Works matching DE "AGRICULTURAL remote sensing"
Results: 319
REMOTE SENSING ANALYSIS OF BOYNTON MOUNDS COMPLEX (8PB100), PALM BEACH COUNTY, FLORIDA.
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- Florida Anthropologist, 2024, v. 77, n. 1, p. 1
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РАЗРАБОТКА АЛГОРИТМА ДОЛГОВРЕМЕННОГО УЧЕТА ИСПОЛЬЗОВАНИЯ СЕЛЬХОЗЗЕМЕЛЬ С ПРИМЕНЕНИЕМ ГИС-ТЕХНОЛОГИИ И ПРОМЕЖУТОЧНЫХ ДАННЫХ ДИСТАНЦИОННОГО ЗОНДИРОВАНИЯ
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- Journal of Geography & Environmental Management, 2024, v. 72, n. 1, p. 61, doi. 10.26577/JGEM.2024.v72.i1.05
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COMPORTAMENTO ESPECTRAL DA CANA-DE-AÇÚCAR UTILIZANDO IMAGENS SENTINEL-2.
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- Revista Foco (Interdisciplinary Studies Journal), 2023, v. 16, n. 5, p. 1, doi. 10.54751/revistafoco.v16n5-062
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Two classification methods for developing and interpreting productivity zones using site properties.
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- Plant & Soil, 2006, v. 288, n. 1/2, p. 357, doi. 10.1007/s11104-006-9126-z
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Evapotranspiration between satellite overpasses: methodology and case study in agricultural dominant semi-arid areas.
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- Meteorological Applications, 2016, v. 23, n. 4, p. 714, doi. 10.1002/met.1596
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Environmental remote sensing: beyond fundamental knowledge.
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- Ecology, 2017, v. 98, n. 10, p. 2733, doi. 10.1002/ecy.1921
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Super-resolution cropland mapping with Sentinel-2 images based on a self-training learning network.
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- Remote Sensing Letters, 2024, v. 15, n. 11, p. 1143, doi. 10.1080/2150704X.2024.2411068
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Winter wheat planting area extraction using SAR change detection.
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- Remote Sensing Letters, 2021, v. 12, n. 10, p. 951, doi. 10.1080/2150704X.2021.1951873
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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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Evaluation of the Standardized Precipitation Index as an early predictor of seasonal vegetation production anomalies in the Sahel.
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- Remote Sensing Letters, 2017, v. 8, n. 4, p. 301, doi. 10.1080/2150704X.2016.1264020
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Spectral Characterization of Avocado Persea Americana Mill. Cv. Hass Using Spectrometry and Imagery from the Visible to Near-Infrared Range.
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- Revista Tecno Lógicas, 2023, v. 26, n. 56, p. 1, doi. 10.22430/22565337.2567
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ResNet50 in remote sensing and agriculture: evaluating image captioning performance for high spectral data.
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- Environmental Earth Sciences, 2024, v. 83, n. 23, p. 1, doi. 10.1007/s12665-024-11950-2
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Potential of TerraSAR-X and Sentinel 1 imagery to map deforested areas and derive degradation status in complex rain forests of Ecuador.
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- International Forestry Review, 2017, v. 19, n. 1, p. 102, doi. 10.1505/146554817820888636
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Novel artificial intelligence assisted Landsat-8 imagery analysis for mango orchard detection and area mapping.
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- PLoS ONE, 2024, v. 19, n. 6, p. 1, doi. 10.1371/journal.pone.0304450
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Extraction and spatio-temporal analysis of phenological dates of winter wheat in north Henan Province of China from 2003 to 2018 based on MODIS NDVI time series.
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- PLoS ONE, 2024, v. 19, n. 4, p. 1, doi. 10.1371/journal.pone.0300486
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Advancing crop classification in smallholder agriculture: A multifaceted approach combining frequency-domain image co-registration, transformer-based parcel segmentation, and Bi-LSTM for crop classification.
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- PLoS ONE, 2024, v. 19, n. 2, p. 1, doi. 10.1371/journal.pone.0299350
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Concept graph construction and applied research of agricultural remote sensing.
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- International Journal of Remote Sensing, 2024, v. 45, n. 13, p. 4428, doi. 10.1080/01431161.2024.2365812
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Crop classification methods and influencing factors of reusing historical samples based on 2D-CNN.
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- International Journal of Remote Sensing, 2023, v. 44, n. 10, p. 3278, doi. 10.1080/01431161.2023.2218972
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The feasibility of using a low-cost near-infrared, sensitive, consumer-grade digital camera mounted on a commercial UAV to assess Bambara groundnut yield.
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- International Journal of Remote Sensing, 2022, v. 43, n. 2, p. 393, doi. 10.1080/01431161.2021.1974116
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Remote sensing of agriculture – South/Southeast Asia research initiative special issue.
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- International Journal of Remote Sensing, 2019, v. 40, n. 21, p. 8071, doi. 10.1080/01431161.2019.1617507
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Spatial-temporal patterns of features selected using random forests: a case study of corn and soybeans mapping in the US.
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- International Journal of Remote Sensing, 2019, v. 40, n. 1, p. 269, doi. 10.1080/01431161.2018.1512769
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Application of an unmanned aerial system for monitoring paddy productivity using the GRAMI-rice model.
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- International Journal of Remote Sensing, 2018, v. 39, n. 8, p. 2441, doi. 10.1080/01431161.2018.1425567
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A machine-learning approach to forecasting remotely sensed vegetation health.
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- International Journal of Remote Sensing, 2018, v. 39, n. 6, p. 1800, doi. 10.1080/01431161.2017.1410296
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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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Height estimation of sugarcane using an unmanned aerial system (UAS) based on structure from motion (SfM) point clouds.
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- International Journal of Remote Sensing, 2017, v. 38, n. 8-10, p. 2218, doi. 10.1080/01431161.2017.1285082
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Determining tree height and crown diameter from high-resolution UAV imagery.
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- International Journal of Remote Sensing, 2017, v. 38, n. 8-10, p. 2392, doi. 10.1080/01431161.2016.1264028
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Forestry applications of UAVs in Europe: a review.
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- International Journal of Remote Sensing, 2017, v. 38, n. 8-10, p. 2427, doi. 10.1080/01431161.2016.1252477
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Evaluation of UAV imagery for mapping Silybum marianum weed patches.
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- International Journal of Remote Sensing, 2017, v. 38, n. 8-10, p. 2246, doi. 10.1080/01431161.2016.1252475
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Using unmanned aerial vehicles for high-resolution remote sensing to map invasive Phragmites australis in coastal wetlands.
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- International Journal of Remote Sensing, 2017, v. 38, n. 8-10, p. 2199, doi. 10.1080/01431161.2016.1239288
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Development of a high-resolution aerial remote-sensing system for precision agriculture.
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- International Journal of Remote Sensing, 2017, v. 38, n. 8-10, p. 2053, doi. 10.1080/01431161.2016.1225182
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Characterizing canopy structural complexity for the estimation of maize LAI based on ALS data and UAV stereo images.
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- International Journal of Remote Sensing, 2017, v. 38, n. 8-10, p. 2106, doi. 10.1080/01431161.2016.1235300
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Use of remotely sensed data to characterize weed competition in forest plantations.
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- International Journal of Remote Sensing, 2017, v. 38, n. 8-10, p. 2448, doi. 10.1080/01431161.2016.1230290
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Detection of asbestos-containing materials in agro-ecosystem by the use of airborne hyperspectral CASI-1500 sensor including the limited use of two UAVs equipped with RGB cameras.
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- International Journal of Remote Sensing, 2017, v. 38, n. 8-10, p. 2135, doi. 10.1080/01431161.2016.1226528
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Juvenile tree classification based on hyperspectral image acquired from an unmanned aerial vehicle.
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- International Journal of Remote Sensing, 2017, v. 38, n. 8-10, p. 2273, doi. 10.1080/01431161.2016.1219076
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Blockchain: implications for Photogrammetry.
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- Photogrammetric Record, 2021, v. 36, n. 173, p. 6, doi. 10.1111/phor.12351
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A decadal record of soil moisture space–time variability over a south‐east Australian catchment.
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- Hydrological Processes, 2022, v. 36, n. 12, p. 1, doi. 10.1002/hyp.14770
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Reviewer list.
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- Soil Use & Management, 2022, v. 38, n. 3, p. 1505, doi. 10.1111/sum.12835
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先验知识融合语义特征的冬小麦田块精细提取方法.
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- Transactions of the Chinese Society of Agricultural Engineering, 2025, v. 41, n. 4, p. 164, doi. 10.11975/j.issn.1002-6819.202410212
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协同 Sentinel-2和GF-3 多特征优选的农作物识别.
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- Transactions of the Chinese Society of Agricultural Engineering, 2025, v. 41, n. 4, p. 153, doi. 10.11975/j.issn.1002-6819.202406135
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基于物候特征的时序 SAR 水稻指数构建及验证.
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- Transactions of the Chinese Society of Agricultural Engineering, 2024, v. 40, n. 20, p. 157, doi. 10.11975/j.issn.1002-6819.202405053
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2000－2020 年湖北省耕地利用集约化和规模化耦合协调特征.
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- Transactions of the Chinese Society of Agricultural Engineering, 2023, v. 39, n. 3, p. 219, doi. 10.11975/j.issn.1002-6819.202209250
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基于无人机图像混合像元分解模型提高小麦基本苗数的反演精度.
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- Transactions of the Chinese Society of Agricultural Engineering, 2022, v. 38, n. 17, p. 142, doi. 10.11975/j.issn.1002-6819.2022.17.015
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基于时空滤波 Sentinel-1 时序数据的田块尺度岭南作物分布提取.
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- Transactions of the Chinese Society of Agricultural Engineering, 2022, v. 38, n. 5, p. 158, doi. 10.11975/j.issn.1002-6819.2022.05.019
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基于点特征检测的农业航空遥感图像配准算法.
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- Transactions of the Chinese Society of Agricultural Engineering, 2020, v. 36, n. 3, p. 71, doi. 10.11975/j.issn.1002-6819.2020.03.009
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基于卷积神经网络和重排序的农业遥感图像检索.
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- Transactions of the Chinese Society of Agricultural Engineering, 2019, v. 35, n. 17, p. 138, doi. 10.11975/j.issn.1002-6819.2019.15.018
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基于时序Landsat 遥感数据的新疆开孔河流域农作物类型识别.
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- Transactions of the Chinese Society of Agricultural Engineering, 2019, v. 35, n. 16, p. 180, doi. 10.11975/j.issn.1002-6819.2019.16.020
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基于卷积神经网络和重排序的农业遥感图像检索.
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- Transactions of the Chinese Society of Agricultural Engineering, 2019, v. 35, n. 15, p. 138, doi. 10.11975/j.issn.1002-6819.2019.15.018
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Artificial Intelligence (AI) as a complementary technology for agricultural Remote Sensing (RS) in plant physiology teaching.
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- REiDoCrea: Revista Electrónica de Investigación y Docencia Creativa, 2022, v. 11, p. 695
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Potential utilization of satellite remote sensing for field-based agricultural studies.
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- Chemical & Biological Technologies in Agriculture, 2021, v. 8, n. 1, p. 1, doi. 10.1186/s40538-021-00253-4
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Bridging the Gap Between Remote Sensing and Plant Phenotyping—Challenges and Opportunities for the Next Generation of Sustainable Agriculture.
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- Frontiers in Plant Science, 2021, v. 12, p. 1, doi. 10.3389/fpls.2021.749374
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