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Early or in-season crop type mapping using remote sensing data is important for crop managements to maximize crop yield. Existing remote sensing-based approaches in the literature rely mainly on the timing and temporal frequency of satellite data acquisition during a growing season to obtain optimal identification feature (OIF) to complete crop mapping. At present, the optimal features for mapping crop types are commonly generated by using different feature selection methods, and thereby are not generic in nature. In this paper, a novel approach based on the OIF knowledge graph is proposed for identification of crop types within the crop growing season. The concept of OIF was proposed based on the one-class classification and optimization theory, which relates a specific crop type to its OIF. The OIF knowledge graph can be created to describe the seasonal trend of OIF in a season. Eventually, the OIF knowledge graphs can be incorporated into the moment-preserving segmentation method to automatically identify crop types within the growing season. To evaluate the performance of the new method, crop mapping was conducted using 2019 Sentinel-2 data in Heilongjiang province, China, and Illinois, USA, respectively. Results demonstrate that integrating the OIF knowledge graph and moment-preserving thresholding method is effective in identifying crop types without ground-truth data. The overall accuracy higher than 90 % (Kappa coefficient greater than 0.90), and the producer's accuracies for corn, soybean, and rice are generally greater than 93 % in Heilongjiang province. The proposed approach has the advantage of simplifying crop identification process while maintaining a satisfactory identification accuracy in shorter time than conventional methods. [ABSTRACT FROM AUTHOR]

• First experimental verification for synthesizing crystalline cellulose nanoribbon from inexpensive starch in one pot by two phosphorylases. • Degree of polymerization of insoluble synthetic CCNR was affected by the primer concentration based on the fixed concentration of maltodextrin. • CCNR can be used as a Pickering emulsions stabilizer and showed excellent emulsifiability. Nanocellulose has many promising applications such as a green ingredient for Pickering emulsion. Traditional strategies to produce nanocellulose, which are acid or enzymatic hydrolysis and mechanical methods on natural complicated cellulose, are hard to control and can result in significant pollutants during the processes. Herein, we demonstrated a facile and sustainable method for the biocatalytic production of insoluble synthetic crystalline cellulose nanoribbon (CCNR) from cheap maltodextrin by coupling α-glucan phosphorylase (αGP) and cellodextrin phosphorylase (CDP) using cellobiose as a primer. And by optimizing the combination of different αGP and CDP, it turned out that the optimal enzyme combination is αGP from Thermotoga maritime and CDP from Clostridium thermocellum , in which CDP was attached to a family 9 cellulose-binding module. The product yield and degree of polymerization (DP) of insoluble synthetic CCNR was affected by the primer concentration at a fixed concentration of maltodextrin. After optimization of reaction conditions, the highest product yield of insoluble synthetic CCNR was 44.92 % and the highest DP of the insoluble synthetic CCNR was 24 from 50 g 1−1 maltodextrin. This insoluble synthetic CCNR can be used as a Pickering emulsions stabilizer, showing excellent emulsifiability. This study provides a promising alternative for cost-efficient production of insoluble synthetic CCNR which was used as a green emulsion stabilizer. [ABSTRACT FROM AUTHOR]

D-Mannitol (hereinafter as mannitol) is a six-carbon sugar alcohol with diverse applications in food and pharmaceutical industries. To overcome the drawbacks of the chemical hydrogenation method commonly used for mannitol production at present, there is a need to search for novel prospective mannitol production strategies that are of high yield and low cost. In this study, we present a novel approach for the stoichiometric synthesis of mannitol via an in vitro synthetic enzymatic biosystem using the low-cost starch as substrate. By dividing the overall reaction pathway into three modules which could be executed sequentially in one pot, our design aimed at the stoichiometric conversion of starch-based materials into mannitol in an ATP-independent and cofactor-balanced manner. At optimized conditions, high product yields of around 95-98% were achieved using both 10 g/L and 50 g/L maltodextrin as substrate, indicating the potential of our designed system for industrial applications. This study not only provides a high-efficient strategy for the synthesis of mannitol but also expands the product scope of sugar alcohols by the in vitro synthetic enzymatic biosystems using low-cost starch-based materials as the input. Key points: • We described a design-build-test-learn pipeline to construct in vitro biosystems. • The designed system comprised six key enzymes and another three enzymes. • The system converted maltodextrin stoichiometrically to mannitol in one pot. [ABSTRACT FROM AUTHOR]

Risk Assessment and Response Strategies for Extreme Climate Events in Key Nodes of the Belt and Road [ABSTRACT FROM AUTHOR]

This study presents a novel approach in which a dual network (DN) composite, comprising polyvinyl alcohol (PVA) and ribbon-like nanocellulose (RC), was synthesized in one step using the volume exclusion effect involved in enzyme-catalyzed cellulose synthesis. Additionally, the impact of PVA as a crowding reagent during enzymatic catalysis on the in situ formation of nanocellulose and its resulting aspect ratio was explored. In contrast, the other two composites were created by incorporating enzyme-catalyzed synthetic block cellulose (BC) and its acid-hydrolyzed regenerated disc-shaped cellulose (DC) into the PVA. Subsequently, the mechanism by which three distinct types of nanocellulose, varying in morphology and size, was explored to elucidate their contributions to enhancing the properties of PVA. The results demonstrated that PVA/RC outperformed PVA/BC and PVA/DC. The elevated aspect ratio and intricate network structure of RCs not only significantly bolster the mechanical robustness of PVA/RC, leading in an 86.40 % surge in tensile strength and a remarkable 277.03 % rise in tensile modulus in comparison to pure PVA, but also induce a slight enhancement in elongation at break. Moreover, the thermal stability and biodegradability of PVA/RC was enhanced. Collectively, this study introduces an innovative strategy for the efficient fabrication of biodegradable composites with enhanced properties. [Display omitted] [ABSTRACT FROM AUTHOR]

• The community livability evaluation is quantitative, practical and fine-grained. • The evaluation criteria span dimensions of environment, traffic and population. • Remote sensing, surface observation and geospatial big data are used. • Most communities are distributed within the central and marginal urban areas. • Communities in urban area tend to have robustly high livability. Effective evaluation of community livability is in urgent need to avoid increased livability at the expense of sustainability. However, studies concerning community livability evaluation were still conceptual, qualitative or conducted at city or regional scales. The availability of abundant, fine-grained, and multi-source data in the big data era laid the foundation for comprehensive livability evaluation at much finer scales. This paper proposed a quantitative and practical method for up-to-date livability evaluation at individual community scale in China. Nine evaluation criteria were identified spanning dimensions of environment, traffic, convenience, and population. These criteria were calculated respectively from remote sensing, surface observation and geospatial big data. The Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) was applied for community livability evaluation, and the uncertainty and sensitivity of evaluation results were assessed. The livability evaluation in the case study area of Haidian District, Beijing, China demonstrated the practicality and effectiveness of the framework. A total number of 1242 communities in Haidian District were evaluated. Communities in urban area were generally associated with higher evaluation scores and lower uncertainties than those in rural area. The careful selection of criteria weights with high sensitivity, i.e., green space coverage within community and driving time to schools, can potentially significantly reduce the uncertainty of the livability evaluation. The community scale livability evaluation is expected to bridge the gap between theoretical concepts and practical implementations of livability evaluation, and enables the development of more effective and locally specific regulations and policies to improve community livability. [ABSTRACT FROM AUTHOR]

In this study, an empirical assessment approach for the risk of crop loss due to water stress was developed and used to evaluate the risk of winter wheat loss in China, the United States, Germany, France and the United Kingdom. We combined statistical and remote sensing data on crop yields with climate data and cropland distribution to model the effect of water stress from 1982 to 2011. The average value of winter wheat loss due to water stress for the three European countries was about −931 kg/ha, which was higher than that in China (−570 kg/ha) and the United States (−367 kg/ha). Our study has important implications for the operational assessment of crop loss risk at a country or regional scale. Future studies should focus on using higher spatial resolution remote sensing data, combining actual evapotranspiration to estimate water stress, improving the method for downscaling of statistical crop yield data and establishing more sophisticated zoning methods. [ABSTRACT FROM AUTHOR]

In this study, an object-based image analysis (OBIA) approach was developed to classify field crops using multi-temporal SPOT-5 images with a random forest (RF) classifier. A wide range of features, including the spectral reflectance, vegetation indices (VIs), textural features based on the grey-level co-occurrence matrix (GLCM) and textural features based on geostatistical semivariogram (GST) were extracted for classification, and their performance was evaluated with the RF variable importance measures. Results showed that the best segmentation quality was achieved using the SPOT image acquired in September, with a scale parameter of 40. The spectral reflectance and the GST had a stronger contribution to crop classification than the VIs and GLCM textures. A subset of 60 features was selected using the RF-based feature selection (FS) method, and in this subset, the near-infrared reflectance and the image acquired in August (jointing and heading stages) were found to be the best for crop classification. [ABSTRACT FROM AUTHOR]

Human diets strongly rely on wheat, maize, rice and soybean; research on the potential crop productivity of these four main crops could provide the basis for increasing global crop yields. The evaluation model of realistic potential crop productivity based on remote sensing and agro-ecological zones was proposed in this study to provide reliable reference data for world food security. The statistical data on these four main crops yields were obtained from the FAO. The model was used to investigate the potential production of four staple crops in the world. The distributions of the realistic potential productivity of four staple crops (winter wheat, maize, rice and soybean) were produced. In the main producing countries of the four staple crops, statistical analysis was conducted on the realistic potential productivity (RPP) of the four staple crops, the highest productivity (HP) during the period 1983-2011 and the gap between RPP and HP. [ABSTRACT FROM AUTHOR]

β-Nicotinamide mononucleotide (NMN) is an important precursor in the synthesis of nicotinamide adenine dinucleotide (NAD+) and confers multiple health benefits, resulting in the rapid growth of NMN market capacity in the fields of food and health care. To overcome the drawbacks of NMN production by the existing chemical or microbial fermentation method, there is an urgent need to develop a prospective NMN production strategy with low cost, low pollution, and high yield. In this study, we demonstrated an artificial in vitro multi-enzyme cascade biocatalysis using starch and nicotinamide (Nam) as substrates for the synthesis of NMN in one-pot. This multi-enzyme cascade reaction was optimized in terms of pH value, buffer concentration, inorganic phosphate concentration, enzyme composition, and phosphoenolpyruvate concentration. Under optimized conditions, a high molar yield of 87.8% for NMN was achieved using 3.2 mM Nam as substrate, and a molar yield of 55.37% for NMN was also achieved under the initial Nam concentration of 9.21 mM. This in vitro enzymatic platform provides an environmental friendliness biomanufacturing technology for the production of NMN, showing a highly promising alternative approach for NMN production. • We created an innovative platform for the biomanufacturing of NMN from starch and Nam via in vitro multi-enzyme cascade biosystem. • This NMN production method is environmentally friendly. • The system can efficiently convert maltodextrin and Nam to NMN. [ABSTRACT FROM AUTHOR]

An antibacterial cellulose oligomer was synthesized by cellodextrin phosphorylase (CDP) from glucose 1-phosphate (G1P) using a glucoside with an antibacterial group as a primer, then a hydrophilic cellulose molecular coating with excellent anti-fouling and anti-bacterial properties on PVDF membrane surface was carried out by dissolving the synthetic cellulose into a homogeneous solution. [Display omitted] • The antibacterial cellulose was synthesized from G1P in one step catalyzed by CDP. • Hydrophilic coating fabricated in one step showed high water flux. • Cellulose coating showed excellent anti-fouling and anti-bacterial performances. Synthetic cellulose prepared by enzyme has the advantages of easy functionalization, easy preparation, and biocompatibility, making it an ideal material for constructing hydrophilic coating with functionalities. In this study, β-D glucose with phenolic group (arbutin) as a primer reacted with glucose 1-phosphate (G1P) under the catalysis of Clostridium thermocellum (CtCDP) to synthesize antibacterial cellooligomer. Then, the synthetic cellooligomer was dissolved into a homogeneous organic solution to prepare a functional hydrophilic coating composed of cellulose molecular chains on the surface of polyvinylidene fluoride (PVDF) membranes. The molecular structure of enzymatic antibacterial cellooligomer was characterized by fourier transform infrared spectroscopy (FTIR) and 1H nuclear magnetic resonance spectroscopy (1H NMR). ATR-FTIR confirmed the successful preparation of cellulose coatings. The performance test results showed that the strong interaction at the interface between cellooligomer and PVDF membrane promoted the uniform distribution of cellulose molecules on the membrane surface, which greatly improved the hydrophilicity of PVDF membrane and enhanced the stability of the coating bonded to the substrate. Moreover, the cellulose molecular coating had excellent antifouling performance, outstanding antibacterial activity, and anti-adhesion to Staphylococcus aureus (S. aureus). This work provides a versatile platform for the time-saving and cost-effective fabrication of antifouling and antibacterial membranes. [ABSTRACT FROM AUTHOR]

The North China Plain (NCP) was selected as the study area and the effects of water and heat were analysed to determine the dominant factor affecting winter wheat growth. The mean, minimum and maximum temperatures, precipitation and soil moisture data were selected to analyse the correlations between the leaf area index (the growth indicator) and these factors using long time series half-monthly data (2–5 months) (from 1982 to 2010). The results showed that temperature was the main factor affecting the growth of winter wheat in the NCP. The growth of winter wheat had weak correlations with precipitation and soil moisture and the influence of water on winter wheat growth was smaller than the influence of heat. In the northern part of the NCP, mainly including the north-west region of Shandong Province and the southern region of Hebei Province, irrigation was necessary in late February and early March. [ABSTRACT FROM AUTHOR]

Automatic land cover update was an effective means to obtain objective and timely land cover maps without human disturbance. This study investigated the efficacy of multi-temporal remote sensing data and advanced non-parametric classifier on improving the classification accuracy of the automatic land cover update approach integrating iterative training sample selection and Markov Random Fields model when the historical remote sensing data were unavailable. The results indicated that two-temporal remote sensing data acquired in one crop growth season could significantly improve the classification accuracy of the automatic land cover update approach by approximately 3–4%. However, the support vector machine (SVM) classifier was not suitable to be integrated in the automatic land cover update approach, because the huge initially selected training samples made the training of the SVM classifier unrealizable. [ABSTRACT FROM PUBLISHER]

Winter wheat biomass was estimated using HJ CCD and MODIS data, combined with a radiation use efficiency model. Results were validated with ground measurement data. Winter wheat biomass estimated with HJ CCD data correlated well with observed biomass in different experiments (coefficients of determinationR2of 0.507, 0.556 and 0.499;n = 48). In addition,R2values between MODIS estimated and observed biomass are 0.420, 0.502 and 0.633. Even if we downscaled biomass estimated using HJ CCD data to MODIS pixel size (9 × 9 HJ CCD pixels to approximate that MODIS pixel),R2values between estimated and observed biomass were still higher than those from MODIS. We conclude that estimation with remote sensing data, such as the HJ CCD data with high spatial resolution and shorter revisit cycle, can show more detail in spatial pattern and improve the application of remote sensing on a local scale. There is also potential for applying the approach to many other studies, including agricultural production estimation, crop growth monitoring and agricultural ecosystem carbon cycle studies. [ABSTRACT FROM AUTHOR]

Crop type identification is the basis of crop acreage estimation and plays a key role in crop production prediction and food security analysis. However, the accuracy of crop type identification using remote-sensing data needs to be improved to support operational agriculture-monitoring tasks. In this paper, a new method integrating high-spatial resolution multispectral data with features extracted from coarse-resolution time-series vegetation index data is proposed to improve crop type identification accuracy in Hungary. Four crop growth features, including peak value, date of peak occurrence, average rate of green-up, and average rate for the senescence period were extracted from time-series Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) profiles and spatially enhanced to 30 m resolution using resolution merge tools based on a multiplicative method to match the spatial resolution of Landsat Thematic Mapper (TM) data. A maximum likelihood classifier (MLC) was used to classify the TM and merged images. Independent validation results indicated that the average overall classification accuracy was improved from 92.38% using TM to 94.67% using the merged images. Based on the classification results using the proposed method, acreages of two major summer crops were estimated and compared to statistical data provided by the United States Department of Agriculture (USDA). The proposed method was able to achieve highly satisfactory crop type identification results. [ABSTRACT FROM PUBLISHER]

Crop acreage estimation is important for the assurance of food security and establishment of national socio-economic development planning. During the current development period, rapid, accurate and reliable estimation for crop acreage is particularly significant in China since the estimation can be affected by many factors such as ecological degradation and farmland reduction. Spatial sampling technology plays an important and irreplaceable role in crop acreage investigation and estimation. However, the effects of sampling factors on estimation are unclear. This study analyzed data on late-season rice in paddy field of Hunan province of China, an area with significant flooded paddy rice agriculture and mixed rice cropping patterns, in order to quantitatively evaluate the influence and sensibility of various monitoring factors (sampling ratio, sampling grid, and sample distribution) on sampling efficiency of the existing space sampling techniques for estimating crop planting acreage. Nine kinds of sampling units and 31 kinds of sampling ratio levels were designed. Spatial stratified sampling was used, and the late rice planting proportion was considered as the stratification symbol. 1000 times repeated trials were conducted based on every kind of sampling plan. Spatial distribution (Variance to Mean Ratio, VMR) of every sampling units and sampling ratio levels were determined. Spatial statistics methods and manifold accuracy evaluation indices (relative estimation error and standard deviation) were used to analyze the acreage estimation results obtained based on the different sampling plans. Then a comprehensive model based on sampling grid, sampling ratio, and sample distribution was developed to assess the sampling monitoring error rate of crop acreage estimation. The result demonstrated that: 1) With the increasing of the sampling grid, the average estimation error increased (R2=0.92), and when the sampling grid was less than 5 km, the estimation error rate was controlled within 5%, the standard deviation was not more than 0.12; 2) With the increasing of the sampling ratio, the average estimation error decreased (R2=0.82), and when the sampling ratio was greater than 0.4%, the estimation error rate was controlled within 5%, and the standard deviation was less than 0.12; 3) Under the condition in which the sampling ratio had been determined, the sample spatial distribution of the sample was an important factor affecting the accuracy of sampling. With the sample distribution tending to cluster distribution, the average estimation error rate increased, and when the variance to mean ratio (VMR) was less than 0.7 the estimation error rate and the standard deviation was controlled within 5% and 0.1, respectively; 4) The quantitative model reflecting the influence of the three factors on crop acreage estimation accuracy was developed. In summary, this study revealed the influence rules and sensibility of sampling factors (sampling ratio, sampling grid, and sample distribution) on crop acreage estimation. In addition, a good method was developed for optimizing spatial sampling and improving the accuracy of crop acreage estimation based on the particular sampling program. [ABSTRACT FROM AUTHOR]

Hydrophilic PVDF membrane was fabricated in single step via cellulose molecular coating prepared by homogeneous solution of cellulose oligomer synthesized by enzyme catalysis (EC)/Trifluoroacetic acid (TFA). Both smooth and rough coatings can be prepared by regulating the aggregative structure of cellulose molecular coatings. The chemical structures of the cellulose and its coating were confirmed by 1H Nuclear Magnetic Resonance Spectroscopy (1H NMR) and Fourier Transform Infrared Spectroscopy (FTIR). The aggregation structure of the cellulose coating was characterized by wide-angle X-ray diffraction (WAXD). X-ray Photoelectron Spectroscopy (XPS) studies unveiled the strong binding of cellulose molecular to PVDF membranes at the interface. Scanning Electron Microscopy (SEM) morphologies show that the cellulose molecules were uniformly coated on the surface and cross section of the membranes. Water contact angle measurement suggested that cellulose molecular coating possessed high hydrophilicity and wetting rate, giving the PVDF membranes high water flux and excellent anti-fouling performances. The coated membranes also show excellent adsorption and desorption performances for crystal violet (CV). The superior performances of incompletely crystallized cellulose coating over amorphous cellulose coating were attributed to the increased specific surface area resulting from the surface roughness. The construction of cellulose molecular coating with controlled aggregation structure provides a novel efficient technology in the field of purification of wastewater, opening up a new approach to the microstructural manipulation of the hydrophilic coating. A novel strategy to prepare the cellulose molecular coating with versatile functions. [Display omitted] • Hydrophilic PVDF membrane was fabricated in one step via cellulose molecular coating. • The surface roughness of coating can be regulated based on the aggregation state. • Cellulose coating showed high water flux and excellent anti-fouling performances. [ABSTRACT FROM AUTHOR]

• SCCO obtained by bioprocessing was used as a Pickering emulsion stabilizer. • The Pickering emulsions were stable in pH ranges of 2–12 and ionic strength from 0 to 2 M. • The Pickering emulsions showed good tolerance to temperature changes from 2 to 60 °C. • The Pickering emulsions remain stable for no less than 6 months. Nanocellulose are promising Pickering emulsion stabilizers for being sustainable and non-toxic. In this work, semicrystalline cellulose oligomers (SCCO), which were synthesized from maltodextrin using cellobiose as primer by in vitro enzymatic biosystem, were exploited as stabilizers for oil-in-water Pickering emulsions. At first, the morphology, structure, thermal and rheological properties of SCCO suspensions were characterized, showing that SCCO had a sheet morphology and typical cellulose-Ⅱ structure with 56 % crystallinity. Then the kinetic stabilities of emulsions containing various amounts of SCCO were evaluated against external stress such as pH, ionic strength, and temperature. Noting that SCCO-Pickering emulsions exhibited excellent stabilities against changes in centrifugation, pH, ionic strengths, and temperatures, and it was also kinetically stable for up to 6 months. Both SCCO suspensions and their emulsions exhibited gel-like structures and shear-thinning behaviors. These results demonstrated great potential of SCCO to be applied as nanocellulosic emulsifiers in food, cosmetic and pharmaceutical industries. [ABSTRACT FROM AUTHOR]

• This paper is the first to map explicitly phenological transition zones. • Both "phenological" and "transitional" characteristics of PTZs were highlighted. • The PTZ dynamics were sensitive, rapid and dramatic in response to climate change. • Three different types of gradient, inclusive and mosaic PTZs were identified. • The method can be easily extended to other areas with no or minor adjustments. Spatially explicit mapping of the Phenological Transition Zone (PTZ) is the prerequisite to PTZ dynamics monitoring and characterization, and is significant for more effective management of natural resources, biodiversity, and conservation areas in the context of climate change. However, to our knowledge there have been no studies to delineate spatially explicit PTZs and there are some potential issues associated with the mapping of other biogeographical transition zones. This paper developed a method to explicitly delineate PTZs continuously from 2003 to 2017 in the study area of the Forest-Grassland Mosaic Region (FGMR) in northeastern China, highlighting both their transitional and phenological characteristics. Annual Normalized Difference Vegetation Index (NDVI) time series were fed into the dynamic-time-warping (DTW) based fuzzy c-medoids (FCMdd) clustering to produce 12 fuzzy phenoregions. Potential PTZ pixels were identified for each pair of fuzzy phenoregions via the fuzzy intersection operator. The Delaunay tessellation field estimator was applied to convert the discrete potential PTZ pixels to continuous weighted density surface. The 7 consistently and 8 intermittently existing PTZs were exhaustively derived via thresholding. Three different types of gradient, inclusive and mosaic PTZs were identified. The PTZ dynamics were rapid and dramatic due to its phenological and transitional nature. This PTZ mapping method can be easily extended to other areas with no or minor adjustments, and it can also be adapted to map other biogeographical transition zones. [ABSTRACT FROM AUTHOR]

• NNVAI is proposed to indicate winter wheat damage caused by a single frost event. • Growth-stage-specific relations between NNVAI and frost features were explored. • Winter wheat is sensitive to different features of frost at different growth stages. • Developed monitoring framework to do two-phase assessment of winter wheat damage. Frost events pose a threat to normal development of winter wheat and increase risk of production loss, even given the context of global warming. On the Huang-Huai-Hai Plain (HHHP), which is the largest region of winter wheat cultivation in China, frost damage has become one of the major meteorological catastrophes. In recent years, both frequency of occurrence and severity of frost events in this region have exhibited an upward trend. For timely and accurate monitoring of single-event frost damage of winter wheat, this study developed a frost-monitoring framework that considers the processes of the evolution of a frost event and the response of winter wheat, as well as the lag between them. The framework adopts a NDVI valley area index (NVAI) to represent the gradual response process of winter wheat to frost. In this framework, the normalized NVAI (NNVAI) is used to indicate the degree of damage, and a two-phased damage assessment (i.e., T1 and T2) is adopted to resolve the contradiction between the urgency of obtaining information on frost severity, the spatial distribution of frost damage, and the delayed response process of winter wheat to frost. Using prediction models based on historical statistics, assessment at the T1 stage estimates the possible damage after the end of the frost, and assessment at the T2 stage evaluates the actual damage caused by frost when the winter wheat has recovered. Application of the proposed framework to a frost event that occurred on the HHHP during March 4–9, 2018 revealed that the average absolute percentage error between actual and predicted damage across the entire region was 33 %. Moreover, the prediction of damage was made 13 days ahead of the occurrence of actual damage. Thus, the framework could provide guidance to enable action to be taken to reduce the impact of frost. The proposed monitoring framework fully considers the processes of the evolution of a frost event and the response of winter wheat to frost, and it learns potential patterns from historical frost events to provide early prediction of potential frost damage. Consequently, the framework is considered suitable for wide practical application. [ABSTRACT FROM AUTHOR]

Grape is an economic crop of great importance and is widely cultivated in China. With the development of remote sensing, abundant data sources strongly guarantee that researchers can identify crop types and map their spatial distributions. However, to date, only a few studies have been conducted to identify vineyards using satellite image data. In this study, a vineyard is identified using satellite images, and a new approach is proposed that integrates the continuous wavelet transform (CWT) and a convolutional neural network (CNN). Specifically, the original time series of the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and green chlorophyll vegetation index (GCVI) are reconstructed by applying an iterated Savitzky-Golay (S-G) method to form a daily time series for a full year; then, the CWT is applied to three reconstructed time series to generate corresponding scalograms; and finally, CNN technology is used to identify vineyards based on the stacked scalograms. In addition to our approach, a traditional and common approach that uses a random forest (RF) to identify crop types based on multi-temporal images is selected as the control group. The experimental results demonstrated the following: (i) the proposed approach was comprehensively superior to the RF approach; it improved the overall accuracy by 9.87% (up to 89.66%); (ii) the CWT had a stable and effective influence on the reconstructed time series, and the scalograms fully represented the unique time-related frequency pattern of each of the planting conditions; and (iii) the convolution and max pooling processing of the CNN captured the unique and subtle distribution patterns of the scalograms to distinguish vineyards from other crops. Additionally, the proposed approach is considered as able to be applied to other practical scenarios, such as using time series data to identify crop types, map landcover/land use, and is recommended to be tested in future practical applications. [ABSTRACT FROM AUTHOR]

Many methods have been used to generate gridded population maps by downscaling demographic data. As one of these methods, the accuracy of the dasymetric model depends heavily on the covariates. Point-of-interest (POI) data, as important covariates, have been widely used for population estimation. However, POIs are often used indiscriminately in existing studies. A few studies further used selected categories of POIs identified based only on the nonspatial quantitative relationship between the POIs and population. In this paper, the spatial association between the POIs and population distribution was considered to identify the POIs with a strong spatial correlation with the population distribution, i.e., population-sensitive POIs. The ability of population-sensitive POIs to improve the fine-grained population mapping accuracy was explored by comparing the results of random forest dasymetric models driven by population-sensitive POIs, all POIs, and no POIs, along with the same sets of multisource remote sensing and social sensing data. The results showed that the model driven by population-sensitive POI had the highest accuracy. Population-sensitive POIs were also more effective in improving the population mapping accuracy than were POIs selected based only on their quantitative relationship with the population. The model built using population-sensitive POIs also performed better than the two popular gridded population datasets WorldPop and LandScan. The model we proposed in this study can be used to generate accurate spatial population distribution information and contributes to achieving more reliable analyses of population-related social problems. [ABSTRACT FROM AUTHOR]

Urban surface water mapping is essential for studying its role in urban ecosystems and local microclimates. However, fast and accurate extraction of urban water remains a great challenge due to the limitations of conventional water indexes and the presence of shadows. Therefore, we proposed a new urban water mapping technique named the Two-Step Urban Water Index (TSUWI), which combines an Urban Water Index (UWI) and an Urban Shadow Index (USI). These two subindexes were established based on spectral analysis and linear Support Vector Machine (SVM) training of pure pixels from eight training sites across China. The performance of the TSUWI was compared with that of the Normalized Difference Water Index (NDWI), High Resolution Water Index (HRWI) and SVM classifier at twelve test sites. The results showed that this method consistently achieved good performance with a mean Kappa Coefficient (KC) of 0.97 and a mean total error (TE) of 5.82%. Overall, classification accuracy of TSUWI was significantly higher than that of the NDWI, HRWI, and SVM (p-value < 0.01). At most test sites, TSUWI improved accuracy by decreasing the TEs by more than 45% compared to NDWI and HRWI, and by more than 15% compared to SVM. In addition, both UWI and USI were shown to have more stable optimal thresholds that are close to 0 and maintain better performance near their optimum thresholds. Therefore, TSUWI can be used as a simple yet robust method for urban water mapping with high accuracy. [ABSTRACT FROM AUTHOR]

Defoliation and accelerating ripening are important measures for cotton mechanization, and judging the time of defoliation and accelerating the ripening and harvest of cotton relies heavily on the boll opening rate, making it a crucial factor to consider. The traditional methods of cotton opening rate determination are time-consuming, labor-intensive, destructive, and not suitable for a wide range of applications. In this study, the relationship between the change rate of the vegetation index obtained by the unmanned aerial vehicle multi-spectrum and the ground boll opening rate was established to realize rapid non-destructive testing of the boll opening rate. The normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI) had good prediction ability for the boll opening rate. NDVI in the training set had an R2 of 0.912 and rRMSE of 15.387%, and the validation set performance had an R2 of 0.929 and rRMSE of 13.414%. GNDVI in the training set had an R2 of 0.901 and rRMSE of 16.318%, and the validation set performance had an R2 of 0.909 and rRMSE of 15.225%. The accuracies of the models based on GNDVI and NDVI were within the acceptable range. In terms of predictive models, random forests achieve the highest accuracy in predictions. Accurately predicting the cotton boll opening rate can support decision-making for harvest and harvest aid spray timing, as well as provide technical support for crop growth monitoring and precision agriculture. [ABSTRACT FROM AUTHOR]

Currently, remote sensing crop identification is mostly based on all available images acquired throughout crop growth. However, the available image and data resources in the early growth stage are limited, which makes early crop identification challenging. Different crop types have different phenological characteristics and seasonal rhythm characteristics, and their growth rates are different at different times. Therefore, making full use of crop growth characteristics to augment crop growth difference information at different times is key to early crop identification. In this study, we first calculated the differential features between different periods as new features based on images acquired during the early growth stage. Secondly, multi-temporal difference features of each period were constructed by combination, then a feature optimization method was used to obtain the optimal feature set of all possible combinations in different periods and the early key identification characteristics of different crops, as well as their stage change characteristics, were explored. Finally, the performance of classification and regression tree (Cart), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), and Support Vector Machine (SVM) classifiers in recognizing crops in different periods were analyzed. The results show that: (1) There were key differences between different crops, with rice changing significantly in period F, corn changing significantly in periods E, M, L, and H, and soybean changing significantly in periods E, M, N, and H. (2) For the early identification of rice, the land surface water index (LSWI), simple ratio index (SR), B11, and normalized difference tillage index (NDTI) contributed most, while B11, normalized difference red-edge3 (NDRE3), LSWI, the green vegetation index (VIgreen), red-edge spectral index (RESI), and normalized difference red-edge2 (NDRE2) contributed greatly to corn and soybean identification. (3) Rice could be identified as early as 13 May, with PA and UA as high as 95%. Corn and soybeans were identified as early as 7 July, with PA and UA as high as 97% and 94%, respectively. (4) With the addition of more temporal features, recognition accuracy increased. The GBDT and RF performed best in identifying the three crops in the early stage. This study demonstrates the feasibility of using crop growth difference information for early crop recognition, which can provide a new idea for early crop recognition. [ABSTRACT FROM AUTHOR]

Because canola is a major oilseed crop, accurately determining its planting areas is crucial for ensuring food security and achieving UN 2030 sustainable development goals. However, when canola is extracted using remote-sensing data, winter wheat causes serious interference because it has a similar growth cycle and spectral reflectance characteristics. This interference seriously limits the classification accuracy of canola, especially in mixed planting areas. Here, a novel canola flower index (CFI) is proposed based on the red, green, blue, and near-infrared bands of Sentinel-2 images to improve the accuracy of canola mapping, based on the finding that spectral reflectance of canola on the red and green bands is higher than that of winter wheat during the canola flowering period. To investigate the potential of the CFI for extracting canola, the IsoData, support vector machine (SVM), and random forest (RF) classification methods were used to extract canola based on Sentinel-2 raw images and CFI images. The results show that the average overall accuracy and kappa coefficient based on CFI images were 94.77% and 0.89, respectively, which were 1.05% and 0.02, respectively, higher than those of the Sentinel-2 raw images. Then we found that a threshold of 0.14 on the CFI image could accurately distinguish canola from non-canola vegetation, which provides a solution for automatic mapping of canola. The overall classification accuracy and kappa coefficient of this threshold method were 96.02% and 0.92, which were very similar to those of the SVM and RF methods. Moreover, the advantage of the threshold classification method is that it reduces the dependence on training samples and has good robustness and high classification efficiency. Overall, this study shows that CFI and Sentinel-2 images provide a solution for automatic and accurate canola extraction. [ABSTRACT FROM AUTHOR]

Early crop identification can provide timely and valuable information for agricultural planting management departments to make reasonable and correct decisions. At present, there is still a lack of systematic summary and analysis on how to obtain real-time samples in the early stage, what the optimal feature sets are, and what level of crop identification accuracy can be achieved at different stages. First, this study generated training samples with the help of historical crop maps in 2019 and remote sensing images in 2020. Then, a feature optimization method was used to obtain the optimal features in different stages. Finally, the differences of the four classifiers in identifying crops and the variation characteristics of crop identification accuracy at different stages were analyzed. These experiments were conducted at three sites in Heilongjiang Province to evaluate the reliability of the results. The results showed that the earliest identification time of corn can be obtained in early July (the seven leaves period) with an identification accuracy up to 86%. In the early stages, its accuracy was 40~79%, which was low, and could not reach the satisfied accuracy requirements. In the middle stages, a satisfactory recognition accuracy could be achieved, and its recognition accuracy was 79~100%. The late stage had a higher recognition accuracy, which was 90~100%. The accuracy of soybeans at each stage was similar to that of corn, and the earliest identification time of soybeans could also be obtained in early July (the blooming period) with an identification accuracy up to 87%. Its accuracy in the early growth stage was 35~71%; in the middle stage, it was 69~100%; and in the late stage, it was 92~100%. Unlike corn and soybeans, the earliest identification time of rice could be obtained at the end of April (the flooding period) with an identification accuracy up to 86%. In the early stage, its accuracy was 58~100%; in the middle stage, its accuracy was 93~100%; and in the late stage, its accuracy was 96~100%. In terms of crop identification accuracy in the whole growth stage, GBDT and RF performed better than other classifiers in our three study areas. This study systematically investigated the potential of early crop recognition in Northeast China, and the results are helpful for relevant applications and decision making of crop recognition in different crop growth stages. [ABSTRACT FROM AUTHOR]

Fractional vegetation cover (FVC) is an important parameter for earth surface process simulations, climate modeling, and global change studies. Currently, several FVC products have been generated from coarse resolution (~1 km) remote sensing data, and have been widely used. However, coarse resolution FVC products are not appropriate for precise land surface monitoring at regional scales, and finer spatial resolution FVC products are needed. Time-series coarse spatial resolution FVC products at high temporal resolutions contain vegetation growth information. Incorporating such information into the finer spatial resolution FVC estimation may improve the accuracy of FVC estimation. Therefore, a method for estimating finer spatial resolution FVC from coarse resolution FVC products and finer spatial resolution satellite reflectance data is proposed in this paper. This method relies on the coupled PROSAIL radiative transfer model and a statistical crop growth model built from the coarse resolution FVC product. The performance of the proposed method is investigated using the time-series Global LAnd Surface Satellite FVC product and Landsat-7 Enhanced Thematic Mapper Plus reflectance data in a cropland area of the Heihe River Basin. The direct validation of the FVC estimated using the proposed method with the ground measured FVC data ( R^\mathrm \mathbf 2 = 0.6942 , RMSE =0.0884), compared with the widely used dimidiate pixel model ( R^\mathrm \mathbf 2 = 0.7034 , RMSE = 0.1575), shows that the proposed method is feasible for estimating finer spatial resolution FVC with satisfactory accuracy, and it has the potential to be applied at a large scale. [ABSTRACT FROM PUBLISHER]

The spatial distribution of the population is uneven for various reasons, such as urban-rural differences and geographical conditions differences. As the basic element of the natural structure of the population, the age structure composition of populations also varies considerably across the world. Obtaining accurate and spatiotemporal population age structure maps is crucial for calculating population size at risk, analyzing populations mobility patterns, or calculating health and development indicators. During the past decades, many population maps in the form of administrative units and grids have been produced. However, these population maps are limited by the lack of information on the change of population distribution within a day and the age structure of the population. Urban functional regions (UFRs) are closely related to population mobility patterns, which can provide information about population variation intraday. Focusing on the area within the Beijing Fifth Ring Road, the political and economic center of Beijing, we showed how to use the temporal scaling factors obtained by analyzing the population survey sampling data and population dasymetric maps in different categories of UFRs to realize the intraday variation mapping of elderly individuals and children. The population dasymetric maps were generated on the basis of covariates related to population. In this article, 50 covariates were calculated from remote sensing data and geospatial data. However, not all covariates are associate with population distribution. In order to improve the accuracy of dasymetric maps and reduce the cost of mapping, it is necessary to select the optimal subset for the dasymetric model of elderly and children. The random forest recursive feature elimination (RF-RFE) algorithm was introduced to obtain the optimal subset of different age groups of people and generate the population dasymetric model in this article, as well as to screen out the optimal subset with 38 covariates and 26 covariates for the dasymetric models of the elderly and children, respectively. An accurate UFR identification method combining point of interest (POI) data and OpenStreetMap (OSM) road network data is also introduced in this article. The overall accuracy of the identification results of UFRs was 70.97%, which is quite accurate. The intraday variation maps of population age structure on weekdays and weekends were made within the Beijing Fifth Ring Road. Accuracy evaluation based on sampling data found that the overall accuracy was relatively high— R 2 for each time period was higher than 0.5 and root mean square error (RMSE) was less than 0.05. On weekdays in particular, R 2 for each time period was higher than 0.61 and RMSE was less than 0.02. [ABSTRACT FROM AUTHOR]