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Objectives: The micro‐nano structure of 3D‐printed porous titanium (Ti) alloy with excellent performance in avoiding stress shielding and promoting bone tissue differentiation provides a new opportunity for the development of bone implants, but it necessitates higher requirements for bone tissue differentiation and the antibacterial properties of bone implants in clinical practice. Methods: This study investigated the preparation, antimicrobial properties, and osteogenesis‐promoting ability of the 3D printed porous Ti alloy anodic oxidized Ag‐carrying (Ag@3D‐TiO2) scaffolds. The 3D printed porous Ti alloy (3D‐Ti), anodized 3D printed porous Ti alloy (3D‐TiO2), and Ag@3D‐TiO2 scaffolds were synthesized using electron beam melting. The antimicrobial properties of the scaffolds were examined using antibacterial tests and their cytocompatibility was assessed using a cell proliferation assay and acridine orange/ethidium bromide (AO/EB) staining. In vitro cellular assays were used to investigate the effects of the scaffold microstructural features on cell activity, proliferation, and osteogenesis‐related genes and proteins. In vivo animal experiments were used to evaluate the anti‐inflammatory and osteogenesis‐promoting abilities of the scaffolds. Results: The Ag@3D‐TiO2 scaffolds exhibited sustained anti‐microbial activity over time, enhanced cell proliferation, facilitated osteogenic differentiation, and increased extracellular matrix mineralization. In addition, alkaline phosphatase (ALP), collagen type I (COL‐I), and osteocalcin (OCN)‐related genes and proteins were upregulated. In vivo animal implantation experiments, the anti‐inflammatory effect of the Ag@3D‐TiO2 scaffolds were observed using histology, and a large amount of fibrous connective tissue was present around it; the Ag@3D‐TiO2 scaffolds were more bio‐compatible with the surrounding tissues compared with 3D‐Ti and 3D‐TiO2; a large amount of uniformly distributed neoplastic bone tissue existed in their pores, and the chronic systemic toxicity test showed that the 3D‐Ti, 3D‐TiO2, and Ag@3D‐TiO2 scaffolds are biologically safe. Conclusion: The goal of this study was to create a scaffold that exhibits antimicrobial properties and can aid bone growth, making it highly suitable for use in bone tissue engineering. [ABSTRACT FROM AUTHOR]

Global warming and algal blooms have been two of the most pressing problems faced by the world today. In recent decades, numerous studies indicated that global warming promoted the expansion of algal blooms. However, research on how algal blooms respond to global warming is scant. Global warming coupled with eutrophication promoted the rapid growth of phytoplankton, which resulted in an expansion of algal blooms. Algal blooms are affected by the combined effects of global warming, including increases in temperatures, CO2 concentration, and nutrient input to aquatic systems by extreme weather events. Since the growth of phytoplankton requires CO2, they appear to act as a carbon sink. Unfortunately, algal blooms will release CH4, CO2, and inorganic nitrogen when they die and decompose. As substrate nitrogen increases from decompose algal biomass, more N2O will be released by nitrification and denitrification. In comparison to CO2, CH4 has 28-fold and N2O has 265-fold greenhouse effect. Moreover, algal blooms in the polar regions may contribute to melting glaciers and sea ice (will release greenhouse gas, which contribute to global warming) by reducing surface albedo, which consequently would accelerate global warming. Thus, algal blooms and global warming could form feedback loops which prevent human survival and development. Future researches shall examine the mechanism, trend, strength, and control strategies involved in this mutual feedback. Additionally, it will promote global projects of environmental protection combining governance greenhouse gas emissions and algal blooms, to form a geoengineering for regulating the cycles of carbon, nitrogen, and phosphorus. [ABSTRACT FROM AUTHOR]

Currently, the main methods for detecting plant diseases are sampling and manual visual inspection. However, these methods are time-consuming, laborious and prone to misinterpretation. Rapid advances in Deep Learning (DL) techniques offer new possibilities. This study focused on analysing the confounding factors among three types of wheat rust (stripe rust, leaf rust and stem rust) and aimed to achieve higher classification accuracy. The following approaches were used: (1) Images were collected from several crops and diseases: Wheat Rusts Dataset (WRD), Wheat Common Disease Dataset (WDD), and Common Poaceae Disease Dataset (PDD); (2) Seven common convolutional neural network (CNN) models were made and their performance compared. DenseNet121 was selected as the base model, and its classification results further analysed. The results of the above analyses were then considered using phenotypic morphology and model structure analysis, as well as potential confounder discussions; (3) Adjustments and optimisations were made based on the identified confounding factors. The final improved model, designated Imp-DenseNet, achieved the following accuracies with different datasets: Top-1 accuracy = 98.32% (WRD), Top-3 accuracy = 97.30% (WDD) and Top-5 accuracy = 96.60% (PDD) (Top-x Accuracy refers to the accuracy of the top-ranked category that matches or containing the actual results). The study revealed the potential factors contributing to the confusion among the three wheat rusts and successfully achieved higher accuracy. It can provide a new perspective for future research on other diseases of wheat or other crops. [Display omitted] • Confusion factors of wheat rust were analysed from different perspectives • Combine knowledges of plant protection, plant phenotype morphology analysis, DL and CV • Remodel DenseNet using attention mechanism, SPP, and factorisation thoughts • Verify validity of methods with Grad-CAM, Yolo v8 and Transformer [ABSTRACT FROM AUTHOR]

Titanium alloy Ti6Al4V is a commonly used bone implant material, primarily prepared as a porous material to better match the elastic modulus of human bone. However, titanium alloy is biologically inert and does not have antibacterial properties. At the same time, the porous structure with a large specific surface area also increases the risk of infection, leading to surgical failure. In this paper, we prepared three porous samples with different porosities of 60%, 75%, and 85%, respectively (for short, 3D-60, 3D-75, and 3D-85) using 3D printing technology and clarified the mechanical properties. Through tensile experiments, when the porosity was 60%, the compressive modulus was within the elastic modulus of human bone. Anodic oxidation technology carried out the surface modification of a 3D-printed porous titanium alloy with 60% porosity. Through change, the different voltages and times on the TiO2 oxide layer on the 3D-printed porous titanium alloy are different, and it reveals the growth mechanism of the TiO2 oxide layer on a 3D-printed unique titanium alloy. The surface hydrophilic and antibacterial properties of 3D-printed porous titanium alloy were significantly improved after modification by anodic oxidation. [ABSTRACT FROM AUTHOR]

The acquisition of maize tassel phenotype information plays a vital role in studying maize growth and improving yield. Unfortunately, detecting maize tassels has proven challenging because of the complex field environment, including image resolution, varying sunlight conditions, plant varieties, and planting density. To address this situation, the present study uses unmanned aerial vehicle (UAV) remote sensing technology and a deep learning algorithm to facilitate maize tassel identification and counting. UAVs are used to collect maize tassel images in experimental fields, and RetinaNet serves as the basic model for detecting maize tassels. Small maize tassels are accurately identified by optimizing the feature pyramid structure in the model and introducing attention mechanisms. We also study how mapping differences in image resolution, brightness, plant variety, and planting density affect the RetinaNet model. The results show that the improved RetinaNet model is significantly better at detecting maize tassels than the original RetinaNet model. The average precision in this study is 0.9717, the precision is 0.9802, and the recall rate is 0.9036. Compared with the original model, the improved RetinaNet improves the average precision, precision, and recall rate by 1.84%, 1.57%, and 4.6%, respectively. Compared with mainstream target detection models such as Faster R-CNN, YOLOX, and SSD, the improved RetinaNet model more accurately detects smaller maize tassels. For equal-area images of differing resolution, maize tassel detection becomes progressively worse as the resolution decreases. We also analyze how detection depends on brightness in the various models. With increasing image brightness, the maize tassel detection worsens, especially for small maize tassels. This paper also analyzes the various models for detecting the tassels of five maize varieties. Zhengdan958 tassels prove the easiest to detect, with R2 = 0.9708, 0.9759, and 0.9545 on 5, 9, and 20 August 2021, respectively. Finally, we use the various models to detect maize tassels under different planting densities. At 29,985, 44,978, 67,466, and 89,955 plants/hm2, the mean absolute errors for detecting Zhengdan958 tassels are 0.18, 0.26, 0.48, and 0.63, respectively. Thus, the detection error increases gradually with increasing planting density. This study thus provides a new method for high-precision identification of maize tassels in farmland and is especially useful for detecting small maize tassels. This technology can be used for high-throughput investigations of maize phenotypic traits. [ABSTRACT FROM AUTHOR]

The Loess Plateau is a typical ecologically sensitive area that can easily be perturbed by the effects of human activities and global climate change. Therefore, it is necessary to develop tools to monitor the environmental quality in the LP quickly and accurately. To reveal the spatio-temporal changes in environmental quality in the LP from 2000 to 2020, we used the Moderate-Resolution Imaging Spectroradiometer (MODIS) products on the Google Earth Engine platform and constructed the remote sensing ecological index (RSEI) through principal component analysis (PCA). Then, Sen–Mann–Kendall methods were applied to determine the changing trend of the environmental quality of the LP. Finally, natural and anthropogenic factors affecting the environmental quality were probed using a geographical detector model. The results showed that: (1) the average RSEI values in 2000, 2010 and 2020 were 0.396, 0.468 and 0.511, respectively, displaying an upward trend from 2000 to 2020, with a growth rate of 0.005 year − 1 . The overall environment quality was moderate (0.4–0.6). (2) In terms of spatial distribution, the environmental quality was excellent in the southeast and poor in the northwest of the LP. The areas with improved environmental quality (84.51%) were located in all the counties, whereas the areas with degraded environmental quality (8.11%) occurred in the north and southeast of the study area. (3) Greenness, heat, wetness, dryness and land use types were prominent factors affecting RSEI throughout the study period; additionally, the total industrial gross domestic product showed a growing influence. The contribution of multi-factor interaction was stronger than that of single factors. The results will provide a reference and a new research perspective for local environmental protection and regional planning. [ABSTRACT FROM AUTHOR]

Nutrients and hydrodynamic mixing are both important environmental factors affecting the growth of Microcystis. Effects of nutrient combined with hydrodynamic mixing in the dominance of Microcystis in phytoplankton community is unclear. Here we present data on the effects of hydrodynamic mixing (HM), nutrient addition (+ N + P), and hydrodynamic mixing combined nutrient addition (HM + N + P) on dominance of Microcystis in a microcosm experiments. Results showed that the average number of Microcystis in HM + N + P was 10.09, 3.79, and 1.95 times that of the control (9.19 × 106 cells L−1), + N + P, and HM group, respectively. Moreover, the mean colony sizes of Microcystis in HM + N + P group were 2.08, 1.91, and 1.21 times that of the control (24.42 μm), + N + P,HM group, respectively. Pseudanabaena limnetica replaced Microcystis as the dominant species in control, + N + P, and HM group at end of the experiment, with 82.21%, 90.65%, and 50.37% of total algae number, respectively. However, in HM + N + P group, Microcystis dominated throughout the experimental period, accounting for 73.56–94.67% of the total algae abundance. Results demonstrated that N and P combined with hydrodynamic mixing promoted abundance, colony size of Microcystis, which plays an important role in the dominance of Microcystis in phytoplankton community in natural ecosystems. [ABSTRACT FROM AUTHOR]

• A novel model (LabTNet) is proposed to estimate crop biochemical traits. • LabTNet jointly estimates multiple wheat traits by hyperspectral data. • LabTNet exhibits superior predictive performance than existing methods. • Grad-CAM analyzed hyperspectral attention regions of LabTNet. Accurate estimation of crop leaf and canopy biochemical traits, such as leaf dry matter content (Cm), leaf equivalent water thickness (Cw), leaf area index (LAI), dry leaf biomass (DLB), leaf total water content (LW), and fresh leaf biomass (FLB), is essential for monitoring crop growth accurately. The vegetation spectral feature technique combined with statistical regression methods is widely employed for remote sensing crop biochemical traits mapping. However, the crop canopy spectral reflectance is influenced by various crop biochemical traits and uncertainties in geometric changes of light and soil background effects. Consequently, the remote-sensing estimation of crop biochemical traits is limited. A potential solution involves training a deep learning model to understand the physical relationship between crop biochemical traits and canopy spectral reflectance based on a physical radiative transfer model (RTM). The primary focus of this study is to propose a winter-wheat leaf and canopy biochemical traits analysis and mapping method based on hyperspectral remote sensing, utilizing a deep learning network for leaf area index and leaf biochemical traits deep learning network (LabTNet). This study consists of four main tasks: (1) Field-based measurements of winter-wheat spectra and biochemical traits were conducted in two growing seasons. A PROSAIL RTM was also employed to generate a simulated dataset representing comprehensive and complex winter-wheat field conditions. (2) The LabTNet deep learning model was pre-trained using the simulated spectra dataset to acquire knowledge of the physical relationship between crop biochemical traits and canopy spectral reflectance derived from the RTM. Subsequently, the model was re-trained using the field-based spectra dataset from two growing seasons, employing a transfer learning technique. (3) An analysis was conducted to assess the performance of LabTNet against traditional statistical regression methods in estimating crop leaf and canopy biochemical traits. The study used the gradient-weighted class activation mapping (Grad-CAM) technique to analyze the attention regions of input spectra (454:8:950 nm, 960:10:1300 nm, 1450:10:1750 nm, 2000:10:2350 nm) by different convolutional neural network layers in LabTNet, aiming to enhance the interpretability of deep learning models. (4) Winter-wheat leaf and canopy biochemical traits (Cw, Cm, LAI, DLB, LW, and FLB) were mapped using the LabTNet deep learning model. Our research has the following conclusions: (1) Combining the RTM and deep learning techniques yields higher winter-wheat biochemical trait estimates than traditional statistical regression methods. (2) Different LabTNet deep learning model layers focus on distinct areas of canopy reflectance, corresponding to the sensitive regions for various winter-wheat biochemical traits. (3) LabTNet demonstrates similar winter-wheat leaf and canopy biochemical traits estimation performance using visible and near-infrared (VNIR) reflectance data and full-spectral (FS) range hyperspectral reflectance as inputs (Cw: R 2 = 0.603–0.653, RMSE = 0.0015–0.0015 cm; Cm: R 2 = 0.511–0.560, RMSE = 0.0006–0.0007 g/m2; LAI: R 2 = 0.773–0.793, RMSE = 0.65–0.66 m2/m2; LW: R 2 = 0.842–0.847, RMSE = 67.93–70.73 g/m2; DLB: R 2 = 0.747–0.762, RMSE = 21.10–21.89 g/m2; FLB: R 2 = 0.831–0.840, RMSE = 86.26–90.30 g/m2). The combined use of UAV hyperspectral remote sensing and the LabTNet model proves effective in providing high-performance winter-wheat leaf and canopy biochemical trait maps, offering valuable insights for agricultural management. [ABSTRACT FROM AUTHOR]

As a key functional trait, leaf photosynthetic pigment content (LPPC) plays an important role in the health status monitoring and yield estimation of apples. Hyperspectral features including vegetation indices (VIs) and derivatives are widely used in retrieving vegetation biophysical parameters. The fractional derivative spectral method shows great potential in retrieving LPPC. However, the performance of fractional derivatives and machine learning (ML) for retrieving apple LPPC still needs to be explored. The objective of this study is to test the capacity of using fractional derivative and ML methods to retrieve apple LPPC. Here, the hyperspectral data in the 400–2500 nm domains was used to calculate the fractional derivative order of 0.2–2, and then the sensitive bands were screened through feature dimensionality reduction to train ML to build the LPPC estimation model. Additionally, VIs-based ML methods and empirical regression models were developed to compare with the fractional derivative methods. The results showed that fractional derivative-driven ML methods have higher accuracy than the ML methods driven by the original spectra or vegetation index. The results also showed that the ML methods perform better than empirical regression models. Specifically, the best estimates of chlorophyll content and carotenoid content were achieved using support vector regression (SVR) at the derivative order of 0.2 (R2 = 0.78) and 0.4 (R2 = 0.75), respectively. The fractional derivative maintained a good universality in retrieving the LPPC of multiple phenological periods. Therefore, this study highlights that the fractional derivative and ML improved the estimation of apple LPPC. [ABSTRACT FROM AUTHOR]

• The increasing trend of vapor pressure deficit (VPD) change in 1997–2020 is ten times higher than in 1981–1996. • Increasing VPD promoted cropland and grassland growth in Arid, while inhibited the growth of shrubland. • Significant positive or negative difference in the sensitivity to VPD for the same vegetation under different climatic zones. • The vast majority of vegetation showed a significant increase in the inter-annual trend of the sensitivity to VPD. • The relationship of higher sensitivity with lower VPD is not stable under different vegetation and climatic zones. Vapor pressure deficit (VPD) shows significant spatial and temporal variability in the context of global climate change, which is important for studying the implications of climate change on the structure and function of ecosystems to analyze the effects of VPD on vegetation dynamics. Spatial patterns of vegetation sensitivity to VPD have been recently investigated, however, the feedback of different vegetation types to VPD may vary depending on physiological characteristics, it is unclear how different types influence the sensitivity to VPD. In this study, the ERA5-Land reanalysis time-series dataset was used to analyze the spatial and temporal trends of VPD under different vegetation types. It was found that VPD showed an increasing trend in vegetated areas over the past 20 years with large spatial heterogeneity, generally increasing with drying conditions. On this basis, the spatial patterns of vegetation sensitivity to VPD and temporal trends in sensitivity were evaluated over the past 20 years in China using the enhanced vegetation index (EVI) and near-infrared reflectance of vegetation (NIRv) which can describe vegetation dynamics. The results show that the sensitivities under the two indices have high spatial consistency, with northeastern and central China showing positive sensitivities and southern China showing negative sensitivities, respectively. The positive sensitivities are relatively high for Deciduous Broadleaf Forests (DBF), Deciduous Needleleaf Forests (DNF), Grasslands (GL), and Croplands (CL) types, while the negative sensitivities are larger for Shrublands (SL) and Savannas (SA) types. Under different climatic zones, the sensitivity of CL and GL are independent of climatic zones (both showing positive), while the sensitivity of SL is negative in the Humid zone and positive in the Semi-Arid zone. Temporally, the sensitivity showed a slow increasing trend over the last 20 years. In terms of vegetation types, sensitivities of Evergreen Broadleaf Forests (EBF), DBF, GL and CL types showed a significant increasing trend (p < 0.05), except for the SL type, which showed a significant decreasing trend (p < 0.05). The trends of sensitivity are not affected by the differences in vegetation types (all of them show an increasing trend) under arid and semi-arid conditions, while dry sub-humid and humid have a greater impact on sensitivity trends. The finding of an overall increase in sensitivity suggests a mechanism of erratic change in vegetation growth under climate change. Notably, the increased sensitivity of certain vegetation types (especially GL and CL) suggests that these may become progressively vulnerable to increased VPD as global climate change persists, with the risk of moving from facilitation to inhibition of photosynthesis. [ABSTRACT FROM AUTHOR]

Phase II clinical trials in oncology are used to initially evaluate the therapeutic efficacy of a new treatment. In the past, the total response was a frequently used endpoint to access the effectiveness of the treatment. When the total response is modest, clinicians may also be interested in disease control (defined as the total response or stable disease) since it may better predict clinical outcomes. Thus, formally testing both the total response and disease control in a phase II trial has an important clinical implication. In this paper, we propose a new method to construct optimal subset designs for one-stage and two-stage phase II clinical trials with two binary endpoints, i.e. the total response and disease control. A new set of hypotheses under the framework of intersection-union tests is provided in which the treatment is considered promising if both the total response and disease control are good. We show that the power function for each test in a large family of tests is nondecreasing in both the total response rate and disease control rate; identify the parameter configurations at which the maximum Type I error rate and minimum power are achieved and derive level- α tests. We also provide optimal one-stage designs with the minimum sample size and optimal two-stage designs with the least expected total sample size. • A new method of constructing optimal subset designs is proposed. • The new method has potential of establishing more precise inferential conclusions. • The proposed optimal design usually yields a smaller sample size. [ABSTRACT FROM AUTHOR]

Activated carbons with different porosities derived from coffee, which prepared by the potassium hydroxide (KOH) activation method, are used as electrode for supercapacitors. The prepared materials indicated a well‐designed porous structure that provides countless adsorption sites, leading to high supercapacitor performance. Mesopores allow rapid diffusion of electrolyte ions, while micropores usually provide abundant adsorption sites for ions. Therefore, appropriate control over pore‐size distributions is a key factor for good electrochemical performance. The porosity and surface structure of activated coffee grounds (ACGs) are highly dependent on both the activation temperature and KOH/carbonized materials weight ratio. The ACGs were characterized using N2 adsorption at 77 K. The specific surface area increased from 1.75 to 3065 m2/g. Cyclic voltammetry and galvanostatic charge–discharge tests were employed in the evaluation of the electrochemical behavior of each sample. The ACG materials have a high specific capacitance of 127.8 F/g and cycle stability after 2500 cycles. [ABSTRACT FROM AUTHOR]

Conjugated polymers have attracted growing attention for versatile applications in energy storage due to their potential benefits including low‐cost processing, molecular tunability, environmental benignity, and high mechanical flexibility. In particular, polymer‐based organic electrode materials have shown significant progress in supercapacitor (SC) applications with superior electrochemical behaviors. The performances of SCs are closely related to the intrinsic characteristics of different polymers in the nanoscale and the morphological features of the polymer‐based electrode materials obtained by different fabrication techniques in the macroscale. This review summarizes the design and synthesis of both p‐type and n‐type conjugated polymers, highlighting the pros and cons of three synthesis techniques: electrochemical polymerization, chemical polymerization, and in situ polymerization. The performances of conjugated polymers in SCs, their cycling stabilities, and structure‐performance relationships are discussed. Moreover, the existing challenges and future directions of polymer‐based SCs are considered with respect to energy density, stability, and large‐scale production to promote commercialization. [ABSTRACT FROM AUTHOR]

In order to solve the pollution problem of organic pollutants in water, this study proposed a photocatalyst that efficiently utilizes sunlight, and TiO 2 /HTiOF 3 photocatalytic materials were successfully prepared by a hydrothermal method and used for the degradation of rhodamine B (RhB) solution under simulated sunlight. The effect of time and urea addition on the performance of the material was investigated, and the results showed that the sample with a reaction time of 12 h and a urea addition of 0.05 g exhibited 97 % degradation efficiency and the best photocatalytic degradation activity for RhB solution, which was attributed to the construction of a traditional type II heterojunction of the TiO 2 /HTiOF 3 nanocomposite, which improved the carrier separation rate and maintained a strong redox capacity. This work contributes to the construction of efficient semiconductor photocatalytic nano-heterostructures. The preparation of TiO 2 /HTiOF 3 composites by one-step hydrothermal method provides a new idea for the development of catalysts. • Stable HTiOF 3 intermediates were synthesized by introducing urea, and TiO 2 /HTiOF 3 photocatalytic materials were constructed by a one-step hydrothermal method. • The construction of type II heterojunction effectively reduces the interfacial resistance while greatly extending the lifetime of the photogenerated carriers. • Photocatalytic performance of TiO 2 /HTiOF 3 composites is greatly improved over pure TiO 2. [ABSTRACT FROM AUTHOR]

• Introduction of a novel model that integrates OBIA and the attention mechanism for precise orchard mapping. • The contribution of SAR is studied in orchard classification. • The model obtains more accurate classification results than RNN-based and CNN-based deep learning models. • The uncertainty of the super-pixel size on classification accuracy is investigated. Reliable and accurate classification of orchards is important for the dynamic monitoring of large-scale orchards and food security evaluation. At present, the very similar spectral profiles of different fruit trees and the high susceptibility of optical data to interference by weather conditions limit the resolution of orchard classification. Synthetic Aperture Radar (SAR) imagery provides an advanced solution to this problem, with the advantages of being immune to weather conditions and advances in deep learning techniques. This paper presents an orchard classification model (STCM) with optical and SAR fusion, which integrates the advantages of the Simple Non-Iterative Clustering (SNIC) super-pixel algorithm with a deep learning algorithm based on a multi-headed attention mechanism, and it achieves the best classification accuracy of above 0.82 in comparison with existing deep learning models. Meanwhile, the model has exceptional classification accuracy and robustness in a study area with fragmented plots, many fruit tree species, and various distributions of optical images. In the absence of optical data, the classification accuracy of the STCM model using only SAR data is around 0.70, which makes the model have a promising potential application value. This study provides a technical solution for accurately obtaining different orchard categories in high-resolution remotely sensed orchard images. The study helps to improve the management and operation of orchards and provides a strong basis for decision-making in the fruit industry to enhance the sustainability of the global fruit industry. [ABSTRACT FROM AUTHOR]

Polyvinylidene fluoride‐hexafluoropropylene (PVDF‐HFP) is a standard electrolyte membrane and binder material for lithium‐ion batteries (LIBs) because of the benefits of membrane formation and strong mechanical properties. Still, its use is limited due to unavoidable defects such as high crystallinity and poor electrochemical performance. In this paper, a larger specific surface area and three‐dimensional (3D) permeable lamellae carboxylated g‐C3N4 (HCN) were successfully prepared by HNO3 and introduced into the PVDF‐HFP to obtain a high‐performance composite separator. The wettability and electrolyte absorption of PVDF‐HFP separator can be improved through the carboxylated HCN. More crucially, because PVDF‐HFP has several active sites and low crystallinity, adding HCN can greatly increase the ionic conductivity of separators. The ionic conductivity of PH‐HCN at 25 °C is 0.52 S cm−1, 4.7 times higher than the initial PVDF‐HFP membrane, and the oxidation potential can reach 4.8 V. The assembled lithium‐ion batteries half‐cell has a capacity of 71 % at a 5 C rate and 94 % after 400 cycles at a 2 C rate. Therefore, the improved separator has the potential to be applied to high‐performance LIBs. [ABSTRACT FROM AUTHOR]

Herein, the synthesis of MnO 2 nanowires decorated with transition metal-doped carbon quantum dots (MCQDs) is described for use as binder-free flexible solid-state supercapacitors. In particular, the water-soluble Fe and N co-doped MCQDs prepared by amidation reaction with ferric ammonium citrate and urea had ultrafine particle morphologies and showed super-hydrophilicity in aqueous solution, which greatly improved the contact probability between the electrode and the electrolyte. In addition, the co-doped MCQDs increased both the electrical conductivity and specific capacitance of the MnO 2 /carbon-based nanocomposites and limited the collapse and exfoliation of the MnO 2 structure during extended cycling. Compared with pristine MnO 2 electrode, the MCQDs/MnO 2 composite electrode exhibited faster charge-discharge rate and improved cycle stability with increased specific capacitance. The flexible symmetric supercapacitor constructed from the MCQDs/MnO 2 composite had a capacitance retention of 85.6 % after 10,000 charge/discharge cycles at a current density of 2 mA cm−2. In addition, the device showed a maximum power density of 4.8 W cm−2 at an energy density of 2.3 mW h cm−2, and a maximum energy density of 11.16 mW h cm−2 at a power density of 311.6 mW cm−2. The data that support the findings of this study are available from the corresponding author upon reasonable request. • A metal carbon quantum dots (MCQDs)/MnO 2 nanowire composite is described. • The MCQDs/MnO 2 composite was used to construct flexible solid-state supercapacitors. • Super-hydrophilic MCQDs improved the contact probability of the interface. • MCQDs improved the conductivity, specific capacitance and cycling stability of MnO 2. • The flexible symmetric supercapacitor exhibited good electrochemical performance. [ABSTRACT FROM AUTHOR]

Accurate estimation of crop biophysical and biochemical parameters during crop growing seasons is essential for improving site-specific management and yield estimation. The potential ability of fully polarimetric synthetic aperture radar (SAR) data in estimating above-ground biomass of oilseed rape was investigated in this study. The temporal profile of different scattering intensity and polarimetric features during the entire growing season was identified with ground measurements. A polarimetric feature, relying on the polarimetric decomposition method, was put forward to estimate the biomass of oilseed rape. Validation results revealed great potential with a determination coefficient (R2) of 0.85, root mean squared error (RMSE) of 41.6 g/m2, and relative error (RE) of 28.5% for dry biomass, and an R2 of 0.76, RMSE of 527.4 g/m2 and RE of 28.6% for fresh biomass. Moreover, the use of full polarization SAR data was compared with single and dual polarization SAR data. The results suggest that when full polarization SAR data is available, a simpler model, higher saturation point and better accuracy can be achieved in biomass estimation of oilseed rape, which highlights the importance and value of polarimetry information in quantitative crop monitoring. This study provides guidelines for in-season monitoring of crop growth parameters with SAR data, which further improves crop monitoring capability in adverse weather conditions. [ABSTRACT FROM AUTHOR]

Abstract When dealing with multiple growth stages, estimates of above-ground biomass (AGB) based on optical vegetation indices (VIs) are difficult for two reasons: (i) optical VIs saturate at medium-to-high canopy cover, and (ii) organs that grow vertically (e.g., biomass of reproductive organs and stems) are difficult to detect by canopy spectral VIs. Although several significant improvements have been made for estimating AGB by using narrow-band hyperspectral VIs, synthetic aperture radar, laser intensity direction and ranging, the crop surface model technique, and combinations thereof, applications of these new techniques have been limited by cost, availability, data-processing difficulties, and high dimensionality. The present study thus evaluates the use of ultrahigh-ground-resolution image textures, VIs, and combinations thereof to make multiple temporal estimates and maps of AGB covering three winter-wheat growth stages. The selected gray-tone spatial-dependence matrix-based image textures (e.g., variance, entropy, data range, homogeneity, second moment, dissimilarity, contrast, correlation) are calculated from 1-, 2-, 5-, 10-, 15-, 20-, 25-, and 30-cm-ground-resolution images acquired by using an inexpensive RGB sensor mounted on an unmanned aerial vehicle (UAV). Optical-VI data were obtained by using a ground spectrometer to analyze UAV-acquired RGB images. The accuracy of AGB estimates based on optical VIs varies, with validation R 2: 0.59–0.78, root mean square error (RMSE): 1.22–1.59 t/ha, and mean absolute error (MAE): 1.03–1.27 t/ha. The most accurate AGB estimate was obtained by combining image textures and VIs, which gave R 2: 0.89, MAE: 0.67 t/ha, and RMSE: 0.82 t/ha. The results show that (i) the eight selected textures from ultrahigh-ground-resolution images were significantly related to AGB, (ii) the combined use of image textures from 1- to 30-cm-ground-resolution images and VIs can improve the accuracy of AGB estimates as compared with using only optical VIs or image textures alone; and (iii) high AGB values from winter-wheat reproductive growth stages can be accurately estimated by using this method; (iv) high estimates of winter-wheat AGB (8–14 t/ha) using the proposed combined method (DIS1, SE30, B460, B560, B670, EVI2 using MSR) show a 22.63% (nRMSE) improvement compared with using only spectral VIs (LCI, NDVI using MSR), and a 21.24% (nRMSE) improvement compared with using only image textures (COR1, DIS1, SE30, EN30 using MSR). Thus, the combined use of image textures and VIs can help improve estimates of AGB under conditions of high canopy coverage. [ABSTRACT FROM AUTHOR]

Abstract A simple hydrothermal method is developed for the growth of NiCo 2 O 4 nano-needle arrays on a filter paper carbon (FC) substrate for use as a high performance electrode for supercapacitors. The as-synthesized NiCo 2 O 4 /C composites exhibited high specific capacitance (995.2 F g−1 at a current density of 10 A g−1) and excellent cycling performance (no capacitance loss after 3000 cycles). The 3D framework of the FC substrate not only prevents the aggregation of NiCo 2 O 4 nanoparticles, but also improves its electrical conductivity, thus leading to excellent electrochemical performance. Moreover, an asymmetric supercapacitor was assembled by using the NiCo 2 O 4 /C composites and activated carbon as the positive and negative electrode, respectively, and provided a maximum energy density of 20.87 Wh kg−1 at a power density of 374.6 W kg−1 and a maximum power density of 7.48 kW kg−1 at an energy density of 11.43 Wh kg−1 within a potential window of 0–1.5 V, demonstrating the potential of the composites for use in high performance energy storage systems. Highlights • Filter paper derived carbon (FC)/NiCo 2 O 4 was prepared by hydrothermal method. • The FC with open channels increased the electron and ion transport rates. • The FC-based supercapacitor exhibited good capacity and cycling stability. [ABSTRACT FROM AUTHOR]

Methods to obtain accurate phenotypic data of the seedling stage of maize are receiving ever-increasing research attention because such data are very important for crop growth and for estimating crop yield. To obtain such data, we propose herein an algorithm that uses computer vision to accurately recognize maize seedlings from a digital image. First, the red–green–blue (RGB) images acquired by a manned ground vehicle (MGV) and an unmanned aerial vehicle (UAV) are transformed into grayscale image to clarify the details of the images, and the Otsu threshold-segmentation method, which is based on threshold optimization, is used to separate the maize seedlings from the soil background. Next, the external pressure method is used to segment the results of skeleton extraction. After removing the skeleton burr by using the skeleton-deburring method based on saliency theory, the principal component analysis is used to determine the direction of the principal axis of the maize-seedling skeleton. The principal axis is then introduced as a reference to identify the direction angle of the principle axis in binary images. This process allows us to obtain from RGB images the multiple plant parameters that characterize the maize seedling stage. To verify these statistical computer-generated results, we compare them with field measurements of the maize plots. The result of applying the proposed algorithm to the MGV and UAV data sets correlate strongly ($R = 0.77$ –0.86) with the manually collected data. An average of 0.014 s is required to calculate the number of maize seedlings in a plot from two images showing different vegetation coverage, which shows that the proposed algorithm is computationally efficient. These results indicate that the proposed method provides accurate phenotypic data on maize seedlings. [ABSTRACT FROM AUTHOR]

Accurate and high-throughput identification of the initial anthesis of soybean varieties is important for the breeding and screening of high-quality soybean cultivars in field trials. The objectives of this study were to identify the initial day of anthesis (IADAS) of soybean varieties based on remote sensing multispectral time-series images acquired by unmanned aerial vehicles (UAVs), and analyze the differences in the initial anthesis of the same soybean varieties between two different climatic regions, Shijiazhuang (SJZ) and Xuzhou (XZ). First, the temporal dynamics of several key crop growth indicators and spectral indices were analyzed to find an effective indicator that favors the identification of IADAS, including leaf area index (LAI), above-ground biomass (AGB), canopy height (CH), normalized-difference vegetation index (NDVI), red edge chlorophyll index (CIred edge), green normalized-difference vegetation index (GNDVI), enhanced vegetation index (EVI), two-band enhanced vegetation index (EVI2) and normalized-difference red-edge index (NDRE). Next, this study compared several functions, like the symmetric gauss function (SGF), asymmetric gauss function (AGF), double logistic function (DLF), and fourier function (FF), for time-series curve fitting, and then estimated the IADAS of soybean varieties with the first-order derivative maximal feature (FDmax) of the CIred edge phenology curves. The relative thresholds of the CIred edge curves were also used to estimate IADAS, in two ways: a single threshold for all of the soybean varieties, and three different relative thresholds for early, middle, and late anthesis varieties, respectively. Finally, this study presented the variations in the IADAS of the same soybean varieties between two different climatic regions and discussed the probable causal factors. The results showed that CIred edge was more suitable for soybean IADAS identification compared with the other investigated indicators because it had no saturation during the whole crop lifespan. Compared with DLF, AGF and FF, SGF provided a better fitting of the CIred edge time-series curves without overfitting problems, although the coefficient of determination (R2) and root mean square error (RMSE) were not the best. The FDmax of the SGF-fitted CIred edge curve (SGF_CIred edge) provided good estimates of the IADAS, with an RMSE and mean average error (MAE) of 3.79 days and 3.00 days, respectively. The SGF-fitted_CIred edge curve can be used to group the soybean varieties into early, middle and late groups. Additionally, the accuracy of the IADAS was improved (RMSE = 3.69 days and MAE = 3.09 days) by using three different relative thresholds (i.e., RT50, RT55, RT60) for the three flowering groups compared to when using a single threshold (RT50). In addition, it was found that the IADAS of the same soybean varieties varied greatly when planted in two different climatic regions due to the genotype–environment interactions. Overall, this study demonstrated that the IADAS of soybean varieties can be identified efficiently and accurately based on UAV remote sensing multispectral time-series data. [ABSTRACT FROM AUTHOR]

MnO 2 /biomass-derived porous carbon (BPC) composites have been prepared by a hydrothermal method, in which the BPC 3D porous carbon structure was based on a banana peel. The banana peel, after freeze drying, can maintain its hierarchical natural porous structure, which provides enough growth space for MnO 2 and reduces the agglomeration of MnO 2 particles. The MnO 2 /BPC composites were characterized by XRD, FT-IR, XPS, TGA, SEM, TEM, BET. The electrochemical performance of the composites was tested in three-electrode supercapacitors using 1 M Na 2 SO 4 aqueous solution as an electrolyte. Due to the large amounts of hierarchical pores and large pore volume, the as-prepared composites exhibited good electrochemical performance. Electrochemical measurements indicated that the MnO 2 /BPC composites applied in supercapacitors had a specific capacitance of 139.6 F g −1 at 300 mA g −1 , and exhibited a good cycling stability with a capacitance retention ratio of 92.3% after 1000 cycles (at 1 A g −1 ). The MnO 2 /BPC composites with 3D porous structure are promising materials in the application of supercapacitors. [ABSTRACT FROM AUTHOR]

Water productivity (WP) is a key element of agricultural water management in agricultural irrigated regions. The objectives of this study were: (i) to estimate biomass of winter wheat using spectral indices; (ii) integrate the estimation of biomass data with the AquaCrop model using a lookup table for higher accuracy biomass simulation; (iii) show estimation accuracy of the data assimilation method in yield and WP. Spectral variables and concurrent biomass, yield and WP of samples were acquired at the Xiaotangshan experimental site in Beijing, China, during the 2008/2009, 2009/2010, 2010/2011 and 2011/2012 winter wheat growing seasons. The results showed that all spectral indices had a highly significant relationship with biomass, especially normalized difference matter index, with R and RMSE values of 0.84 and 1.43 t/ha, respectively. Simulation of biomass and yield by the AquaCrop model were in good agreement with the measured biomass and yield of winter wheat. The results showed that the data assimilation method (R = 0.79 and RMSE = 0.12 kg/m) could be used to estimate WP. The result indicated that the AquaCrop model could be used to estimate yield and WP with the aid of remote sensing for improving agricultural water resources management. [ABSTRACT FROM AUTHOR]

• We introduce leaf N profile characteristic coefficient K m on its remote estimates. • Diverse datasets are used for developing robust models for remote estimates of K m. • Four regression algorithms are tested with various possible sets of predictors. • A robust method is proposed to estimate K m from canopy reflectance for winter wheat. The characteristic coefficient of vertical leaf nitrogen (N) profile is a canopy parameter that indicates the attenuation steepness of leaf N from the top of canopy downward. It is sensitive not only to crop production, grain yield and quality, and light- and N-use efficiency but also to N deficiency in the crop. We introduce this coefficient by exploring a robust method to estimate it from canopy spectral reflectance. We analyze comprehensively various approaches based on diverse datasets of winter wheat. We test and compare the accuracy and stability of models by using the adjusted and weighted coefficient of determination (wR adj 2), the mean absolute error (MAE), and the mean and coefficient of variation (CV) over multiple seasons. The analysis focuses mainly on the coefficient of mass-based leaf N profile (K m); nevertheless, a comparison with the coefficient of area-based leaf N profile is presented. The results indicate that the most robust model to estimate K m of winter wheat is K m = (1.8037 R G V I - 0.9702 + 0.0786 e x p (0.6315 / D A S F)) / 2 , where RGVI is the red and green ratio vegetation index, and DASF is the directional area scattering factor. The mean wR adj 2 is 0.663 with CV = 8.2% and the mean MAE is 0.117 with CV = 12.8% over three seasons including various situations. This makes it possible to assess K m at large areas and follow its dynamics over multiple periods in a timely and nondestructive manner. [ABSTRACT FROM AUTHOR]

Land-use maps are thematic materials reflecting the current situation, geographical diversity, and classification of land use and are an important scientific foundation that can assist decision-makers in adjusting land-use structures, agricultural zoning, regional planning, and territorial improvement according to local conditions. Spectral reflectance and radar signatures of time series are important in distinguishing land-use types. However, their impact on the accuracy of land-use mapping and decision making remains unclear. Also, the many spatial and temporal heterogeneous landscapes in southern Xinjiang limit the accuracy of existing land-use classification products. Therefore, our objective herein is to develop reliable land-use products for the highly heterogeneous environment of the southern Xinjiang Uygur Autonomous Region using the freely available public Sentinel image datasets. Specifically, to determine the effect of temporal features on classification, several classification scenarios with different temporal features were developed using multi-temporal Sentinel-1, Sentinel-2, and terrain data in order to assess the importance, contribution, and impact of different temporal features (spectral and radar) on land-use classification models and determine the optimal time for land-use classification. Furthermore, to determine the optimal method and parameters suitable for local land-use classification research, we evaluated and compared the performance of three decision-tree-related classifiers (classification and regression tree, random forest, and gradient tree boost) with respect to classifying land use. Yielding the highest average overall accuracy (95%), kappa (95%), and F1 score (98%), we determined that the gradient tree boost model was the most suitable for land-use classification. Of the four individual periods, the image features in autumn (25 September to 5 November) were the most accurate for all three classifiers in relation to identifying land-use classes. The results also show that the inclusion of multi-temporal image features consistently improves the classification of land-use products, with pre-summer (28 May–20 June) images providing the most significant improvement (the average OA, kappa, and F1 score of all the classifiers were improved by 6%, 7%, and 3%, respectively) and fall images the least (the average OA, kappa, and F1 score of all the classifiers were improved by 2%, 3%, and 2%, respectively). Overall, these analyses of how classifiers and image features affect land-use maps provide a reference for similar land-use classifications in highly heterogeneous areas. Moreover, these products are designed to describe the highly heterogeneous environments in the study area, for example, identifying pear trees that affect local economic development, and allow for the accurate mapping of alpine wetlands in the northwest. [ABSTRACT FROM AUTHOR]

Crop evapotranspiration (ET c ) is an important indicator used in managing agriculture water and monitoring crop growth. The objectives of this study were to: (1) analyze the seasonal dynamics of crop coefficients (K c ) and basal crop coefficient (K cb ) derived from vegetation indices (VIs) based on a time series of Huanjing (HJ) satellite images during 2011 and 2013; (2) investigate daily and monthly variations of ET c at key growth stages of winter wheat using lysimeter or eddy covariance systems; (3) compare the performance of two Huanjing-1A/B satellite-based FAO-56 models (the FAO-56 dual-crop coefficient model and the vegetation indices-reference evapotranspiration (VIs-ET o ) method) to the ET c measurements; (4) select the best ET c model for estimating daily ET c (mm/day) at the Xiaotangshan experimental site and its surrounding farmland in conjunction with HJ satellite overpasses from March to May 2011. The VIs and concurrent ET c were acquired at the Xiaotangshan experimental site, Beijing, China, during the 2011 and 2013 winter wheat growing seasons. The results showed that the overall tendencies of crop coefficient patterns (K cb and K c ), ET c and ET o first increased and then decreased at key growth stages of winter wheat. The cumulative ET c of water consumption was highest at the heading-filling stage in May. Similar changes in cumulative ET c were found during April–May 2011 and 2013. The estimation accuracy of ET c was better based on FAO-56 dual-crop coefficient model ( R 2 = 0.88 and RMSE = 1.06 mm/day in 2011 and R 2 = 0.84 and RMSE = 0.55 mm/day in 2013) than the VI-ET o method ( R 2 = 0.77 and RMSE = 1.22 mm/day in 2011 and R 2 = 0.67 and RMSE = 0.81 mm/day in 2013). The results indicated that the FAO-56 dual-crop coefficient model and VI-ET o methods were used to estimate ET c in winter wheat. Two Huanjing-1A/B satellite-based FAO-56 models were used to timely estimate ET c during the winter wheat mid-season, and ET c was used to adjust agricultural water management practices. [ABSTRACT FROM AUTHOR]

Hierarchical carbon foams ( HCFs) with micro-, meso-, and macropores were successfully synthesized via a two-step process: (1) polymerization in oil-in-water (O/W) emulsions without any hard templates and (2) carbonization at 850°C. With the aim of both enhancing the stability of the emulsion and forming a micro- and mesoporous structure during the carbonization process, potassium citrate was introduced in an aqueous solution of resorcinol and formaldehyde. A series of HCFs were fabricated by changing the mass ratio of potassium citrate to total carbon sources from 0.25 to 1.5. The effect of potassium citrate on the porous structure of HCFs was investigated through nitrogen sorption tests. The prepared HCFs exhibited well-developed porous structures of micro-, meso- and macropores and high surface areas. The structural characteristics of the HCFs, including pore size distribution, surface area, and porosity, were significantly dependent on the amount of potassium citrate. It was concluded that potassium citrate greatly contributed to the formation of carbon foams with nano-sized pore structures and high porosity. Interestingly, it was found that when the mass ratio of potassium citrate to total carbon sources was 0.5, the HCFs showed the highest specific surface area (~1360 m2/g). Furthermore, the capacitive performances of the HCFs were evaluated in a 6.0 M KOH aqueous solution using typical electrochemical methods such as cyclic voltammetry and galvanostatic charge/discharge tests. The capacitance of the HCFs tended to increase with the increase in surface area. In particular, the HCFs with the highest surface area also exhibited excellent electrochemical properties (high capacitance of 224 F/g at 1.0 A/g, high rate capability of 191 F/g at 10.0 A/g). These features may be attributed to both the resulting interconnected pore structure that is easily accessible to ions and the high surface area. We believe that this synthesis strategy can be easily extended to the preparation of hierarchical porous carbon materials with a desirable pore structure by tuning the organic monomer. [ABSTRACT FROM AUTHOR]

• Multispectral data are effective in discriminating tea plant varieties. • Texture information are essential elements in discriminating tea plant varieties. • Data fusion of multispectral image can improve tea plant discrimination rate. Discrimination of tea plant (Camellia sinensis L.) varieties is of significant value for the efficient management of cultivation and resources optimization of tea industry. Phenotypic and spectral characteristics of tea plants are important indicators for determining the quality of tea cultivars to a certain extent. However, few studies have used spectral image information to identify tea plant varieties. The aim of this research was the discrimination of 16 types of high-yield tea plant varieties using a multispectral camera. This methodology involved image registration, calibration, segmentation, information extraction, and data fusion. The hue (H), saturation (S), and value (V), texture information, and several spectral vegetation indices were acquired from the multispectral image of the tea plant canopy. The successive projection algorithm (SPA) was used to analyze the original parameters. Three classification methods were applied to tea plant variety discrimination: Bayes discriminant analysis (BDA), support vector machine (SVM), and extreme learning machine (ELM). The results indicated that SPA based on fusing data combined with the SVM classification model, achieved a feasible method to identify tea plant varieties. Additionally, the method achieved accuracy in the training, test, and validation sets, reaching 97.00%, 90.52%, and 88.67%, respectively. This study proposed a new perspective on multispectral image information as an identifier of tea plant varieties. The explored model will be helpful for the development of portable instruments for commercial applications in variety identification and phenotype recognition of tea plants. [ABSTRACT FROM AUTHOR]

• Vegetation indices show a strong correlation with nitrogen status in tea plants. • The combination of multispectral data and hyperspectral data is effective in monitoring nitrogen level. • Fusing data of image information and spectral information has remarkable ability to predict nitrogen content in tea plants. Nitrogen (N) plays a pivotal role in management of tea plantation, with significant impacts on the growth, productivity, and nutrition status of tea plants. The existing methods for N content monitoring of tea leaves are complicated and can not realize in suite and in real time way. This study proposed a method for estimating the N content of tea plants in field conditions based on a combination of a multispectral imaging system and hyperspectral data. A total of 32 parameters were extracted from five tea gardens using calibrated multispectral images of the tea plant canopy, and 27 indices were selected by Pearson correlation analysis. A total of 28 wavelengths selected by competitive adaptive reweighted sampling from hyperspectral data were combined with 27 multispectral indices as the original data. Subsequently, five variables of fused data (H, VOG, BGI, 1664 nm and 1665 nm) were selected by variable combination population analysis based on the 55 combination parameters. Partial least squares regression, random forest regression, and support vector machine regression (SVR) models all showed excellent performance for both the calibration and prediction sets. The overall results indicated that the infused data of multispectral and hyperspectral data combined with SVR are effective in monitoring the N level under field conditions, and the R2 (coefficient of determination) and root mean square error values of the prediction were 0.9186 and 0.0560, respectively. The findings of this study are important in retaining the nutritional and quality attributes of agricultural commodities. [ABSTRACT FROM AUTHOR]

• Accurately forecast the first flowering period of two typical apple producing areas in China. • It fills in the research of forecasting the first flowering period of apple from point scale to regional scale. • The phenological survey method of replacing time with space is very effective for the calibration of sequential model. • The prediction of the apple first flowering on a regional scale has important application value for optimizing orchard management. China is one of the largest apple-producing countries in the world, with large orchards and diverse climates. Accurately forecasting the first-flowering time of apple trees can assist orchard managers in their deciding when to apply anti-freeze. The temperature-driven sequential model from previous studies can be used to forecast the flowering phenology of deciduous fruit trees. However, this model requires many years of observational data for calibration, so flowering forecasts based on traditional phenological models cannot be implemented in areas that lack such historical data. To overcome this problem, the present work combines a spatial rather than a temporal phenological survey method with 1-km-gridded temperature products to calibrate the chill and heat requirement parameters of the sequential model. We then use the model to forecast the first-flowering on a regional scale for Luochuan and Linyi, which are two main apple-producing areas of China. The results show that the proposed method accurately forecasts regional flowering. The root mean squared errors (RMSE) for Luochuan and Linyi were 4.7 and 4.4 days, respectively, and the normalized RMSEs were all less than 5.19%. We expect the proposed regional first-flowering forecast method to be an important aid to optimize orchard management. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key parameter in investigating environmental, ecological processes and climate change at various scales, and is also valuable in the studies of evapotranspiration, soil moisture conditions, surface energy balance, and urban heat islands. However, it is difficult to acquire satellite LSTs with both high spatial and temporal resolutions due to tradeoffs between these parameters. The Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) algorithm was initially designed to predict surface reflectance and is based on the assumption that MODIS and Landsat surface reflectance are highly consistent over homogeneous areas. However, the ESTARFM method prediction results degrade somewhat when the method is used for heterogeneous fine-grained landscapes. This research extended the ESTARFM model from reflectivity range to thermal infrared for estimation of daily temperature at 90 m resolution combined MODIS and ASTER. The implementation of ESTARFM requires input of the search window size, selection of spectrally similar pixels, determination of the weight of similar pixels, and computation of the correction coefficient and temporal weight. The calculation of weights for spectrally similar pixels involves weighing the contribution of neighboring pixels to the computation of a central pixel. Using a local moving window, neighboring spectrally similar pixels were included for the computation of the LST corresponding to a central pixel with the temporal weights of the two dates. In this study, we used multiple bands, i.e., red, NIR, and LST bands, as the input variables and generated high spatial-temporal resolution land surface temperature, combining temporal change information from multi-temporal MODIS with high-resolution spatial resolution from ASTER. The objective of this paper was to evaluate the ESTARFM method using ground measurements coordination with ASTER LST products collected in an arid region of Northwest China during the first thematic Multi-Scale Observation Experiment on Evapotranspiration (MUSOEXE) over heterogeneous land surfaces in 2012, as part of the Heihe Watershed Allied Telemetry Experimental Research (HiWATER) .We didn't modified the model even if the linear hypothesis was directly applied in LST prediction, which may result in uncertainty and errors. The remote sensing data were acquired with from Jun to September in 2012. The results showed that ESTARFM was positively and linearly related with the actual measured TIR. The correlation coefficient values were all found to be greater than 0.71. The mean absolute error and root mean square error were all below 2.00 K and 2.60 K, respectively. From the feature of scattering plots between the predicted and observed LST, the data points fell close to the diagonal line in each panel, indicated that the predictions were all in good agreement with the observations. Overall, the values of mean absolute error and root mean square error between the predicted and the observed LST were quiet small; whereas the correlation coefficient values between the predicted LSTs and ASTER LST products were all found to be greater than 0.95.It should be noted that some pixels in the scatter plots showed differences between the predicted and observed LSTs. These discrepancies revealed a major limitation to the method; i.e., ESTARFM does not capture land cover that has been altered between two imaging dates. Thus, changes in land cover or other surface conditions can lead to prediction errors. In addition, the fusion results showed that value of correlation coefficient was better in non-vegetation area than vegetation and water area, and up to 0.91 especially in the August 27, 2012. However, the application of the ESTARFM and its variants to LST prediction is immature in terms of methodology. Many critical issues have not been solved, especially with respect to the determination of the search window size, conversion coefficient improvement, and thermal landscape heterogeneity. [ABSTRACT FROM AUTHOR]

• Synthetic Minority Oversampling Technique and Edited Nearest Neighbors are applied. • A SMOTE-ENN-XGBoost model is developed to detect lodging in imbalanced datasets. • The developed model is explained with SHAP values. • Deeper insight is obtained into how the model behaves with these explanations. • Complex and interrelated impacts of the selected features are captured and analyzed. Remote sensing image is becoming an increasingly popular tool for crop lodging detection because it conveniently provides features for building machine learning models and predicting lodging. However, difficulties in interpreting machine learning models and their predictions limit the confidence of using remote sensing images to detect lodging. In addition, the lodging datasets used for modeling are difficult to balance under natural conditions. Designing a robust and interpretable classification model for the detection of lodging in an imbalanced distribution dataset poses a particularly difficult challenge. In this study, visible and multi-spectral images were collected with a UAV to extract relevant features from remote sensing images. In a preliminary step, Synthetic Minority Oversampling Technique (SMOTE) and Edited Nearest Neighbors (ENN) method were used to treat imbalanced datasets. The SMOTE-ENN-XGBoost model is proposed for the efficient identification of maize lodging at the plot scale. The SMOTE-ENN-XGBoost model achieved an F1-score of 0.930 and a recall of 0.899 on a testing set, suggesting that it can be used for modeling lodging detection. Additionally, the SHapley Additive exPlanations (SHAP) approach was employed to interpret the identification and prioritization of features that determine lodging classification and activity prediction. The results showed that canopy structure and textural features are relatively stable compared with spectral features, which are susceptible to the external environment when modeling is employed to detect lodging. This work also showed that canopy structural, spectral, and textural information should be considered simultaneously rather than separately when detecting crop lodging in a crop breeding program in order to prevent differences in expression controlled by the interaction between genotype and environment obscuring the change in a single feature before and after lodging. For practical applications of machine learning models in crop lodging detection, such insights are of critical relevance. Taken together, the results of this study encourage further applications of remote sensing techniques to build interpretable machine learning models. [ABSTRACT FROM AUTHOR]

Ear height (EH) and ear–plant height ratio (ER) are important agronomic traits in maize that directly affect nutrient utilization efficiency and lodging resistance and ultimately relate to maize yield. However, challenges in executing large-scale EH and ER measurements severely limit maize breeding programs. In this paper, we propose a novel, simple method for field monitoring of EH and ER based on the relationship between ear position and vertical leaf area profile. The vertical leaf area profile was estimated from Terrestrial Laser Scanner (TLS) and Drone Laser Scanner (DLS) data by applying the voxel-based point cloud method. The method was validated using two years of data collected from 128 field plots. The main factors affecting the accuracy were investigated, including the LiDAR platform, voxel size, and point cloud density. The EH using TLS data yielded R2 = 0.59 and RMSE = 16.90 cm for 2019, R2 = 0.39 and RMSE = 18.40 cm for 2021. In contrast, the EH using DLS data had an R2 = 0.54 and RMSE = 18.00 cm for 2019, R2 = 0.46 and RMSE = 26.50 cm for 2021 when the planting density was 67,500 plants/ha and below. The ER estimated using 2019 TLS data has R2 = 0.45 and RMSE = 0.06. In summary, this paper proposed a simple method for measuring maize EH and ER in the field, the results will also offer insights into the structure-related traits of maize cultivars, further aiding selection in molecular breeding. [ABSTRACT FROM AUTHOR]

Arable land is the foundation of the national economy. How to make the best of arable land resources has become a focus problem of modern science and technology information. The rapid development of agricultural condition remote sensing monitoring technology provides more scientific ways and information technology for monitoring the arable land in real-time. In order to obtain the information of Shaanxi Province agricultural condition monitoring for managing arable land more efficiently, this thesis aimed to study the crop planting patterns and types of arable land, and took the main crops (wheat, spring maize, summer maize, rice and rape) of arable land in Shaanxi Province as the research object. Firstly, the remote sensing datasets of 250 m MOD09Q1 time series during 2003-2012 were used, and the Savitzky-Golay filtering method of TIMESAT software was used to reconstruct the NDVI time series datasets. Secondly, combined with the agricultural meteorological station datasets, TM 30 m land cover classification data, and the main crops' information and crop phenological information in Shaanxi Province, we extracted the change trends of typical terrain feature and determined the interannual dynamic thresholds. According to the threshold of a peak and crop growth period and other information, the crop planting patterns and crop types were identified. Thirdly, owing to the mixed pixel that the major factor affected the classification accuracy of the low spatial resolution remote sensing, therefore, the IDL optimization function (CONSTRAINED_MIN) was used to obtain each crop types' abundance figure by the method of non-negative least squares. Two kinds of precision validation methods of spatial and quantitative were adopted in this paper. The total classification accuracy and Kappa coefficient were 88.18% and 59.64% respectively according to spatial comparative analysis. The classification results were revised by the crop types' abundance figure, and the overall consistency of classification was 87.56% according to quantitative analysis, and the validation results for the rice and other grains had good consistency (93.74%, 92.36%), while the winter wheat and maize followed (83.68%, 84.61%). Through the analysis of mixed pixels, the overall consistency of estimating crop acreage increased by 6.23%, the consistency of winter wheat, maize, rice and other grains increased by 6.35%, 8.01%, 7.26% and 4.85%. The results indicate that using the Savitzky-Golay filtering method to reconstruct NDVI time series datasets could meet the requirement of the classification. Combining phonological information with time series datasets and using the classification method presented in this thesis could identify the crop planting patterns and crop types effectively in large scale. Using the IDL optimization function (CONSTRAINED_MIN) to analyze the mixed pixels, the crop acreages were calculated accurately. [ABSTRACT FROM AUTHOR]

Wheat lodging is one of the most popular agriculture disaster. It has a great influence on the yield formation of wheat and causes huge loss every year. However, available means for monitoring crop lodging in large area is very limited. The potential capability of radar remote sensing for monitoring wheat lodging was explored in this study. After the backscattering coefficient of wheat field parcels were extracted at different polarization mode, radar backscattering behavior of typical lodging wheat parcels and the one of typical normal wheat parcels was dynamically compared at different growth stages and at different polarization mode, based on 5 multi-temporal Radarsat-2 images that covered an entire wheat growth cycle from sowing to near harvest. Results showed that there were distinct differences between lodging and normal wheat parcels in all polarization channels. When compared to normal wheat, the backscattering coefficient at HH (H, horizontal polarization) polarization decreased remarkably, while the one at VV (V, vertical polarization) polarization increased and the one at HV polarization increased slightly for lodging wheat. It was also found that polarimetric feature from synthetic aperture radar (SAR) data was very sensitive to wheat lodging and the sensibility was caused by unique structural characteristics of wheat vegetation and inherent polarization characteristics of SAR observation. Then a method based on radar polarimetric index, which took the full advantage of this sensibility, was put forward to monitoring wheat lodging. And the method was validated by in-situ data collected in 28 wheat parcels in Shangkuli Farmland in Inner Mongolia, China, at heading and filling stages of spring wheat. The result revealed that the polarimetric index, especially based on the ratio of dual-polarization backscattering coefficient (HH/VV and HH/HV), had excellent performance for distinguishing lodging from normal wheat parcels: all the 11 lodging wheat parcels with different severity were detected successfully from 28 parcels. In addition, the results were compared with the ones by single polarization channel data. It showed that the polarimetric index method had good anti-inference ability such as resisting influence of wheat growth difference, and can better reflect the intrinsic feature of lodging. It should be mentioned that the monitoring result may be influenced by harvest situation and vegetation water content etc. Moreover, while optical remote sensing relied on its spectral features to monitor crop lodging, radar remote sensing utilized polarimetric features to monitor crop lodging since SAR observation had advantage in reflecting the structural variation of lodging. Therefore, radar remote sensing have great potential for crop lodging monitoring, and the study presented a simple and effective method for monitoring wheat lodging in large area. [ABSTRACT FROM AUTHOR]

Highlights: [•] The complementary relationship model (CR) was employed to estimate actual ET. [•] The high temporal ET was validated by Lysimeter observation. [•] The CR model was combined with IKONOS image to estimate high spatial resolution instantaneous and daily ET. [Copyright &y& Elsevier]

Highlights: [•] PLSR of band depth ratio together with optimal NDVI-like estimated biomass best. [•] The PLSR model based on BDR was better than optimal NDVI-like/SAVI-like. [•] The REP got lower estimation accuracy than optimal NDVI-like/SAVI-like. [•] The performance of REP was better than that of NDVI/SAVI. [ABSTRACT FROM AUTHOR]

A better understanding of wheat nitrogen status is important for improving N fertilizer management in precision farming. In this study, four different sensors were evaluated for their ability to estimate winter wheat nitrogen. A Gaussian process regression (GPR) method with the sequential backward feature removal (SBBR) routine was used to identify the best combinations of vegetation indices (VIs) sensitive to wheat N indicators for different sensors. Wheat leaf N concentration (LNC), plant N concentration (PNC), and the nutrition index (NNI) were estimated by the VIs through parametric regression (PR), multivariable linear regression (MLR), and Gaussian process regression (GPR). The study results reveal that the optical fluorescence sensor provides more accurate estimates of winter wheat N status at a low-canopy coverage condition. The Dualex Nitrogen Balance Index (NBI) is the best leaf-level indicator for wheat LNC, PNC and NNI at the early wheat growth stage. At the early growth stage, Multiplex indices are the best canopy-level indicators for LNC, PNC, and NNI. At the late growth stage, ASD VIs provide accurate estimates for wheat N indicators. This study also reveals that the GPR with SBBR analysis method provides more accurate estimates of winter wheat LNC, PNC, and NNI, with the best VI combinations for these sensors across the different winter wheat growth stages, compared with the MLR and PR methods. [ABSTRACT FROM AUTHOR]

The normalized difference vegetation index (NDVI) is an important agricultural parameter that is closely correlated with crop growth. In this study, a novel method combining the dynamic time warping (DTW) model and the long short-term memory (LSTM) deep recurrent neural network model was developed to predict the short and medium-term winter wheat NDVI. LSTM is well-suited for modelling long-term dependencies, but this method may be susceptible to overfitting. In contrast, DTW possesses good predictive ability and is less susceptible to overfitting. Therefore, by utilizing the combination of these two models, the prediction error caused by overfitting is reduced, thus improving the final prediction accuracy. The combined method proposed here utilizes the historical MODIS time series data with an 8-day time resolution from 2015 to 2020. First, fast Fourier transform (FFT) is used to decompose the time series into two parts. The first part reflects the inter-annual and seasonal variation characteristics of winter wheat NDVI, and the DTW model is applied for prediction. The second part reflects the short-term change characteristics of winter wheat NDVI, and the LSTM model is applied for prediction. Next, the results from both models are combined to produce a final prediction. A case study in Hebei Province that predicts the NDVI of winter wheat at five prediction horizons in the future indicates that the DTW–LSTM model proposed here outperforms the LSTM model according to multiple evaluation indicators. The results of this study suggest that the DTW–LSTM model is highly promising for short and medium-term NDVI prediction. [ABSTRACT FROM AUTHOR]

Abstract: Fractional vegetation cover (FVC) is an important indicator of mountain ecosystem status. A study on the seasonal changes of FVC can be beneficial for regional eco-environmental security, which contributes to the assessment of mountain ecosystem recovery and supports mountain forest planning and landscape reconstruction around megacities, for example, Beijing, China. Remote sensing has been demonstrated to be one of the most powerful and feasible tools for the investigation of mountain vegetation. However, topographic and atmospheric effects can produce enormous errors in the quantitative retrieval of FVC data from satellite images of mountainous areas. Moreover, the most commonly used analysis approach for assessing FVC seasonal fluctuations is based on per-pixel analysis regardless of the spatial context, which results in pixel-based FVC values that are feasible for landscape and ecosystem applications. To solve these problems, we proposed a new method that incorporates the use of a revised physically based (RPB) model to correct both atmospheric and terrain-caused illumination effects on Landsat images, an improved vegetation index (VI)-based technique for estimating the FVC, and an adaptive mean shift approach for object-based FVC segmentation. An array of metrics for segmented FVC analyses, including a variety of area metrics, patch metrics, shape metrics and diversity metrics, was generated. On the basis of the individual segmented FVC values and landscape metrics from multiple images of different dates, remote sensing of the seasonal variability of FVC was conducted over the mountainous area of Beijing, China. The experimental results indicate that (a) the mean value of the RPB–NDVI in all seasons was increased by approximately 10% compared with that of the atmospheric correction-NDVI; (b) a strong consistency was demonstrated between ground-based FVC observations and FVC estimated through remote sensing technology (R 2 =0.8527, RMSE=0.0851); and (c) seasonal changes in the landscape characteristics existed, and the landscape diversity reached its maximum in May and June in the study area. [Copyright &y& Elsevier]

Metal–organic framework materials(ZIF-9) were loaded on Sodium alginate/polyvinyl(PVA/SA) hydrogel by in situ growth method for adsorption of heavy metal Cu (II). The structure of hydrogels composited with MOFs and polymers was designed to improve the poor mechanical properties of natural polymer gel materials and the inconvenience of powdered MOFs materials in practical applications. The adsorption results showed that the optimum adsorption process of Cu (II) pH was 5.0. The adsorption kinetics and isotherm suggest that the adsorption process follows the Freundlich isotherm and the pseudo-second-order models. The experimental maximum adsorption capacity was 98.98 mg/g, 2.6 times and 1.5 times higher than ordinary SA and PVA/SA hydrogel spheres. Synthesized hydrogel spheres were characterized by FT-IR, SEM, XRD, and XPS, which confirmed that MOF materials have grown in situ on PVA/SA hydrogel spheres. More importantly, PVA/SA@ZIF-9 exhibited exceptional mechanical stability and showed excellent recycling capability in the cyclic adsorption process. [ABSTRACT FROM AUTHOR]

Nitrogen(N) is one of the most important elements for crop growth and yield formation. Insufficient or excessive application of N fertilizers can limit crop yield and quality, especially as excessive N fertilizers can damage the environment and proper fertilizer application is essential for agricultural production. Efficient monitoring of crop N content is the basis of precise fertilizer management, and therefore to increase crop yields and improve crop quality. Remote sensing has gradually replaced traditional destructive methods such as field surveys and laboratory testing for crop N diagnosis. With the rapid advancement of remote sensing, a review on crop N monitoring is badly in need of better summary and discussion. The purpose of this study was to identify current research trends and key issues related to N monitoring. It begins with a comprehensive statistical analysis of the literature on remote sensing monitoring of N in rice and wheat over the past 20 years. The study then elucidates the physiological mechanisms and spectral response characteristics of remote sensing monitoring of canopy N. The following section summarizes the techniques and methods applied in remote sensing monitoring of canopy N from three aspects: remote sensing platforms for N monitoring; correlation between remotely sensed data and N status; and the retrieval methods of N status. The influential factors of N retrieval were then discussed with detailed classification. However, there remain challenges and problems that need to be addressed in the future studies, including the fusion of multisource data from different platforms, and the uncertainty of canopy N inversion in the presence of background factors. The newly developed hybrid model integrates the flexibility of machine learning with the mechanism of physical models. It could be problem solving, which has the advantages of processing multi-source data and reducing the interference of confounding factors. It could be the future development direction of crop N inversion with both high precision and universality. [ABSTRACT FROM AUTHOR]

Abstract: Land surface temperature (LST) is a key parameter in numerous environmental studies. Surface heterogeneity induces uncertainty in estimating subpixel temperature. To take an advantage of simultaneous, multi-resolution observations at coincident nadirs by the Advanced Spaceborne Thermal Emission Reflection Radiometer (ASTER) and the MODerate-resolution Imaging Spectroradiometer (MODIS), LST products from the two sensors were examined for a portion of suburb area in Beijing, China. We selected Soil-Adjusted Vegetation Index (SAVI), Normalized Multi-band Drought Index (NMDI), Normalized Difference Built-up Index (NDBI) and Normalized Difference Water Index (NDWI) as representative remote sensing indices for four land cover types (vegetation, bare soil, impervious and water area), respectively. By using support vector machines, the overall classification accuracy of the four land cover types with inputs of the four remote sensing indices, extracted from ASTER visible near infrared (VNIR) bands and shortwave infrared (SWIR) bands, reached 97.66%, and Kappa coefficient was 0.9632. In order to lower the subpixel temperature estimation error caused by re-sampling of remote sensing data, a disaggregation method for subpixel temperature using the remote sensing endmember index based technique (DisEMI) was established in this study. Firstly, the area ratios and statistical information of endmember remote sensing indices were calculated from ASTER VNIR/SWIR data at 990m and 90m resolutions, respectively. Secondly, the relationship between the 990m resolution MODIS LST and the corresponding input parameters (area ratios and endmember indices at the 990m resolution) was trained by a genetic algorithm and self-organizing feature map artificial neural network (GA-SOFM-ANN). Finally, the trained models were employed to estimate the 90m resolution subpixel temperature with inputs of area ratios and endmember indices at the 90m resolution. ASTER LST product was used for verifying the estimated subpixel temperature, and the verified results indicate that the estimated temperature distribution was basically consistent with that of ASTER LST product. A better agreement was found between temperatures derived by our proposed method (DisEMI) and the ASTER 90m data (R 2 =0.709 and RMSE=2.702K). [Copyright &y& Elsevier]

This paper presents a new forest leaf area index (LAI) inversion method from multisource and multiangle data combined with a radiative transfer model and the strategy of k -means clustering and artificial neural network (ANN). Four scenes of Landsat-5 Thematic Mapper (L5TM) and Beijing-1 small satellite multispectral sensors (BJ1) images, acquired at different times, were selected to construct multisource and multiangle image data in this study. Considering a vertical distribution of forest LAI from both overstory and understory, a hybrid model of the invertible forest reflectance model (INFORM) was used to support the retrieval of forest LAI to eliminate the dependence of understory vegetation. The simulated data from INFORM outputs, added with a random noise, were first clustered by k -means method, and were then trained by ANN to obtain the inversion model for each group (cluster). Next, the inversion model was applied to the different combinations of multiangle data to retrieve the forest LAI. Finally, a validation of inverted results with Moderate Resolution Imaging Spectroradiometer LAI product and field measurements was conducted. The experimental results indicate that the accuracy of the inverted forest LAI can be improved through the addition of observation angle data, if the quality of the image data is ensured. The inversion accuracy of LAI with the multiangle image data is improved by 30% compared to the average accuracy of the inverted LAI with the single angle data after considering the addition of random noise to the ANN training data. [ABSTRACT FROM AUTHOR]

Future mid-infrared satellite missions exploring the Earth will feature advanced high spatial resolution and directional imaging instruments. Consistent end-to-end simulation of them is an important task, and is sometimes the only way to adapt and optimize a sensor and its observation conditions, to choose and test algorithms for data processing, to estimate errors and to evaluate the capabilities of the whole sensor system. However, contrary to other wavelength ranges, the mid-infrared is highly dependent on atmospheric scattering and emission. Therefore, simulation of atmospheric radiative transfer for remote sensing images will remain a challenging task, because few studies on this topic include a full treatment of atmospheric effects. With a given resolution and directional capabilities of the instrument, and combining with land surface temperature and emissivity data obtained from airborne imagery, TOA (top of atmosphere) radiance images have been simulated pixel by pixel, coupling the atmospheric radiative transfer analytic model extended from MODTRAN4 and the atmospheric adjacency effect model derived from point spread function (for atmospheric directional and adjacency effect). In this way, all major scattering and emission contributions of atmosphere were considered. Based on different atmospheric conditions and geometrical relations between the scene, the Sun and the sensor, simulated TOA radiance images were produced according to simulated workflows, 10-m spatial resolution and a spectral range of 3.5-3.9 μm. Analysis of results indicates that the analytic model and adjacency effect model are more suitable for mid-infrared imaging simulation than other existing models. This paper describes the principle of the two models, the applied methodology, the set-up of the actual image simulations, and then discusses the final results obtained. [ABSTRACT FROM AUTHOR]

Abstract: This paper shows that the join designs [Butler, N.A., 2005. Classification of efficient two-level fractional factorial design of resolution IV or more. J. Statist. Plann. Inference 131, 145–159] include all designs with the maximum number of clear two-factor interactions (2FIs) for . The designs are shown to have concise grid representations which provide simple interpretations of the 2FIs in a design. In addition, a better upper bound on the maximum number of clear 2FIs in a design than the one obtained by Tang et al. [2002. Bounds of the maximum members of clear factor interactions for designs of resolution III and IV. Canad. J. Statist. 30, 127–136] is given. [Copyright &y& Elsevier]

Abstract: It is known by Zhang and Park (J. Statist. Plann. Inference 91 (2000) 107) that there are no minimum aberration (MA) designs with respect to both treatments and blocks for blocked regular mixed-level factorial designs. So it should be compromised between the block wordlength pattern and treatment wordlength pattern. Two methods are considered in this article. The first is MA blocking scheme of an MA design. The other is to combine the components of the two wordlength pattern vectors into one combined wordlength pattern according to the modified hierarchical assumptions and an appropriate ordering of the numbers of alias or confounding relations. The relationship between the two types of optimal blocked designs is investigated. A complete catalogue of optimal blocked regular mixed factorial designs of the above two types with 16 or 32 runs is given. [Copyright &y& Elsevier]

Accurate determination of phenological information of crops is essential for field management and decision-making. Remote sensing time-series data are widely used for extracting phenological phases. Existing methods mainly extract phenological phases directly from individual remote sensing time-series, which are easily affected by clouds, noise, and mixed pixels. This paper proposes a novel method of phenological phase extraction based on the time-weighted dynamic time warping (TWDTW) algorithm using MODIS Normalized Difference Vegetation Index (NDVI) 5-day time-series data with a spatial resolution of 500 m. Firstly, based on the phenological differences between winter wheat and other land cover types, winter wheat distribution is extracted using the TWDTW classification method, and the results show that the overall classification accuracy and Kappa coefficient reach 94.74% and 0.90, respectively. Then, we extract the pure winter-wheat pixels using a method based on the coefficient of variation, and use these pixels to generate the average phenological curve. Next, the difference between each winter-wheat phenological curve and the average winter-wheat phenological curve is quantitatively calculated using the TWDTW algorithm. Finally, the key phenological phases of winter wheat in the study area, namely, the green-up date (GUD), heading date (HD), and maturity date (MD), are determined. The results show that the phenological phase extraction using the TWDTW algorithm has high accuracy. By verification using phenological station data from the Meteorological Data Sharing Service System of China, the root mean square errors (RMSEs) of the GUD, HD, and MD are found to be 9.76, 5.72, and 6.98 days, respectively. Additionally, the method proposed in this article is shown to have a better extraction performance compared with several other methods. Furthermore, it is shown that, in Hebei Province, the GUD, HD, and MD are mainly affected by latitude and accumulated temperature. As the latitude increases from south to north, the GUD, HD, and MD are delayed, and for each 1° increment in latitude, the GUD, HD, and MD are delayed by 4.84, 5.79, and 6.61 days, respectively. The higher the accumulated temperature, the earlier the phenological phases occur. However, latitude and accumulated temperature have little effect on the length of the phenological phases. Additionally, the lengths of time between GUD and HD, HD and MD, and GUD and MD are stable at 46, 41, and 87 days, respectively. Overall, the proposed TWDTW method can accurately determine the key phenological phases of winter wheat at a regional scale using remote sensing time-series data. [ABSTRACT FROM AUTHOR]