SPOT4 (Take 5) special issue is now complete

The last time I showed this post on the front page, in March, I thought the special issue about SPOT4 (Take5) was complete, but we were lacking a paper about snow cover, which came a little late, but still on time to join the special issue. The paper by Jean-Pierre Dedieu and colleagues was just released :

• On the Importance of High-Resolution Time Series of Optical Imagery for Quantifying the Effects of Snow Cover Duration on Alpine Plant Habitat
  
  by Jean-Pierre Dedieu, Bradley Z. Carlson, Sylvain Bigot, Pascal Sirguey, Vincent Vionnet and Philippe Choler
  
  

14 papers have finally been published in the SPOT4 (Take5) special issue in MDPI remote sensing. As this journal is an open access journal, all the papers may be accessed from the special issue webpage.

• Operational products validation (L1C and L2A), guess who wrote it !
  • Hagolle, O.; Sylvander, S.; Huc, M.; Claverie, M.; Clesse, D.; Dechoz, C.; Lonjou, V.; Poulain, V. SPOT-4 (Take 5): Simulation of Sentinel-2 Time Series on 45 Large Sites. Remote Sens.2015, 7, 12242-12264.
• Directional effects modeling (M.Claverie et al)
  • Claverie, M.; Vermote, E.; Franch, B.; He, T.; Hagolle, O.; Kadiri, M.; Masek, J. Evaluation of Medium Spatial Resolution BRDF-Adjustment Techniques Using Multi-Angular SPOT4 (Take5) Acquisitions. Remote Sens.2015, 7, 12057-12075.
• Gap filling of cloud affected data
  • Doxani, G.; Mitraka, Z.; Gascon, F.; Goryl, P.; Bojkov, B.R. A Spectral Unmixing Model for the Integration of Multi-Sensor Imagery: A Tool to Generate Consistent Time Series Data. Remote Sens. 2015, 7, 14000-14018.
• The Sentinel-2 project products validation using SPOT4 (Take5)
  • Bontemps, S.; Arias, M.; Cara, C.; Dedieu, G.; Guzzonato, E.; Hagolle, O.; Inglada, J.; Matton, N.; Morin, D.; Popescu, R.; Rabaute, T.; Savinaud, M.; Sepulcre, G.; Valero, S.; Ahmad, I.; Bégué, A.; Wu, B.; de Abelleyra, D.; Diarra, A.; Dupuy, S.; French, A.; ul Hassan Akhtar, I.; Kussul, N.; Lebourgeois, V.; Le Page, M.; Newby, T.; Savin, I.; Verón, S.R.; Koetz, B.; Defourny, P. Building a Data Set over 12 Globally Distributed Sites to Support the Development of Agriculture Monitoring Applications with Sentinel-2. Remote Sens. 2015, 7, 16062-16090.
• Land cover (crop type)  operational production method, tested on 12 different sites within Sen2Agri project
  • Inglada, J.; Arias, M.; Tardy, B.; Hagolle, O.; Valero, S.; Morin, D.; Dedieu, G.; Sepulcre, G.; Bontemps, S.; Defourny, P.; Koetz, B. Assessment of an Operational System for Crop Type Map Production Using High Temporal and Spatial Resolution Satellite Optical Imagery. Remote Sens.2015, 7, 12356-12379.
• Land cover (crop type) classification method based on bio-physical variables
  • Waldner, F.; Lambert, M.-J.; Li, W.; Weiss, M.; Demarez, V.; Morin, D.; Marais-Sicre, C.; Hagolle, O.; Baret, F.; Defourny, P. Land Cover and Crop Type Classification along the Season Based on Biophysical Variables Retrieved from Multi-Sensor High-Resolution Time Series. Remote Sens. 2015, 7, 10400-10424.
• Land cover (crop mask) classification when field work is not possible
  • Matton, N.,Sepulcre Canto,G.,Waldner,F.; Valero, S.;Morin,D.;Inglada, J.; Arias, M.; Bontemps, S.; Koetz, B.; Defourny, P., An Automated Method for Annual Cropland Mapping along the Season for Various Globally-Distributed Agrosystems Using High Spatial and Temporal Resolution Time Series. Remote Sens. 2015, 7, 13208-13232.
• Dynamic Land cover (crop mask) classification when field data are available
  • Valero, S.; Morin, D.; Inglada, J.; Sepulcre, G.; Arias, M.; Hagolle, O.; Dedieu, G.; Bontemps, S.; Defourny, P.; Koetz, B. Production of a Dynamic Cropland Mask by Processing Remote Sensing Image Series at High Temporal and Spatial Resolutions. Remote Sens. 2016, 8, 55.

• Biophysical variables
  • LAI, fAPAR and fCOVER estimates and validation
    • Li, W.; Weiss, M.; Waldner, F.; Defourny, P.; Demarez, V.; Morin, D.; Hagolle, O.; Baret, F. A Generic Algorithm to Estimate LAI, FAPAR and FCOVER Variables from SPOT4_HRVIR and Landsat Sensors: Evaluation of the Consistency and Comparison with Ground Measurements. Remote Sens. 2015, 7, 15494-15516.
  • Validation of a forage production index
    • Roumiguié, A.; Jacquin, A.; Sigel, G.; Poilvé, H.; Hagolle, O.; Daydé, J. Validation of a Forage Production Index (FPI) Derived from MODIS fCover Time-Series Using High-Resolution Satellite Imagery: Methodology, Results and Opportunities. Remote Sens. 2015, 7, 11525-11550.
• Irrigation
  • Saadi, S.; Simonneaux, V.; Boulet, G.; Raimbault, B.; Mougenot, B.; Fanise, P.; Ayari, H.; Lil
    
    i-Chabaane, Z. Monitoring Irrigation Consumption Using High Resolution NDVI Image Time Series: Calibration and Validation in the Kairouan Plain (Tunisia).Remote Sens. 2015, 7, 13005-13028.
• Biodiversity
  • Szulkin, M.; Zelazowski, P.; Marrot, P.; Charmantier, A. Application of High Resolution Satellite Imagery to Characterize Individual-Based Environmental Heterogeneity in a Wild Blue Tit Population. Remote Sens. 2015, 7, 13319-13336.
• Monitoring of suspended particle matter in the water of estuaries
  
  
  
  • Gernez, P.; Lafon, V.; Lerouxel, A.; Curti, C.; Lubac, B.; Cerisier, S.; Barillé, L. Toward Sentinel-2 High Resolution Remote Sensing of Suspended Particulate Matter in Very Turbid Waters: SPOT4 (Take5) Experiment in the Loire and Gironde Estuaries.Remote Sens. 2015, 7, 9507-9528.