Search Results - Sense data.

Monitoring the grasslands of the Sahel using NOAA AVHRR data: Niger 1983 by Justice, C.O., Hiernaux, Pierre H.Y.

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Harnessing net primary productivity data for monitoring sustainable development of agriculture by Robinson, Nathaniel P., Cox, Cindy M., Koo, Jawoo

“…This study was undertaken to assess the utility of remotely sensed net primary productivity (NPP) data to measure agricultural sustainability by applying a new methodology that captures spatial variability and trends in total NPP and in NPP removed at harvest. …”
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The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data by Pastorello, G., Trotta, C., Canfora, E., Chu, H., Christianson, D., Cheah, Y., Poindexter, C., Chen, J., Elbashandy, A., Humphrey, M., Merbold, Lutz

“…These sites, independently managed and operated, voluntarily contributed their data to create global datasets. Data were quality controlled and processed using uniform methods, to improve consistency and intercomparability across sites. …”
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Advancing water security in Africa with new high-resolution discharge data by Akpoti, Komlavi, Velpuri, Naga Manohar, Mizukami, N., Kagone, S., Leh, Mansoor, Mekonnen, Kirubel, Owusu, Afua, Tinonetsana, Primrose, Phiri, Michael, Madushanka, Lahiru, Perera, Tharindu, Paranamana, Thilina Prabhath, Parrish, G. E. L., Senay, G. B., Seid, Abdulkarim

“…VegDischarge v1, which covers over 64,000 river segments in Africa, is a natural river discharge dataset produced by coupled modeling; the agro-hydrologic VegET model and the mizuRoute routing model for the period 2001-2021. Using remote sensing data and hydrological modeling system, the 1-km runoff field simulated by VegET, was routed with mizuRoute. …”
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Global transpiration data from sap flow measurements: the SAPFLUXNET database by Poyatos, Rafael, Granda, Víctor, Flo, Víctor, Adams, Mark A., Adorján, Balázs, Aguadé, David, Aidar, Marcos P. M., Allen, Scott, Alvarado-Barrientos, M. Susana, Anderson-Teixeira, Kristina J., Peri, Pablo Luis, Martínez-Vilalta, Jordi

“…The “sapfluxnetr” R package – designed to access, visualize, and process SAPFLUXNET data – is available from CRAN.…”
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Tree Biomass Equations from Terrestrial LiDAR: A Case Study in Guyana by Lau, A., Calders, K., Bartholomeus, H., Martius, C., Raumonen, P., Herold, M., Vicari, M., Sukhdeo, H., Singh, J., Goodman, R.C.

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Spatiotemporal trends in Anopheles funestus breeding habitats by Aduvukha, G.R., Abdel-Rahman, E.M., Mudereri, B.T., Mutanga, O., Odindi, J., Tonnang, H.E.

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Banana mapping in heterogenous smallholder farming systems using high-resolution remote sensing imagery and machine learning models with implications for banana bunchy top disease... by Alabi, T.R., Adewopo, Julius, Duke, O.P., Kumar, P. Lava

“…The ML algorithms performed comparatively well in classifying the land cover, with a mean overall accuracy (OA) of about 93% and a Kappa coefficient (KC) of 0.89 for the UAV data. The model using fused SAR and Sentinel 2A data gave an OA of 90% and KC of 0.86. …”
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Climate Change Vulnerability Assessment in Mangrove-Dependent Communities of Manoka Island, Littoral Region of Cameroon by Fongnzossie, E., Sonwa, D.J., Mbevo, P., Kentatchime, F., Mokam, A., Tatuebu Tagne, C., Rim, L.F.E.A.

“…We used household surveys, focus group discussions, field observation, GIS, and remote sensing to collect data on variables of exposure, sensitivity, and adaptive capacity. …”
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Multimodal data integration to model, predict, and understand changes in plant biodiversity : a systematic review by Martinez, Emilce Soledad, Tejada-Gutiérrez, Eva, Sorribas, Albert, Mateo-Fornes, Jordi, Solsona, Francesc, Defacio, Raquel Alicia, Alves, Rui

“…The integration of multimodal data to analyze, model, and predict changes in plant biodiversity is critical for addressing global conservation challenges. …”
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Garden pea: agronomic, color and quality characterization using morphological and molecular data by Esposito, Maria Andrea, Bermejo, Carolina J., Guindon, María Fernanda, Palacios Martínez, Laura Tatiana, Gatti, Ileana

“…A Cluster analysis combining morphological and molecular data allowed the formation of five highly differentiated groups regarding expressed and underlying variability. …”
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A global dataset of crowdsourced land cover and land use reference data by Fritz, Steffen, See, Linda, Perger, Christoph, McCallum, Ian, Schill, Christian, Schepaschenko, Dmitry, Duerauer, Martina, Karner, Mathias, Dresel, Christopher, Laso-Bayas, Juan-Carlos, Lesiv, Myroslava, Moorthy, Inian, Salk, Carl F., Danylo, Ohla, Sturn, Tobias, Albrecht, Franziska, You, Liangzhi, Kraxner, Florian, Obersteiner, Michael

“…Global land use is typically more difficult to map and in many cases cannot be remotely sensed. In-situ or ground-based data and high resolution imagery are thus an important requirement for producing accurate land cover and land use datasets and this is precisely what is lacking. …”
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Comparison of ERS wind-scatterometer and SSM/I data for Sahelian vegetation monitoring by Frison, P.L., Mougin, E., Jarlan, L., Karam, M.A., Hiernaux, Pierre H.Y.

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Modelling agricultural drought: a review of latest advances in big data technologies by Houmma, I.H., El Mansouri, L., Gadal, S., Garba, M., Hadria, R.

“…This article reviews the main recent applications of multi-sensor remote sensing and Artificial Intelligence techniques in multivariate modelling of agricultural drought. …”
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Evaluation of hydrologic impact of an irrigation curtailment program using Landsat satellite data by Velpuri, Naga Manohar, Senay, Gabriel B., Schauer, M., Garcia, C. A., Singh, R.K., Friedrichs, M., Kagone, S., Haynes, J., Conlon, T.

“…Geological Survey station discharge data to evaluate the hydrologic impact of the curtailment program. …”
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On the Ground or in the Air? A Methodological Experiment on Crop Residue Cover Measurement in Ethiopia by Kosmowski, Frederic, Stevenson, James R., Campbell, J., Ambel, A.A., Tsegay, A. H.

Subjects: “…data…”
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Yield prediction, pest and disease diagnosis, soil fertility mapping, precision irrigation scheduling, and food quality assessment using machine learning and deep learning algorith... by Ajith, S., Vijayakumar, S., Elakkiya, N.

Subjects: “…data analysis…”
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In-person training report on: Agronomic Data Collection and Management Tools for Better Agricultural Research by Attaher, Samar, Atassi, Layal, Al-Shamaa, Khaled, Devkota Wasti, Mina Kumari, Nangia, Vinay

“…In this sense, it is critical to improve agronomic research techniques and contexts by utilizing digital tools that enable researchers to develop and share digital data sets that are FAIR (findable, accessible, interoperable, and reusable). …”
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A global benchmark map of water productivity for rainfed and irrigated wheat by Zwart, Sander J., Bastiaanssen, Wim G.M., Fraiture, Charlotte de, David, S.

“…The WATPRO water productivity model for wheat, using remote sensing data products as input, was applied at a global scale with global data sets of the NDVI and surface albedo to benchmark water productivity of wheat for the beginning of this millennium. …”
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Interpretation of climate change and agricultural adaptations by local household farmers: A case study at Bin County, Northeast China by Yu, Qiang-Yi, Wu, Wen-Bin, Liu, Zhen-Huan, Verburg, Peter H., Xia, Tian, Yang, Peng, Lu, Zhong-Jun, You, Liangzhi, Tang, Hua-Jun

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