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Reliable and accurate crop yield prediction at the field scale is critical for meeting the global demand for reliable food sources. In this study, we tested the viability of VENμS satellite data as an alternative to other popular and publicly available multispectral satellite data to predict winter wheat yield and produce a yield prediction map for a field located in southwestern Ontario, Canada, in 2020. Random forest (RF) and support vector regression (SVR) were the two machine learning techniques employed. Our results indicate that machine learning models paired with vegetation indices (VIs) derived from VENμS imagery can accurately predict winter wheat yield 1~2 months prior to harvest, with the most accurate predictions achieved during the early fruit development stage. While both machine learning approaches were viable, SVR produced the most accurate prediction with an R2 of 0.86 and an RMSE of 0.3925 t/ha using data collected from tillering to the early fruit development stage. NDRE-1, NDRE-2, and REP from various growth stages were ranked among the top seven variables in terms of importance for the prediction. These findings provide valuable insights into using high-resolution satellites as tools for non-destructive yield potential analysis. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Flood events can cause extensive damage to physical infrastructure, pose risks to human life, and necessitate the reoccupation and rehabilitation of affected areas. A key parameter for flood vulnerability assessment is the first floor height (FFH), which also plays an important role in setting insurance premiums. Traditional methods for FFH estimation rely on ground surveys and site inspections, yet these approaches are both time‐consuming and labor‐intensive. In this study, we propose an alternative approach based on measurements derived from Google Street View (GSV) images and Deep Learning (DL). We employ the YOLOv5s algorithm, which belongs to a family of compound‐scaled object detection models trained on the COCO dataset, for the detection of crucial building elements such as the Front Door (FD), stairs, and overall building extent. Additionally, we utilized the YOLOv5s algorithm to identify basement windows and assess the existence of basements. To validate our methodology, we conducted tests in both the Greater Toronto Area (GTA) and the state of Virginia in the United States. The results demonstrate an achievement of RMSE and Bias values of 81 cm and −50 cm for GTA, and 95 cm and −20 cm for the Virginia region, respectively. [ABSTRACT FROM AUTHOR]

Building land-use type classification using earth observation data is essential for urban planning and emergency management. Municipalities usually do not hold a detailed record of building land-use types in their jurisdictions, and there is a significant need for a detailed classification of this data. Earth observation data can be beneficial in this regard, because of their availability and requiring a reduced amount of fieldwork. In this work, we imported Google Street View (GSV), light detection and ranging-derived (LiDAR-derived) features, and orthophoto images to deep learning (DL) models. The DL models were trained on building land-use type data for the Greater Toronto Area (GTA). The data was created using building land-use type labels from OpenStreetMap (OSM) and web scraping. Then, we classified buildings into apartment, house, industrial, institutional, mixed residential/commercial, office building, retail, and other. Three DL-derived classification maps from GSV, LiDAR, and orthophoto images were combined at the decision level using the proposed ranking classes based on the F1 score method. For comparison, the classifiers were combined using fuzzy fusion as well. The results of two independent case studies, Vancouver and Fort Worth, showed that the proposed fusion method could achieve an overall accuracy of 75%, up to 8% higher than the previous study using CNNs and the same ground truth data. Also, the results showed that while mixed residential/commercial buildings were correctly detected using GSV images, the DL models confused many houses in the GTA with mixed residential/commercial because of their similar appearance in GSV images. [ABSTRACT FROM AUTHOR]

In this research, an ammonium perchlorate/polydopamine (AP/PDA) core–shell composite was prepared in a non-aqueous solution to reduce the mechanical sensitivity of ammonium perchlorate (AP). The result showed that the AP/PDA core–shell composite could be successfully constructed in ethyl acetate solution with an AP recovery rate that reached 86%. The mechanical sensitivity of the obtained AP/PDA core–shell composite was significantly reduced with a PDA content of only 0.76%. The DSC and TG also indicated that the coating of PDA showed catalytic activity in the thermal decomposition of AP with a lower decomposition temperature and a decreased Ea value of AP. Thus, this study proposed a simple strategy for achieving a good balanced between harnessing the energy and ensuring the safety of ammonium perchlorate by significantly reducing its mechanical sensitivity by using a very low polydopamine coating layer content, and this shows great potential for the design and fabrication of insensitive energetic composites for use in propellants. [ABSTRACT FROM AUTHOR]

Crop classification is indispensable for agricultural monitoring and food security, but early-season mapping has remained challenging. Synthetic aperture radar (SAR), such as RADARSAT Constellation Mission (RCM) and Sentinel-1, can meet higher requirements on the reliability of satellite data acquisition with all-weather and all-day imaging capability to supply dense observations in the early crop season. This study applied the local window attention transformer (LWAT) to time-series SAR data, including RCM and Sentinel-1, for early-season crop classification. The performance of this integration was evaluated over crop-dominated regions (corn, soybean and wheat) in southwest Ontario, Canada. Comparative analyses against several machine learning and deep learning methods revealed the superiority of the LWAT, achieving an impressive F1-score of 97.96% and a Kappa coefficient of 97.08% for the northern crop region and F1-scores of 98.07% and 97.02% for the southern crop region when leveraging time-series data from RCM and Sentinel-1, respectively. Additionally, by the incremental procedure, the evolution of accuracy determined by RCM and Sentinel-1 was analyzed, which demonstrated that RCM performed better at the beginning of the season and could achieve comparable accuracy to that achieved by utilizing both datasets. Moreover, the beginning of stem elongation of corn was identified as a crucial phenological stage to acquire acceptable crop maps in the early season. This study explores the potential of RCM to provide reliable prior information early enough to assist with in-season production forecasting and decision making. [ABSTRACT FROM AUTHOR]

Urban floods can affect people's lives and properties, and therefore, urban flood mapping is crucial for reliable risk assessment and the development of effective mitigation strategies. With the advent of high spatial and temporal resolution satellite images, remote sensing has become popular for urban flood mapping. Synthetic aperture RADAR (SAR) sensors can capture image data during a flood event because their emitted signal can penetrate through the clouds. However, they have some limitations, such as layover, shadowing, and speckle noise, that might challenge their usage, especially for urban flood mapping. Deep learning (DL) algorithms have been widely used for automatic urban flood mapping using remote sensing data, but the flood mapping accuracy achieved using SAR and DL algorithms is still uncertain. This paper proposes a DL-based change detection framework, convolutional Siamese network (CSN), for flood mapping in three urban areas: parts of Ottawa, ON and Gatineau, QC, Abbotsford, BC, and Leverkusen, Germany. The datasets applied were Sentinel-1 and dual-polarized RADARSAT Constellation Mission (RCM) data. The applied data were captured in C-band, and their resolutions were 10 m and 5 m for Sentinel-1 and RCM, respectively. Comparison with other DL-based segmentation algorithms, including Unet, Unet++, DeepLabV3+, and Siamese-Unet, confirmed the reliability of the proposed CSN. Although a promising flood recall rate of about 0.7 was achieved, it was inferred from the flood precision and F1 score that Sentinel-1 data medium resolution might hinder its application for urban flood mapping. Further, RCM data were also tested in both urban and non-urban areas, and a precision of 0.79 was achieved for the non-urban case. Experiments on two existing datasets, SEN12FLOOD and SEN1FLOOD11, showed that the proposed CSN achieved a higher precision index of 0.75 on SEN12-FLOOD than SEN1FLOOD11 dataset, with a precision of 0.2, because of different labeling formats between the two datasets. [ABSTRACT FROM AUTHOR]

Obtaining precise and rapid crop height is essential to facilitate agricultural production services, field management, disaster monitoring, and yield assessment. With the capability to penetrate vegetation and record vertical structure information, Polarimetric Synthetic Aperture Radar (PolSAR) holds significant potential for application in vegetation height inversion. The Water Cloud Model (WCM) and its enhanced versions are extensively utilized for estimating crop heights from PolSAR data owing to their physical significance and simplicity. However, the method is not practical for stalk crops due to the neglect of double-bounce scattering considerations. Therefore, according to the growth characteristics of stalk crops, a three-component polarimetric coherent backscattering model considering crop target double-bounce scattering is established by simplifying the Random Volume over Ground (RVoG) coherent scattering model. The empirical coefficient is introduced to simplify the model into a semi-empirical for crop height inversion. The suitability of applying the RVoG-B three-component model for crop height inversion at the early stage in corn fields was assessed using Multi-temporal C-band PolSAR RADARSAT-2 data in three polarimetric channels. The results show that the HV channel exhibits superior potential in inverting the height of corn compared with the HH and the VV channels. The results of corn height inversion demonstrate that the RVoG-B three-component semi-empirical model performs effectively in estimating corn height, with its inversion accuracy having an RMSE ranging from 11.66cm to 24.51cm. This study demonstrates the potential application of the RVoG-B three-component semi-empirical model for inverting crop height at the early stage dominated by double-bounce scattering. [ABSTRACT FROM AUTHOR]

Copyright of Professional Geographer is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Due to nonlinear components in the charging piles of electric vehicles, harmonics and nonstationary signals in the electric vehicle charging load bring voltage and current distortion, seriously affecting the accuracy of the power-related calculation in nonsinusoidal environments. This paper proposed a new approach to calculate the active power and root mean square values from decomposed components using the adaptive chirp mode decomposition (ACMD) method on voltage and current. The advantage of the ACMD-based method is that it correctly provides the power-related quantities of harmonics or nonstationary components for the electric vehicle charging load. The performance of the proposed method was verified using synthetic signals and simulation tests. The experimental results presented better estimations for each quantity defined in IEEE Standard 1459-2010, compared with the discrete wavelet transform approach. [ABSTRACT FROM AUTHOR]

Winter wheat is one of the main crops in Canada. Near real-time forecasting of within-field variability of yield in winter wheat at the early stages is essential for precision farming. However, the crop yield modelling based on high spatial resolution satellite data is generally affected by the lack of continuous satellite observations, resulting in reducing the generalization ability of the models and increasing the difficulty of near real-time crop yield forecasting at the early stages. In this study, the correlations between Sentinel-2 data (vegetation indices and reflectance) and yield data collected by combine harvester were investigated and a generalized multivariate linear regression (MLR) model was built and tested with data acquired in different years. In addition, three simple unsupervised domain adaptation (DA) methods were adopted for improving the generalization ability of yield prediction. The winter wheat yield prediction using multiple vegetation indices showed higher accuracy than using single vegetation index. The optimum stage for winter wheat yield forecasting varied with different fields when using vegetation indices, while it was consistent when using multispectral reflectance and the optimum stage for winter wheat yield prediction was at the end of flowering stage. This study demonstrated that the simple mean matching (MM) performed better than other DA methods and it was found that "DA then MLR at the optimum stage" performed better than "MLR directly at the early stages" for winter wheat yield forecasting at the early stages. The results indicated that the DA had a great potential in near real-time crop yield forecasting at the early stages. [ABSTRACT FROM AUTHOR]

Both the Southern Annular Mode (SAM) and El Niño–Southern Oscillation (ENSO) are critical factors contributing to Antarctic sea ice variability on interannual time scales. However, their joint effects on sea ice are complex and remain unclear for each austral season. In this study, satellite sea ice concentration (SIC) observations and atmospheric reanalysis data are utilized to assess the impacts of combined SAM and ENSO on seasonal Antarctic sea ice changes. The joint SAM–ENSO impacts on southern high latitudes are principally controlled by the strength and position of the wave activity and associated atmospheric circulation anomalies affected by their interactions. In-phase events (La Niña/positive SAM and El Niño/negative SAM) are characterized with an SIC dipole located in the Weddell/Bellingshausen Seas and Amundsen/Ross Seas, while out-of-phase events (El Niño/positive SAM and La Niña/negative SAM) experience significant SIC anomalies in the Indian Ocean and western Pacific Ocean. Sea ice budget analyses are conducted to separate the dynamic and thermodynamic contributions inducing the sea ice intensification anomalies. The results show that in-phase intensification anomalies also display a pattern similar to the SIC dipole and are mainly driven by the direct thermodynamic forcing at the ice edge and thermodynamic responses to meridional sea ice drift in the inner pack, especially in autumn and winter. Dynamic processes caused by zonal sea ice drift also play an important role during out-of-phase conditions in addition to the same mechanisms during in-phase conditions. [ABSTRACT FROM AUTHOR]

Antarctic sea ice variations are affected by moisture and heat from low‐ and mid‐latitudes, more than 90% of which are transported by atmospheric rivers (ARs). This study employs the ERA5 reanalysis and satellite observations to detect all the ARs over the Antarctic sea ice and analyze the general contributions of ARs on sea ice changes from 1979 to 2020. Though AR frequency is low in all seasons, ARs give rise to intense sea ice reduction at a rate of more than 10%/day in marginal ice zone. Thermodynamic processes of sea ice dominate the AR‐induced variations, associated with anomalous atmospheric conditions during ARs. Warm, moist and cloudy weather causes considerable melting by enhancing sensible heat flux in cold seasons but has restrictive influences in summer due to blocked solar radiation. Heavy precipitation during ARs is also nonnegligible, especially during the summer melt. Plain Language Summary: Atmospheric rivers are long, narrow, transient water vapor band, and vast research focus on atmospheric rivers (ARs) landfall at mid‐latitudes because they can lead to deleterious hazards (flooding, storm surge, etc.). Little attention has been paid to the impacts of ARs on polar areas, though more than 90% of water vapor into high latitude is transported by ARs which have profound implications on the extremely dry and cold polar region. Antarctic sea ice, as a part of the cryosphere, is important to global climate, but its overall variation pattern during ARs is poorly understood. By analyzing sea ice change and atmospheric perturbations during ARs, we find that the infrequent ARs cause a drastic sea ice reduction at the sea ice edge. And the decline is mainly attributed to the melting, while dynamic processes (i.e., sea ice advection and convergence) have minor contribution to the sea ice change. Warm, humid and cloudy weather during ARs is vital to the sea ice melting in cold months, but is not that important in summer from the long‐term perspective. The heavy rain may also contribute to substantial melting, notably in summer. Key Points: Low‐frequency atmospheric rivers (ARs) can lead to prominent sea ice reduction over marginal ice zone primarily through thermodynamic processesWarm, moist and cloudy weather during ARs boosts sea ice melting in cold seasons but has a limited effect in summer [ABSTRACT FROM AUTHOR]

Why are some cities more compact than others? We argue that riskier housing markets have a costlier real option; developers would require greater compensation to build now—thus, developers are being incentivized to delay, giving rise to more compact cities. We test this hypothesis cross‐sectionally for Canada's 11 largest census metropolitan areas. We interpret satellite imageries and estimate a hierarchical spatial autoregressive model to account for both the hierarchical and the spatial structure of our data. Our results show that, on average, a one‐percentage‐point increase in price risk reduces urban land coverage in the fringe by about 0.7 percentage point. [ABSTRACT FROM AUTHOR]

In the aftermath of a natural hazard, rapid and accurate building damage assessment from remote sensing imagery is crucial for disaster response and rescue operations. Although recent deep learning-based studies have made considerable improvements in assessing building damage, most state-of-the-art works focus on pixel-based, multi-stage approaches, which are more complicated and suffer from partial damage recognition issues at the building-instance level. In the meantime, it is usually time-consuming to acquire sufficient labeled samples for deep learning applications, making a conventional supervised learning pipeline with vast annotation data unsuitable in time-critical disaster cases. In this study, we present an end-to-end building damage assessment framework integrating multitask semantic segmentation with semi-supervised learning to tackle these issues. Specifically, a multitask-based Siamese network followed by object-based post-processing is first constructed to solve the semantic inconsistency problem by refining damage classification results with building extraction results. Moreover, to alleviate labeled data scarcity, a consistency regularization-based semi-supervised semantic segmentation scheme with iteratively perturbed dual mean teachers is specially designed, which can significantly reinforce the network perturbations to improve model performance while maintaining high training efficiency. Furthermore, a confidence weighting strategy is embedded into the semi-supervised pipeline to focus on convincing samples and reduce the influence of noisy pseudo-labels. The comprehensive experiments on three benchmark datasets suggest that the proposed method is competitive and effective in building damage assessment under the circumstance of insufficient labels, which offers a potential artificial intelligence-based solution to respond to the urgent need for timeliness and accuracy in disaster events. [ABSTRACT FROM AUTHOR]

Summary: A large amount of the world's existing infrastructure is reaching the end of its service life, requiring intervention in the form of structural rehabilitation or replacement. A critical aspect of such asset management is the condition assessment of these structures to evaluate their existing health and dictate the scheduling and extent of required rehabilitation. It has been demonstrated that human‐based manual inspections face logistical constraints and are expensive, time extensive, and subjective, depending on the knowledge of the inspection. Recently, autonomous vision‐based techniques have been proposed as an alternative, more accurate method for the inspection of deteriorating structures. Convolutional neural networks (CNNs) have demonstrated state‐of‐the‐art accuracy with respect to damage classification for concrete structures and are often implemented to process images taken from vision‐based sensors such as cameras, smartphones, and drones. However, these archetypes require a large database of annotated images to train the network to an accurate level, which is not readily available for real‐life structures. Moreover, CNNs are limited to the extent by which they are trained; they are often only trained for binary damage classification of a singular material model. This paper addresses these challenges of CNNs through the application of a generative adversarial network (GANs) for multiclass damage detection of concrete structures. The proposed GAN is trained using the SDNET2018 dataset to detect cracking, spalling, pitting, and construction joints in concrete surfaces. Moreover, transfer learning is implemented to transfer the learned features of the GAN to a CNN architecture to allow for accurate image classification. It is concluded that, for a 0%–30% reduction in the amount of labeled data used, the proposed GAN method has comparable accuracy to traditional CNNs. [ABSTRACT FROM AUTHOR]

Compared with a monoculture planting mode, the practice of crop rotations improves fertilizer efficiency and increases crop yield. Large-scale crop rotation monitoring relies on the results of crop classification using remote sensing technology. However, the limited crop classification accuracy cannot satisfy the accurate identification of crop rotation patterns. In this paper, a crop classification and rotation mapping scheme combining the random forest (RF) algorithm and new statistical features extracted from time-series ground range direction (GRD) Sentinel-1 images. First, the synthetic aperture radar (SAR) time-series stacks are established, including VH, VV, and VH/VV channels. Then, new statistical features named the objected generalized gamma distribution (OGΓD) features are introduced to compare with other object-based features for each polarization. The results showed that the OGΓD σVH achieved 96.66% of the overall accuracy (OA) and 95.34% of the Kappa, improving around 4% and 6% compared with the object-based backscatter in VH polarization, respectively. Finally, annual crop-type maps for five consecutive years (2017–2021) are generated using the OGΓD σVH and the RF. By analyzing the five-year crop sequences, the soybean-corn (corn-soybean) is the most representative rotation in the study region, and the soybean-corn-soybean-corn-soybean (together with corn-soybean-corn-soybean-corn) has the highest count with 100 occurrences (25.20% of the total area). This study offers new insights into crop rotation monitoring, giving the basic data for government food planning decision-making. [ABSTRACT FROM AUTHOR]

The crucial role that Antarctic sea ice plays in the global climate system is strongly linked to its thickness. While field observations are too sparse in the Southern Ocean to determine long-term trends of the Antarctic sea ice thickness (SIT) on a hemispheric scale, satellite radar altimetry data can be applied with a promising prospect. The European Space Agency's Sea Ice Climate Change Initiative project (ESA SICCI) generates sea ice thickness derived from Envisat, covering the entire Southern Ocean year-round from 2002 to 2012. In this study, the SICCI Envisat Antarctic SIT is first compared with an Ice, Cloud, and land Elevation Satellite (ICESat) SIT product retrieved with a modified ice density algorithm. Both data sets are compared to SIT estimates from upward-looking sonar (ULS) in the Weddell Sea, showing mean differences (MDs) and standard deviations (SDs, in parentheses) of 1.29 (0.65) m for Envisat - ULS (- denotes "minus" and the same below), while we find 1.11 (0.81) m for ICESat - ULS. The inter-comparisons are conducted for all seasons except for winter, based on the ICESat operating periods. According to the results, the differences between Envisat and ICESat SIT reveal significant temporal and spatial variations. More specifically, the smallest seasonal SIT MD (SD) of 0.00 m (0.39 m) for Envisat - ICESat is found in spring (October–November), while a larger MD (SD) of 0.52 (0.68 m) and 0.57 m (0.45 m) exists in summer (February–March) and autumn (May–June). It is also shown that from autumn to spring, mean Envisat SIT decreases while mean ICESat SIT increases. Our findings suggest that both overestimation of Envisat sea ice freeboard potentially caused by radar backscatter originating from inside the snow layer and the Advanced Microwave Scanning Radiometer for EOS (AMSR-E, where EOS stands for Earth Observing System) snow depth biases and sea ice density uncertainties can possibly account for the differences between Envisat and ICESat SIT. [ABSTRACT FROM AUTHOR]

Crop yield is related to household food security and community resilience, especially in smallholder agricultural systems. As such, it is crucial to accurately estimate within-season yield in order to provide critical information for farm management and decision making. Therefore, the primary objective of this paper is to assess the most appropriate method, indices, and growth stage for predicting the groundnut yield in smallholder agricultural systems in northern Malawi. We have estimated the yield of groundnut in two smallholder farms using the observed yield and vegetation indices (VIs), which were derived from multitemporal PlanetScope satellite data. Simple linear, multiple linear (MLR), and random forest (RF) regressions were applied for the prediction. The leave-one-out cross-validation method was used to validate the models. The results showed that (i) of the modelling approaches, the RF model using the five most important variables (RF5) was the best approach for predicting the groundnut yield, with a coefficient of determination (R2) of 0.96 and a root mean square error (RMSE) of 0.29 kg/ha, followed by the MLR model (R2 = 0.84, RMSE = 0.84 kg/ha); in addition, (ii) the best within-season stage to accurately predict groundnut yield is during the R5/beginning seed stage. The RF5 model was used to estimate the yield for four different farms. The estimated yields were compared with the total reported yields from the farms. The results revealed that the RF5 model generally accurately estimated the groundnut yields, with the margins of error ranging between 0.85% and 11%. The errors are within the post-harvest loss margins in Malawi. The results indicate that the observed yield and VIs, which were derived from open-source remote sensing data, can be applied to estimate yield in order to facilitate farming and food security planning. [ABSTRACT FROM AUTHOR]

Copyright of Advances in Atmospheric Sciences is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Journal of Remote Sensing is the property of Editorial Office of Journal of Remote Sensing & Science Publishing Co. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Soil moisture content (SMC) is an indispensable basic element for crop growth and development in agricultural production. Obtaining accurate information on SMC in real time over large agricultural areas has important guiding significance for crop yield estimation and production management. In this study, the paper reports on the retrieval of SMC from RADARSAT-2 polarimetric SAR data. The proposed SMC retrieval algorithm includes vegetation correction based on a ratio method and roughness correction based on the optimal roughness method. Three vegetation description parameters (i.e., RVI, LAI, and NDVI) serve as vegetation descriptors in the parameterization of the algorithm. To testify the vegetation correction result of the algorithm, the water cloud model (WCM) was compared with the algorithm. The calibrated integrated equation model (CIEM) was employed to describe the backscattering from the underlying soil. To improve the accuracy of SMC retrieval, the CIEM model was optimized by using the optimal roughness parameter and the normalization method of reference incidence angle. Validation against ground measurements showed a high correlation between the measured and estimated SMC when the NDVI serves as vegetation descriptor (R2 = 0.68, RMSE = 4.15 vol.%, p < 0.01). The overall estimation performance of the proposed SMC retrieval algorithm is better than that of the WCM. It demonstrates that the proposed algorithm has an operational potential to estimate SMC over wheat fields during the growing season. [ABSTRACT FROM AUTHOR]

Crop identification and classification are of great significance to agricultural land use management. The physically constrained general model-based decomposition (PCGMD) has proven to be a promising method in comparison with the typical four-component decomposition methods in scattering mechanism interpretation and identifying vegetation types. However, the robustness of PCGMD requires further investigation from the perspective of final applications. This paper aims to validate the efficiency of the PCGMD method on crop classification for the first time. Seven C-band time-series RADARSAT-2 images were exploited, covering the entire growing season over an agricultural region near London, Ontario, Canada. Firstly, the response and temporal evolution of the four scattering components obtained by PCGMD were analyzed. Then, a forward selection approach was applied to achieve the highest classification accuracy by searching an optimum combination of multi-temporal SAR data with the random forest (RF) algorithm. For comparison, the general model-based decomposition method (GMD), the original and its three improved Yamaguchi four-component decomposition approaches (Y4O, Y4R, S4R, G4U), were used in all tests. The results reveal that the PCGMD method is highly sensitive to seasonal crop changes and matches well with the real physical characteristics of the crops. Among all test methods used, the PCGMD method using six images obtained the optimum classification performance, reaching an overall accuracy of 91.83%. [ABSTRACT FROM AUTHOR]

Antarctic sea ice is an important component of the Earth system. However, its role in the Earth system is still unclear due to limited Antarctic sea-ice thickness (SIT) data. A reliable sea-ice reanalysis can be useful to study Antarctic SIT and its role in the Earth system. Among various Antarctic sea-ice reanalysis products, the Global Ice-Ocean Modeling and Assimilation System (GIOMAS) output is widely used in the research of Antarctic sea ice. As more Antarctic SIT observations with quality control are being released, a further evaluation of Antarctic SIT from GIOMAS is conducted in this study based on in situ and satellite observations. Generally, though only sea-ice concentration is assimilated, GIOMAS can basically reproduce the observed variability in sea-ice volume and its changes in the trend before and after 2013, indicating that GIOMAS is a good option to study the long-term variation in Antarctic sea ice. However, due to deficiencies in the model and asymmetric changes in SIT caused by assimilation, GIOMAS underestimates Antarctic SIT especially in deformed ice regions, which has an impact on not only the mean state of SIT but also the variability. Thus, besides the further development of the model, assimilating additional sea-ice observations (e.g., SIT and sea-ice drift) with advanced assimilation methods may be conducive to a more accurate estimation of Antarctic SIT. [ABSTRACT FROM AUTHOR]

Soil moisture content (SMC) is a significant factor affecting crop growth and development. However, SMC estimation, based on synthetic aperture radar (SAR), is influenced by a variety of surface parameters, such as vegetation cover and surface roughness. As a result, determining the SMC across agricultural areas (e.g., wheat fields) remotely (i.e., without ground measurement) is difficult to achieve. In this study, a model-based polarization decomposition method was used to decompose the original SAR signal into different scattering components that represented different scattering mechanisms. The different volume scattering models were applied, and then the results were compared in order to remove the scattering contribution from vegetation canopy, and extract the surface scattering components related to the soil moisture. Finally, by combining extensively used surface scattering models (e.g., CIEM and Dubois), and a method of roughness parameters optimization, a lookup table was developed to estimate the soil moisture during the wheat growth period. When CIEM is applied, the R2 and RMSE of the SMC are 0.534, 5.62 vol.%, and for the Dubois model, 0.634, 5.16 vol.%, respectively, which indicates that this approach provides good estimation performance for measuring soil moisture during the wheat growing season. [ABSTRACT FROM AUTHOR]

Deforestation drives climate change and reinforces food insecurity in forest‐dependent communities. What drives deforestation varies by location and is shaped by livelihood systems. But how locals perceive restoration is crucial for developing restoration policies. Evidence suggests that applying sustainable farming strategies can potentially restore forests and sustain livelihoods. Applying a broad‐based conceptualization of deforestation and restoration in policymaking, however, results in missed opportunities for addressing deforestation and restoration. Here, we explore the drivers of deforestation, the perceptions of restoration, and the challenges to restoration among smallholder farmers in northern Malawi and examine how agroecology can contribute to restoring degraded agroecosystems. Participants report agricultural land expansion, charcoal production, climate change, burnt brick production, and government subsidies as the major drivers of deforestation. We observed that although perceptions of forest restoration reflect farmers' traditional ecological knowledge (TEK) to include reclamation of degraded farmlands, reconstruction of native tree species, and replacement of felled trees on farmlands, there are challenges including splitting families to gain access to more subsidized fertilizers and food aid, embedded cultural practices, growing demand for charcoal in cities, and weak ecosystem governance structures that hinder the effectiveness of restoration efforts. We, however, do find that agroecological intensification can increase yield from smaller farmlands and allow for larger and longer‐lasting fallows of spare lands which regenerate forests. Key overarching implications of these findings include the need to integrate livelihoods more explicitly into restoration plans, accounting for TEK in restoration policies in forest‐dependent communities and encouraging the adoption of agroecology. [ABSTRACT FROM AUTHOR]

To improve productivity, reduce production costs, and minimize the environmental impacts of agriculture, the advancement of nitrogen (N) fertilizer management methods is needed. The objective of this study is to compare the use of Unmanned Aerial Vehicle (UAV) multispectral imagery and PlanetScope satellite imagery, together with plant height, leaf area index (LAI), soil moisture, and field topographic metrics to predict the canopy nitrogen weight (g/m2) of wheat fields in southwestern Ontario, Canada. Random Forests (RF) and support vector regression (SVR) models, applied to either UAV imagery or satellite imagery, were evaluated for canopy nitrogen weight prediction. The top-performing UAV imagery-based validation model used SVR with seven selected variables (plant height, LAI, four VIs, and the NIR band) with an R2 of 0.80 and an RMSE of 2.62 g/m2. The best satellite imagery-based validation model was RF, which used 17 variables including plant height, LAI, the four PlanetScope bands, and 11 VIs, resulting in an R2 of 0.92 and an RMSE of 1.75 g/m2. The model information can be used to improve field nitrogen predictions for the effective management of N fertilizer. [ABSTRACT FROM AUTHOR]

Mixed lineage leukemia (MLL) T10 is a relatively rare partner for the KMT2A lysine (K)-specific methyltransferase 2A gene. The common features and coexisting mutations of acute myeloid leukemia (AML) patients with KMT2A-MLLT10 remain unknown. In this study, 10 adult AML patients with KMT2A-MLLT10 fusions were picked up from 496 AML patients by using RT-polymerase chain reaction (PCR) and/or fluorescence in situ hybridization, and then screened for mutations in the 49 genes panel with next-generation sequencing and PCR, followed by direct Sanger sequencing. Of the 10 unique individuals identified, 6 were male and 4 were female (M:F ratio, 1.5:1) with ages ranging from 19 to 52 years (median 39.5 years). Most (90%, 9/10) patients with KMT2A-MLLT10 were accompanied by additional mutations. Twelve mutated genes were detected, averaging 2.1 mutations per patient (range, 0–4). The most frequently mutated gene was NRAS (n = 5). Clinical and laboratory data pointed to common features: French American British-M5 subtype (n = 7), a high rate of relapse, and biomarkers CD33 (n = 10), CD117 (n = 9), CD13 (n = 8), and CD64 (n = 8). Overall, most patients harbored at least one mutation. A high incidence of mutations affecting the RAS signaling pathway or RAS regulating components was found in 50% (5/10) patients. The overall survival is about 12.0 months. Allogeneic-hematopoietic stem cell transplantation trends to improve survival in selected patients. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Plant Science is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

A socio-ecological health model will be used to address underlying factors for injury risk and design injury prevention programs and policies that address regional and economic disparities. Injury risk, is affected by the social determinants of health including social and economic inequities, which are more pronounced in low and middle-income countries (LMIC) like Kenya (World Health Organization [4]). [Extracted from the article]

Copyright of Papers in Regional Science is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Ocean–sea-ice coupled models constrained by various observations provide different ice thickness estimates in the Antarctic. We evaluate contemporary monthly ice thickness from four reanalyses in the Weddell Sea: the German contribution of the project Estimating the Circulation and Climate of the Ocean Version 2 (GECCO2), the Southern Ocean State Estimate (SOSE), the Ensemble Kalman Filter system based on the Nucleus for European Modelling of the Ocean (NEMO-EnKF) and the Global Ice–Ocean Modeling and Assimilation System (GIOMAS). The evaluation is performed against reference satellite and in situ observations from ICESat-1, Envisat, upward-looking sonars and visual ship-based sea-ice observations. Compared with ICESat-1, NEMO-EnKF has the highest correlation coefficient (CC) of 0.54 and lowest root mean square error (RMSE) of 0.44 m. Compared with in situ observations, SOSE has the highest CC of 0.77 and lowest RMSE of 0.72 m. All reanalyses underestimate ice thickness near the coast of the western Weddell Sea with respect to ICESat-1 and in situ observations even though these observational estimates may be biased low. GECCO2 and NEMO-EnKF reproduce the seasonal variation in first-year ice thickness reasonably well in the eastern Weddell Sea. In contrast, GIOMAS ice thickness performs best in the central Weddell Sea, while SOSE ice thickness agrees most with the observations from the southern coast of the Weddell Sea. In addition, only NEMO-EnKF can reproduce the seasonal evolution of the large-scale spatial distribution of ice thickness, characterized by the thick ice shifting from the southwestern and western Weddell Sea in summer to the western and northwestern Weddell Sea in spring. We infer that the thick ice distribution is correlated with its better simulation of northward ice motion in the western Weddell Sea. These results demonstrate the possibilities and limitations of using current sea-ice reanalysis for understanding the recent variability of sea-ice volume in the Antarctic. [ABSTRACT FROM AUTHOR]

Soil moisture (Mv) estimation and monitoring over agricultural areas using Synthetic Aperture Radar (SAR) are often affected by vegetation cover during the growing season. Volume scattering and vegetation attenuation can complicate the received SAR backscatter signal when microwave interacts with the vegetation canopy. To address the existing problems, this article employed the model-based polarimetric decomposition method considering the two-way attenuation to remove the volume scattering and vegetation attenuation. A deorientation process of SAR data was applied to remove the influence of randomly distributed target orientation angles before the polarimetric decomposition. To parameterize the two-way attenuation, Radar Vegetation Index derived from the SAR intensity images was adopted. The Dubois model was used to describe backscattering from the underlying bare soil. Since the soil roughness parameters are difficult to measure under vegetation cover, the optimum surface roughness method was used to parameterize the Dubois model. This soil moisture retrieval algorithm was applied to the polarimetric multitemporal RADARSAT-2 SAR data over soybean fields. The validation indicates the root-mean-square error of 9.2 vol.% and 8.2 vol.% at HH and VV polarization, respectively, over the entire soybean growing period, suggesting that the proposed method is capable of reducing the effect of vegetation cover for soil moisture monitoring over the soybean field. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Copyright of Canadian Journal of Remote Sensing is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Remanufacturing is one of the important means to achieve circular economy and improve the reuse of resources. But, compared with the reuse of old parts, most ordinary Chinese consumers are not familiar with remanufacturing. Because of this, the development of China's remanufacturing industry is hindered. This paper introduces two kinds of consumer goods with different attributes, namely MP4 (the hedonic product) and cartridge (the functional product). The empirical study on the consumption behavior of Chinese consumers when they are faced with a variety of recycling options was performed. Empirical studies are divided into two stages: participants need to give hypothetical purchase decisions when facing situations of two products (new products and remanufactured products) and three products including refurbishment products, respectively. This paper analyzes the purchase intention and decision-making process of Chinese consumers for remanufactured products, new products, and refurbished products in these two situations. The consumers' willingness to pay for remanufactured products and refurbished products is also part of the study. The experimental results verify that consumers have a different selection mechanism for new products, remanufactured products, and refurbished products, and there is also a certain relationship between this selection mechanism and the attributes of the product itself. The research shows that due to the different product attributes, consumers pay different attention to the environmental protection, quality, brand, price, and new and old preferences of products. The result of the model shows that the choice behavior of different products and their willingness to pay are also affected by different levels of these attentions. Through the research results, this paper finds conclusions like refurbished products have an impact on the development of remanufactured products, and consumers pay more attention to price but do not pay attention to environmental protection. The conclusion of the study provides references and practical implications for Chinese remanufacturing enterprises to formulate market strategy, for the government to formulate relevant policies, and for OEM production. [ABSTRACT FROM AUTHOR]

The article presents the modified structure of the two-level digital frequency synthesizer (TLDFS), which combines the properties of classical digital frequency synthesizers (DFS) and Poisson pulse sequence generators (PPSG). The analysis of the statistical characteristics of synthesizer output signal, obtained in computer modelling with the use of appropriate software, has been carried out, which allowed determining the effective range of values of its control codes. The proposed generators can be effectively used to simulate various natural and technical processes, in particular, to simulate the output signals of dosimetric detectors during the design, adjustment and testing of dosimetric devices. [ABSTRACT FROM AUTHOR]

Urban flooding is a reoccurring disaster, and its frequency and intensity are likely to increase in the future due to the increasing frequency of storm events. Up-to-date monitoring on the distribution of flood hazards in cities is necessary and valuable for urban planning. This research combines two common urban flooding approaches, namely hydraulic and GIS models, in a case study of London, Ontario, Canada. The hydraulic–GIS combined model employs the hydraulic concept in a simplified GIS frame, hence avoiding heavy computation in the hydraulic model and arbitrary coefficients in a GIS model. We used a binary logistic regression model to integrate the hydraulic concept in a GIS model. The multi-criteria GIS model built by binary logistic regression was able to simulate the results from the hydraulic model with good consistency. Such a strategy serves as a promising prototype for addressing similar geographical modelling issues, where the time-consuming physical model can be potentially replaced by a simplified GIS model. Furthermore, the impervious surface percentage is an important input in the hydraulic model. This research experimented different impervious surface percentages as input to the hydraulic model and found that a spatially variable impervious surface percentage achieves better agreement with hydraulic modelling than that of uniform (25% and 42%) impervious surface percentages. [ABSTRACT FROM AUTHOR]

As one of the low-carbon products, remanufactured products are being paid more attention in more and more countries. But the low willingness of Chinese consumers to pay for them makes it difficult for remanufactured products companies to move forward in the Chinese market. This study explores the factors that affect consumers' willingness to pay for remanufactured goods, through the hierarchical linear regression method, based on questionnaires. The results show that demographic variables (age, education, occupation, and income), individual subjective variables (environmental awareness, secondhand preferences, and Chinese quality trust) and product perception variables (MP4 quality perceived risk, face risk, and product impact on the environment) have a significant impact on the willingness to pay for remanufactured goods. [ABSTRACT FROM AUTHOR]

Error due to inhomogeneity is the main problem of thermocouples (TCs), e.g., during the operation of a type K TC, this error can reach 11-30 °C. Thus, metrological reliability of TCs is threatened by this error because there is a high risk of exceeding the permissible error when the temperature distribution along the TC legs changes. Such a large error, in turn, can threaten a proper operation or even safety of a measured object. A TC with controlled temperature field was proposed to cope with this error. An information-measuring system to perform proper measurements, measurement data acquisition and collection to construct mathematical models is proposed. Its property is high diurnal stability of ±(0.0025+0,002(X/XMAX–1) %. The requirements for the information-measuring system and its structure are considered in this paper. In particular, one of the key problems of such a sensor is how stable is its own temperature field under the influence of the temperature field of a measured object. The experimental studies were carried out using the developed system. They showed that the coefficient of penetration of the temperature field of the measured object is about 0.04. This allows decreasing error due to inhomogeneity by about 10-20 times. [ABSTRACT FROM AUTHOR]

In recent years, a new genre of hyperspectral unmixing methods based on nonnegative matrix factorization (NMF) have been proposed. Unlike traditional spectral unmixing methods, the NMF-based hyperspectral unmixing methods no longer depend on pure pixels in the original image. The NMF is based on linear algebra, which requires that the hyperspectral data cube is converted from 3-D cube to a 2-D matrix. Due to this conversion, the spatial information in the relative positions of the pixels is lost. With the emergence of multilinear algebra, the tensorial representation of hyperspectral imagery that preserves spectral and spatial information has become popular. The tensor-based spectral unmixing was first realized in 2017 using the matrix-vector nonnegative tensor factorization (MVNTF) decomposition. Using the construction of MVNTF spectral unmixing, this letter proposes to integrate three additional constraints (sparseness, volume, and nonlinearity) to the cost function. As we show in this letter, we found that the three constraints greatly improved the impervious surface area fraction/classification results. The constraints also shortened the processing time. [ABSTRACT FROM AUTHOR]

Distribution of impervious surface area (ISA) is an important input in a wide range of urban ecosystem studies. The future launch of the German hyperspectral satellite environmental mapping and analysis program (EnMAP) in 2019 provides new opportunities for timely and global ISA extraction. The previously proposed EnMAP applications heavily relied on existing reference endmembers, which may be impractical on a global scale. To overcome this defect, we suggest to use the nonnegative matrix factorization (NMF) method to extract the endmember directly from EnMAP imagery. Three traditional unmixing method (e.g., N-Findr, pixel purity index, and independent component analysis) and four NMF-based methods with different constraints (e.g., sparseness, convex volume, and nonlinearity) were used to obtain series of endmember sets, ISA fraction, and classification maps. In results, the NMF-based methods outperformed the three traditional unmixing method, by achieving 0.5–0.6 R-squared values in the linear regression models between predicted and reference ISA percentages, and over 85% overall accuracy in ISA classification maps. We found that the NMF-based spectral unmixing methods are suitable to work with the EnMAP image, when reference endmember data are unavailable. In addition, we processed the widely used Hydice urban test image with the same methods and compared the resulting ISA percentage/classification maps with the EnMAP results, considering the different features of the Hydice and EnMAP sensors. In the results, it is proved that the EnMAP image has great potential in ISA mapping on a global scale, with reasonable overall accuracy and economical efficiency. [ABSTRACT FROM AUTHOR]

Up-to-date spatial information on ground movements and land use is useful for emergency management of coastal regions. Time series InSAR techniques have proven to be effective tools for providing the former; however, InSAR results alone cannot be used to characterize the relationship between movements and land use. The focus of this study is to evaluate the potential to use high resolution radar satellite imagery for monitoring urban land subsidence associated with the construction of new building in Canadian coastal cities. To do this, we propose to integrate InSAR and polarimetric SAR information for deformation analysis. The methodology included multidimensional small baseline subset (MSBAS) InSAR analysis, polarimetric SAR change detection, and integration of the coherence, deformation, and polarimetric information for identifying the urban surface movements related to new buildings. The study was conducted in the Vancouver region, BC using RADARSAT-2 satellite data of ultra-fine mode and fine-quad mode acquired during 2010-2016. Results demonstrated that the integration of polarimetric and InSAR data permitted identification of ground movement, and the association of these movements to the new constructions in the urban environment. Several locations have been experiencing subsidence at a rate of up to 10 cm/year, and horizontal motion of 5 cm/year. [ABSTRACT FROM AUTHOR]

Agriculture is an important sector in Canada, and annual crop inventories are required in many agricultural applications. Multi-temporal polarimetric synthetic aperture radar (SAR) data have great potential in crop classification due to its less dependency on weather condition. This study, for the first time, investigated the effects of the Minimum Noise Fraction (MNF) transformation of multi-temporal RADARSAT-2 polarimetric SAR data on the performance of cropland classification through the discussing of the performance of different polarimetric SAR parameter sets, and the impact of the timing of RADARSAT-2 datasets in southwestern Ontario. The random forest classifier was adopted due to its excellent ability in crop classification. The results illustrated that the elements of coherency matrix performed the best in agricultural land cover classification. The multi-temporal polarimetric SAR data acquired from the end of June to November gave the best classification accuracy, and an overall accuracy of 90% can be achieved using two images acquired in the middle of September and October. The MNF transformation can further improve the classification accuracy, and this accuracy was competitive with the accuracy produced using the integration of optical and polarimetric SAR data. [ABSTRACT FROM AUTHOR]

Increasing studies have been conducted to investigate the potential of polarimetric synthetic aperture radar (SAR) in crop growth monitoring due to the capability of penetrating the clouds, haze, light rain, and vegetation canopy. This study investigated the sensitivity of 16 parameters derived from C-band Radarsat-2 polarimetric SAR data to crop height and fractional vegetation cover (FVC) of corn and wheat. The in-situ measured crop height and FVC were collected from 29 April to 30 September 2013, at the study site in southwest Ontario, Canada. A total of 10 Radarsat-2 polarimetric SAR images were acquired throughout the same growing season. It was observed that at the early growing stage, the corn height was strongly correlated with the SAR parameters including HV (R2 = 0.88), HH-VV (R2 = 0.84), and HV/VV (R2 = 0.80), and the corn FVC was significantly correlated with HV (R2 = 0.79) and HV/VV (R2 = 0.92), but the correlation became weaker at the later growing stage. The sensitivity of the SAR parameters to wheat variables was very low and only HV and Yamaguchi helix scattering showed relatively good but negative correlations with wheat height (R2 = 0.57 andR2 = 0.39) at the middle growing stage. These findings indicated that Radarsat-2 polarimetric SAR (C-band) has a great potential in crop height and FVC estimation for broad-leaf crops, as well as identifying the changes in crop canopy structures and phenology. [ABSTRACT FROM PUBLISHER]