Exploitation of SAR and optical sentinel data to detect rice crop and estimate seasonal dynamics of leaf area index

Remote Sensing

Volumn 9, Issue 3, 2017, Pages

  Campos Taberner, Manuel ^a   García Haro, Francisco Javier ^a   Camps Valls, Gustau ^a   Grau Muedra, Gonçal ^a   Nutini, Francesco ^b   Busetto, Lorenzo ^b   Katsantonis, Dimitrios ^c   Stavrakoudis, Dimitris ^d   Minakou, Chara ^d   Gatti, Luca ^e   Barbieri, Massimo ^e   Holecz, Francesco ^e   Stroppiana, Daniela ^b   Boschetti, Mirco ^b  

^a UNIVERSITY OF VALENCIA   (Spain)

^b INSTITUTE FOR ELECTROMAGNETIC SENSING OF THE ENVIRONMENT   (Italy)

^c INSTITUTE OF TECHNOLOGY OF AGRICULTURAL PRODUCTS   (Greece)

^d ARISTOTLE UNIVERSITY OF THESSALONIKI   (Greece)

^e Sarmap   (Switzerland)

Author keywords

Gaussian process regression; Leaf area index (LAI); Rice map; Sentinel 1A; Sentinel 2A

Indexed keywords

GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); RADIATIVE TRANSFER; TIME SERIES;

GAUSSIAN PROCESS REGRESSION; GROUND MEASUREMENTS; LEAF AREA INDEX; RADIATIVE TRANSFER MODEL; SENTINEL-1; SENTINEL-2A; SPATIAL CONSISTENCY; TEMPORAL CONSISTENCY;

CROPS;

EID: 85019346314     PISSN: None     EISSN: 20724292     Source Type: Journal    
DOI: 10.3390/rs9030248     Document Type: Article

References (51)

1. Gobron, N.; Verstraete, M. ECV T11: Leaf Area Index. In Assessment of the Status of the Development of Standards for the Terrestrial Essential Climate Variables; FAO: Rome, Italy, 2009.
2. Chen, J.M.; Black, T.A. Defining leaf area index for non-flat leaves. Plant Cell Environ. 1992, 15, 421-429.
3. The Global Climate Observing System (GCOS). Systematic Observation Requirements for Satellite-Based Productsfor Climate; GCOS: Geneva, Switzerland, 2011.
4. Busetto, L.; Casteleyn, S.; Granell, C.; Pepe, M.; Barbieri, M.; Campos-Taberner, M.; Casa, R.; Confalonieri, R.; Crema, A.; García-Haro, F.J.; et al. Downstream services for rice crop monitoring in Europe: From regional to local scale. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2017, accepted.
5. Baret, F.; Houlès, V.; Guérif, M. Quantification of plant stress using remote sensing observations and crop models: The case of nitrogen management. J. Exp. Bot. 2007, 58, 869.
6. Aboelghar, M.; Arafat, S.; Yousef, M.A.; El-Shirbeny, M.; Naeem, S.; Massoud, A.; Saleh, N. Using SPOT data and leaf area index for rice yield estimation in Egyptian Nile delta. Egypt. J. Remote Sens. Space Sci. 2011, 14, 81-89.
7. Confalonieri, R.; Rosenmund, A.S.; Baruth, B. An improved model to simulate rice yield. Agron. Sustain. Dev. 2009, 29, 463-474.
8. Launay, M.; Guerif, M. Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications. Agric. Ecosyst. Environ. 2005, 111, 321-339.
9. Mosleh, M.K.; Hassan, Q.K.; Chowdhury, E.H. Application of Remote Sensors in Mapping Rice Area and Forecasting Its Production: A Review. Sensors 2015, 15, 769-791.
10. Dorigo, W.; Zurita-Milla, R.; de Wit, A.; Brazile, J.; Singh, R.; Schaepman, M. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. Int. J. Appl. Earth Obs. Geoinf. 2007, 9, 165-193.
11. Curnel, Y.; de Wit, A.J.; Duveiller, G.; Defourny, P. Potential performances of remotely sensed (LAI) assimilation in (WOFOST) model based on an (OSS) Experiment. Agric. For. Meteorol. 2011, 151, 1843-1855.
12. Holecz, F.; Barbieri, M.; Collivignarelli, F.; Gatti, L.; Nelson, A.; Setiyono, T.D.; Boschetti, M.; Manfron, G.; Brivio, P.A.; Quilang, J.; et al. An operational remote sensing based service for rice production estimation at national scale. In Proceedings of the Living Planet Symposium, Edinburgh, UK, 9-13 September 2013.
13. Ata-Ul-Karim, S.T.; Zhu, Y.; Yao, X.; Cao, W. Determination of critical nitrogen dilution curve based on leaf area index in rice. Field Crop. Res. 2014, 167, 76-85.
14. Confalonieri, R.; Debellini, C.; Pirondini, M.; Possenti, P.; Bergamini, L.; Barlassina, G.; Bartoli, A.; Agostoni, E.; Appiani, M.; Babazadeh, L.; et al. A new approach for determining rice critical nitrogen concentration. J. Agric. Sci. 2011, 149, 633-638.
15. Gobron, N.; Verstraete, M. ECV T11: Leaf Area Index. In Assessment of the Status of the Development of Standards for the Terrestrial Essential Climate Variables; FAO: Rome, Italy, 2009.
16. Chen, J.M.; Black, T.A. Defining leaf area index for non-flat leaves. Plant Cell Environ. 1992, 15, 421-429.
17. The Global Climate Observing System (GCOS). Systematic Observation Requirements for Satellite-Based Productsfor Climate; GCOS: Geneva, Switzerland, 2011.
18. Busetto, L.; Casteleyn, S.; Granell, C.; Pepe, M.; Barbieri, M.; Campos-Taberner, M.; Casa, R.; Confalonieri, R.; Crema, A.; García-Haro, F.J.; et al. Downstream services for rice crop monitoring in Europe: From regional to local scale. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2017, accepted.
19. Baret, F.; Houlès, V.; Guérif, M. Quantification of plant stress using remote sensing observations and crop models: The case of nitrogen management. J. Exp. Bot. 2007, 58, 869.
20. Aboelghar, M.; Arafat, S.; Yousef, M.A.; El-Shirbeny, M.; Naeem, S.; Massoud, A.; Saleh, N. Using SPOT data and leaf area index for rice yield estimation in Egyptian Nile delta. Egypt. J. Remote Sens. Space Sci. 2011, 14, 81-89.
21. Confalonieri, R.; Rosenmund, A.S.; Baruth, B. An improved model to simulate rice yield. Agron. Sustain. Dev. 2009, 29, 463-474.
22. Launay, M.; Guerif, M. Assimilating remote sensing data into a crop model to improve predictive performance for spatial applications. Agric. Ecosyst. Environ. 2005, 111, 321-339.
23. Mosleh, M.K.; Hassan, Q.K.; Chowdhury, E.H. Application of Remote Sensors in Mapping Rice Area and Forecasting Its Production: A Review. Sensors 2015, 15, 769-791.
24. Dorigo, W.; Zurita-Milla, R.; de Wit, A.; Brazile, J.; Singh, R.; Schaepman, M. A review on reflective remote sensing and data assimilation techniques for enhanced agroecosystem modeling. Int. J. Appl. Earth Obs. Geoinf. 2007, 9, 165-193.
25. Curnel, Y.; de Wit, A.J.; Duveiller, G.; Defourny, P. Potential performances of remotely sensed (LAI) assimilation in (WOFOST) model based on an (OSS) Experiment. Agric. For. Meteorol. 2011, 151, 1843-1855.
26. Holecz, F.; Barbieri, M.; Collivignarelli, F.; Gatti, L.; Nelson, A.; Setiyono, T.D.; Boschetti, M.; Manfron, G.; Brivio, P.A.; Quilang, J.; et al. An operational remote sensing based service for rice production estimation at national scale. In Proceedings of the Living Planet Symposium, Edinburgh, UK, 9-13 September 2013.
27. Ata-Ul-Karim, S.T.; Zhu, Y.; Yao, X.; Cao, W. Determination of critical nitrogen dilution curve based on leaf area index in rice. Field Crop. Res. 2014, 167, 76-85.
28. Confalonieri, R.; Debellini, C.; Pirondini, M.; Possenti, P.; Bergamini, L.; Barlassina, G.; Bartoli, A.; Agostoni, E.; Appiani, M.; Babazadeh, L.; et al. A new approach for determining rice critical nitrogen concentration. J. Agric. Sci. 2011, 149, 633-638.
29. Darvishzadeh, R.; Matkan, A.A.; Ahangar, A.D. Inversion of a Radiative Transfer Model for Estimation of Rice Canopy Chlorophyll Content Using a Lookup-Table Approach. IEEE J. Sel. Top. Appl. Earth Obs. Remote Sens. 2012, 5, 1222-1230.
30. Baret, F.; Hagolle, O.; Geiger, B.; Bicheron, P.; Miras, B.; Huc, M.; Berthelot, B.; Nino, F.; Weiss, M.; Samain, O.; et al. LAI, fAPAR and fCover CYCLOPES global products derived from VEGETATION: Part 1: Principles of the algorithm. Remote Sens. Environ. 2007, 110, 275-286.
31. Claverie, M.; Vermote, E.F.; Weiss, M.; Baret, F.; Hagolle, O.; Demarez, V. Validation of coarse spatial resolution LAI and FAPAR time series over cropland in southwest France. Remote Sens. Environ. 2013, 139, 216-230.
32. Müller-Wilm, U. Sentinel-2 MSI-Level-2A Prototype Processor Installation and User Manual; Telespazio VEGA Deutschland GmbH: Darmstadt, Germany, 2016.
33. Richter, R.; Schlaepfer, D. Atmospheric/Topographic Correction for Satellite Imagery: ATCOR-2/3 User Guide Vers. 8.0.2; DLR-German Aerospace Center, Remote Sensing Data Center: Wessling, Germany, 2011.
34. Louis, J.; Charantonis, A.; Berthelot, B. Cloud Detection for Sentinel-2; ESA Special Publication: Noordwijk, The Netherlands, 2010.
35. Clevers, J.; Gitelson, A. Remote estimation of crop and grass chlorophyll and nitrogen content using red-edge bands on Sentinel-2 and -3. Int. J. Appl. Earth Obs. Geoinf. 2013, 23, 344-351.
36. Mandanici, E.; Bitelli, G. Preliminary Comparison of Sentinel-2 and Landsat 8 Imagery for a Combined Use. Remote Sens. 2016, 8, 1014.
37. Nielsen, A.A.; Conradsen, K.; Simpson, J.J. Multivariate Alteration Detection (MAD) and MAF Postprocessing in Multispectral, Bitemporal Image Data: New Approaches to Change Detection Studies. Remote Sens. Environ. 1998, 64, 1-19.
38. Nelson, A.; Setiyono, T.; Rala, A.; Quicho, E.; Raviz, J.; Abonete, P.; Maunahan, A.; Garcia, C.; Bhatti, H.; Villano, L.; et al. Towards an Operational SAR-Based Rice Monitoring System in Asia: Examples from Demonstration Sites across Asia in the RIICE Project. Remote Sens. 2014, 6, 10773-10812.
39. Bordogna, G.; Kliment, T.; Frigerio, L.; Brivio, P.A.; Crema, A.; Stroppiana, D.; Boschetti, M.; Sterlacchini, S. A Spatial Data Infrastructure Integrating Multisource Heterogeneous Geospatial Data and Time Series: A Study Case in Agriculture. ISPRS Int. J. Geoinf. 2016, 5, 73.
40. Congalton, R.G. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 1991, 37, 35-46.
41. Ranghetti, L.; Busetto, L.; Crema, A.; Fasola, M.; Cardarelli, E.; Boschetti, M. Testing estimation of water surface in Italian rice district from MODIS satellite data. Int. J. Appl. Earth Obs. Geoinf. 2016, 52, 284-295.
42. EL HAJJ, M.; Baghdadi, N.; Cheviron, B.; Belaud, G.; Zribi, M. Integration of remote sensing derived parameters in crop models: Application to the PILOTE model for hay production. Agric. Water Manag. 2016, 176, 67-79.
43. Storey, J.; Roy, D.P.; Masek, J.; Gascon, F.; Dwyer, J.; Choate, M. A note on the temporary misregistration of Landsat-8 Operational Land Imager (OLI) and Sentinel-2 Multi Spectral Instrument (MSI) imagery. Remote Sens. Environ. 2016, 186, 121-122.
44. Morisette, J.; Baret, F.; Privette, J.; Myneni, R.; Nickeson, J.; Garrigues, S.; Shabanov, N.;Weiss, M.; Fernandes, R.; Leblanc, S.; et al. Validation of global moderate-resolution LAI products: A framework proposed within the CEOS land product validation subgroup. IEEE Trans. Geosci. Remote Sens. 2006, 44, 1804-1817.
45. Boschetti, M.; Stroppiana, D.; Brivio, P.; Bocchi, S. Multi-year monitoring of rice crop phenology through time series analysis of MODIS images. Int. J. Remote Sens. 2009, 30, 4643-4662.
46. De Bernardis, C.; Vicente-Guijalba, F.; Martinez-Marin, T.; Lopez-Sanchez, J.M. Particle Filter Approach for Real-Time Estimation of Crop Phenological States Using Time Series of NDVI Images. Remote Sens. 2016, 8, 610.
47. Boschetti, M.; Busetto, L.; Manfron, G.; Laborte, A.; Asilo, S.; Pazhanivelan, S.; Nelson, A. PhenoRice: A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series. Remote Sens. Environ. 2017, under revision.
48. Nutini, F.; Boschetti, M.; Brivio, P.A.; Bocchi, S.; Antoninetti, M. Land-use and land-cover change detection in a semi-arid area of Niger using multi-temporal analysis of Landsat images. Int. J. Remote Sens. 2013, 34, 4769-4790.
49. Heumann, B.W.; Seaquist, J.; Eklundh, L.; Jönsson, P. AVHRR derived phenological change in the Sahel and Soudan, Africa, 1982-2005. Remote Sens. Environ. 2007, 108, 385-392.
50. Fang, H.; Li, W.; Wei, S.; Jiang, C. Seasonal variation of leaf area index (LAI) over paddy rice fields in NE China: Intercomparison of destructive sampling, LAI-2200, digital hemispherical photography (DHP), and AccuPAR methods. Agric. For. Meteorol. 2014, 198, 126-141.
51. Verrelst, J.; Rivera, J.P.; Moreno, J.; Camps-Valls, G. Gaussian processes uncertainty estimates in experimental Sentinel-2 LAI and leaf chlorophyll content retrieval. ISPRS J. Photogramm. Remote Sens. 2013, 86, 157-167.