Gao Rui, Torres-Rua Alfonso, Nassar Ayman, Alfieri Joseph, Aboutalebi Mahyar, Hipps Lawrence, Bambach Ortiz Nicolas, Mcelrone Andrew J, Coopmans Calvin, Kustas William, White William, McKee Lynn, Del Mar Alsina Maria, Dokoozlian Nick, Sanchez Luis, Prueger John H, Nieto Hector, Agam Nurit
Utah State University, Old Main Hill, Logan, UT 84322, USA.
U.S. Department of Agriculture, Agricultural Research Service, Hydrology and Remote Sensing Laboratory, Beltsville, MD 20705, USA.
Proc SPIE Int Soc Opt Eng. 2021;11747. doi: 10.1117/12.2586259. Epub 2021 Apr 12.
Accurate quantification of the partitioning of evapotranspiration (ET) into transpiration and evaporation fluxes is necessary to understanding ecosystem interactions among carbon, water, and energy flux components. ET partitioning can also support the description of atmosphere and land interactions and provide unique insights into vegetation water status. Previous studies have identified leaf area index (LAI) estimation as a key descriptor of biomass conditions needed for the estimation of transpiration and evaporation. LAI estimation in clumped vegetation systems, such as vineyards and orchards, has proven challenging and is strongly related to crop phenological status and canopy management. In this study, a feature extraction model based on previous research was built to generate a total of 202 preliminary variables at a 3.6-by-3.6-meter-grid scale based on submeter-resolution information from a small Unmanned Aerial Vehicle (sUAV) in four commercial vineyards across California. Using these variables, a machine learning model called eXtreme Gradient Boosting (XGBoost) was successfully built for LAI estimation. The XGBoost built-in function requires only six variables relating to vegetation indices and temperature to produce high-accuracy LAI estimation for the vineyard. Using the six-variable XGBoost-based LAI map, two versions of the Two-Source Energy Balance (TSEB) model, TSEB-PT and TSEB-2T were used for energy balance and ET partitioning. Comparing these results with the Eddy-Covariance (EC) tower data, showed that TSEB-PT outperforms TSEB-2T on the estimation of sensible heat flux (within 13% relative error) and surface heat flux (within 34% relative error), while TSEB-2T outperforms TSEB-PT on the estimation of net radiation (within 14% relative error) and latent heat flux (within 2% relative error). For the mature vineyard (north block), TSEB-2T performs better than TSEB-PT in partitioning the canopy latent heat flux with 6.8% relative error and soil latent heat flux with 21.7% relative error; however, for the younger vineyard (south block), TSEB-PT performs better than TSEB-2T in partitioning the canopy latent heat flux with 11.7% relative error and soil latent heat flux with 39.3% relative error.
准确量化蒸散量（ET）在蒸腾和蒸发通量之间的分配，对于理解生态系统中碳、水和能量通量成分之间的相互作用至关重要。ET分配还可以支持对大气和陆地相互作用的描述，并为植被水分状况提供独特见解。先前的研究已将叶面积指数（LAI）估计确定为估计蒸腾和蒸发所需生物量状况的关键描述符。在葡萄园和果园等丛生植被系统中，LAI估计已被证明具有挑战性，并且与作物物候状态和冠层管理密切相关。在本研究中，基于先前的研究构建了一个特征提取模型，根据来自加利福尼亚州四个商业葡萄园的小型无人机（sUAV）的亚米级分辨率信息，在3.6×3.6米的网格尺度上生成了总共202个初步变量。利用这些变量，成功构建了一个名为极端梯度提升（XGBoost）的机器学习模型用于LAI估计。XGBoost内置函数仅需要六个与植被指数和温度相关的变量，即可对葡萄园进行高精度的LAI估计。利用基于六变量XGBoost的LAI地图，使用了两源能量平衡（TSEB）模型的两个版本，即TSEB-PT和TSEB-2T进行能量平衡和ET分配。将这些结果与涡度协方差（EC）塔数据进行比较，结果表明，在感热通量估计（相对误差在13%以内）和地表热通量估计（相对误差在34%以内）方面，TSEB-PT优于TSEB-2T；而在净辐射估计（相对误差在14%以内）和潜热通量估计（相对误差在2%以内）方面，TSEB-2T优于TSEB-PT。对于成熟葡萄园（北地块），TSEB-2T在分配冠层潜热通量时相对误差为6.8%，分配土壤潜热通量时相对误差为21.7%，表现优于TSEB-PT；然而，对于较年轻的葡萄园（南地块），TSEB-PT在分配冠层潜热通量时相对误差为11.7%，分配土壤潜热通量时相对误差为39.3%，表现优于TSEB-2T。
