Selective electrooxidation of 5-hydroxymethylfurfural at low working potentials promoted by 3D hierarchical Cu(OH)2@Ni3Co1-layered double hydroxide architecture with oxygen vacancies.

Selective electrooxidation of 5-hydroxymethylfurfural (HMF) to 2,5-furandicarboxylic acid (FDCA) is of great significance in the manufacture of fine chemicals, liquid fuels, pharmaceuticals, plastics, etc., but still suffers from the high potential input, resulting in high electricity consumption. Developing active, low-cost and stable electrocatalysts is crucial for this electrochemical reaction at low working potentials. Herein, a three-dimensional (3D) hierarchical Cu(OH)2@Ni3Co1-layered double hydroxide architecture with abundant oxygen vacancies (Vo) was synthesized by facile electrodeposition of Ni3Co1-LDH nanosheets on copper foam (CF) supported-Cu(OH)2 nanorods (CF/Cu(OH)2@Ni3Co1-LDH) for the selective electrooxidation of HMF to FDCA. The 3D hierarchical architecture of the Cu(OH)2 nanorod core loaded with Ni3Co1-LDH nanosheet shell facilitates the rapid transfer of charges and exposes more active sites. The synergistic effect of the core-shell nanoarray structure, atomic level dispersion of Ni and Co on LDH laminates, and rich Vo gives 98.12% conversion of HMF, 98.64% yield and 91.71% selectivity for FDCA at a low working potential of 1.0 V vs. RHE. In addition, CF/Cu(OH)2@Ni3Co1-LDH exhibits superior stability by maintaining 93.26% conversion of HMF, 93.65% yield and 91.57% selectivity of FDCA after eight successive cycles, showing the immense potential of utilizing electrochemical conversion for biomass.

Accurate estimation of fractional vegetation cover for winter wheat by integrated unmanned aerial systems and satellite images.

Accurate estimation of fractional vegetation cover (FVC) is essential for crop growth monitoring. Currently, satellite remote sensing monitoring remains one of the most effective methods for the estimation of crop FVC. However, due to the significant difference in scale between the coarse resolution of satellite images and the scale of measurable data on the ground, there are significant uncertainties and errors in estimating crop FVC. Here, we adopt a Strategy of Upscaling-Downscaling operations for unmanned aerial systems (UAS) and satellite data collected during 2 growing seasons of winter wheat, respectively, using backpropagation neural networks (BPNN) as support to fully bridge this scale gap using highly accurate the UAS-derived FVC (FVCUAS) to obtain wheat accurate FVC. Through validation with an independent dataset, the BPNN model predicted FVC with an RMSE of 0.059, which is 11.9% to 25.3% lower than commonly used Long Short-Term Memory (LSTM), Random Forest Regression (RFR), and traditional Normalized Difference Vegetation Index-based method (NDVI-based) models. Moreover, all those models achieved improved estimation accuracy with the Strategy of Upscaling-Downscaling, as compared to only upscaling UAS data. Our results demonstrate that: (1) establishing a nonlinear relationship between FVCUAS and satellite data enables accurate estimation of FVC over larger regions, with the strong support of machine learning capabilities. (2) Employing the Strategy of Upscaling-Downscaling is an effective strategy that can improve the accuracy of FVC estimation, in the collaborative use of UAS and satellite data, especially in the boundary area of the wheat field. This has significant implications for accurate FVC estimation for winter wheat, providing a reference for the estimation of other surface parameters and the collaborative application of multisource data.

CoFe-Layered Double Hydroxide Coupled with Pd Particles for Electrocatalytic Ethanol Oxidation.

Electrocatalytic efficiency and stability have emerged as critical issues in the ethanol oxidation reaction (EOR) of direct ethanol fuel cells. In this paper, Pd/Co1Fe3-LDH/NF as an electrocatalyst for EOR was prepared by a two-step synthetic strategy. Metal-oxygen bonds formed between Pd nanoparticles and Co1Fe3-LDH/NF guaranteed structural stability and adequate surface-active site exposure. More importantly, the charge transfer of the formed Pd-O-Co(Fe) bridge could effectively modulate the electrical structure of hybrids, improving the facilitated absorption of OH- radicals and oxidation of COads. Benefiting from the interfacial interaction, exposed active sites, and structural stability, the observed specific activity for Pd/Co1Fe3-LDH/NF (17.46 mA cm-2) was 97 and 73 times higher than those of commercial Pd/C (20%) (0.18 mA cm-2) and Pt/C (20%) (0.24 mA cm-2), respectively. Besides, the jf/jr ratio representing the resistance to catalyst poisoning was 1.92 in the Pd/Co1Fe3-LDH/NF catalytic system. These results provide insights into optimizing the electronic interaction between metals and the support of electrocatalysts for EOR.

ZIF-67-Derived NiCo-Layered Double Hydroxide@Carbon Nanotube Architectures with Hollow Nanocage Structures as Enhanced Electrocatalysts for Ethanol Oxidation Reaction.

The rational design of efficient Earth-abundant electrocatalysts for the ethanol oxidation reaction (EOR) is the key to developing direct ethanol fuel cells (DEFCs). Among these, the smart structure is highly demanded for highly efficient and stable non-precious electrocatalysts based on transition metals (such as Ni, Co, and Fe). In this work, high-performance NiCo-layered double hydroxide@carbon nanotube (NiCo-LDH@CNT) architectures with hollow nanocage structures as electrocatalysts for EOR were prepared via sacrificial ZIF-67 templates on CNTs. Comprehensive structural characterizations revealed that the as-synthesized NiCo-LDH@CNTs architecture displayed 3D hollow nanocages of NiCo-LDH and abundant interfacial structure between NiCo-LDH and CNTs, which could not only completely expose active sites by increasing the surface area but also facilitate the electron transfer during the electrocatalytic process, thus, improving EOR activity. Benefiting from the 3D hollow nanocages and interfacial structure fabricated by the sacrificial ZIF-67-templated method, the NiCo-LDH@CNTs-2.5% architecture exhibited enhanced electrocatalytic activity for ethanol oxidation compared to single-component NiCo-LDH, where the peak current density was 11.5 mA·cm-2, and the jf/jb value representing the resistance to catalyst poisoning was 1.72 in an alkaline environment. These results provide a new perspective on the fabrication of non-precious metal electrocatalysts for EOR in DEFCs.

Combining spectral and texture feature of UAV image with plant height to improve LAI estimation of winter wheat at jointing stage.

Introduction: Leaf area index (LAI) is a critical physiological and biochemical parameter that profoundly affects vegetation growth. Accurately estimating the LAI for winter wheat during jointing stage is particularly important for monitoring wheat growth status and optimizing variable fertilization decisions. Recently, unmanned aerial vehicle (UAV) data and machine/depth learning methods are widely used in crop growth parameter estimation. In traditional methods, vegetation indices (VI) and texture are usually to estimate LAI. Plant Height (PH) unlike them, contains information about the vertical structure of plants, which should be consider. Methods: Taking Xixingdian Township, Cangzhou City, Hebei Province, China as the research area in this paper, and four machine learning algorithms, namely, support vector machine(SVM), back propagation neural network (BPNN), random forest (RF), extreme gradient boosting (XGBoost), and two deep learning algorithms, namely, convolutional neural network (CNN) and long short-term memory neural network (LSTM), were applied to estimate LAI of winter wheat at jointing stage by integrating the spectral and texture features as well as the plant height information from UAV multispectral images. Initially, Digital Surface Model (DSM) and Digital Orthophoto Map (DOM) were generated. Subsequently, the PH, VI and texture features were extracted, and the texture indices (TI) was further constructed. The measured LAI on the ground were collected for the same period and calculated its Pearson correlation coefficient with PH, VI and TI to pick the feature variables with high correlation. The VI, TI, PH and fusion were considered as the independent features, and the sample set partitioning based on joint x-y distance (SPXY) method was used to divide the calibration set and validation set of samples. Results: The ability of different inputs and algorithms to estimate winter wheat LAI were evaluated. The results showed that (1) The addition of PH as a feature variable significantly improved the accuracy of the LAI estimation, indicating that wheat plant height played a vital role as a supplementary parameter for LAI inversion modeling based on traditional indices; (2) The combination of texture features, including normalized difference texture indices (NDTI), difference texture indices (DTI), and ratio texture indices (RTI), substantially improved the correlation between texture features and LAI; Furthermore, multi-feature combinations of VI, TI, and PH exhibited superior capability in estimating LAI for winter wheat; (3) Six regression algorithms have achieved high accuracy in estimating LAI, among which the XGBoost algorithm estimated winter wheat LAI with the highest overall accuracy and best results, achieving the highest R2 (R2 = 0.88), the lowest RMSE (RMSE=0.69), and an RPD greater than 2 (RPD=2.54). Discussion: This study provided compelling evidence that utilizing XGBoost and integrating spectral, texture, and plant height information extracted from UAV data can accurately monitor LAI during the jointing stage of winter wheat. The research results will provide a new perspective for accurate monitoring of crop parameters through remote sensing.

Regulation of Structure and Anion-Exchange Performance of Layered Double Hydroxide: Function of the Metal Cation Composition of a Brucite-like Layer.

As anion-exchange materials, layered double hydroxides (LDHs) have attracted increasing attention in the fields of selective adsorption and separation, controlled drug release, and environmental remediation. The metal cation composition of the laminate is the essential factor that determines the anion-exchange performance of LDHs. Herein, we review the regulating effects of the metal cation composition on the anion-exchange properties and LDH structure. Specifically, the internal factors affecting the anion-exchange performance of LDHs were analyzed and summarized. These include the intercalation driving force, interlayer domain environment, and LDH morphology, which significantly affect the anion selectivity, anion-exchange capacity, and anion arrangement. By changing the species, valence state, size, and mole ratio of the metal cations, the structural characteristics, charge density, and interlayer spacing of LDHs can be adjusted, which affect the anion-exchange performance of LDHs. The present challenges and future prospects of LDHs are also discussed. To the best of our knowledge, this is the first review to summarize the essential relationship between the metal ion composition and anion-exchange performance of laminates, providing important insights for regulating the anion-exchange performance of LDHs.

Evolution of Chemical Bonding and Crystalline Swelling-Shrinkage of Montmorillonite upon Temperature Changes Probed by in Situ Fourier Transform Infrared Spectroscopy and X-ray Diffraction.

Clay minerals are distributed in Earth's crust and troposphere and in Martian crust where temperature varies. Understanding the changes of chemical bonding and crystalline swelling-shrinkage of montmorillonite (Mnt) upon temperature changes is fundamental for studying its surface reactivity and interaction in specific surroundings. However, such an issue remains poorly understood. Here, in situ high- and low-temperature Fourier transform infrared (HT- and LT-FTIR) spectroscopy and X-ray diffraction (HT- and LT-XRD) were performed to study the evolution of chemical bonding and crystalline swelling-shrinkage of sodium-montmorillonite (NaMnt) upon temperature changes. The FTIR results show that the hydroxyl content in NaMnt decreased when the temperature increased from 20 to 700 °C, while it is independent of temperature from 0 to -150 °C. The formation of hydroxyls at the "broken" layer edges of NaMnt is related to adsorbed water molecules on the surfaces, and its content increased when the particle size of the NaMnt decreased. The water molecules in the interlayer space of NaMnt could bond to the tetrahedral sheet of NaMnt through Si2O-H2O bonds. HT- and LT-XRD results reveal that all of those water molecules in NaMnt were removed after heating to 100 °C in the heating chamber. The NaMnt was transformed from a state of monolayer interlayer water molecules at 20 °C to a dehydrated state at 100 °C, and then to a dehydroxylated state at 700 °C. Accordingly, the basal spacings of NaMnt were changed from 1.27 to 0.97 nm and then to 0.96 nm, respectively. When NaMnt was cooled from 20 to -268 °C, a crystalline swelling of NaMnt occurred with an increase of 0.03 nm of basal spacing. This work demonstrates that high/low temperature has a remarkable effect on the hydroxyls and the water molecules in NaMnt, which in turn affects its swelling-shrinkage performance. These findings provide some in-depth understanding of the changes of chemical bonding and crystalline swelling-shrinkage of montmorillonite upon temperature changes and the reasons behind these, which might be helpful for the design of engineering Mnt in high-/low-temperature applications.

Wheat yield estimation using remote sensing data based on machine learning approaches.

Accurate predictions of wheat yields are essential to farmers'production plans and to the international trade in wheat. However, only poor approximations of the productivity of wheat crops in China can be obtained using traditional linear regression models based on vegetation indices and observations of the yield. In this study, Sentinel-2 (multispectral data) and ZY-1 02D (hyperspectral data) were used together with 15709 gridded yield data (with a resolution of 5 m × 5 m) to predict the winter wheat yield. These estimates were based on four mainstream data-driven approaches: Long Short-Term Memory (LSTM), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Regression (SVR). The method that gave the best estimate of the winter wheat yield was determined, and the accuracy of the estimates based on multispectral and hyperspectral data were compared. The results showed that the LSTM model, for which the RMSE of the estimates was 0.201 t/ha, performed better than the RF (RMSE = 0.260 t/ha), GBDT (RMSE = 0.306 t/ha), and SVR (RMSE = 0.489 t/ha) methods. The estimates based on the ZY-1 02D hyperspectral data were more accurate than those based on the 30-m Sentinel-2 data: RMSE = 0.237 t/ha for the ZY-1 02D data, which is about a 5% improvement on the RSME of 0.307 t/ha for the 30-m Sentinel-2 data. However, the 10-m Sentinel-2 data performed even better, giving an RMSE of 0.219 t/ha. In addition, it was found that the greenness vegetation index SR (simple ratio index) outperformed the traditional vegetation indices. The results highlight the potential of the shortwave infrared bands to replace the visible and near-infrared bands for predicting crop yields Our study demonstrates the advantages of the deep learning method LSTM over machine learning methods in terms of its ability to make accurate estimates of the winter wheat yield.

Estimation of wheat tiller density using remote sensing data and machine learning methods.

The tiller density is a key agronomic trait of winter wheat that is essential to field management and yield estimation. The traditional method of obtaining the wheat tiller density is based on manual counting, which is inefficient and error prone. In this study, we established machine learning models to estimate the wheat tiller density in the field using hyperspectral and multispectral remote sensing data. The results showed that the vegetation indices related to vegetation cover and leaf area index are more suitable for tiller density estimation. The optimal mean relative error for hyperspectral data was 5.46%, indicating that the results were more accurate than those for multispectral data, which had a mean relative error of 7.71%. The gradient boosted regression tree (GBRT) and random forest (RF) methods gave the best estimation accuracy when the number of samples was less than around 140 and greater than around 140, respectively. The results of this study support the extension of the tested methods to the large-scale monitoring of tiller density based on remote sensing data.

Monitoring Crop Growth During the Period of the Rapid Spread of COVID-19 in China by Remote Sensing.

The status of crop growth under the influence of COVID-19 is an important information for evaluating the current food security in China. This article used the cloud computing platform of Google Earth Engine, to access and analyze Sentinel-2, MODIS, and other multisource remote sensing data in the last five years to monitor the growth of crops in China, especially in Hubei province, during the period of the rapid spread of COVID-19 (i.e., from late January to mid-March 2020), and compared with the growth over the same period under similar climate conditions in the past four years. We further analyzed the indirect effects of COVID-19 on crop growth. The results showed that: the area of the crops with better growth (51%) was much more than that with worse growth (22%); the crops with better and worse growth were mainly distributed in the North China Plain (the main planting areas of winter wheat in China) and the South China regions (such as Guangxi, Guangdong province), respectively. The area of the crops with a similar growth occupied 27%. In Hubei province, the area of the crops with better growth (61%) was also more than that with worse growth (27%). It was found that there was no obvious effect from COVID-19 on the overall growth of crops in China during the period from late January to mid-March 2020 and the growth of crops was much better than that during the same period in previous years. The findings in this study are helpful in evaluating the impact of the COVID-19 on China's agriculture, which are conducive to serve the relevant agricultural policy formulation and to ensure food security.

Dual-valve parallel prediction control for an electro-hydraulic servo system.

To improve the dynamic response performance of a high-flow electro-hydraulic servo system, scholars have conducted considerable research on the synchronous and time-sharing controls of multiple valves. However, most scholars have used offline optimization to improve control performance. Thus, control performance cannot be dynamically adjusted or optimized. To repeatedly optimize the performance of multiple valves online, this study proposes a method for connecting a high-flow proportional valve in parallel with a low-flow servo valve. Moreover, this study proposes an algorithm in which a proportional-integral-derivative system and multivariable predictive control system are used as an inner loop and outer loop, respectively. The simulation and experimental results revealed that dual-valve parallel control could effectively improve the control accuracy and dynamic response performance of an electro-hydraulic servo system and that the proportional-integral-derivative-multivariable predictive control controller could further dynamically improve the control accuracy.

Unusual Improvement of Pseudocapacitance of Nanocomposite Electrodes: Three-Dimensional Amorphous Carbon Frameworks Triggered by TiO2 Nanocrystals.

Both nanocrystals and carbon materials have attracted considerable attention in lithium-ion batteries (LIBs) because of their fast kinetics for lithium storage or long-life cycles. However, the easy aggregation of nanocrystals and high-temperature doping process of carbon materials seriously hindered their application in LIBs. Here, we report the development of unprecedented TiO2-x@C nanocomposite electrodes through a unique "melting low-temperature pyrolysis" strategy. It is found that the continuous and interconnected three-dimensional amorphous carbon frameworks (3DCFs) in the composites are closely connected with TiO2 nanocrystals by Ti-O-C covalent bonding, forming robust 3D framework architectures. Interestingly, we find that TiO2 nanocrystals can greatly improve the pseudocapacitance of TiO2-x@C nanocomposite electrodes with increasing cycles, which significantly exceeds previously reported TiO2-based anodes and carbon materials. Furthermore, for the first time, the unusual improvement of pseudocapacitance of TiO2-x@C electrodes is carefully investigated by means of dQ/dV curves and electrochemical kinetic analysis to reveal the extra contribution of lithium storage. 3DCF, a "lithium-ion reservoir", possesses an unexpected capacity enhancement behavior that is triggered by TiO2 nanocrystals and exhibits bicontinuous pathways for both rapid ion and electron transport. In this case, TiO2 nanocrystals stabilizing the 3DCF acted as a conductive agent during charge and discharge. Our findings confirm that the 3DCF triggered by TiO2 nanocrystals boosted the electrochemical performance of TiO2-x@C nanocomposite electrodes, especially the pseudocapacitance enhancement. The unique characteristics of ingenious combination of TiO2 nanocrystals and amorphous carbon materials make them attain superior electrochemical properties in all known TiO2- and carbon-based anodes (289 mA h g-1 at 5 A g-1 after 4000 cycles). Above all, our findings reveal previously ignored fundamental aspects of pseudocapacitance improvement of nanocomposite electrodes and offer new hope for structural design and carbon coating process of high-performance anode materials.

Insights into the Rheological Behavior of Aqueous Dispersions of Synthetic Saponite: Effects of Saponite Composition and Sodium Polyacrylate.

Synthetic saponite (Sap) easily delaminates in water to form a transparent sol and hydrogel with excellent rheological performance and is thus widely used in paints, cosmetics, and nanomaterials. The thixotropic property of Sap hydrogels is heavily dependent on the nature of Sap and the external electrolyte and polyelectrolyte; yet, details on the relationship between rheological behaviors of saponite hydrogels and Sap composition and polyelectrolyte remain unclear. In this work, thixotropic rheological behaviors of a series of synthetic Sap hydrogels, with and without added sodium polyacrylate polyelectrolyte (NaPA), were investigated. The Sap samples, with a Si/Al molar ratio from 5 to 25, were successfully synthesized using hydrothermal methods and characterized by powder X-ray diffraction, Fourier transform infrared spectroscopy, and scanning electron microscopy. The rheological performances of aqueous Sap dispersions and particle sizes and ζ-potentials of Sap were measured. The results showed that the crystallinity of the Sap increased with an increasing Si/Al molar ratio. All Sap samples, with the exception of the Sap with a Si/Al molar ratio of 5, dispersed well in water (3 wt %) to form hydrogels. The rheological behaviors of the hydrogels were related to the chemical composition and the layer charge of the Sap. The Sap with a Si/Al molar ratio of 25 had higher viscosity due to improved delamination. The addition of the NaPA, an anionic polyelectrolyte, into the hydrogels decreased the viscosity and altered the thixotropic properties such that the hydrogel becomes a sol. The addition of NaPA facilitated the dispersion and delamination of Sap, because under the electric field of negatively charged Sap particles in the hydrogel, the anionic NaPA was instantaneously polarized and thereby entered the hydration layer of the Sap particles.

Medium-chain triglyceride/water Pickering emulsion stabilized by phosphatidylcholine-kaolinite for encapsulation and controlled release of curcumin.

Pickering emulsions have received widespread attention for encapsulating lipophilic guests in the biomedical and food fields. However, control of the stabilities and demulsification of Pickering emulsions to allow the release of encapsulated species remains a challenge in gastrointestinal conditions. In this work, phosphatidylcholine-kaolinite was prepared by modification of natural kaolinite with phosphatidylcholine and was used as an emulsifier to stabilize medium-chain triglyceride (MCT)/water Pickering emulsions for encapsulating curcumin, a natural antioxidant drug. Simulated gastric and intestinal digestion and a cell uptake assay were implemented for the curcumin-loaded MCT/water Pickering emulsion to study its demulsification and the bioavailability of curcumin. The results revealed that the wettability of phosphatidylcholine-kaolinite could be tailored by controlling the modification temperature so that it could control the emulsion stability. The prepared phosphatidylcholine-kaolinite, with a three-phase contact angle of 123°, was an optimal emulsifier for the enhanced stabilization of the MCT/water Pickering emulsion, especially in the presence of gastric acid. The phosphatidylcholine-kaolinite distributed at the water-oil interface and formed a dense shell structure on the surfaces of the emulsion droplets, controlling the demulsification efficiency to release the encapsulated curcumin. Only 18.9% of the curcumin was released in the simulated gastric conditions after 120 min of digestion due to the demulsification of the MCT/water Pickering emulsion, while it was completely released after 150 min of digestion in simulated intestinal conditions, as expected. This Pickering emulsion stabilized by phosphatidylcholine-kaolinite is a promising delivery system for lipophilic foods or drugs to enhance their bioavailability.

Two-Dimensional Cr-Doped MoO2.5(OH)0.5 Nanosheets: A Promising Anode Material for Lithium-Ion Batteries.

α-MoO3 has gained growing attention as an anode material of lithium-ion batteries (LIBs) because it has a high theoretical specific capacity of 1111 mA h g-1 and unique layer structure. However, the electrochemical reactions of MoO3 exhibit sluggish kinetics and structural instability caused by pulverization during charge and discharge. Herein, we report new two-dimensional Cr-doped MoO2.5(OH)0.5 (doped MoO2.5(OH)0.5) ultrathin nanosheets prepared by a facile hydrothermal process. The formation of the ultrathin nanosheets was clarified by a "doping-adsorption" model. Compared with doped MoO3, doped MoO2.5(OH)0.5 has larger expanded spacing of the {0 l0} crystal planes for fast Li+ storage. The electrodes after cycling were investigated by ex situ transmission electron microscopy in combination with X-ray photoelectron spectroscopy analysis to reveal the reversible conversion reaction mechanism of doped MoO2.5(OH)0.5 nanosheets. Interestingly, for doped MoO2.5(OH)0.5 nanosheet electrodes, it was found that the as-formed intermediate Li xMoO3 nanodots were well-dispersed in the mesoporous amorphous matrix and had an expanded (040) crystal plane after 10 cycles. These unique structural features increased the effective surface of intermediate products Li xMoO3 to react with Li+ and shortened the diffusion length and thus promoted the electrochemical reactions of doped MoO2.5(OH)0.5. Additionally, the presence of Cr also played a critical role in the reversible decomposition of Li2O and enhanced specific capacity. When employed as an anode in LIBs, doped MoO2.5(OH)0.5 nanosheets show superior reversible capacity (294 mA h g-1 at 10 A g-1 after 2000 cycles). Moreover, the reversible capacity after electrochemical activation is quite stable throughout the cycling, thereby presenting a potential candidate anode material for LIBs.

[Estimation of winter wheat protein content based on new indexes].

Wheat protein content is an important indicator often employed in wheat sale price. Spectral indexes and concurrent winter wheat protein content (WWPC) samples were obtained across three years. Data from 2008/2009 and 2009/2010 were utilized to build the new ratio indexes and product indexes, and then selected grey relational method and partial least squares method were used to improve the estimation accuracy of WWPC, data from 2011/2012 was utilized to validate model. The results showed that the correlation coefficients between ratio indexes and WWPC were better than that between single indexesand WWPC. The r of single indexes and ratio indexes were 0.726 and 0.751, respectively, and the product indexes were used to improve the parts of single indexes. The estimation accuracy of WWPC was improved by using GRA-PLS, the determination coefficients (R2) of single indexes, ratio indexes and product indexes were 0.537, 0.631 and 0.521, respectively, and corresponding root mean square errors (RMSE) were 0.665%, 0.564% and 0.574%, respectively. The results indicated that it was feasible to estimate WWPC by building new ratio indexes and product indexes, and then applying the GRA-PLS.

[Using extraction of red edge position to validate consistency of hyperspectral imaging and non-imaging data].

Using Pushbroom imaging spectrometer (PIS) and FieldSpec ProFR2500 (ASD), spectral reflectances of winter wheat and maize at different stages were collected synchronously. In order to validate the reliability of imaging spectral data, the red edge position of hyperspectral data for PIS and ASD were extracted by different algorithms, respectively. The following results were obtained: (1) The original spectrum of both instruments had high inosculation in red light region (670-740 nm); (2) With the spectra collected under laboratory condition (maize leaf), the extracted red edge position was is concentrated between 700 and 720 nm for the two instruments; (3) With the spectra collected undre field condition (wheat leaf), the extracted red edge position for PIS and ASD were different, the red edge position of PIS data was in 760 nm, while it was in 720 nm for ASD data. The main reason might be that the imaging spectral data were influenced by oxygen absorbtion; (4) the red edge rangeability of PIS and ASD were different, but the trends were the same. The above results could provide some references for hyperspectral imaging data's extensive application.

[Study on relationship between new characteristic parameters of spectral curve and chlorophyll content for rice].

It is of significance to monitor chlorophyll content with hyperspectral data for crop growth diagnosis in field. In the study, with the point of view that spectral curve shapes display "tall, low, fat and thin" morphological changes, we proposed some new characteristic parameters from spectral curve such as the ascensive or degressive velocities of segments composing peak or valley shapes in spectral curve, and angles formed by the lines fitting the segments of two sides of peak or valley curves, and used the normalized spectra to analyze correlation between these parameters and rice chlorophyll content. The result shows that (1) there is a good negative correlation between rice chlorophyll content and normalized reflectance spectra from 520-740 nm; (2) characteristic parameters from green peak region of spectral curve display better correlation with rice chlorophyll content, which makes it possible to utilize the parameters to monitor crop chlorophyll content, and will provide new ideas and methods for carrying out crop growth diagnosis with hyperspectral data.

[Accuracy of winter wheat identification based on multi-temporal CBERS-02 images].

Chinese-Brazil Earth Resources Satellite No. 2 (CBERS-02) has good spatial resolution and abundant spectral information, and a strong ability in detecting vegetation. Based on five CBERS-02 images in winter wheat growth season, the spectral distance between winter wheat and other ground targets was calculated, and then, winter wheat was classified from each individual image or their combinations by using supervised classification. The train and validation samples were derived from high resolution Aerial Images and SPOT5 images. The accuracies and analyses were evaluated for CBERS-02 images at early growth stages, and the results were compared to those of TM images acquired in the same phenological calendars. The results showed that temporal information was the main factor affecting the classification accuracy in winter wheat, but the characteristics of different sensors could affect the classification accuracy. The multi-temporal images could improve the classification accuracy, compared with the results derived from signal stage, with the producer accuracy of optimum periods combination improved 20.0% and user accuracy improved 7.83%. Compared with TM sensor, the classification accuracy from CBERS-02 was a little lower.