Search Results - "Rembold, Felix"

Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection by Rembold, Felix, Atzberger, Clement, Savin, Igor, Rojas, Oscar

“…Low resolution satellite imagery has been extensively used for crop monitoring and yield forecasting for over 30 years and plays an important role in a growing…”
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Comparison of Global Land Cover Datasets for Cropland Monitoring by Pérez-Hoyos, Ana, Rembold, Felix, Kerdiles, Hervé, Gallego, Javier

“…Accurate and reliable information on the spatial distribution of major crops is needed for detecting possible production deficits with the aim of preventing…”
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Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets by Atzberger, Clement, Rembold, Felix

“…For large areas, it is difficult to assess the spatial distribution and inter-annual variation of crop acreages through field surveys. Such information,…”
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Automatic detection of charcoal kilns on Very High Resolution images with a computer vision approach in Somalia by Verhegghen, Astrid, Martinez-Sanchez, Laura, Bolognesi, Michele, Meroni, Michele, Rembold, Felix, Vojnović, Petar, van der Velde, Marijn

“…In Somalia’s drylands, charcoal production is a major driver of forest degradation enabled by civil conflict and institutional weakness. Up to now, the extent…”
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A new global hybrid map of annual herbaceous cropland at a 500 m resolution for the year 2019 by Fritz, Steffen, Lesiv, Myroslava, See, Linda, Schepaschenko, Dmitry, Pérez Guzmán, Katya, Laso Bayas, Juan Carlos, Schepaschenko, Maria, Georgieva, Ivelina, Collivignarelli, Francesco, Meroni, Michele, Kerdiles, Hervé, Rembold, Felix, McCallum, Ian

“…Abstract The global spatial extent of croplands is a crucial input to global and regional agricultural monitoring and modeling systems. Although many new…”
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Abstracts of Presentations to the Working Session on Improving Predictive Modeling of Mycotoxin Risk for Africa Held at the 3rd ASM2022 on 7 September 2022, in Stellenbosch, South Africa by Rembold, Felix, Mvumi, Brighton, Miller, David, Omari, Rose, Battilani, Paola, Galani, Yamdeu Joseph Hubert, Louw, Wiana, Falade, Titilayo D O, Schweiger, Wolfgang, Ermolli, Monica

“…In 2008, the African Postharvest Losses Information Systems project (APHLIS, accessed on 6 September 2022) developed an algorithm for estimating the scale of…”
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New Functionalities and Regional/National Use Cases of the Anomaly Hotspots of Agricultural Production (ASAP) Platform by Rembold, Felix, Meroni, Michele, Otieno, Viola, Kipkogei, Oliver, Mwangi, Kenneth, de Sousa Afonso, João Maria, Ihadua, Isidro Metódio Tuleni Johannes, José, Amílcar Ernesto A., Zoungrana, Louis Evence, Taieb, Amjed Hadj, Urbano, Ferdinando, Dimou, Maria, Kerdiles, Hervé, Vojnovic, Petar, Zampieri, Matteo, Toreti, Andrea

“…The Anomaly hotSpots of Agricultural Production (ASAP) Decision Support System was launched operationally in 2017 for providing timely early warning…”
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Increasing the prospective capacity of global crop and rangeland monitoring with phenology tailored seasonal precipitation forecasts by Meroni, Michele, Vojnović, Petar, Zampieri, Matteo, Materia, Stefano, Rembold, Felix, Kipkogei, Oliver, Toreti, Andrea

“…Droughts are more and more often a limiting factor to agricultural production and can have severe negative effects on food security in vulnerable countries…”
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Seasonal forecasts of the rainy season onset over Africa: Preliminary results from the FOCUS-Africa project by Zampieri, Matteo, Toreti, Andrea, Meroni, Michele, Bojovic, Dragana, Octenjak, Sara, Marcos-Matamoros, Raül, Materia, Stefano, Chang'a, Ladislaus, Merchades, Mecklina, Chaves Montero, María del Mar, Rembold, Felix, Troccoli, Alberto, Roy, Indrani, Hoteit, Ibrahim

“…Precipitation seasonality is the main factor controlling vegetation phenology in many tropical and subtropical regions. Anticipating the rain onset is of…”
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Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel by Meroni, Michele, Rembold, Felix, Verstraete, Michel M, Gommes, Rene, Schucknecht, Anne, Beye, Gora

“…In the Sahel region, moderate to coarse spatial resolution remote sensing time series are used in early warning monitoring systems with the aim of detecting…”
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Correction: Rembold, F.; Atzberger, C.; Savin, I.; Rojas, O. Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection. Remote Sens. 2013, 5, 1704-1733 by Rembold, Felix, Atzberger, Clement, Savin, Igor, Rojas, Oscar

“…Due to an oversight by the authors, in the upper graph in Figure 4 [1] only the determination coefficients for Morocco are correct. Those for the other three…”
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Near real-time vegetation anomaly detection with MODIS NDVI: Timeliness vs. accuracy and effect of anomaly computation options by Meroni, Michele, Fasbender, Dominique, Rembold, Felix, Atzberger, Clement, Klisch, Anja

“…For food crises early warning purposes, coarse spatial resolution NDVI data are widely used to monitor vegetation conditions in near real-time (NRT). Different…”
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Comparing land surface phenology of major European crops as derived from SAR and multispectral data of Sentinel-1 and -2 by Meroni, Michele, d'Andrimont, Raphaël, Vrieling, Anton, Fasbender, Dominique, Lemoine, Guido, Rembold, Felix, Seguini, Lorenzo, Verhegghen, Astrid

“…The frequent acquisitions of fine spatial resolution imagery (10 m) offered by recent multispectral satellite missions, including Sentinel-2, can resolve…”
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Yield forecasting with machine learning and small data: What gains for grains? by Meroni, Michele, Waldner, François, Seguini, Lorenzo, Kerdiles, Hervé, Rembold, Felix

“…•We present a system to forecast crop yield with machine learning at regional level.•This fully automated system selects the best features and machine learning…”
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Is deeper always better? Evaluating deep learning models for yield forecasting with small data by Sabo, Filip, Meroni, Michele, Waldner, François, Rembold, Felix

“…Predicting crop yields, and especially anomalously low yields, is of special importance for food insecure countries. In this study, we investigate a flexible…”
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Comparison of global and regional land cover maps with statistical information for the agricultural domain in Africa by Fritz, Steffen, See, Linda, Rembold, Felix

“…Achieving food security in particular in Africa continues to pose a major challenge to humankind. It is clear that the future agricultural potential of Africa…”
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ASAP: A new global early warning system to detect anomaly hot spots of agricultural production for food security analysis by Rembold, Felix, Meroni, Michele, Urbano, Ferdinando, Csak, Gabor, Kerdiles, Hervé, Perez-Hoyos, Ana, Lemoine, Guido, Leo, Olivier, Negre, Thierry

“…Monitoring crop and rangeland conditions is highly relevant for early warning and response planning in food insecure areas of the world. Satellite remote…”
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A comparison of global agricultural monitoring systems and current gaps by Fritz, Steffen, See, Linda, Bayas, Juan Carlos Laso, Waldner, François, Jacques, Damien, Becker-Reshef, Inbal, Whitcraft, Alyssa, Baruth, Bettina, Bonifacio, Rogerio, Crutchfield, Jim, Rembold, Felix, Rojas, Oscar, Schucknecht, Anne, Van der Velde, Marijn, Verdin, James, Wu, Bingfang, Yan, Nana, You, Liangzhi, Gilliams, Sven, Mücher, Sander, Tetrault, Robert, Moorthy, Inian, McCallum, Ian

“…Global and regional scale agricultural monitoring systems aim to provide up-to-date information regarding food production to different actors and decision…”
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Strengthening agricultural decisions in countries at risk of food insecurity: The GEOGLAM Crop Monitor for Early Warning by Becker-Reshef, Inbal, Justice, Christina, Barker, Brian, Humber, Michael, Rembold, Felix, Bonifacio, Rogerio, Zappacosta, Mario, Budde, Mike, Magadzire, Tamuka, Shitote, Chris, Pound, Jonathan, Constantino, Alessandro, Nakalembe, Catherine, Mwangi, Kenneth, Sobue, Shinichi, Newby, Terence, Whitcraft, Alyssa, Jarvis, Ian, Verdin, James

“…•Crop Monitor provides consensus crop assessments for countries at risk.•The goal is to reduce ambiguity in crop assessments for food security…”
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Image time series processing for agriculture monitoring by Eerens, Herman, Haesen, Dominique, Rembold, Felix, Urbano, Ferdinando, Tote, Carolien, Bydekerke, Lieven

“…Given strong year-to-year variability, increasing competition for natural resources, and climate change impacts on agriculture, monitoring global crop and…”
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