Felix Rembold
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Assessing drought probability for agricultural areas in Africa with coarse resolution remote sensing imagery
O Rojas, A Vrieling, F Rembold
Remote sensing of Environment 115 (2), 343-352, 2011
Using low resolution satellite imagery for yield prediction and yield anomaly detection
F Rembold, C Atzberger, I Savin, O Rojas
Remote Sensing 5 (4), 1704-1733, 2013
A comparison of global agricultural monitoring systems and current gaps
S Fritz, L See, JCL Bayas, F Waldner, D Jacques, I Becker-Reshef, ...
Agricultural systems 168, 258-272, 2019
Comparison of global and regional land cover maps with statistical information for the agricultural domain in Africa
S Fritz, L See, F Rembold
International Journal of Remote Sensing 31 (9), 2237-2256, 2010
Comparison of global land cover datasets for cropland monitoring
A Pérez-Hoyos, F Rembold, H Kerdiles, J Gallego
Remote Sensing 9 (11), 1118, 2017
Image time series processing for agriculture monitoring
H Eerens, D Haesen, F Rembold, F Urbano, C Tote, L Bydekerke
Environmental Modelling & Software 53, 154-162, 2014
ASAP: A new global early warning system to detect anomaly hot spots of agricultural production for food security analysis
F Rembold, M Meroni, F Urbano, G Csak, H Kerdiles, A Perez-Hoyos, ...
Agricultural systems 168, 247-257, 2019
Use of aerial photographs, Landsat TM imagery and multidisciplinary field survey for land-cover change analysis in the lakes region (Ethiopia)
F Rembold, S Carnicelli, M Nori, GA Ferrari
International Journal of Applied Earth Observation and Geoinformation 2 (3-4 …, 2000
Analysis of GAC NDVI data for cropland identification and yield forecasting in Mediterranean African countries
F MASELLI, F Rembold
Photogrammetric Engineering and Remote Sensing 67 (5), 593-602, 2001
Comparing land surface phenology of major European crops as derived from SAR and multispectral data of Sentinel-1 and-2
M Meroni, R d'Andrimont, A Vrieling, D Fasbender, G Lemoine, ...
Remote sensing of environment 253, 112232, 2021
Mapping the spatial distribution of winter crops at sub-pixel level using AVHRR NDVI time series and neural nets
C Atzberger, F Rembold
Remote Sensing 5 (3), 1335-1354, 2013
Historical extension of operational NDVI products for livestock insurance in Kenya
A Vrieling, M Meroni, A Shee, AG Mude, J Woodard, CK de Bie, ...
International Journal of Applied Earth Observation and Geoinformation 28 …, 2014
Near real-time vegetation anomaly detection with MODIS NDVI: Timeliness vs. accuracy and effect of anomaly computation options
M Meroni, D Fasbender, F Rembold, C Atzberger, A Klisch
Remote sensing of environment 221, 508-521, 2019
Strengthening agricultural decisions in countries at risk of food insecurity: The GEOGLAM Crop Monitor for Early Warning
I Becker-Reshef, C Justice, B Barker, M Humber, F Rembold, R Bonifacio, ...
Remote Sensing of Environment 237, 111553, 2020
A phenology-based method to derive biomass production anomalies for food security monitoring in the Horn of Africa
M Meroni, MM Verstraete, F Rembold, F Urbano, F Kayitakire
International Journal of Remote Sensing 35 (7), 2472-2492, 2014
Mapping charcoal driven forest degradation during the main period of Al Shabaab control in Southern Somalia
F Rembold, SM Oduori, H Gadain, P Toselli
Energy for Sustainable Development 17 (5), 510-514, 2013
Rapid mapping and impact estimation of illegal charcoal production in southern Somalia based on WorldView-1 imagery
M Bolognesi, A Vrieling, F Rembold, H Gadain
Energy for sustainable development 25, 40-49, 2015
Investigating the relationship between the inter-annual variability of satellite-derived vegetation phenology and a proxy of biomass production in the Sahel
M Meroni, F Rembold, MM Verstraete, R Gommes, A Schucknecht, ...
Remote Sensing 6 (6), 5868-5884, 2014
The use of MODIS data to derive acreage estimations for larger fields: A case study in the south-western Rostov region of Russia
S Fritz, M Massart, I Savin, J Gallego, F Rembold
International Journal of Applied Earth Observation and Geoinformation 10 (4 …, 2008
Yield forecasting with machine learning and small data: What gains for grains?
M Meroni, F Waldner, L Seguini, H Kerdiles, F Rembold
Agricultural and Forest Meteorology 308, 108555, 2021
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Articles 1–20