Kang Yanghui, Gao Feng, Anderson Martha, Kustas William, Nieto Hector, Knipper Kyle, Yang Yun, White William, Alfieri Joseph, Torres-Rua Alfonso, Alsina Maria Mar, Karnieli Arnon
Hydrology and Remote Sensing Laboratory, US Department of Agriculture, Agricultural Research Service, Beltsville, MD USA.
Department of Environmental Science, Policy, and Management, University of California, Berkeley, Berkeley, CA USA.
Irrig Sci. 2022;40(4-5):531-551. doi: 10.1007/s00271-022-00798-8. Epub 2022 Jun 9.
Remote sensing estimation of evapotranspiration (ET) directly quantifies plant water consumption and provides essential information for irrigation scheduling, which is a pressing need for California vineyards as extreme droughts become more frequent. Many ET models take satellite-derived Leaf Area Index (LAI) as a major input, but how uncertainties of LAI estimations propagate to ET and the partitioning between evaporation and transpiration is poorly understood. Here we assessed six satellite-based LAI estimation approaches using Landsat and Sentinel-2 images against ground measurements from four vineyards in California and evaluated ET sensitivity to LAI in the thermal-based two-source energy balance (TSEB) model. We found that radiative transfer modeling-based approaches predicted low to medium LAI well, but they significantly underestimated high LAI in highly clumped vine canopies (RMSE ~ 0.97 to 1.27). Cubist regression models trained with ground LAI measurements from all vineyards achieved high accuracy (RMSE ~ 0.3 to 0.48), but these empirical models did not generalize well between sites. Red edge bands and the related vegetation index (VI) from the Sentinel-2 satellite contain complementary information of LAI to VIs based on near-infrared and red bands. TSEB ET was more sensitive to positive LAI biases than negative ones. Positive LAI errors of 50% resulted in up to 50% changes in ET, while negative biases of 50% in LAI caused less than 10% deviations in ET. However, even when ET changes were minimal, negative LAI errors of 50% led to up to a 40% reduction in modeled transpiration, as soil evaporation and plant transpiration responded to LAI change divergently. These findings call for careful consideration of satellite LAI uncertainties for ET modeling, especially for the partitioning of water loss between vine and soil or cover crop for effective vineyard irrigation management.
蒸散量（ET）的遥感估算可直接量化植物的水分消耗，并为灌溉调度提供重要信息，这对于加利福尼亚州的葡萄园来说是一项紧迫需求，因为极端干旱变得越来越频繁。许多ET模型将卫星衍生的叶面积指数（LAI）作为主要输入，但LAI估算的不确定性如何传播到ET以及蒸发和蒸腾之间的分配情况却知之甚少。在此，我们使用陆地卫星和哨兵 - 2图像评估了六种基于卫星的LAI估算方法，并与加利福尼亚州四个葡萄园的地面测量数据进行对比，同时在基于热的双源能量平衡（TSEB）模型中评估了ET对LAI的敏感性。我们发现，基于辐射传输建模的方法能较好地预测低至中等的LAI，但在高度丛生的葡萄树冠层中，它们显著低估了高LAI（均方根误差约为0.97至1.27）。使用所有葡萄园的地面LAI测量数据训练的Cubist回归模型具有较高的准确性（均方根误差约为0.3至0.48），但这些经验模型在不同地点之间的通用性不佳。哨兵 - 2卫星的红边波段及相关植被指数（VI）包含与基于近红外和红波段的VI互补的LAI信息。TSEB ET对正的LAI偏差比负偏差更敏感。LAI正误差50%会导致ET变化高达50%，而LAI负偏差50%导致ET偏差小于10%。然而，即使ET变化最小，LAI负误差50%也会导致模拟蒸腾量减少高达40%，因为土壤蒸发和植物蒸腾对LAI变化的响应不同。这些发现要求在ET建模时仔细考虑卫星LAI的不确定性，特别是在葡萄藤与土壤或覆盖作物之间水分损失的分配方面，以实现有效的葡萄园灌溉管理。
