystem using multiple satellite-based models

<div><p>Estimating cropland latent heat flux (LE) from continental to global scales is vital to modeling crop production and managing water resources. Over the past several decades, numerous LE models were developed, such as the moderate resolution imaging spectroradiometer LE (MOD16) algorithm, revised remote sensing-based Penman–Monteith LE algorithm (RRS), the Priestley–Taylor LE algorithm of the Jet Propulsion Laboratory (PT-JPL) and the modified satellite-based Priestley-Taylor LE algorithm (MS-PT). However, these LE models have not been directly compared over the global cropland ecosystem using various algorithms. In this study, we evaluated the performances of these four LE models using 34 eddy covariance (EC) sites. The results showed that mean annual LE for cropland varied from 33.49 to 58.97 W/m² among the four models. The interannual LE slightly increased during 1982–2009 across the global cropland ecosystem. All models had acceptable performances with the coefficient of determination (R²) ranging from 0.4 to 0.7 and a root mean squared error (RMSE) of approximately 35 W/m². MS-PT had good overall performance across the cropland ecosystem with the highest R², lowest RMSE and a relatively low bias. The reduced performances of MOD16 and RRS, with R² ranging from 0.4 to 0.6 and RMSEs from 30 to 39 W/m², might be attributed to empirical parameters in the structure algorithms and calibrated coefficients.</p></div

Seasonal cropland average LE during 1982~2010.

<p>DJF represents December, January, and February. MAM represents March, April, and May. JJA represents June, July, and August. SON represents September, October, and November.</p

Spatial patterns of long-term cropland LE trends of four models from 1982 to 2010.

<p>Spatial patterns of long-term cropland LE trends of four models from 1982 to 2010.</p

Interannual variations of average LE over the cropland ecosystem during 1982–2010 predicted by the four models.

<p>Interannual variations of average LE over the cropland ecosystem during 1982–2010 predicted by the four models.</p

Comparison between the seasonal patterns of LE (W/m²) obtained by MOD16, RRS, PT-JPL and MS-PT.

<p>Comparison between the seasonal patterns of LE (W/m²) obtained by MOD16, RRS, PT-JPL and MS-PT.</p

Taylor diagram for LE estimates using four LE algorithms driven by EC sites data.

<p>Six points were plotted on the semi-polar-style graph, with the circle representing the four models and field observation.</p

Spatial distribution of the correlation coefficients, RMSE and Bias for estimated daily LE calculated by MOD16, RRS, PT-JPL and MS-PT over the period of 2000–2009 (S1 File).

<p>Spatial distribution of the correlation coefficients, RMSE and Bias for estimated daily LE calculated by MOD16, RRS, PT-JPL and MS-PT over the period of 2000–2009 (<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0183771#pone.0183771.s001" target="_blank">S1 File</a>).</p

Estimated annual global terrestrial LE for cropland, averaged for 1982~2010 at a spatial resolution of 0.05°, from the four models, driven by MERRA data.

<p>Estimated annual global terrestrial LE for cropland, averaged for 1982~2010 at a spatial resolution of 0.05°, from the four models, driven by MERRA data.</p