As a species for ecological restoration in northern China, Tamarix ramosissima plays an important role in river protection, flood control, regional climate regulation, and landscape construction with vegetation. Two sampling sites were selected in the hillside and floodplain habitats along the Lanzhou City, and the xylems of T. ramosissima and potential water sources were collected, respectively. The Bayesian mixture model (MixSIAR) and soil water excess (SW-excess) were applied to analyze the relationship on different water pools and the utilization ratios of T. ramosissima to potential water sources in two habitats. The results showed that the slope and intercept of local meteoric water line (LMWL) in two habitats were smaller compared with the global meteoric water line (GMWL), which indicated the existence of drier climate and strong evaporation in the study area, especially in the hillside habitat. Except for the three months in hillside, the SW-excess of T. ramosissima were negative, which indicated that xylems of T. ramosissima are more depleted in δ2H than the soil water line. In growing seasons, the main water source in hillside habitat was deep soil water (80~150 cm) and the utilization ratio was 63 ± 17% for T. ramosissima, while the main water source in floodplain habitat was shallow soil water (0~30 cm), with a utilization ratio of 42.6 ± 19.2%, and the water sources were different in diverse months. T. ramosissima has a certain adaptation mechanism and water-use strategies in two habitats, and also an altered water uptake pattern in acquiring the more stable water. This study will provide a theoretical basis for plant water management in ecological environment protection in the Loess Plateau.
Leaf area index (LAI) is an essential indicator for crop growth monitoring and yield prediction. Real-time, non-destructive, and accurate monitoring of crop LAI is of great significance for intelligent decision-making on crop fertilization, irrigation, as well as for predicting and warning grain productivity. This study aims to investigate the feasibility of using spectral and texture features from unmanned aerial vehicle (UAV) multispectral imagery combined with machine learning modeling methods to achieve maize LAI estimation. In this study, remote sensing monitoring of maize LAI was carried out based on a UAV high-throughput phenotyping platform using different varieties of maize as the research target. Firstly, the spectral parameters and texture features were extracted from the UAV multispectral images, and the Normalized Difference Texture Index (NDTI), Difference Texture Index (DTI) and Ratio Texture Index (RTI) were constructed by linear calculation of texture features. Then, the correlation between LAI and spectral parameters, texture features and texture indices were analyzed, and the image features with strong correlation were screened out. Finally, combined with machine learning method, LAI estimation models of different types of input variables were constructed, and the effect of image features combination on LAI estimation was evaluated. The results revealed that the vegetation indices based on the red (650 nm), red-edge (705 nm) and NIR (842 nm) bands had high correlation coefficients with LAI. The correlation between the linearly transformed texture features and LAI was significantly improved. Besides, machine learning models combining spectral and texture features have the best performance. Support Vector Machine (SVM) models of vegetation and texture indices are the best in terms of fit, stability and estimation accuracy (R2 = 0.813, RMSE = 0.297, RPD = 2.084). The results of this study were conducive to improving the efficiency of maize variety selection and provide some reference for UAV high-throughput phenotyping technology for fine crop management at the field plot scale. The results give evidence of the breeding efficiency of maize varieties and provide a certain reference for UAV high-throughput phenotypic technology in crop management at the field scale.
Lanzhou lies at the western Loess Plateau, China, and has a typical semi-arid temperate continental climate. Plants in this area are exposed to a prolonged dry season. In this study, we measured the stable isotopes of hydrogen (δD) and oxygen (δ18O) of the local precipitation, river water, soil water, plant xylem water, and leaf water at four sampling sites during the 2016 growing season. Our results showed that plants relied mostly on wet season precipitation at sites N1, N2, and N3 because this recharged the soil after the long dry season. Leaf phenology had a significant effect on evaporation distance (ED) value, and evergreen plants have adapted to water tapping from deep soil water sources during the dry season. The ED values of trees and shrubs were quite different in the dry season, indicating water competition among different plant species was mitigated due to water resource partitioning. Moreover, plants at site N4 relied on a water source admixed with river water throughout the whole growing season. The mean value of xylem-to-leaf water deuterium enrichment (ɛl/x) was −0.91 ± 0.36‰ over all plant species, seasons, and sampling sites. Plant species, leaf phenology, and seasons were found to be the primary factors influencing the ɛl/x, while growth form and elevation had negligible effects.
The stable isotopes (δD and δ18O) in soil water allow tracing of the flow and transportation of water in the soil. However, there are few studies on the use of soil water stable isotopes to explore the soil water in the floodplain, especially in determining the soil water source and infiltration mechanism. The Bayesian mixing model (MixSIAR) was integrated with the line conditioned excess (lc-excess) of stable isotopes (δD and δ18O) in precipitation, soil water (0–150 cm), river water, and groundwater to determinate the source and recharge mechanisms of two different soil profile types in the floodplain of the upper Yellow River in Lanzhou, China. The results showed that soil water below 80 cm was affected by river water recharge, affecting soil water content and stable isotopic composition at S1 sampling points (profile parallel to river water); this effect was not observed at S2 (profile is higher than the river water) sampling points. The isotopic compositions of the soil water sources at the two sampling points (S1: δD = −77.41‰, δ18O = −11.01‰; S2: δD = −74.02‰, δ18O = −10.56‰) were depleted more than those in the long-term amount-weighted precipitation isotopes (δD = −56.30‰, δ18O = −8.17‰). The isotope signatures of soil water sources are similar to the isotope characteristics of some high-intensity precipitation events (≥30 mm/day), indicating that soil water originates from a fraction of the total precipitation. The piston flow (60%) and the preferential flow (40%) coexist, but soil moisture and rainfall intensity will affect the sequence of the two infiltration methods. This study provides insights for understanding the hydrological process of the upper Yellow River and evaluating groundwater quality and protecting the floodplain environment.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.