UAV multispectral technology is used to obtain leaf area index (LAI) and aboveground biomass (AGB) information on Cinnamomum camphora (C. camphora) and to diagnose the growth condition of Cinnamomum camphora dwarf forests in a timely and rapid manner, which helps improve the precision management of Cinnamomum camphora dwarf forests. Multispectral remote sensing images provide large-area plant spectral information, which can provide a detailed quantitative assessment of LAI, AGB and other plant physicochemical parameters. They are very effective tools for assessing and analyzing plant health. In this study, the Cinnamomum camphora dwarf forest in the red soil area of south China is taken as the research object. Remote sensing images of Cinnamomum camphora dwarf forest canopy are obtained by the multispectral camera of an unmanned aerial vehicle (UAV). Extreme gradient boosting (XGBoost), gradient boosting decision tree (GBDT), random forest (RF), radial basis function neural network (RBFNN) and support vector regression (SVR) algorithms are used to study the correlation and estimation accuracy between the original band reflectance, spectral indices and LAI and AGB of Cinnamomum camphora. The results of this study showed the following: (1) The accuracy of model estimation based on RF is significantly different for different model inputs, while the other four models have small differences. (2) The accuracy of the XGBoost-based LAI model was the highest; with original band reflectance as the model input, the R² of the model test set was 0.862, and the RMSE was 0.390. (3) The accuracy of the XGBoost-based AGB model was the highest; with spectral indices as the model input, the R² of the model test set was 0.929, and the RMSE was 587.746 kg·hm⁻². (4) The XGBoost model was the best model for the LAI and AGB estimation of Cinnamomum camphora, which was followed by GBDT, RF, RFNN, and SVR. This research result can provide a theoretical basis for monitoring a Cinnamomum camphora dwarf forest based on UAV multispectral technology and a reference for rapidly estimating Cinnamomum camphora growth parameters.

Plant leaf water content significantly influences photosynthetic efficiency and crop yield. Leaf water content (LWC) and equivalent water thickness (EWT) are indicators that reflect the water state within plant tissues, and they play a crucial role in assessing plant water supply and usage. In recent years, there has been a growing focus on the rapid and precise determination of plant water content. In this study, Cinnamomum camphora (C. camphora) was chosen as the subject of investigation. After acquiring spectral data, three types of vegetation indices were computed: the empirical vegetation index, the random combination dual-band vegetation index, and the ‘trilateral’ parameter. Four groups of optimal spectral index screening strategies were established, namely an empirical vegetation index group (G1), a random combination dual-band vegetation index group (G2), a ‘trilateral’ parameter group (G3), and a mixed group (G4). Three algorithms, specifically random forest (RF), radial basis function neural network (RBFNN), and support vector machine (SVM), were employed for the estimation of leaf water content (LWC) and equivalent water thickness (EWT) in mature C. camphora. The results demonstrated that the G4 group displayed superior performance, yielding five optimal spectral indices for LWC: water index (WI), optimized soil-adjusted vegetation index (OSAVI), difference vegetation index (DVI) at wavelengths 734 and 956 nm, first-order difference vegetation index (DVI-FD) at wavelengths 1009 and 774 nm, and red-edge amplitude (Dr). With regard to EWT estimation, the five optimal spectral indices encompassed the red-edge normalized difference vegetation index (RE-NDVI), simple ratio water index (SRWI), difference vegetation index (DVI) at wavelengths 700 and 1167 nm, first-order difference vegetation index (DVI-FD) at wavelengths 1182 and 1514 nm, and red-edge area (SDr). Utilizing these indices as inputs significantly enhanced the accuracy of the models, with the RF model emerging as the most effective for estimating LWC and EWT in C. camphora. Based on the LWC estimation model of the G4 group and the RF algorithm, the determination coefficient (R²) for both the training and test sets reached 0.848 and 0.871, respectively. The root mean square error (RMSE) was 0.568% for the training set and 0.582% for the test set, while the average relative error (MRE) stood at 0.806% and 0.642%, respectively. Regarding the EWT estimation model, R² values of 0.887 and 0.919 were achieved for the training and test sets, accompanied by RMSE values of 0.6 × 10⁻³ g·cm⁻² and 0.7 × 10⁻³ g·cm⁻², and MRE values of 3.198% and 2.901%, respectively. These findings lay a solid foundation for hyperspectral moisture monitoring in C. camphora and offer valuable reference for the rapid assessment of crop growth status.

Plant hydraulic conductivity (K) refers to the rate of water flow (kg s⁻¹) per unit pressure drop (MPa), which drives flow through the plant organ system. It is an important eco-physiology index for measuring plant water absorption and transport capacity. A field study was conducted in the arid region of the Heihe River Basin in northwestern China, plant hydraulic conductivity was measured by high-pressure flowmeter (HPFM) to investigate the characteristics of hydraulic conductivity of typical dominant desert plants (Reaumuria soongarica M., Nitraria sphaerocarpa M., and Sympegma regelii B.) and their relationship with functional traits of leaves, stems, and roots, and explaining their adaptation strategies to desert environment from the perspective of plant organs hydraulic conductivity. The results showed that the hydraulic conductivity of the leaves and stems of R. soongarica and N. sphaerocarpa (K_LA, leaf hydraulic conductivity per unit leaf area; K_LW, leaf hydraulic conductivity per unit leaf weight; K_SLA, stem hydraulic conductivity per unit leaf area; K_SLW, stem hydraulic conductivity per unit leaf weight) were significantly lower than those of S. regelii, while their fine root (K_RL, root hydraulic conductivity per unit leaf length; K_RSA, root hydraulic conductivity per unit root surface area) and whole root (K_TRW, whole root hydraulic conductivity per unit root weight) of hydraulic conductivity were significantly higher than those of S. regelii. In addition, K_LA and K_LW, K_SLA and K_SLW, and K_RL and K_RSA in three desert plants all exhibited consistent trends. Correlation analysis illustrated that the hydraulic conductivity of leaves and stems had a significantly positive correlation, but they had no significant negative correlation with the specific leaf weight (SLW, specific leaf weight). The hydraulic conductivity of fine root weight (K_RW, root hydraulic conductivity per unit root weight) and specific root surface area (SRSA, specific root surface area) showed significantly positive correlation (r = 0.727, P < 0.05). The results demonstrated that the R. soongarica and N. sphaerocarpa preserved their water content through the strong leaf absorption capacity of soil water and the low water dispersion rates of leaves to adapt to the harsher arid habitat, which is more drought tolerant than S. regelii.

Climate change is a very serious threat to the agricultural sector and potentially brings new problems to the sustainability of agricultural production systems. This paper aims to know how to improve crop yield by changing the nitrogen application ratio and sowing time under future climate change conditions based on the CERES-Rice model. The CERES-Rice model was calibrated and validated with a three-year field experiment (2018–2020), which was coupled with four N rates (50, 100, 150, and 200 kg/ha) and three different N ratios (B:T:S = 3:1:0; B:T:S = 5:3:2; B:T:S = 6:3:1). The results showed that the CERES-Rice model had better simulation effect on the phenophase (n-RMSE < 15%, d > 0.9 and R² = 0.978) and yield (n-RMSE < 10%, d > 0.9 and R² = 0.910) of double-harvest rice. The calibrated model was used to evaluate the growth period and yield of double-harvest rice under the RCP4.5 climate scenario and the results revealed that future yields of double-harvest rice in Nanchang are lower than those in experimental years, especially for early rice. Adjusting the nitrogen application ratio and sowing time can improve the yield of double-harvest rice to a certain extent, and the nitrogen application ratio of 5:3:2 has the best effect. In 2021–2035, the best yield of double-harvest rice can be obtained when the sowing date of early rice is about 15 days earlier and the sowing date of late rice is about 10 days earlier than the experiment year. From 2035 to 2050, the sowing date of early rice and late rice will be advanced by about 10 days, and the total yield of double-harvest rice will be higher. In 2050–2070, the total yield of double-harvest rice may reach the best when the sowing date is delayed by 10–15 days. Therefore, reasonably changing the sowing date of double-harvest rice and the nitrogen application regime of early rice can be used as a possible adaptive strategy to cope with the yield reduction in double-harvest rice in future climate scenarios.

To explore an effective analysis model and method for estimating Cinnamomum camphora’s (C. camphora’s) growth using unmanned aerial vehicle (UAV) multispectral technology, we obtained C. camphora’s canopy spectral reflectance using a UAV-mounted multispectral camera and simultaneously measured four single-growth indicators: Soil and Plant Analyzer Development (SPAD)value, aboveground biomass (AGB), plant height (PH), and leaf area index (LAI). The coefficient of variation and equal weighting methods were used to construct the comprehensive growth monitoring indicators (CGMI) for C. camphora. A multispectral inversion model of integrated C. camphora growth was established using the multiple linear regression (MLR), partial least squares (PLS), support vector regression (SVR), random forest (RF), radial basis function neural network (RBFNN), back propagation neural network (BPNN), and whale optimization algorithm (WOA)-optimized BPNN models. The optimal model was selected based on the coefficient of determination (R²), normalized root mean square error (NRMSE) and mean absolute percentage error (MAPE). Our findings indicate that apparent differences in the accuracy with different model, and the WOA–BPNN model is the best model to invert the growth potential of C. camphora, the R² of the model test set was 0.9020, the RMSE was 0.0722, and the MAPE was 7%. The R² of the WOA–BPNN model improved by 18%, the NRMSE decreased by 33%, and the MAPE decreased by 9% compared with the BPNN model. This study provides technical support for the modern field management of C. camphora essential oil and other dwarf forestry industries.

The root system is an important organ for nutrient uptake and biomass accumulation in plants, while biomass allocation directly affects essential oils content, which plays an essential role in plant growth and development and resistance to adverse environmental conditions. This study was undertaken to investigate the differences and correlation of biomass allocation, root traits and essential oil content (EOC), as well as the adaptations of camphor tree with different chemical types to the ionic rare earth tailing sand habitats. Data from 1-year old cutting seedlings of C. camphora showed that the biomass of C. camphora cuttings was mainly distributed in root system, with the ratio of root biomass 49.9–72.13% and the ratio of root to canopy 1.00–2.64. The total biomass was significantly positively correlated with root length (RL), root surface area (RSA) and dry weight of fine roots (diameter ≤ 2 mm) (P < 0.05). Root biomass and leaf biomass were negatively and positively with specific root length (SRL) and specific root surface area (SRSA), respectively. Leaf biomass presented a positive effect on EOC (P < 0.05), with the correlation coefficient of 0.808. The suitability sort of these camphor trees was as follows: C. camphora β-linalool, C. camphora α-linaloolII, C. camphora α-linaloolI being better adapted to the ionic rare earth tailings substrate, C. camphora citral being the next, and C. porrectum β-linalool and C. camphora borneol being the least adaptive. EOC played a positive role in the adaptation of C. camphora (R² = 0.6099, P < 0.05). Therefore camphor tree with linalool type is the appropriate choice in the ecological restoration of ionic rare earth tailings. The study could provide scientific recommendations for the ecological restoration of ionic rare earth tailings area combined with industrial development.