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5 results for “Field Delineation”
Data for "Site-Specific Management Zones Delineation Based on Apparent Soil Electrical Conductivity in Two Contrasting Fields of Southern Brazil"
<p>This dataset contains soybean yield, soil property values, and apparent electrical conductivity (ECa) from two farms located in the South of Brazil. The data description and methods can be found in the paper:</p> <p><strong>Bottega, E. L., Safanelli, J. L., Zeraatpisheh, M., Amado, T. J. C., Queiroz, D. M. de, & Oliveira, Z. B. de. (2022). Site-Specific Management Zones Delineation Based on Apparent Soil Electrical Conductivity in Two Contrasting Fields of Southern Brazil. In Agronomy (Vol. 12, Issue 6, p. 1390). MDPI AG. https://doi.org/10.3390/agronomy12061390</strong></p> <p>Abstract:</p> <p>Management practices that aim to increase the profitability of agricultural production with minimal environmental impact must consider within-field soil variability, and this site-specific management can be addressed by precision agriculture (PA). Thus, this work aimed to investigate which key soil attributes are distinguishable management zones (MZ) delineated based on the soil apparent electrical conductivity (ECa), using fuzzy k-means, in two fields with contrasting soil textures in southern Brazil. For this, a grid scheme (50 × 50 m) was applied to measure ECa, conduct soil sampling for analysis, and determine soybean yield. The MZ were delineated based on the ECa spatial distribution, and statistical non-parametric tests (p < 0.05) were employed to compare the soil chemical and physical attributes among MZ. The management zones were able to distinguish the average values of Clay, Silt, pH, Ca<sup>2+</sup>, Mg<sup>2+</sup>, SB, Al<sup>3+</sup>, H<sup>+</sup> + Al<sup>3+</sup>, AS%, and BS%. In the field classified as sandy clay loam texture, management zones were able to differentiate the average values of soybean yield, Clay, Ca<sup>2+</sup>, Mg<sup>2+</sup>, SB, and CEC. Thus, this study supports the ECa as an efficient tool for delineating MZ of contrasting cropland soils in southern Brazil to understand the within-field soil variability and adjust the inputs accordingly.</p>
Ukraine Field Boundary Delineation 2022
<h2><strong>Authors:</strong></h2> <div> <p>Yuval Sadeh<sup>a,b,*</sup>, <sup> </sup>Josef Wagner<sup>c</sup>, Shabarinath S. Nair<sup>c</sup>, Oleksandra Oliinyk<sup>b</sup>, Enguerran Belles<sup>c</sup>, Ayman Bibih<sup>c</sup>, Léna D'Harboullé<sup>c</sup>, Hamza Bendhamane<sup>c</sup>, Manav Gupta<sup>c</sup>,<sup> </sup>Inbal Becker-Reshef<sup>b,c</sup></p> <p><sup>a</sup>School of Earth, Atmosphere and Environment, Monash University, Clayton, 3800, Victoria, Australia.</p> <p><sup>b</sup>Department of Geographical Sciences, University of Maryland, College-Park, 20742, Maryland, USA</p> <p><sup>c</sup>ICube, University of Strasbourg, Illkirch-Graffenstaden, 67400, France</p> <p>* Corresponding author: Yuval Sadeh (yuval.sadeh@monash.edu)</p> </div> <div> </div> <h2><strong>Information:</strong></h2> <p>These files present a comprehensive dataset of delineated field boundaries across Ukraine, generated using time-series of high-resolution satellite imagery. The delineation process employs Planet’s bi-weekly image composites at 3-meter spatial resolution, processed through Google Earth Engine (GEE), resulting in the delineation of over 5 million fields, covering the years 2021 to 2023. The accuracy of these delineations was evaluated using the Intersection over Union (IoU) metric, against a reference dataset of 12,331 field boundaries derived from combine harvesters equipped with GPS technology, achieving median IoU of 0.77 for the delineated field boundaries across all Oblasts and years.</p>
Ukraine Field Boundary Delineation 2021
<div><strong>Authors:</strong></div> <div> <p>Yuval Sadeh<sup>a,b,*</sup>, <sup> </sup>Josef Wagner<sup>c</sup>, Shabarinath S. Nair<sup>c</sup>, Alex Olin<sup>b</sup>, Enguerran Belles<sup>c</sup>, Ayman Bibih<sup>c</sup>, Léna D'Harboullé<sup>c</sup>, Hamza Bendhamane<sup>c</sup>, Manav Gupta<sup>c</sup>,<sup> </sup>Inbal Becker-Reshef<sup>b,c</sup></p> <p><sup>a</sup>School of Earth, Atmosphere and Environment, Monash University, Clayton, 3800, Victoria, Australia.</p> <p><sup>b</sup>Department of Geographical Sciences, University of Maryland, College-Park, 20742, Maryland, USA</p> <p><sup>c</sup>ICube, University of Strasbourg, Illkirch-Graffenstaden, 67400, France</p> <p>* Corresponding author: Yuval Sadeh (yuval.sadeh@monash.edu)</p> </div> <div> </div> <div><strong>Information:</strong></div> <p>These files present a comprehensive dataset of delineated field boundaries across Ukraine, generated using time-series of high-resolution satellite imagery. The delineation process employs Planet’s bi-weekly image composites at 3-meter spatial resolution, processed through Google Earth Engine (GEE), resulting in the delineation of over 5 million fields, covering the years 2021 to 2023. The accuracy of these delineations was evaluated using the Intersection over Union (IoU) metric, against a reference dataset of 12,331 field boundaries derived from combine harvesters equipped with GPS technology, achieving median IoU of 0.77 for the delineated field boundaries across all Oblasts and years.</p>
The synthetic and field seismic datasets for "ClinoformNet-1.0: stratigraphic forward modeling and deep learning for seismic clinoform delineation"
<p>This is the synthetic and field siesmic dataset used in manuscript "Three-Dimensional Implicit Structural Modeling Using Convolutional Neural Network". The dimensions of the large-scale and small-scale synthetic seismic datasets are 1600×256 pixels and 900×256 pixels. The field seismic data contains the subsets of the F3 Block, Australia Poseidon, Alaska North Slope seismic data.</p>
Ukraine Field Boundary Delineation 2023
<h2><strong>Authors:</strong></h2> <div> <p>Yuval Sadeh<sup>a,b,*</sup>, <sup> </sup>Josef Wagner<sup>c</sup>, Shabarinath S. Nair<sup>c</sup>, Oleksandra Oliinyk<sup>b</sup>, Enguerran Belles<sup>c</sup>, Ayman Bibih<sup>c</sup>, Léna D'Harboullé<sup>c</sup>, Hamza Bendhamane<sup>c</sup>, Manav Gupta<sup>c</sup>,<sup> </sup>Inbal Becker-Reshef<sup>b,c</sup></p> <p><sup>a</sup>School of Earth, Atmosphere and Environment, Monash University, Clayton, 3800, Victoria, Australia.</p> <p><sup>b</sup>Department of Geographical Sciences, University of Maryland, College-Park, 20742, Maryland, USA</p> <p><sup>c</sup>ICube, University of Strasbourg, Illkirch-Graffenstaden, 67400, France</p> <p>* Corresponding author: Yuval Sadeh (yuval.sadeh@monash.edu)</p> </div> <div> </div> <h2><strong>Information:</strong></h2> <p>These files present a comprehensive dataset of delineated field boundaries across Ukraine, generated using time-series of high-resolution satellite imagery. The delineation process employs Planet’s bi-weekly image composites at 3-meter spatial resolution, processed through Google Earth Engine (GEE), resulting in the delineation of over 5 million fields, covering the years 2021 to 2023. The accuracy of these delineations was evaluated using the Intersection over Union (IoU) metric, against a reference dataset of 12,331 field boundaries derived from combine harvesters equipped with GPS technology, achieving median IoU of 0.77 for the delineated field boundaries across all Oblasts and years.</p>
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