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Long timeseries of Earth observation data for the characterization of agricultural crops across large scales are of high interest to crop modelers, scientists, and decision makers in the fields of agricultural and environmental policy as well as crop monitoring and food security. They are particularly important for regression-based crop monitoring systems that rely on historic information. The major challenge lies in identifying pixels from satellite imagery that represent pure enough cropdoi:10.3390/rs11222668 fatcat:37cvr73npve2daw4yoiqz756bq
more »... ls. Here, we present a data-driven semi-automatic approach to identify pure pixels of two crop groups (i.e., winter and spring crops and summer crops) based on a MODIS–NDVI timeseries. We applied this method to the European Union at a 250 m spatial resolution. Pre-processed and smoothed, daily normalized difference vegetation index (NDVI) data (2001–2017) were used to first extract the phenological data. To account for regional characteristics (varying climate, agro-management, etc.), these data were clustered by administrative units and by year using a Gaussian mixture model. The number of clusters was pre-defined using data from regional agricultural acreage statistics. After automatic labelling, clusters were filtered based on agronomic knowledge and phenological information extracted from the same timeseries. The resulting pure pixels were validated with two different datasets, one based on high-resolution Sentinel-2 data (5 sites, 2 years) and one based on a regional crop map (1 site, 7 years). For the winter and spring crop class, pixel purity amounted to 93% using the first validation dataset and to 73% using the second one, averaged over the different years. For summer crops, the respective values were 61% (91% without one critical validation site) and 72%. The phenological analyses revealed a clear trend towards an earlier NDVI peak (approximately −0.28 days/year) for winter and spring crops across Europe. We expect that this dataset will be useful for various applications, from crop model calibration to operational crop monitoring and yield forecasting.
Pietro Alessandro Brivio, Laure Hossard and Giacinto Manfron tested and provided the feedback for the revision of the S4A smart app. Simone Sterlacchini was the main tester of the S4A geoportal. ...doi:10.3390/ijgi5120234 fatcat:xjhezfuwxrhpreseeaftzkuw4q
., Manfron, G., Lopez-Lozano, R., Baruth, B., Berg, M. v. d., Dentener, F., Ceglar, A., Chatzopoulos, T., Zampieri, M. (2019). ... (Auteur de correspondance), Belward, A., PerezDominguez, I., Naumann, G., Luterbacher, J., Cronie, O., Seguini, L., Manfron, G., Lopez-Lozano, R., Baruth, B., Berg, M. v. d., Dentener, F., Ceglar, A., ...doi:10.1029/2019ef001170 fatcat:gsmj7rlkubcchfo6k5p576nqc4
Identifying managed flooding in paddy fields is commonly used in remote sensing to detect rice. Such flooding, followed by rapid vegetation growth, is a reliable indicator to discriminate rice. Spectral indices (SIs) are often used to perform this task. However, little work has been done on determining which spectral combination in the form of Normalised Difference Spectral Indices (NDSIs) is most appropriate for surface water detection or which thresholds are most robust to separate water fromdoi:10.1371/journal.pone.0088741 pmid:24586381 pmcid:PMC3930609 fatcat:dzsyq4splzgwjfing7ydimfdqu
more »... other surfaces in operational contexts. To address this, we conducted analyses on satellite and field spectral data from an agronomic experiment as well as on real farming situations with different soil and plant conditions. Firstly, we review and select NDSIs proposed in the literature, including a new combination of visible and shortwave infrared bands. Secondly, we analyse spectroradiometric field data and satellite data to evaluate mixed pixel effects. Thirdly, we analyse MODIS data and Landsat data at four sites in Europe and Asia to assess NDSI performance in real-world conditions. Finally, we test the performance of the NDSIs on MODIS temporal profiles in the four sites. We also compared the NDSIs against a combined index previously used for agronomic flood detection. Analyses suggest that NDSIs using MODIS bands 4 and 7, 1 and 7, 4 and 6 or 1 and 6 perform best. A common threshold for each NDSI across all sites was more appropriate than locally adaptive thresholds. In general, NDSIs that use band 7 have a negligible increase in Commission Error over those that use band 6 but are more sensitive to water presence in mixed land cover conditions typical of moderate spatial resolution analyses. The best performing NDSI is comparable to the combined index but with less variability in performance across sites, suggesting a more succinct and robust flood detection method.
Land Applications of Radar Remote Sensing
Author details Andrew Nelson 1* , Mirco Boschetti 2 , Giacinto Manfron 2 , Franceco Holecz 3 , Francesco Collivignarelli 3 , Luca Gatti 3 , Massimo Barbieri 3 , Lorena Villano 1 , Parvesh Chandna 4 and ...doi:10.5772/57443 fatcat:57d7mbdvevdl3chczdunkipjq4
Remote Sensing for Agriculture, Ecosystems, and Hydrology XIV
Rice farming, one of the most important agricultural activities in the world producing staple food for nearly one-fifth of the global population, covers 153 MHa every year corresponding to a production of more than 670 Mton. Retrieve updated information on actual rice cultivated areas and on key phenological stages occurrence is fundamental to support policy makers, rice farmers and consumers providing the necessary information to increase food security and control market prices. In particular,doi:10.1117/12.974662 fatcat:ofkrjsbrefh4pnxisobxroge3e
more »... remote sensing is very important to retrieve spatial distributed information on large scale fundamental to set up operational agro-ecosystem monitoring tool. The present work wants to assess the reliability of automatic image processing algorithm for the identification of rice cultivated areas. A method, originally tested for Asian tropical rice areas, was applied on temperate European Mediterranean environment. Modifications of the method have been evaluated to adapt the original algorithm to the different experimental conditions. Finally, a novel approach based on phenological detection analysis has been tested on Northern Italy rice district. Rice detection was conducted using times series of Vegetation Indices derived by MODIS MOD09A1 products for the year 2006 and the accuracy of the maps was assessed using available thematic cartography. Error matrix analysis shows that the new proposed method, applied in a fully automatic way, is comparable to the results of the original approach when it is customized and adapted for the specific study area. The new algorithm minimizes the use of external data and provides also spatial distributed information on crop phenological stages.
Following Manfron et al.  , these minima and maxima were then evaluated with agronomically based criteria to identify which ones correspond to the transplanting and heading dates, respectively. ...doi:10.3390/rs70606535 fatcat:4nnnxzrqofhchjptpxoqufq6yy
In the eastern Democratic Republic of Congo, agriculture represents the most important economic sector, and land control can be considered a perpetual source of conflict. Knowledge of the existing production system distribution is fundamental for both informing national land tenure reforms and guiding more effective agricultural development interventions. The present paper focuses on existing agricultural production systems in Katoyi collectivity, Masisi territory, where returning Internallydoi:10.3390/land10121368 fatcat:6tiecf3cnfe47l4mk5b5q7vlmi
more »... Externally Displaced People are resettling. We aim to define a repeatable methodology for building evidence-based and updated knowledge concerning the spatial distribution of the two existing production systems: subsistence-oriented agriculture (SOA) and business-oriented agriculture (BOA). To this aim, we used a supervised object-based classification approach on remotely sensed Sentinel-2 imagery to classify land cover. To classify production systems further within the "agriculture" and "pasture" land use classes, binary classification based on an entropy value threshold was performed. An iterative approach was adopted to define the final HNDVI threshold that minimised commission and omission errors and maximised overall accuracy and class separability. The methodology achieved acceptable observed accuracy (OA equal to 80–90% respectively for agricultural and pasture areas) in the assessment. SOA and BOA respectively covered 24.4 and 75.6% of the collectivity area (34606 ha). The results conclude that land use and entropy analysis can draw an updated picture of existing land distribution among different production systems, supporting better-adapted intervention strategies in development cooperation and pro-poor agrarian land tenure reforms in conflict-ridden landscapes.
This work is based on the improvement of a recently proposed remote sensed based methodology for mapping and monitoring rice crop fields (Boschetti et al., 2009; Manfron et al., 2012a; Manfron et al., ... Applications of these methods are in the work of Zhang et al., (2003), Sakamoto et al., (2010), Manfron et al., (2012). v. ...doi:10.13130/manfron-giacinto_phd2016-01-15 fatcat:tomd3hw2pfcxnkbq7zoc3wka2i
Proceedings of 3rd International Electronic Conference on Remote Sensing
Information related to the impact of wildfire disturbances on ecosystems is of paramount interest to account for environmental loss, to plan strategies for facilitating ecosystem restoration, and to monitor the dynamics of vegetation restoration. Phenological metrics can represent a good candidate to monitor and quantify vegetation recovery after natural hazards like wildfire disturbances. Satellite observations have been demonstrated to be a suitable tool for wildfire disturbed areasdoi:10.3390/ecrs-3-06200 fatcat:6tsepgo3evfptepmgcx52jsv5y
more »... , allowing both the identification of burned areas and the monitoring of vegetation recovery. This research study aims to identify post-fire vegetation restoration dynamics for the area surrounding Naples (Italy), affected by severe wildfires events in 2017. Sentinel-2 satellite data were used to extract phenological metrics from the estimated Leaf Area Index (LAI) and to relate such metrics to environmental variables in order to evaluate the vegetation restoration and landslide susceptibility for different land use classes.
Manevski, Kiril Manferdini, Anna Maria Manfreda, Salvatore Manfron, Giacinto Mangiarotti, Sylvain Manjoro, Munyaradzi Mannaerts, Chris Manry, Michael Mantovani, Matteo Mao, Jiafu Março, Paulo ...doi:10.3390/rs70100627 fatcat:shjrih5jvjechkiyisrttr3p5u
Mancini, Francesco Manconi, Andrea Mandal, Sohom Mandanici, Emanuele Mandlburger, Gottfried Manevski, Kiril Manfreda, Salvatore Manfron, Giacinto Mansberger, Reinfried Mantas, Vasco.M. ...doi:10.3390/rs9010062 fatcat:azu3fgff4nanpinqaaalfhcwgy
Manconi, Andrea Mandava, Ajay Kumar Mandlburger, Gottfried Manevski, Kiril Manfron, Giacinto Mangiarotti, Sylvain Mani, Sneh Mann, Roger Mannaerts, Chris Mantas, Vasco M Mantovani, Matteo Manuel ...doi:10.3390/rs8010081 fatcat:tewflx4robeulikaupjih77jue
Pietro De Marinis, Giacinto Manfron, Arianna Facchi, Giorgio Provolo and Guido Sali, Remote Sensing and landsecurization in Nord Kivu, DRC 9. ...doi:10.13135/2531-8772/3674 fatcat:w7ssitc4vbhcbkzdp5tgc73piq
David García-León would like to thank Giacinto Manfron for his assistance and support in the processing of the remote sensing data used in this study. ...doi:10.3390/agronomy10060809 fatcat:sgt6qd467fffve53hzk4x4ftlq
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