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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
2013
Remote Sensing
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NDVI/yield linear regressions for cereals in North Africa (from Maselli and Rembold [46] ; modified). ...
doi:10.3390/rs5115572
fatcat:2jlkn7xitrhordod2jr44t2qoq
Image time series processing for agriculture monitoring
2014
Environmental Modelling & Software
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A review on this evolution has been published by Rembold et al. (2013) . ...
doi:10.1016/j.envsoft.2013.10.021
fatcat:dw7lisfduvblzih2sbdzyxoov4
Comparison of Global Land Cover Datasets for Cropland Monitoring
2017
Remote Sensing
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Author Contributions: Ana Pérez-Hoyos and Felix Rembold conceived and designed the research; Ana Pérez-Hoyos conducted the analysis and drafted the manuscript; Javier Gallego provided some essential suggestions ...
Author Contributions: Ana Pérez-Hoyos and Felix Rembold conceived and designed the research; Ana Pérez-Hoyos conducted the analysis and drafted the manuscript; Javier Gallego provided some essential suggestions ...
doi:10.3390/rs9111118
fatcat:mewedztngfduvgajmwhu67ffm4
Using Low Resolution Satellite Imagery for Yield Prediction and Yield Anomaly Detection
2013
Remote Sensing
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The two failed seasons in 2010 and 2011
Figure 4 . 4 NDVI/yield linear regressions for cereals in North Africa (from Maselli and Rembold
Figure 7 . 7 Simplified scheme of a crop process model. ...
For example, Maselli and Rembold [46] used the regression between historical yields and cumulated NDVI at pixel level to derive fractions of agricultural area by pixel and restrict yield estimates to ...
doi:10.3390/rs5041704
fatcat:oq23wwe56vbzfeobfaaadfw2jq
Nlcam modulates midline convergence during anterior neural plate morphogenesis
2010
Developmental Biology
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., 2006; Rembold et al., 2006b) . ...
Injection and transplantation Injections were carried out essentially as described [Rembold] . ...
doi:10.1016/j.ydbio.2009.12.003
pmid:20005219
fatcat:fgd6sgz6fzfk7aq6sagfwrb4ny
Portability of neural nets modelling regional winter crop acreages using AVHRR time series
2012
European Journal of Remote Sensing
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The current paper is an extension and refinement of the mentioned conference papers Rembold, 2009, 2010] . ...
Alternative architectures with larger number of hidden nodes were evaluated [Atzberger and Rembold, 2009 ] but resulted in overfitting problems. ...
doi:10.5721/eujrs20124532
fatcat:mizyvljwifhv5gnse3vqmrlwru
Yield forecasting with machine learning and small data: what gains for grains?
[article]
2021
arXiv
pre-print
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., 2016; Rembold et al., 2013) . ...
provided in a ready-to-use format by the Anomaly hotSpot of Agricultural Production (ASAP) early warning decision support system of the European Commission Joint Research Centre (Meroni et al., 2019b; Rembold ...
arXiv:2104.13246v2
fatcat:2htwmdrpojcf7m5h3gjsehgwo4
Mapping the Spatial Distribution of Winter Crops at Sub-Pixel Level Using AVHRR NDVI Time Series and Neural Nets
2013
Remote Sensing
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In-season predictions improved compared to the work of Rembold and Maselli [20, 21] using the same dataset and linear prediction models. ...
Several studies by Rembold and Maselli used Spectral Angular Mapping (SAM) for measuring inter-annual crop area changes based on NDVI time series from NOAA-AVHRR [20, 21] . ...
doi:10.3390/rs5031335
fatcat:ccl6xdizz5gwbghbz4s22ze2t4
Near real-time vegetation anomaly detection with MODIS NDVI: Timeliness vs. accuracy and effect of anomaly computation options
2019
Remote Sensing of Environment
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Rojas et al., 2011; Sepulcre-Canto et al., 2012; Rembold et al., 2018) . ...
Rembold Table 3 Ranked concordance between anomaly classes of NxHy and NfHf. ...
doi:10.1016/j.rse.2018.11.041
pmid:30774156
pmcid:PMC6360378
fatcat:skzxhrseyve5rbzsfsbuxtnry4
Evaluation of the Standardized Precipitation Index as an early predictor of seasonal vegetation production anomalies in the Sahel
2016
Remote Sensing Letters
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Mitigating drought impacts requires timely and locationspecific information on drought occurrence to ensure appropriate responses (Rembold et al. 2016) . ...
To keep the time frame consistent for the two RFE sources, we computed the SPI using data for the 1983-2013 period, using the SPIRITS software (Rembold et al., 2015) . ...
doi:10.1080/2150704x.2016.1264020
fatcat:jwldzc7sg5aqxm5piq4ftx4vru
Mapping areas invaded by Prosopis juliflora in Somaliland on Landsat 8 imagery
2015
Remote Sensing for Agriculture, Ecosystems, and Hydrology XVII
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Prosopis juliflora is a fast growing tree species originating from South and Central America with a high invasion potential in semi-arid areas around the globe. It was introduced to East Africa for the stabilization of dune systems and for providing fuel wood after prolonged droughts and deforestation in the 1970s and 1980s. In many dry lands in East Africa the species has expanded rapidly and has become challenging to control. The species generally starts its colonization on deep soils with
doi:10.1117/12.2193133
fatcat:dw2pqcem3vcclewtfr2y6qlu2q
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... h water availability while in later stages or on poorer soils, its thorny thickets expand into drier grasslands and rangelands. Abandoned or low input farmland is also highly susceptible for invasion as P. juliflora has competitive advantages to native species and is extremely drought tolerant. In this work we describe a rapid approach to detect and map P. juliflora invasion at country level for the whole of Somaliland. Field observations were used to delineate training sites for a supervised classification of Landsat 8 imagery collected during the driest period of the year (i.e., from late February to early April). The choice of such a period allowed to maximise the spectral differences between P. juliflora and other species present in the area, as P. juliflora tends to maintain a higher vigour and canopy water content than native vegetation, when exposed to water stress. The results of our classification map the current status of invasion of Prosopis in Somaliland showing where the plant is invading natural vegetation or agricultural areas. These results have been verified for two spatial subsets of the whole study area with very high resolution (VHR) imagery, proving that Landsat 8 imagery is highly adequate to map P. juliflora. The produced map represents a baseline for understanding spatial distribution of P. juliflora across Somaliland but also for change detection and monitoring of long term dynamics in support to P. juliflora management and control activities.
Remote sensing time series analysis for crop monitoring with the SPIRITS software: new functionalities and use examples
2015
Frontiers in Environmental Science
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Globally, a number of operational systems have implemented such type of analysis as recently reviewed in Rembold et al. (2013) and Atzberger (2013) . ...
The mentioned vegetation indices and biophysical variables are mainly derived from space measurements in the visible to near infrared reflected domain (Rembold et al., 2013) . ...
doi:10.3389/fenvs.2015.00046
fatcat:jcvfz2q35ffihfrw4bwx3n7pm4
Historical extension of operational NDVI products for livestock insurance in Kenya
2014
International Journal of Applied Earth Observation and Geoinformation
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Droughts induce livestock losses that severely affect Kenyan pastoralists. Recent index insurance schemes have the potential of being a viable tool for insuring pastoralists against drought-related risk. Such schemes require as input a forage scarcity (or drought) index that can be reliably updated in near realtime, and that strongly relates to livestock mortality. Generally, a long record (>25 years) of the index is needed to correctly estimate mortality risk and calculate the related
doi:10.1016/j.jag.2013.12.010
fatcat:u76musyj7baydgrf7ukgkh6fs4
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... premium. Data from current operational satellites used for large-scale vegetation monitoring span over a maximum of 15 years, a time period that is considered insufficient for accurate premium computation. This study examines how operational NDVI datasets compare to, and could be combined with the non-operational recently constructed 30-year GIMMS AVHRR record to provide a near-real time drought index with a long term archive for the arid lands of Kenya. We compared six freely available, near-real time NDVI products: five from MODIS and one from SPOT-VEGETATION. Prior to comparison, all datasets were averaged in time for the two vegetative seasons in Kenya, and aggregated spatially at the administrative division level at which the insurance is offered. The feasibility of extending the resulting aggregated drought indices back in time was assessed using jackknifed R 2 statistics (leave-one-year-out) for the overlapping period 2002-2011. We found that division-specific models were more effective than a global model for linking the division-level temporal variability of the index between NDVI products. Based on our results, good scope exists for historically extending the aggregated drought index, thus providing a longer operational record for insurance purposes. We showed that this extension may have large effects on the calculated insurance premium. Finally, we discuss several possible improvements to the drought index.
Investigating the Relationship between the Inter-Annual Variability of Satellite-Derived Vegetation Phenology and a Proxy of Biomass Production in the Sahel
2014
Remote Sensing
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Felix Rembold and Rene Gommes supported the interpretation of the correlation analysis. Anne Schucknecht and Gora Beye assisted in the analysis of ground measurements and in the validation exercise. ...
doi:10.3390/rs6065868
fatcat:ubazklahjfbkdcmxoupdkebw3e
Comparing land surface phenology of major European crops as derived from SAR and multispectral data of Sentinel-1 and -2
2021
Remote Sensing of Environment
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Phenology is also used to monitor the crop progress in food security monitoring applications (Rembold et al. 2019 ) and can represent a key indicator for the control of the European Common Agricultural ...
doi:10.1016/j.rse.2020.112232
pmid:33536689
pmcid:PMC7841528
fatcat:g2a6xd4msjblppov6yo6x4teqe
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