Leaf transpiration of drought tolerant plant can be captured by hyperspectral reflectance using PLSR analysis

Abstract

A clear understanding of plant transpiration is a crucial step for water cycle and climate modeling, especially for arid ecosystems in which water is one of the major constraints. Traditional field measurements of leaf scale transpiration are always time-consuming and often unfeasible in the context of large spatial and temporal scales. This study focused on a dominant native plant in the arid land of central Asia, Haloxylon ammondendron, with the aim of deriving the leaf-scale transpiration through hyperspectral reflectance using Partial Least Squares Regression (PLSR) analysis. The results revealed that the PLSR model based on the first-order derivative spectra at wavelengths selected through stepwise regression analysis can closely trace leaf transpiration with a high accuracy (R 2 = 0.78, RMSE = 1.62 µmol g -1 s -1 ). The accuracy is also relatively stable even at a spectral resolution of 10 nm, which is very close to the bandwidths of several running satellite-borne hyperspectral sensors such as Hyperion. The results also proved that the first-order derivative spectra within the shortwave infrared (SWIR) domain, especially at 2435, 2440, 2445, and 2470 nm, were critical for PLSR models to predict leaf transpiration. These findings highlight a promising strategy for developing remote sensing methods to potentially characterize transpiration at broad scales.

Estimating growth and photosynthetic properties of wheat grown in simulated saline field conditions using hyperspectral reflectance sensing and multivariate analysis

“…8 , who stated that the selected wavelengths within SWIR (e.g., 1330 and 1500 nm), which are almost independent of the variations in pigment content and photosynthetic capacity, were indicative of stress-induced alterations in several photosynthetic-related traits. Wang and Jin 25 also reported that wavelengths selected by PLSR-VIP and MLR within the SWIR domain, especially at 2435, 2440, 2445, and 2470 nm, were found to be effective for tracking changes in E under drought stress conditions.…”

“…In particular, PLSR is able to correlate data when the number of independent variables greatly exceeds the number of dependent variables 22,23 . Therefore, many studies have applied PLSR to estimate various crop physiological, biochemical, nutrient, and structural parameters such as the photosynthetic capacity 24 , leaf transpiration rate 25 , leaf water potential and Gs 8 , pigment, nitrogen, and potassium content 9,16,26 , green and dry biomass 27,28 , leaf area index 18 , and grain yield 29–31 . Therefore, in this study, we hypothesized that the combinations of all hyperspectral data with the PLSR method could improve the estimation of crop parameters across different environmental conditions and genotypes compared with published SRIs.…”

Hyperspectral reflectance sensing to assess the growth and photosynthetic properties of wheat cultivars exposed to different irrigation rates in an irrigated arid region

“…Unfortunately, there are currently no traditional methods whereby multiple morphophysiological parameters can be measured simultaneously. Furthermore, measurement of these parameters based on plant sampling techniques is generally tedious, destructive, and time consuming, and often inappropriate for tracking the dynamics of physiological parameters or for fulfilling the requirement for real-time evaluation of morphological parameters [ 12 , 13 ]. Importantly, although the parameters related to photosynthetic efficiency ( Pn , Gs , and E ) can be simultaneously measured in a rapid and non-destructive manner using a portable gas exchange system, this method provides information on the photosynthetic status solely on a single leaf.…”

Assessing Grapevine Biophysical Parameters From Unmanned Aerial Vehicles Hyperspectral Imagery

“…In this context, UAV based HS sensors ( Figure 1 ) are gaining particular attention due to their well-known ability to provide deep spectral characterization of vegetation and soil targets. HS imagery has been applied to quantify leaf area index ( Haboudanea et al, 2004 ; Delegido et al, 2013 ), plant biomass ( Cho et al, 2007 ; Fu et al, 2014 ), pigment contents ( Yi et al, 2014 ), plant nitrogen content ( Ryu et al, 2011 ; Inoue et al, 2012 ), and leaf nitrogen and phosphorus concentrations ( Ramoelo et al, 2013 ; Zhang et al, 2013 ), soil moisture content ( Ge et al, 2021 ), as well as plant water status and transpiration ( Wang and Jin, 2015 ; Marshall et al, 2016 ).…”

“…For example, in plant condition detection, the red-edge location (REP), which is the wavelength of the maximum first derivative in the range of 690–750 nm, has been successfully used. As a result, a number of derivative hyperspectral indices (dHVIs) have been developed and are now being used to calculate biophysical and biochemical quantities ( Demetriades-Shah et al, 1990 ; Imanishi et al, 2004 ; Wang and Jin, 2015 ). Demetriades-Shah et al (1990) and Zarco-Tejada et al (2003a , b) found that indices based on derivative spectra are more efficient than reflectance-based indices.…”