Evaluating NDVI Data Continuity Between SPOT-VEGETATION and PROBA-V Missions for Operational Yield Forecasting in North African Countries
| Autor: | Michele Meroni, Josh Hooker, Hamid Mahyou, Myriam Haffani, Moncef Ben Moussa, Ismael Haythem, Raúl López-Lozano, Nabil Sghaier, Talhaoui Wafa, Olivier Leo, Mustapha Dali, Riad Balaghi, Mouanis Lahlou, Dominique Fasbender |
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| Přispěvatelé: | JRC Institute for Environment and Sustainability (IES), European Commission - Joint Research Centre [Ispra] (JRC), Institut national de la recherche agronomique [Maroc] (INRA Maroc), National Institute for Agricultural Research of Algeria, Partenaires INRAE, Centre National de Cartographie et de la Télédétection / National Center for Mapping and Remote Sensing [Tunisie] (CNCT), Ministère de la Défense, Tunisie, Institut Agronomique et Vétérinaire Hassan II (IAV), Environnement Méditerranéen et Modélisation des Agro-Hydrosystèmes (EMMAH), Avignon Université (AU)-Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement (INRAE) |
| Rok vydání: | 2016 |
| Předmět: |
Monitoring
010504 meteorology & atmospheric sciences Meteorology Mean squared error [SDV]Life Sciences [q-bio] Yield (finance) PROBA-V normalized difference vegetation index 0211 other engineering and technologies 02 engineering and technology crop yield forecasting sensors source transition 01 natural sciences Normalized Difference Vegetation Index Predictive models vegetation Data continuity Statistics Analytical models different spatial quality PROBA-V Missions SPOT-VEGETATION Missions Electrical and Electronic Engineering normalized difference vegetation index (NVDI) 021101 geological & geomatics engineering 0105 earth and related environmental sciences Mathematics Vegetation mapping Estimation NDVI Data Continuity Anomaly (natural sciences) crop area Agriculture Vegetation Enhanced vegetation index Project for On-Board Autonomy-Vegetation (PROBA-V) Crop monitoring crops Operational Yield Forecasting North African Countries [SDE]Environmental Sciences root mean square error General Earth and Planetary Sciences cereal yield forecasts Instruments Système Pour l'Observation de la Terre (SPOT)-VEGETATION (VGT) Forecasting |
| Zdroj: | IEEE Transactions on Geoscience and Remote Sensing IEEE Transactions on Geoscience and Remote Sensing, Institute of Electrical and Electronics Engineers, 2016, 54 (2), pp.795-804. ⟨10.1109/TGRS.2015.2466438⟩ |
| ISSN: | 1558-0644 0196-2892 |
| DOI: | 10.1109/tgrs.2015.2466438 |
| Popis: | International audience; After 15 years, the Système Pour l'Observation de la Terre (SPOT)-VEGETATION (VGT) program reached the end of its life in May 2014 and was replaced by the Project for On-Board Autonomy-Vegetation (PROBA-V) mission. Exploiting the period of overlap between instruments, this study compares the normalized difference vegetation index (NDVI) of two instruments from the point of view of the user interested in operational crop monitoring. The comparison is performed for Morocco, Algeria, and Tunisia, where NDVI is used to derive anomaly maps, temporal profiles, and cereal yield forecasts. A relevant scatter due to unexplained unsystematic variability exists between anomaly values. A mismatch between anomaly classes is observed for 20%-30% of the crop area. However, when the NDVI is averaged over cropland and administrative units to derive temporal profiles, the two data sources show a high agreement. Results for yield estimation comparison indicate an overall high agreement, and both the (null) hypotheses that the model predictions and the root mean square error (RMSE) in yield estimation are not different, when using PROBA-V instead of SPOT-VGT, cannot be rejected in all cases for Morocco and Algeria. On the contrary, in Tunisia, where RMSE is lower using PROBA-V, the hypothesis of no difference in RMSE is rejected. These findings therefore indicate that yield estimation performances are not affected (Morocco and Algeria) or improved (Tunisia) by the source transition. Finally, despite the same nominal spatial resolution, the different spatial quality of the sensors was found to have an effect on yield estimation in areas characterized by sharp transitions between cropland and desert. |
| Databáze: | OpenAIRE |
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