Hyperspectral sensing of photosynthesis, stomatal conductance, and transpiration for citrus tree under drought condition

Abstract

Obtaining variation in water use and photosynthetic capacity is a promising route toward yield increases, but it is still too laborious for large-scale rapid monitoring and prediction. We tested the application of hyperspectral reflectance as a high-throughput phenotyping approach for early identification of water stress and rapid assessment of leaf photosynthetic traits in citrus trees. To this end, photosynthetic CO2 assimilation rate (Pn), stomatal conductance (Cond) and transpiration rate (Trmmol) were measured with gas-exchange approaches alongside measurements of leaf hyperspectral reflectance from citrus grown across a gradient of soil drought levels. Water stress caused Pn, Cond and Trmmol rapid and continuous decreases in whole drought period. Upper layer was more sensitive to drought than middle and lower layers. Original reflectance spectra of three drought treatments were surprisingly of low diversity and could not track drought responses, whereas specific hyperspectral spectral vegetation indices (SVIs) and absorption features or wavelength position variables presented great potential. Performance of four machine learning algorithms were assessed and random forest (RF) algorithm yielded the highest predictive power for predicting photosynthetic parameters. Our results indicated that leaf hyperspectral reflectance was a reliable and stable method for monitoring water stress and yield increasing in large-scale orchards.

Ground-Based Hyperspectral Remote Sensing for Estimating Water Stress in Tomato Growth in Sandy Loam and Silty Loam Soils

“…Maximum CO 2 assimilation has also been efficiently estimated by PRI under severe drought conditions and this has established that changes in PRI were correlated with water stress in maize (Zhang et al [9]). Photosynthetic variability induced by heat and drought is simultaneously accompanied by complex physiological and biochemical processes, which could constrain the PRI-based estimation of the photosynthetic apparatus (Zhou et al [16], Magney et al [40], Garbulsky et al [41], Wong and Gamon [42], Zhang et al [43], Gamon et al [44], Massonnet et al [45] and Pedrol et al [46]). For optimization of tomato productivity, adequate and well-planned irrigation scheduling is required to ensure adequate water supply during the entire growth cycle.…”