Proximal sensing offers a novel means for determination of the heavy metal concentration in soil, facilitating low cost and rapid analysis over large areas. In this respect, spectral data and model variables play an important role. Thus far, no attempts have been made to estimate soil heavy metal content using continuum-removal (CR), different preprocessing and statistical methods, and different modeling variables. Considering the adsorption and retention of heavy metals in spectrally active constituents in soil, this study proposes a method for determining low heavy metal concentrations in soil using spectral bands associated with soil organic matter (SOM) and visible–near-infrared (Vis–NIR). To rapidly determine the concentration of heavy metals using hyperspectral data, partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) statistical methods and 16 preprocessing combinations were developed and explored to determine an optimal combination. The results showed that the multiplicative scatter correction and standard normal variate preprocessing methods evaluated with the second derivative spectral transformation method could accurately determine soil Cr and Ni concentrations. The root-mean-square error (RMSE) values of Vis–NIR model combinations with PLSR, PCR, and SVMR were 0.34, 3.42, and 2.15 for Cr, and 0.07, 1.78, and 1.14 for Ni, respectively. Soil Cr and Ni showed strong spectral responses to the Vis–NIR spectral band. The R² value of the Vis–NIR-based PLSR model was higher than 0.99, and the RMSE value was 0.07–0.34, suggesting higher stability and accuracy. The results were more accurate for Ni than Cr, and PLSR showed the best performance, followed by SVMR and PCR. This perspective has critical implications for guiding quantitative biogeochemical analysis using proximal sensing data.

Actinidia arguta (Siebold and Zucc.) Planch.ex Miq, called “hardy kiwifruit”, “baby kiwi” or “kiwi berry”, has a unique taste, is rich in nutrients and has high economic value and broad market prospects. Active research on the potential geographic distribution of A. arguta in China aims to provide a reference basis for its resource investigation, conservation, development and utilization and introduction of cultivation. In this study, the Maxent model was used to combine climatic factors, soil factors and geographical factors (elevation, slope and aspect) to predict the current and future (2041–2060 and 2081–2100) potential distribution of A. arguta and to analyze the impact of climate change on it. The results showed that the suitable distribution range of A. arguta in China was 23–43 N and 100–125 E, with a total area of about 3.4451 × 10⁶ km². The highly suitable area of A. arguta was mainly concentrated in the middle and low mountain areas of the south of Shaanxi, the east of Sichuan, the middle and west of Guizhou and the west of Yunnan, presenting a circular distribution. The Jackknife test was used to calculate the main environmental factors affecting the distribution of A. arguta. The first four main factors were annual mean temperature (bio_1), precipitation of the warmest quarter (bio_18), elevation (ELE) and mean temperature of the warmest quarter (bio_10), which provided a contribution up to 81.7%. Under the scenarios of three representative concentrations (SSP1_2.6, SSP2_4.5 and SSP5_8.5) in the future, the area of low and moderate suitable habitat decreased, while the area of highly suitable habitat increased. The migration direction of the centroid in the highly suitable habitat moved to the southwest in the future scenario period.

Salinity stress is a serious constraint for crop productivity, and its extent is continuously across the globe. Therefore, it is direly necessary to develop appropriate measures to mitigate the toxic effects of salinity to meet rising food needs. The present study determined the impact of diverse concentrations of neutral salt (NaCl: 0, 200, 400 mM) and alkaline salt (Na₂CO₃: 0, 25, 50 mM) on the growth status, leaf traits, and clonal component characteristics of reed (Phragmites communis Trin.). The results indicated that increasing the intensity of salt and alkali stress markedly reduced growth (> 30%) and biomass production (> 35%). The impact of alkali stress was more significant as compared to salt stress, and it significantly decreased leaf area and dry matter production and increased the N concentration and N:P ratio. Further, stomata conductance and CO₂ concentration showed a decrease under stress conditions. While water use efficiency (WUE) also showed a decreasing trend under saline conditions, it first showed an increasing trend under alkali stress and then a decreasing trend. Further, plant tiller, rhizome, and seeds were markedly reduced by stresses. Moreover, the concentration of Na⁺ was increased under saline conditions while the concentration of K⁺ and NO₃^- showed a marked decrease, Na⁺/K⁺ showed a substantial increase. Therefore, salinity and alkaline stress can reduce the growth of reeds by disturbing plant physiological and biochemical functioning and nutrient homeostasis.