STEEP: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests
STEEP: A remotely-sensed energy balance model for evapotranspiration estimation in seasonally dry tropical forests
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STEEP：用于估算季节性干旱热带森林蒸散量的遥感能量平衡模型
DOI：
10.1016/j.agrformet.2023.109408
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发表时间：
2023
影响因子：
6.2
通讯作者：
Bezerra U
中科院分区：
文献类型：
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作者：
Bezerra U
使用基于多光谱和热传感器的遥感（RS）产品改进蒸散量（ET）估算一直是水文研究的突破。在大规模应用中，使用基于 RS 的表面能量平衡 (SEB) 模型的方法通常依赖于过度简化。这些模型在季节性干旱热带森林 (SDTF) 中的使用一直具有挑战性，因为这些模型的假设与环境的特殊性之间不相容，例如高度对比的物候阶段或蒸散主要由土壤-水可用性控制。我们根据单源批量传输方程开发了一种基于 RS 的 SEB 模型，称为季节性热带生态系统能量分配 (STEEP)。我们的模型在计算矩粗糙度长度时使用植物面积指数来表示植物的木质结构。我们在传热空气动力阻力的计算中纳入了参数 kB−1 及其使用 RS 土壤湿度的修正。此外，利用Priestley-Taylor方程对端元像素中剩余水可用性引起的λET进行量化。我们使用免费提供的数据在 Google Earth Engine 上实现了该算法。为了评估我们的模型，我们使用了巴西半干旱地区卡廷加（南美洲最大的 SDTF）四个地点的涡度协方差数据。我们的结果表明，STEEP 提高了 ET 估计的准确性，而无需任何额外的气候信息。这种改善在旱季更为明显，一般来说，传统 SEB 模型（例如陆地表面能量平衡算法 (SEBAL)）高估了这些 SDTF 的蒸散量。相对于全球 ET 产品（MOD16 和 PMLv2），STEEP 模型具有相似或优越的行为和性能统计数据。这项工作有助于更好地了解 SDTF 地方和区域范围内能源和水平衡的驱动因素和调节因素。
Improvement of evapotranspiration (ET) estimates using remote sensing (RS) products based on multispectral and thermal sensors has been a breakthrough in hydrological research. In large-scale applications, methods that use the approach of RS-based surface energy balance (SEB) models often rely on oversimplifications. The use of these models for Seasonally Dry Tropical Forests (SDTF) has been challenging due to incompatibilities between the assumptions underlying those models and the specificities of this environment, such as the highly contrasting phenological phases or ET being mainly controlled by soil–water availability. We developed a RS-based SEB model from a one-source bulk transfer equation, called Seasonal Tropical Ecosystem Energy Partitioning (STEEP). Our model uses the plant area index to represent the woody structure of the plants in calculating the moment roughness length. We included the parameterkB−1and its correction using RS soil moisture in the calculation of the aerodynamic resistance for heat transfer. Besides,λETcaused by remaining water availability in endmembers pixels was quantified using the Priestley-Taylor equation. We implemented the algorithm on Google Earth Engine, using freely available data. To evaluate our model, we used eddy covariance data from four sites in the Caatinga, the largest SDTF in South America, in the Brazilian semiarid region. Our results show that STEEP increased the accuracy of ET estimates without requiring any additional climatological information. This improvement is more pronounced during the dry season, which, in general, ET for these SDTF is overestimated by traditional SEB models, such as the Surface Energy Balance Algorithms for Land (SEBAL). The STEEP model had similar or superior behavior and performance statistics relative to global ET products (MOD16 and PMLv2). This work contributes to an improved understanding of the drivers and modulators of the energy and water balances at local and regional scales in SDTF.