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In recent years, rapid development has been achieved in technologies and sensors related to autonomous driving and assistive technologies. In this study, low-cost light detection and ranging (LiDAR) was used to estimate leaf water content (LWC) by measuring LiDAR reflectance instead of morphological measurement (e.g., plant size), which is the conventional method. Experimental results suggest that reflection intensity can be corrected using the body temperature of LiDAR, when using reflection intensity observed by LiDAR. Comparisons of corrected LiDAR observation data and changes in reflectance attributed to leaf drying suggest that the reflectance increases with leaf drying in the 905 nm band observed with a hyperspectral camera. The LWC is estimated with an R2 of 0.950, RMSE of 6.78%, and MAPE of 18.6% using LiDAR reflectance. Although the 905 nm wavelength used by LiDAR is not the main water absorption band, the reflectance is closely related to the leaf structure; therefore, it is believed that the reflectance changes with structural changes accompanying drying, which allows for the indirect estimation of LWC. This can help utilize the reflectance of the 905 nm single-wavelength LiDAR, which, to the best of our knowledge has not been used in plant observations for estimating LWC.

This paper reviews the different remote sensing techniques found in the literature to monitor plant water status, allowing farmers to control the irrigation management and to avoid unnecessary periods of water shortage and a needless waste of valuable water. The scope of this paper covers a broad range of 77 references published between the years 1981 and 2021 and collected from different search web sites, especially Scopus. Among them, 74 references are research papers and the remaining three are review papers. The different collected approaches have been categorized according to the part of the plant subjected to measurement, that is, soil (12.2%), canopy (33.8%), leaves (35.1%) or trunk (18.9%). In addition to a brief summary of each study, the main monitoring technologies have been analyzed in this review. Concerning the presentation of the data, different results have been obtained. According to the year of publication, the number of published papers has increased exponentially over time, mainly due to the technological development over the last decades. The most common sensor is the radiometer, which is employed in 15 papers (20.3%), followed by continuous-wave (CW) spectroscopy (12.2%), camera (10.8%) and THz time-domain spectroscopy (TDS) (10.8%). Excluding two studies, the minimum coefficient of determination (R2) obtained in the references of this review is 0.64. This indicates the high degree of correlation between the estimated and measured data for the different technologies and monitoring methods. The five most frequent water indicators of this study are: normalized difference vegetation index (NDVI) (12.2%), backscattering coefficients (10.8%), spectral reflectance (8.1%), reflection coefficient (8.1%) and dielectric constant (8.1%).

Background Accurate leaf water potential (Ψ w ) determination is crucial in studying plant responses to water deficit. After excision, water potential decreases, even under low evaporative demand conditions, which has been recently attributed to the equilibration of pre-excision Ψ w gradients across the leaf. We assessed the influence of potential re-equilibration on water potential determination by monitoring leaf Ψ w and relative water content decline after excision using different storage methods. Results Even though leaf Ψ w declined during storage under low evaporative demand conditions, this was strongly reduced when covering the leaf with a hydrophobic layer (vaseline) and explained by changes in relative water content. However, residual water loss was variable between species, possibly related to morpho-physiological leaf traits. Provided water loss was minimized during storage, pre-excision leaf transpiration rate did not affect to the magnitude of leaf Ψ w decline after excision, confirming that transpiration-driven Ψ w gradients have no effect on leaf Ψ w determination. Conclusions Disequilibrium in water potentials across a transpiring leaf upon excision is dissipated very quickly, well within the elapsed time between excision and pressurization, therefore, not resulting in overestimation of leaf Ψ w measured immediately after excision. When leaf storage is required, the effectiveness of a storage under low evaporative demand varied among species. Covering with a hydrophobic layer is an acceptable alternative.

Estimating biophysical forest features from optical reflectance is a key approach for enhancing large-scale forest management. The present study estimates Leaf Area Index (LAI), leaf chlorophyll content (Cab), Leaf Mass per Area (LMA) and Equivalent Water Thickness (EWT) in different Eucalyptus sp. plantations using Sentinel-2 images and multiple linear regression models applicable to various Eucalyptus species and genotypes. This study investigates how canopy vertical heterogeneity, sun–sensor geometry, genotype, and related foliage traits influence the accuracy of biophysical variable retrieval. The key findings show that using a weighted average that gives greater importance to lower canopy leaves improved the accuracy of estimating Cab, LMA and EWT by approximately 35%, 22% and 28%, respectively, compared to relying solely on the upper canopy layer. These results indicate that the sensor's reflectance is substantially influenced by contributions from lower canopy layers. Additionally, incorporating sun–sensor geometry information alongside vegetation indices increased the accuracy of empirical model estimates by 13% for LAI and 41% for LMA. Furthermore, including either genotype information or related biophysical variables further improved model accuracy compared to other tested models, with gains of up to 21% for LAI, 11% for Cab, 44% for LMA and 4% for EWT.

Analysing functional traits along environmental gradients can improve our understanding of the mechanisms structuring plant communities. Within forests, vertical gradients in light intensity, temperature and humidity are often pronounced. Vascular epiphytes are particularly suitable for studying the influence of these vertical gradients on functional traits because they lack contact with the soil and thus individual plants are entirely exposed to different environmental conditions, from the dark and humid understorey to the sunny and dry outer canopy. In this study, we analysed multiple aspects of the trait‐based ecology of vascular epiphytes: shifts in trait values with height above ground (as a proxy for vertical environmental gradients) at community and species level, the importance of intra‐ vs. interspecific trait variability, and trait differences among taxonomic groups. We assessed ten leaf traits for 1151 individuals belonging to 83 epiphyte species of all major taxonomic groups co‐occurring in a Panamanian lowland forest. Community mean trait values of many leaf traits were strongly correlated with height and particularly specific leaf area and chlorophyll concentration showed nonlinear, negative trends. Intraspecific trait variability was pronounced and accounted for one‐third of total observed trait variance. Intraspecific trait adjustments along the vertical gradient were common and seventy per cent of all species showed significant trait–height relationships. In addition, intraspecific trait variability was positively correlated with the vertical range occupied by species. We observed significant trait differences between major taxonomic groups (orchids, ferns, aroids, bromeliads). In ferns, for instance, leaf dry matter content was almost twofold higher than in the other taxonomic groups. This indicates that some leaf traits are taxonomically conserved. Our study demonstrates that vertical environmental gradients strongly influence functional traits of vascular epiphytes. In order to understand community composition along such gradients, it is central to study several aspects of trait‐based ecology, including both community and intraspecific trends of multiple traits.

Leaf water content is one of the most common physiological parameters limiting efficiency of photosynthesis and biomass productivity in plants including . Therefore, it is of great significance to determine or predict the water content quickly and non-destructively. In this study, we explored the relationship between leaf water content and diffuse reflectance spectra in . Three multivariate calibrations including partial least squares (PLS), least squares support vector machine regression (LSSVR), and radial basis function (RBF) neural network (NN) were developed for the models of leaf water content determination. The non-linear models including RBF_LSSVR and RBF_NN showed higher accuracy than the PLS and Lin_LSSVR models. Moreover, 75 sensitive wavelengths were identified to be closely associated with the leaf water content in . The RBF_LSSVR and RBF_NN models for predicting leaf water content, based on 75 characteristic wavelengths, obtained the high determination coefficients of 0.9838 and 0.9899, respectively. The results indicated the non-linear models were more accurate than the linear models using both wavelength intervals. These results demonstrated that visible and near-infrared (VIS/NIR) spectroscopy combined with RBF_LSSVR or RBF_NN is a useful, non-destructive tool for determinations of the leaf water content in , and thus very helpful for development of drought-resistant varieties in .

To ensure national food security amidst severe water shortages, agricultural irrigation must be reduced through scientific innovation and technological progress. Efficient monitoring is essential for achieving water-saving irrigation and ensuring the sustainable development of agriculture. UAV hyperspectral remote sensing has demonstrated significant potential in monitoring large-scale crop leaf water content (LWC). In this study, hyperspectral and LWC data were collected for potatoes ( Solanum tuberosum ) during the tuber formation, growth, and starch accumulation stage in both 2021 and 2022. The hyperspectral data underwent mathematical transformation by multivariate scatter correction (MSC) and standard normal transformation (SNV). Next, feature spectral bands of LWC were selected using Competitive Adaptive Reweighted Sampling (CARS) and Random Frog (RF). For comparison, both the full-band and feature band were utilized to establish the estimation models of LWC. Modeling methods included partial least squares regression (PLSR), support vector regression (SVR), and BP neural network regression (BP). Results demonstrate that MSC and SNV significantly enhance the correlation between spectral data and LWC. The efficacy of estimation models varied across different growth stages, with optimal models identified as MSC-CARS-SVR (R 2 = 0.81, RMSE = 0.51) for tuber formation, SNV-CARS-PLSR (R 2 = 0.85, RMSE = 0.42) for tuber growth, and MSC-RF-PLSR (R 2 = 0.81, RMSE = 0.55) for starch accumulation. The RPD values of the three optimal models all exceed 2, indicating their excellent predictive performance. Utilizing these optimal models, a spatial distribution map of LWC across the entire potato canopy was generated, offering valuable insights for precise potato irrigation.

Investigating differences in resistance to alkaline stress among three willow species can provide a theoretical basis for planting willow in saline soils. Therefore we tested three willow species ( Salix matsudana , Salix gordejevii and Salix linearistipularis ), already known for their high stress tolerance, to alkaline stress environment at different pH values under hydroponics. Root and leaf dry weight, root water content, leaf water content, chlorophyll content, photosynthesis and chlorophyll fluorescence of three willow cuttings were monitored six times over 15 days under alkaline stress. With the increase in alkaline stress, the water retention capacity of leaves of the three species of willow cuttings was as follows: S. matsudana  >  S. gordejevii  >  S. linearistipularis and the water retention capacity of the root system was as follows: S. gordejevii  >  S. linearistipularis  >  S. matsudana . The chlorophyll content was significantly reduced, damage symptoms were apparent. The net photosynthetic rate (Pn), rate of transpiration (E), and stomatal conductance (Gs) of the leaves showed a general trend of decreasing, and the intercellular CO 2 concentration (Ci) of S. matsudana and S. gordejevii first declined and then tended to level off, while the intercellular CO 2 concentration of S. linearistipularis first declined and then increased. The quantum yield and energy allocation ratio of the leaf photosystem II (PSII) reaction centre changed significantly (φPo, Ψo and φEo were obviously suppressed and φDo was promoted). The photosystem II (PSII) reaction centre quantum performance index and driving force showed a clear downwards trend. Based on the results it can be concluded that alkaline stress tolerance of three willow was as follows: S. matsudana  >  S. gordejevii  >  S. linearistipularis . However, since the experiment was done on young seedlings, further study at saplings stage is required to revalidate the results.

Leaf water content is a key physiological indicator of plant growth and health status. Constructing leaf water content estimation models based on spectroscopy is an effective method for monitoring plant physiological conditions. To improve the accuracy of leaf water content estimation and develop models applicable to different plants, this study collected 1,680 groups of hyperspectral and water content data from peach tree leaves. Estimation models were established using two methods: \"constructing vegetation indices\" and \"selecting characteristic wavelengths.\" The accuracy and number of wavelengths used in each model were systematically evaluated. The optimal model was used to predict the water content of each pixel in the hyperspectral images, achieving visualization of leaf water distribution. Additionally, 244 groups of hyperspectral and water content data from apple tree and lettuce leaves were collected to validate the generalization ability of the optimal model. Results showed that the optimal models established using the two methods were the linear regression model based on the vegetation index NISDI (3 wavelengths, R = 0.9636, RMSEP=0.0356), and the CARS-RF model (12 wavelengths, R = 0.9861, RMSEP=0.0219). Although the accuracy of the two models was similar, the latter used four times more wavelengths than the former, so the former was chosen as the optimal model. Using the optimal model to estimate the water content of apple tree leaves, the R and RMSEP were 0.9504 and 0.1226, respectively. For lettuce containing only leaf tissue, the R and RMSEP were 0.8211 and 0.1771, respectively. These results indicate that the model has some generalization ability and can accurately estimate the water content of leaves of woody plants in the same family, with some performance degradation across different growth forms. The study results achieved accurate estimation of leaf water content for three types of plants and also provided a reference for establishing plant leaf water content estimation models with generalization ability.