利用遥感高光谱数据估算叶片含水量
Estimating Leaf Water Content using Remotely Sensed Hyperspectral Data
摘要：
植物水分胁迫可能是由于根系/土壤的可用水有限或由于蒸腾作用增加而发生的。这些因素对植物生理和光合能力产生不利影响，以至于已显示对生长和产量具有抑制作用[18]。植物水分胁迫状态的早期识别使得能够应用合适的纠正措施以获得预期的作物产量。此外，通过精准农业方法提高作物产量是气候政策和联合国可持续发展目标的关键组成部分[1]。叶片含水量（LWC）是一种可用于估计含水量和识别胁迫植物的量度。作物生长初期的LWC是衡量作物生产力和产量的重要指标。水分胁迫的影响可以是瞬时的[15]，影响气体交换或长期的，显着减少[9，18，22]。因此，有必要在生长的早期阶段确定潜在的植物水分胁迫[15]，以引入纠正性灌溉并缓解胁迫。LWC还可用于通过测量甚至在人工诱导的水分胁迫下LWC的稳定性来鉴定耐受水分胁迫和盐度的植物基因型[18，25]。此类实验通常采用破坏性程序来获得LWC，这是耗时且劳动密集的。因此，本研究开发了一种非破坏性的方法来估计LWC基于无人机的高光谱数据。
英文摘要：
Plant water stress may occur due to the limited availability of water to the roots/soil or due to increased transpiration. These factors adversely affect plant physiology and photosynthetic ability to the extent that it has been shown to have inhibitory effects in both growth and yield [18]. Early identification of plant water stress status enables suitable corrective measures to be applied to obtain the expected crop yield. Further, improving crop yield through precision agriculture methods is a key component of climate policy and the UN sustainable development goals [1]. Leaf water content (LWC) is a measure that can be used to estimate water content and identify stressed plants. LWC during the early crop growth stages is an important indicator of plant productivity and yield. The effect of water stress can be instantaneous [15], affecting gaseous exchange or long-term, significantly reducing [9, 18, 22]. It is thus necessary to identify potential plant water stress during the early stages of growth [15] to introduce corrective irrigation and alleviate stress. LWC is also useful for identifying plant genotypes that are tolerant to water stress and salinity by measuring the stability of LWC even under artificially induced water stress [18, 25]. Such experiments generally employ destructive procedures to obtain the LWC, which is time-consuming and labor intensive. Accordingly, this research has developed a non-destructive method to estimate LWC from UAV-based hyperspectral data.
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