Characterization Of Boron Doped Nanocrystalline Diamonds

Nanostructured diamond doped with boron was prepared using a hot-filament assisted chemical vapour deposition system fed with an ethyl alcohol, hydrogen and argon mixture. The reduction of the diamond grains to the nanoscale was produced by secondary nucleation and defects induced by argon and boron atoms via surface reactions during chemical vapour deposition. Raman measurements show that the samples are nanodiamonds embedded in a matrix of graphite and disordered carbon grains, while morphological investigations using field electron scanning microscopy show that the size of the grains ranges from 20 to 100 nm. The lowest threshold fields achieved were in the 1.6 to 2.4 V/μm range. © 2008 IOP Publishing Ltd.100PART 5Himpsel, F.J., Knapp, J.A., VanVechten, J.A., Eastman, P.E., (1979) Phys. Rev., 20 B, p. 624Bandis, B., Pate, B.B., (1996) Appl. Phys Lett., 69, p. 366Mammana, V.P., Santos, T.E.A., Mammana, A., Baranauskas, V., Ceragioli, H.J., Peterlevitz, A.C., (2002) Appl. Phys. Lett., 81, p. 3470Baranauskas, V., Fontana, M., Ceragioli, H.J., Peterlevitz, A.C., (2004) Nanotech., 15 (10), pp. S678Shroder, R.E., Nemanich, R.J., Glass, J.T., (1990) Phys. Rev., 41 B, p. 3738Ferrari, A.C., Robertson, J., (2001) Phys. Rev., 63 B. , 121405(R)Jiang, X., Frederick, C.K.Au., Lee, S.T., (2002) J. Appl. Phys., 92 (5), p. 2880Lee, Y.C., Lin, S.J., Lin, I.N., Cheng, H.F., (2005) J. Appl. Phys., 97, p. 05431

Integration of Multispectral and C-Band SAR Data for Crop Classification

The paper debates the impact of sensor configuration diversity on the crop classification performance. More specifically, the analysis accounts for multi-temporal and polarimetric C-Band SAR information used individually and in synergy with Multispectral imagery. The dataset used for the investigation comprises several multi-angle Radarsat-2 (RS2) fullpol acquisitions and RapidEye (RE) images both at fine resolution collected over the Indian Head (Canada) agricultural site area and spanning the summer crop growth cycle from May to September. A quasi-Maximum Likelihood (ML) classification approach applied at per-field level has been adopted to integrate the different data sources. The analysis provided evidence on the overall accuracy enhancement with respect to the individual sensor performances, with 4%-8% increase over a single RE image, a 40%-10% increase over a single 1-pol/full-pol image and 15%-0% increase over multitemporal 1-pol/full-pol RS2 series respectively. A more detailed crop analysis revealed that in particular canola and the cereals benefit from the integration, whereas lentil and flax can experience similar or worse performance when compared to the RE-based classification. Comments and suggestions for further development are presented.Geoscience & Remote SensingCivil Engineering and Geoscience

Author Correction: Ground reference data for sugarcane biomass estimation in São Paulo state, Brazil (Scientific Data, (2018), 5, 1, (180150), 10.1038/sdata.2018.150)

An amendment to this paper has been published and can be accessed via a link at the top of the paper.</p

Author Correction: Ground reference data for sugarcane biomass estimation in São Paulo state, Brazil (Scientific Data, (2018), 5, 1, (180150), 10.1038/sdata.2018.150)

An amendment to this paper has been published and can be accessed via a link at the top of the paper.Mathematical Geodesy and PositioningOptical and Laser Remote Sensin

Data descriptor: Ground reference data for sugarcane biomass estimation in São Paulo state, Brazil

In order to make effective decisions on sustainable development, it is essential for sugarcane-producing countries to take into account sugarcane acreage and sugarcane production dynamics. The availability of sugarcane biophysical data along the growth season is key to an effective mapping of such dynamics, especially to tune agronomic models and to cross-validate indirect satellite measurements. Here, we introduce a dataset comprising 3,500 sugarcane observations collected from October 2014 until October 2015 at four fields in the São Paulo state (Brazil). The campaign included both non-destructive measurements of plant biometrics and destructive biomass weighing procedures. The acquisition plan was designed to maximize cost-effectiveness and minimize field-invasiveness, hence the non-destructive measurements outnumber the destructive ones. To compensate for such imbalance, a method to convert the measured biometrics into biomass estimates, based on the empirical adjustment of allometric models, is proposed. In addition, the paper addresses the precisions associated to the ground measurements and derived metrics. The presented growth dynamics and associated precisions can be adopted when designing new sugarcane measurement campaigns.Mathematical Geodesy and PositioningOptical and Laser Remote Sensin

Erratum: Author Correction: Ground reference data for sugarcane biomass estimation in São Paulo state, Brazil (Scientific data (2018) 5 (180150))

Following publication, it was noticed that the horizontal brackets labelling the two groups of precisions present in Equation 7 are incorrectly rendered in the PDF version of this Data Descriptor. The correct Equation 7 is as follows: (Formula presented.) (Formula presented.) In addition, in the Biomass subsection of the Methods section in both the HTML and PDF versions, the term “ESUs” is incorrectly rendered as “ESU’s” and the term ESUBs is incorrectly rendered as “ESUB’s” Finally, throughout the manuscript, references to sections and subsections include the prefixes “sec:” and “subsec:”, respectively. These prefixes and any hyphen between the reference words that follow the prefixes can be ignored.Mathematical Geodesy and PositioningOptical and Laser Remote Sensin

Sugarcane productivity mapping through C-band and L-band SAR and optical satellite imagery

Space-based remote sensing imagery can provide a valuable and cost-effective set of observations for mapping crop-productivity differences. The effectiveness of such signals is dependent on several conditions that are related to crop and sensor characteristics. In this paper, we present the dynamic behavior of signals from five Synthetic Aperture Radar (SAR) sensors and optical sensors with growing sugarcane, focusing on saturation effects and the influence of precipitation events. In addition, we analyzed the level of agreement within and between these spaceborne datasets over space and time. As a result, we produced a list of conditions during which the acquisition of satellite imagery is most effective for sugarcane productivity monitoring. For this, we analyzed remote sensing data from two C-band SAR (Sentinel-1 and Radarsat-2), one L-band SAR (ALOS-2), and two optical sensors (Landsat-8 and WorldView-2), in conjunction with detailed ground-reference data acquired over several sugarcane fields in the state of São Paulo, Brazil. We conclude that satellite imagery from L-band SAR and optical sensors is preferred for monitoring sugarcane biomass growth in time and space. Additionally, C-band SAR imagery offers the potential for mapping spatial variations during specific time windows and may be further exploited for its precipitation sensitivity.Mathematical Geodesy and PositioningOptical and Laser Remote Sensin

Enabling the Processing of Sentinel-1 TOPS Data with the Open-Source DORIS Software (PPT)

Part of Session 3 - Sentinel-1 TOPS InSAR result

Vegetation Characterization through the Use of Precipitation-Affected SAR Signals

Current space-based SAR offers unique opportunities to classify vegetation types and to monitor vegetation growth due to its frequent acquisitions and its sensitivity to vegetation geometry. However, SAR signals also experience frequent temporal fluctuations caused by precipitation events, complicating the mapping and monitoring of vegetation. In this paper, we show that the influence of a priori known precipitation events on the signals can be used advantageously for the classification of vegetation conditions. For this, we exploit the change in Sentinel-1 backscatter response between consecutive acquisitions under varying wetness conditions, which we show is dependent on the state of vegetation. The performance further improves when a priori information on the soil type is taken into account.After publication of the research paper [1], the authors wish to make the following correction. The link to the affiliation of Ramon F. Hanssen should have been (1). Hence, the affiliation of Ramon F. Hanssen is Geoscience and Remote Sensing at Delft University of Technology. The authors would like to apologize for any inconvenience caused. The change does not affect the scientific results. The manuscript will be updated and the original will remain online on the article webpage, with a reference to this correction. Reference 1. Molijn, R.A.; Iannini, L.; López Dekker, P.; Magalhães, P.S.; Hanssen, R.F. Vegetation Characterization through the Use of Precipitation-Affected SAR Signals. Remote Sens. 2018, 10, 1647. [CrossRef]Mathematical Geodesy and PositioningOptical and Laser Remote Sensin

Enabling the Processing of Sentinel-1 TOPS Data with the Open-Source DORIS Software (PPT)

Part of Session 3 - Sentinel-1 TOPS InSAR resultsMathematical Geodesy and Positionin