Zobrazeno 1 - 10 of 42 pro vyhledávání: '"Hendrik Boogaard"'
1
Akademický článek
TRANCO: Thermo radiometric normalization of crop observations
Autor: Juanma Cintas, Belen Franch, Kristof Van-Tricht, Hendrik Boogaard, Jeroen Degerickx, Inbal Becker-Reshef, Italo Moletto-Lobos, Bertran Mollà-Bononad, Jose A. Sobrino, Sven Gilliams, Zoltan Szantoi
Publikováno v: International Journal of Applied Earth Observations and Geoinformation, Vol 118, Iss , Pp 103283- (2023)
Crop type maps are essential for a wide range of applications such as crop monitoring, and yield estimation. In addition, Earth Observation (EO) systems allow robust and timely mapping of the earth’s surface, usually based on time-series. Yet, exis
Externí odkaz: https://doaj.org/article/caef17ebc6d949f88af2ac0679e2c77b
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2
Akademický článek
Building a community-based open harmonised reference data repository for global crop mapping.
Autor: Hendrik Boogaard, Arun Kumar Pratihast, Juan Carlos Laso Bayas, Santosh Karanam, Steffen Fritz, Kristof Van Tricht, Jeroen Degerickx, Sven Gilliams
Publikováno v: PLoS ONE, Vol 18, Iss 7, p e0287731 (2023)
Reference data is key to produce reliable crop type and cropland maps. Although research projects, national and international programs as well as local initiatives constantly gather crop related reference data, finding, collecting, and harmonizing da
Externí odkaz: https://doaj.org/article/6056ee0a70af48e58eb69e1675bafe1d
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3
Akademický článek
A weakly supervised framework for high-resolution crop yield forecasts
Autor: Dilli Paudel, Diego Marcos, Allard de Wit, Hendrik Boogaard, Ioannis N Athanasiadis
Publikováno v: Environmental Research Letters, Vol 18, Iss 9, p 094062 (2023)
Predictor inputs and labels (e.g. yield data) for crop yield forecasting are not always available at the same spatial resolution. Common statistical and machine learning methods require inputs and labels at the same resolution. Therefore, they cannot
Externí odkaz: https://doaj.org/article/a12310aa6c09406e92f2d96ad3591118
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4
Akademický článek
Evaluation of Satellite Rainfall Estimates for Drought and Flood Monitoring in Mozambique
Autor: Carolien Toté, Domingos Patricio, Hendrik Boogaard, Raymond van der Wijngaart, Elena Tarnavsky, Chris Funk
Publikováno v: Remote Sensing, Vol 7, Iss 2, Pp 1758-1776 (2015)
Satellite derived rainfall products are useful for drought and flood early warning and overcome the problem of sparse, unevenly distributed and erratic rain gauge observations, provided their accuracy is well known. Mozambique is highly vulnerable to
Externí odkaz: https://doaj.org/article/3fd077473a9845c6a21795405c395b24
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7
Harmonize open in-situ data for better re-use
Autor: Hendrik Boogaard, Ian McCallum, Sven Gilliams, Laurent Tits, Sander Janssen, Florian Franziskakis, Ian Jarvis
Open and harmonized in-situ data is crucial for monitoring agricultural resources, addressing food security, and fostering resources sustainability. It will improve capacity to produce relevant, timely, accurate and updated information on agricultura
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::253a9bdfc2d0fc4be0c8851fa3d57432
https://doi.org/10.5194/egusphere-egu23-8850
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8
Toward effective communication of agrometeorological services
Autor: Tanja Cegnar, Hendrik Boogaard, Klara Finkele, Branislava Lalic, Joanna Raymond, Saskia Lifka, David M. Schultz, Vieri Tarchiani
Publikováno v: Advances in Science and Research 20 (2023)
Advances in Science and Research, 20, 9–16
Cegnar, T, Boogaard, H, Finkele, K, Lalic, B, Raymond, J, Lifka, S, Schultz, D M & Tarchiani, V 2023, ' Toward effective communication of agrometeorological services ', Advances in Science and Research, vol. 20, pp. 9-16 . https://doi.org/10.5194/asr-20-9-2023
Agrometeorological services are a subset of climate services targeted to support farmers' tactical and strategic decisions, with the potential to support farmers' capacity to cope with climate variability and change, as well as strengthen their resil
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8f5ec8d4fe0b7333d00a623a0783703c
https://research.wur.nl/en/publications/toward-effective-communication-of-agrometeorological-services
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9
Interpretability of deep learning models for crop yield forecasting
Autor: Dilli Paudel, Allard de Wit, Hendrik Boogaard, Diego Marcos, Sjoukje Osinga, Ioannis N. Athanasiadis
Publikováno v: Computers and Electronics in Agriculture, 206
Computers and Electronics in Agriculture 206 (2023)
Machine learning models for crop yield forecasting often rely on expert-designed features or predictors. The effectiveness and interpretability of these handcrafted features depends on the expertise of the people designing them. Neural networks have
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7f2386ec5031ee03388797505484f75d
https://doi.org/10.1016/j.compag.2023.107663
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10
Machine learning for regional crop yield forecasting in Europe
Autor: Dilli Paudel, Hendrik Boogaard, Allard de Wit, Marijn van der Velde, Martin Claverie, Luigi Nisini, Sander Janssen, Sjoukje Osinga, Ioannis N. Athanasiadis
Publikováno v: Field Crops Research 276 (2022)
Field Crops Research, 276
Crop yield forecasting at national level relies on predictors aggregated from smaller spatial units to larger ones according to harvested crop areas. Such crop areas come from land cover maps or reported statistics, both of which can have errors and
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4a600ac711b584e743e37bc1954576fa
https://research.wur.nl/en/publications/machine-learning-for-regional-crop-yield-forecasting-in-europe
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