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As a desert shrub, Haloxylon ammodendron combines ecological, economic, and social benefits and plays an important role in the ecological conservation of arid desert areas. Understanding its physiological characteristics and its mechanism of light energy utilization is important for the conservation and utilization of H. ammodendron . Therefore, we selected five stands (5-, 11-, 22-, 34-, and 46-year-old) of H. ammodendron as research objects in the study and measured their photosynthetic light response curves by a portable open photosynthesis system (Li-6400) with a red-blue light source (6400-02B). Then, we measured the leaf chlorophyll parameters in the laboratory, calculated the photosynthetic characteristics by using Ye Zipiao’s photosynthetic model, analyzed their variation patterns across stand ages, and explored the relationships between leaf chlorophyll parameters and photosynthetic characteristics. The results showed that leaf chlorophyll parameters and photosynthetic characteristics of H. ammodendron at different stand ages were significantly different. Chl content, P nmax , and LUE max of H. ammodendron were V-shaped with the increase of stand age. The 5-year-old H. ammodendron was in the rapid growth period, synthesized more Chl a+b content (8.47 mg g −1 ) only by using a narrower range of light, and the P nmax and LUE max were the highest with values of 36.21 μmol m −2 s −1 and 0.0344, respectively. For the 22-year-old H. ammodendron , due to environmental stress, the values of Chl a+b content, P nmax , and LUE max were the smallest and were 2.64 mg g −1 , 25.73 μmol m −2 s −1 , and 0.0264, respectively. For the older H. ammodendron , its Chl content, P nmax , and LUE max were not significantly different and tended to stabilize but were slightly higher than those of the middle-aged H. ammodendron . On the other hand, the other photosynthetic parameters did not show significant variation patterns with stand age, such as R d , AQE, LSP, LCP, and I L-sat . In addition, we found that the relationships between Chl a+b content and P nmax and between Chl a+b content and LUE max were highly correlated, except for the older H. ammodendron . Thus, using leaf chlorophyll content as a proxy for photosynthetic capacity and light use efficiency should be considered with caution. This work will provide a scientific reference for the sustainable management of desert ecosystems and vegetation restoration in sandy areas.

The canopy chlorophyll content (CCC) and leaf area index (LAI) are both essential indicators for crop growth monitoring and yield estimation. The PROSAIL model, which couples the properties optique spectrales des feuilles (PROSPECT) and scattering by arbitrarily inclined leaves (SAIL) radiative transfer models, is commonly used for the quantitative retrieval of crop parameters; however, its homogeneous canopy assumption limits its accuracy, especially in the case of multiple crop categories. The adjusted average leaf angle (ALAadj), which can be parameterized for a specific crop type, increases the applicability of the PROSAIL model for specific crop types with a non-uniform canopy and has the potential to enhance the performance of PROSAIL-coupled hybrid methods. In this study, the PROSAIL-D model was used to generate the ALAadj values of wheat, soybean, and maize crops based on ground-measured spectra, the LAI, and the leaf chlorophyll content (LCC). The results revealed ALAadj values of 62 degrees for wheat, 45 degrees for soybean, and 60 degrees for maize. Support vector regression (SVR), random forest regression (RFR), extremely randomized trees regression (ETR), the gradient boosting regression tree (GBRT), and stacking learning (STL) were applied to simulated data of the ALAadj in 50-band data to retrieve the CCC and LAI of the crops. The results demonstrated that the estimation accuracy of singular crop parameters, particularly the crop LAI, was greatly enhanced by the five machine learning methods on the basis of data simulated with the ALAadj. Regarding the estimation results of mixed crops, the machine learning algorithms using ALAadj datasets resulted in estimations of CCC (RMSE: RFR = 51.1 μg cm−2, ETR = 54.7 μg cm−2, GBRT = 54.9 μg cm−2, STL = 48.3 μg cm−2) and LAI (RMSE: SVR = 0.91, RFR = 1.03, ETR = 1.05, GBRT = 1.05, STL = 0.97), that outperformed the estimations without using the ALAadj (namely CCC RMSE: RFR = 93.0 μg cm−2, ETR = 60.1 μg cm−2, GBRT = 60.0 μg cm−2, STL = 68.5 μg cm−2 and LAI RMSE: SVR = 2.10, RFR = 2.28, ETR = 1.67, GBRT = 1.66, STL = 1.51). Similar findings were obtained using the suggested method in conjunction with 19-band data, demonstrating the promising potential of this method to estimate the CCC and LAI of crops at the satellite scale.

Global food security is critical to eliminating hunger and malnutrition. In the changing climate, farmers in developing countries must adopt technologies and farming practices such as precision agriculture (PA). PA-based approaches enable farmers to cope with frequent and intensified droughts and heatwaves, optimising yields, increasing efficiencies, and reducing operational costs. Biophysical parameters such as Leaf Area Index (LAI), Leaf Chlorophyll Content (LCab), and Canopy Chlorophyll Content (CCC) are essential for characterising field-level spatial variability and thus are necessary for enabling variable rate application technologies, precision irrigation, and crop monitoring. Moreover, robust machine learning algorithms offer prospects for improving the estimation of biophysical parameters due to their capability to deal with non-linear data, small samples, and noisy variables. This study compared the predictive performance of sparse Partial Least Squares (sPLS), Random Forest (RF), and Gradient Boosting Machines (GBM) for estimating LAI, LCab, and CCC with Sentinel-2 imagery in Bothaville, South Africa and identified, using variable importance measures, the most influential bands for estimating crop biophysical parameters. The results showed that RF was superior in estimating all three biophysical parameters, followed by GBM which was better in estimating LAI and CCC, but not LCab, where sPLS was relatively better. Since all biophysical parameters could be achieved with RF, it can be considered a good contender for operationalisation. Overall, the findings in this study are significant for future biophysical product development using RF to reduce reliance on many algorithms for specific parameters, thus facilitating the rapid extraction of actionable information to support PA and crop monitoring activities.

Leaf chlorophyll content (LCC) is an indicator of leaf photosynthetic capacity. It is crucial for improving the understanding of plant physiological status. SPAD meters are routinely used to provide an instantaneous estimation of in situ LCC. However, the calibration of meter readings into absolute measures of LCC is difficult, and a generic approach for this conversion remains elusive. This study presents an evaluation of the approaches that are commonly used in converting SPAD readings into absolute LCC values. We compared these approaches using three field datasets and one synthetic dataset. The field datasets consist of LCC measured using a destructive method in the laboratory, as well as the SPAD readings measured in the field for various vegetation types. The synthetic dataset was generated with the leaf radiative transfer model PROSPECT-5 across different leaf structures. LCC covers a wide range from 1.40 μg cm−2 to 86.34 μg cm−2 in the field datasets, and it ranges from 5 μg cm−2 to 80 μg cm−2 in the synthetic dataset. The relationships between LCC and SPAD readings were examined using linear, polynomial, exponential, and homographic functions for the field and synthetic datasets. For the field datasets, the assessments of these approaches were conducted for (i) all three datasets together, (ii) individual datasets, and (iii) individual vegetation species. For the synthetic dataset, leaves with different leaf structures (which mimic different vegetation species) were grouped for the evaluation of the approaches. The results demonstrate that the linear function is the most accurate one for the simulated dataset, in which leaf structure is relatively simple due to the turbid medium assumption of the PROSPECT-5 model. The assumption of leaves in the PROSPECT-5 model complies with the assumption made in the designed algorithm of the SPAD meter. As a result, the linear relationship between LCC and SPAD values was found for the modeled dataset in which the leaf structure is simple. For the field dataset, the functions do not perform well for all datasets together, while they improve significantly for individual datasets or species. The overall performance of the linear (LCC=a∗SPAD+b), polynomial (LCC=a∗SPAD2+b∗SPAD+c), and exponential functions (LCC=0.0893∗10SPADα) is promising for various datasets and species with the R2 > 0.8 and RMSE <10 μg cm−2. However, the accuracy of the homographic functions (LCC=a∗SPAD/b−SPAD) changes significantly among different datasets and species with R2 from 0.02 of wheat to 0.92 of linseed (RMSE from 642.50 μg cm−2 to 5.74 μg cm−2). Other than species- and dataset-dependence, the homographic functions are more likely to produce a numerical singularity due to the characteristics of the function per se. Compared with the linear and exponential functions, the polynomial functions have a higher degree of freedom due to one extra fitting parameter. For a smaller size of data, the linear and exponential functions are more suitable than the polynomial functions due to the less fitting parameters. This study compares different approaches and addresses the uncertainty in the conversion from SPAD readings into absolute LCC, which facilitates more accurate measurements of absolute LCC in the field.

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.

Background Drought stress, the most prevalent abiotic stress, has a significant effect on citrus production worldwide. The differential mechanisms to overcome the drought stress has been reported in citrus rootstock genotypes. This study evaluated nine citrus rootstock genotypes, including indigenous rough lemon variants, for drought tolerance. The genotypes were subjected to well-watered, drought stress, and re-watering conditions to assess morphological, physiological, and biochemical responses. High-throughput imaging techniques were employed to non-destructively assess chlorophyll fluorescence, digital leaf area, and plant tissue water content during drought stress. Results For rapid and accurate screening of rootstocks, phenomics and physio-biochemical tools were used to know morpho-physiological responses to drought. Citrus rootstock genotype X639 demonstrated superior performance under drought stress conditions. It maintained the highest growth in terms of relative shoot increment (8.09%), number of leaves (79.00), and specific leaf area (62.45 cm 2  g −1 ). X639 also excelled in root morphological parameters, including root length, projected area, diameter, surface area, volume, and number of tips, forks, and crossings. Trifoliate hybrids X639 and Troyer citrange exhibited larger stomata (54.73 and 43.82 µm 2 ) compared to mono-foliate species, with minimal impact of drought on stomatal pore area. X639 maintained the highest relative water content, membrane and chlorophyll stability indices, leaf gas exchange parameters, and antioxidant enzyme activity. RLC-1 and RLC-4 genotypes showed pronounced accumulation of leaf proline and antioxidant enzymes during drought, contributing to better recovery after re-watering. Conclusion In this study, Cleopatra mandarin, Grambhiri , and RLC-2 were identified as drought-susceptible rootstocks based on their responses. Rootstock genotypes X639 and RLC-4 proven a superior drought-tolerant genotypes. Their robust root system enables efficient water uptake and the maintenance of water relations during drought stress. The drought tolerance of X639 was evidenced by its ability to maintain plant tissue moisture, membrane and chlorophyll stability, and higher photosystem II efficiency. High-throughput imaging techniques have proven effective in rapidly assessing and differentiating drought-tolerant and drought-susceptible citrus rootstocks based on their photosystem- II efficiency, leaf area, and tissue water content during induced drought stress. These findings will contribute to the selection and development of drought-tolerant citrus rootstocks to improve citrus production under water-limited conditions.

Water deficit (WD) is expected to increase in intensity, frequency and duration in many parts of the world as a consequence of global change, with potential negative effects on plant gas exchange and growth. We review here the parameters that can be derived from measurements made on leaves, in the field, and that can be used to assess the effects of WD on the components of plant photosynthetic rate, including stomatal conductance, mesophyll conductance, photosynthetic capacity, light absorbance, and efficiency of absorbed light conversion into photosynthetic electron transport. We also review some of the parameters related to dissipation of excess energy and to rerouting of electron fluxes. Our focus is mainly on the techniques of gas exchange measurements and of measurements of chlorophyll fluorescence (ChlF), either alone or combined. But we put also emphasis on some of the parameters derived from analysis of the induction phase of maximal ChlF, notably because they could be used to assess damage to photosystem II. Eventually we briefly present the non-destructive methods based on the ChlF excitation ratio method which can be used to evaluate non-destructively leaf contents in anthocyanins and flavonols.

Parthenium hysterophorus L. (Asteraceae) is a highly prevalent invasive species in subtropical regions across the world. It has recently been seen to shift from low (subtropical) to high (sub-temperate) elevations. Nevertheless, there is a dearth of research investigating the adaptive responses and the significance of leaf functional traits in promoting the expansion to high elevations. The current study investigated the variations and trade-offs among 14 leaf traits (structural, photosynthetic, and nutrient content) of P. hysterophorus across different elevations in the western Himalayas, India. Plots measuring 20 × 40 m were established at different elevations (700 m, 1100 m, 1400 m, and 1800 m) to collect leaf trait data for P. hysterophorus . Along the elevational gradient, significant variations were noticed in leaf morphological parameters, leaf nutrient content, and leaf photosynthetic parameters. Significant increases were observed in the specific leaf area, leaf thickness, and chlorophyll a , total chlorophyll and carotenoid content, as well as leaf nitrogen and phosphorus content with elevation. On the other hand, there were reductions in the amount of chlorophyll b , photosynthetic efficiency, leaf dry matter content, leaf mass per area, and leaf water content. The trait-trait relationships between leaf water content and dry weight and between leaf area and dry weight were stronger at higher elevations. The results show that leaf trait variability and trait-trait correlations are very important for sustaining plant fitness and growth rates in low-temperature, high-irradiance, resource-limited environments at relatively high elevations. To summarise, the findings suggest that P. hysterophorus can expand its range to higher elevations by broadening its functional niche through changes in leaf traits and resource utilisation strategies.

Individual inversions of Leaf Area Index (LAI) and Leaf Chlorophyll Content (LCC) have problems due to the mutual interference between these two vegetation parameters on remote sensing signals. We therefore explore synergetic inversion of these two parameters to improve their inversion accuracy. We selected subtropical forest plantations, where canopy reflectance data were collected using a DJI Phantom 4 Multispectral Unmanned Aerial Vehicle (UAV) every month during 2021-2022. Monthly in-situ observations of LAI and Clumping Index (CI) were also made in 23 broadleaf tree plots of dimension 12 m × 12 m. Vegetation Indices (VI) were calculated with the mean reflectance of all pixels at 0.06 m resolution within each sampling plot, and only those VIs with highest sensitivities to LAI or LCC were selected and correlated to LAI and LCC. An empirical model in the form of VI = f(LAI, LCC) was constructed for synergetic inversion of LAI and LCC. For the purpose of comparison, two models VI = f(LAI) and VI = f(LCC) were also constructed and used for the inversions of LAI and LCC, separately. The synergetic inversion model yields R 2  = 0.60 and RMSE = 2.80 cm 2 /cm 2 for LAI and R 2  = 0.45 and RMSE = 32.71 μg/cm 2 for LCC, whereas the separate inversion models result in R 2  = 0.59 and RMSE = 2.82 cm 2 /cm 2 for LAI and R 2  = 0.35 and RMSE = 35.86 μg/cm 2 for LCC. Moreover, we found that the inclusion of VIs containing a red edge band in the synergetic inversion can effectively improve the inversion accuracy. The proposed synergetic inversion method based on multiple VIs would be an effective way to separate the mutual interference between LAI and LCC and improve the accuracy of LCC inversion from remote sensing data.

Accurate and timely monitoring of biochemical and biophysical traits associated with crop growth is essential for indicating crop growth status and yield prediction for precise field management. This study evaluated the application of three combinations of feature selection and machine learning regression techniques based on unmanned aerial vehicle (UAV) multispectral images for estimating the bio-parameters, including leaf area index (LAI), leaf chlorophyll content (LCC), and canopy chlorophyll content (CCC), at key growth stages of winter wheat. The performance of Support Vector Regression (SVR) in combination with Sequential Forward Selection (SFS) for the bio-parameters estimation was compared with that of Least Absolute Shrinkage and Selection Operator (LASSO) regression and Random Forest (RF) regression with internal feature selectors. A consumer-grade multispectral UAV was used to conduct four flight campaigns over a split-plot experimental field with various nitrogen fertilizer treatments during a growing season of winter wheat. Eighteen spectral variables were used as the input candidates for analyses against the three bio-parameters at four growth stages. Compared to LASSO and RF internal feature selectors, the SFS algorithm selects the least input variables for each crop bio-parameter model, which can reduce data redundancy while improving model efficiency. The results of the SFS-SVR method show better accuracy and robustness in predicting winter wheat bio-parameter traits during the four growth stages. The regression model developed based on SFS-SVR for LAI, LCC, and CCC, had the best predictive accuracy in terms of coefficients of determination (R2), root mean square error (RMSE) and relative predictive deviation (RPD) of 0.967, 0.225 and 4.905 at the early filling stage, 0.912, 2.711 μg/cm2 and 2.872 at the heading stage, and 0.968, 0.147 g/m2 and 5.279 at the booting stage, respectively. Furthermore, the spatial distributions in the retrieved winter wheat bio-parameter maps accurately depicted the application of the fertilization treatments across the experimental field, and further statistical analysis revealed the variations in the bio-parameters and yield under different nitrogen fertilization treatments. This study provides a reference for monitoring and estimating winter wheat bio-parameters based on UAV multispectral imagery during specific crop phenology periods.