Feng Fei, Li Xianglan, Yao Yunjun, Liu Meng
State Key Laboratory of Remote Sensing Science, College of Global Change and Earth System Science, Beijing Normal University, Beijing, China.
State Key Laboratory of Remote Sensing Science, School of Geography, Beijing Normal University, Beijing, China.
PLoS One. 2017 Aug 24;12(8):e0183771. doi: 10.1371/journal.pone.0183771. eCollection 2017.
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.
从大陆尺度到全球尺度估算农田潜热通量（LE）对于作物生产建模和水资源管理至关重要。在过去几十年中，开发了许多LE模型，如中分辨率成像光谱仪LE（MOD16）算法、基于遥感的修正彭曼 - 蒙特斯LE算法（RRS）、喷气推进实验室的普里斯特利 - 泰勒LE算法（PT - JPL）以及改进的基于卫星的普里斯特利 - 泰勒LE算法（MS - PT）。然而，尚未使用各种算法在全球农田生态系统上对这些LE模型进行直接比较。在本研究中，我们使用34个涡度相关（EC）站点评估了这四种LE模型的性能。结果表明，四种模型中农田的年平均LE在33.49至58.97 W/m²之间变化。1982 - 2009年期间，全球农田生态系统的年际LE略有增加。所有模型的性能均可接受，决定系数（R²）范围为0.4至0.7，均方根误差（RMSE）约为35 W/m²。MS - PT在整个农田生态系统中具有良好的整体性能，R²最高，RMSE最低且偏差相对较小。MOD16和RRS的性能下降，R²范围为0.4至0.6，RMSE为30至39 W/m²，可能归因于结构算法中的经验参数和校准系数。
