Works matching DE "AGRICULTURAL mapping"
Results: 217
河西魏晋十六国壁画墓农作图 所见农具研究.
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- Agricultural Archaeology, 2022, n. 6, p. 124
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النمذجة المكانية للعوامل الطبيعية المؤثرة على انتاج المحاصيل الزراعية في قضاء الحمدانية.
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- College of Basic Education Researches Journal, 2023, v. 19, n. 3, p. 342
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الملائمة المكانية للعوامل الطبيعية المؤثرة على انتاج المحاصيل الز ا رعية في ناحية القيارة.
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- College of Basic Education Researches Journal, 2023, v. 19, n. 1, p. 461
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Changes in Types and Area of Postharvest Flooded Fields Available to Waterbirds in Tulare Basin, California.
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- Journal of Fish & Wildlife Management, 2013, v. 4, n. 2, p. 351, doi. 10.3996/022013-JFWM-012
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Semi-empirical models for estimating canopy chlorophyll content: the importance of prior information.
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- Remote Sensing Letters, 2023, v. 14, n. 10, p. 1109, doi. 10.1080/2150704X.2023.2266119
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Temporal expansion of annual crop classification layers for the CONUS using the C5 decision tree classifier.
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- Remote Sensing Letters, 2017, v. 8, n. 4, p. 389, doi. 10.1080/2150704X.2016.1271469
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AsiaRiceMap10m: high-resolution annual paddy rice maps for Southeast and Northeast Asia from 2017 to 2019.
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- Earth System Science Data Discussions, 2021, p. 1, doi. 10.5194/essd-2021-211
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A national extent map of cropland and grassland for Switzerland based on Sentinel-2 data.
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- Earth System Science Data Discussions, 2021, p. 1, doi. 10.5194/essd-2021-60
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INTEGRATION OF GEOSPATIAL MODELS FOR AGRICULTURAL DROUGHT VULNERABILITY ASSESSMENT, NAKHON RATCHASIMA, THAILAND.
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- Suranaree Journal of Science & Technology, 2019, v. 26, n. 2, p. 121
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Enhanced crop classification through integrated optical and SAR data: a deep learning approach for multi-source image fusion.
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- International Journal of Remote Sensing, 2024, v. 45, n. 19/20, p. 7605, doi. 10.1080/01431161.2023.2232552
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Obtaining LULC distribution at 30-m resolution from Pixxel's first technology demonstrator hyperspectral imagery.
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- International Journal of Remote Sensing, 2024, v. 45, n. 14, p. 4883, doi. 10.1080/01431161.2024.2370504
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Synthetic Aperture Radar (SAR) image processing for operational space-based agriculture mapping.
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- International Journal of Remote Sensing, 2020, v. 41, n. 18, p. 7112, doi. 10.1080/01431161.2020.1754494
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Correction.
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- 2019
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- Correction Notice
Multiple kernel representation and classification of multivariate satellite-image time-series for crop mapping.
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- International Journal of Remote Sensing, 2018, v. 39, n. 1, p. 149, doi. 10.1080/01431161.2017.1381351
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UAV-derived data for mapping change on a swidden agriculture plot: preliminary results from a pilot study.
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- International Journal of Remote Sensing, 2017, v. 38, n. 8-10, p. 2066, doi. 10.1080/01431161.2017.1295487
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LA INVENCIÓN DE LA RETÍCULA URBANA: ESCUADRA Y ARADO. Una cuestión geométrica y agrícola.
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- Revista de EGA, 2017, v. 22, n. 29, p. 78, doi. 10.4995/ega.2017.7355
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LAND USE LAND COVER CHANGE MAPPING FROM SENTINEL 1B & 2A IMAGERY USING RANDOM FOREST ALGORITHM IN CÔTE D'IVOIRE.
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- Geodesy & Cartography, 2024, v. 50, n. 1, p. 43, doi. 10.3846/gac.2024.18724
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Sustainable agricultural development in Iraq: potentials and challenges.
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- Al-Anbar University Journal of Economic & Administration Sciences, 2022, v. 14, n. 4, p. 163
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Salinity risk assessment using fuzzy multiple criteria evaluation.
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- Soil Use & Management, 2017, v. 33, n. 2, p. 216, doi. 10.1111/sum.12301
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Artificial Neural Networks and Data Mining Techniques for Summer Crop Discrimination: A New Approach.
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- Canadian Journal of Remote Sensing, 2019, v. 45, n. 1, p. 16, doi. 10.1080/07038992.2019.1594734
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Assessment of soil impact on pre- and post-harvest NDVI extrema by machine learning.
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- Soil Science Annual, 2024, v. 75, n. 2, p. 1, doi. 10.37501/soilsa/189540
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Proposal of the correlation between cartographic units on the agricultural soil map and types and subtypes of Polish Soil Classification (6th edition, 2019).
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- Soil Science Annual, 2019, v. 70, n. 2, p. 98, doi. 10.2478/ssa-2019-0010
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Development of soil organic matter measurement system.
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- Soil & Water Research, 2021, v. 16, n. 3, p. 174, doi. 10.17221/18/2021-SWR
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MethaneSAT and NZ involvement.
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- Air Quality & Climate Change, 2024, v. 58, n. 2, p. 61
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- Article
Applicability of ASTER imagery in mapping land use /cover as a basis for biodiversity studies in drylands of northern Ethiopia.
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- African Journal of Ecology, 2008, v. 46, p. 19, doi. 10.1111/j.1365-2028.2008.00925.x
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Identifying suitable conservation targets: can GIS-based modelling serve to link coarse-scale complementarity to local selection of conservation sites?
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- African Journal of Ecology, 2008, v. 46, p. 109, doi. 10.1111/j.1365-2028.2008.00937.x
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The Tropical East African Urticaceae as a sample plant biodiversity study: what does it show about taxa, habitats, existing scientific collections and protected areas?
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- African Journal of Ecology, 2008, v. 46, p. 122, doi. 10.1111/j.1365-2028.2008.00940.x
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Ethnobotany and Prioritization of Some Selected Tree Species in South-western Cameroon.
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- Ethnobotany Research & Applications, 2012, v. 10, p. 235, doi. 10.17348/era.10.0.235-246
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Quantifying and Mapping the Effects of Ecosystem Services on Agricultural Competitiveness: A Case Study in Shandong Province, China.
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- Chinese Geographical Science, 2025, v. 35, n. 2, p. 326, doi. 10.1007/s11769-024-1485-y
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Irrigated Family Farming Panorama in the Latin-American Highlands.
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- Cuadernos de Desarrollo Rural, 2013, v. 10, n. 70, p. 93
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تقُُى األرضٍ انسراعُخ فٍ يُطقخ داقىق ثبستخذاو االستشعبر عٍ ثعذ RS.
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- Journal of Al-Frahids Arts, 2022, v. 14, n. 51, Part 2, p. 207
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تصممممممموم نموغي للتاورات الجوو و وممملاسمممتعمالات اارز اليرافو ني مسممممممرسمممممم ظ اسممممممت دام ن م المعلوماتالجارانو
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- Journal of Al-Frahids Arts, 2019, v. 11, n. 38, Part 1, p. 205
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السالءمة السكانية لمعهامل الطبيعية السؤثرة عمى استعساالت األرض الزراعية في ناحية بعذيقة.
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- Journal of Tikrit University for Humanities, 2021, v. 28, n. 10, p. 230, doi. 10.25130/jtuh.28.2021.11
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Cognitive Mapping in the Study of Agricultural Orality in the Apagua Community.
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- Neutrosophic Sets & Systems, 2024, v. 69, p. 102
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SERGEANT WILLIAM YANERT, CARTOGRAPHER FROM HELL.
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- Geographical Review, 2002, v. 92, n. 4, p. 582, doi. 10.2307/4140936
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Semantic Segmentation of Cabbage in the South Korea Highlands with Images by Unmanned Aerial Vehicles.
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- Applied Sciences (2076-3417), 2021, v. 11, n. 10, p. 4493, doi. 10.3390/app11104493
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Multi-scale correlations between topography and vegetation in a hillside catchment of Honduras.
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- International Journal of Geographical Information Science, 2007, v. 21, n. 2, p. 145, doi. 10.1080/13658810600852263
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Science Mapping of Meta-Analysis in Agricultural Science.
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- Information (2078-2489), 2023, v. 14, n. 11, p. 611, doi. 10.3390/info14110611
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Empirical geographic modeling of switchgrass yields in the United States.
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- GCB Bioenergy, 2010, v. 2, n. 5, p. 248, doi. 10.1111/j.1757-1707.2010.01059.x
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Generalization Enhancement Strategies to Enable Cross-Year Cropland Mapping with Convolutional Neural Networks Trained Using Historical Samples.
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- Remote Sensing, 2025, v. 17, n. 3, p. 474, doi. 10.3390/rs17030474
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Detection of the Optimal Temporal Windows for Mapping Paddy Rice Under a Double-Cropping System Using Sentinel-2 Imagery.
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- Remote Sensing, 2025, v. 17, n. 1, p. 57, doi. 10.3390/rs17010057
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Large-Scale Maize Condition Mapping to Support Agricultural Risk Management.
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- Remote Sensing, 2024, v. 16, n. 24, p. 4672, doi. 10.3390/rs16244672
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Mapping Agricultural Land in Afghanistan's Opium Provinces Using a Generalised Deep Learning Model and Medium Resolution Satellite Imagery.
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- Remote Sensing, 2023, v. 15, n. 19, p. 4714, doi. 10.3390/rs15194714
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Soil Moisture Retrieval in Bare Agricultural Areas Using Sentinel-1 Images.
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- Remote Sensing, 2023, v. 15, n. 14, p. 3502, doi. 10.3390/rs15143502
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Synergy of Sentinel-1 and Sentinel-2 Imagery for Early Seasonal Agricultural Crop Mapping.
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- Remote Sensing, 2021, v. 13, n. 23, p. 4891, doi. 10.3390/rs13234891
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Landsat-Derived Annual Maps of Agricultural Greenhouse in Shandong Province, China from 1989 to 2018.
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- Remote Sensing, 2021, v. 13, n. 23, p. 4830, doi. 10.3390/rs13234830
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Feature Fusion Approach for Temporal Land Use Mapping in Complex Agricultural Areas.
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- Remote Sensing, 2021, v. 13, n. 13, p. 2517, doi. 10.3390/rs13132517
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Mapping Seasonal Agricultural Land Use Types Using Deep Learning on Sentinel-2 Image Time Series.
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- Remote Sensing, 2021, v. 13, n. 2, p. 289, doi. 10.3390/rs13020289
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Deep Learning and Adaptive Graph-Based Growing Contours for Agricultural Field Extraction.
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- Remote Sensing, 2020, v. 12, n. 12, p. 1990, doi. 10.3390/rs12121990
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An Effective Way to Map Land-Use Intensity with a High Spatial Resolution Based on Habitat Type and Environmental Data.
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- Remote Sensing, 2020, v. 12, n. 6, p. 969, doi. 10.3390/rs12060969
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- Article