Estimating Leaf Water Content using Remotely Sensed Hyperspectral Data

TL;DR

This paper presents a non-destructive method to estimate leaf water content (LWC) using UAV-based hyperspectral data, aiding early detection of water stress in plants to improve crop management and yield.

Contribution

The study introduces a novel remote sensing approach to estimate LWC non-destructively, enabling early water stress detection and supporting precision agriculture.

Findings

Successfully estimated LWC from hyperspectral data

Enabled early detection of plant water stress

Provided a scalable method for crop monitoring

Abstract

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.