State Key Laboratory of Remote Sensing Science, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
Faculty of Geo-Information and Earth Observation (ITC), University of Twente, 7500 AE Enschede, The Netherlands.
Sensors (Basel). 2020 May 15;20(10):2811. doi: 10.3390/s20102811.
Reliable estimates of terrestrial latent heat flux (LE) at high spatial and temporal resolutions are of vital importance for energy balance and water resource management. However, currently available LE products derived from satellite data generally have high revisit frequency or fine spatial resolution. In this study, we explored the feasibility of the high spatiotemporal resolution LE fusion framework to take advantage of the Moderate Resolution Imaging Spectroradiometer (MODIS) and Chinese GaoFen-1 Wide Field View (GF-1 WFV) data. In particular, three-fold fusion schemes based on Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) were employed, including fusion of surface reflectance (Scheme 1), vegetation indices (Scheme 2) and high order LE products (Scheme 3). Our results showed that the fusion of vegetation indices and further computing LE (Scheme 2) achieved better accuracy and captured more detailed information of terrestrial LE, where the determination coefficient (R) varies from 0.86 to 0.98, the root-mean-square error (RMSE) ranges from 1.25 to 9.77 W/m and the relative RSME (rRMSE) varies from 2% to 23%. The time series of merged LE in 2017 using the optimal Scheme 2 also showed a relatively good agreement with eddy covariance (EC) measurements and MODIS LE products. The fusion approach provides spatiotemporal continuous LE estimates and also reduces the uncertainties in LE estimation, with an increment in R by 0.06 and a decrease in RMSE by 23.4% on average. The proposed high spatiotemporal resolution LE estimation framework using multi-source data showed great promise in monitoring LE variation at field scale, and may have value in planning irrigation schemes and providing water management decisions over agroecosystems.
可靠的陆面潜热通量（LE）估计值在时空分辨率上都非常重要，这对于能量平衡和水资源管理至关重要。然而，目前基于卫星数据的 LE 产品普遍具有高重复观测频率或精细的空间分辨率。在本研究中，我们探索了利用中分辨率成像光谱仪（MODIS）和中国高分一号宽视场成像仪（GF-1 WFV）数据的高时空分辨率 LE 融合框架的可行性。具体来说，我们采用了基于增强型时空自适应反射融合模型（ESTARFM）的三重融合方案，包括地表反射率融合（方案 1）、植被指数融合（方案 2）和高阶 LE 产品融合（方案 3）。结果表明，融合植被指数并进一步计算 LE（方案 2）可以获得更好的精度，并捕获更详细的陆面 LE 信息，其中决定系数（R）在 0.86 到 0.98 之间，均方根误差（RMSE）在 1.25 到 9.77 W/m 之间，相对均方根误差（rRMSE）在 2%到 23%之间。使用最优方案 2 对 2017 年合并 LE 的时间序列进行分析，也与涡度相关（EC）测量和 MODIS LE 产品具有较好的一致性。该融合方法提供了时空连续的 LE 估计值，并减少了 LE 估计的不确定性，R 值平均增加了 0.06，RMSE 值平均降低了 23.4%。使用多源数据的高时空分辨率 LE 估算框架在监测田间 LE 变化方面具有很大的应用潜力，并且可能在规划灌溉方案和提供农业生态系统的水资源管理决策方面具有价值。
