PhenoRice: A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series

Mirco Boschetti (Corresponding Author), Lorenzo Busetto, Giancinto Manfron, Alice Laborte, S. Asilo, S. Pazhanivelan, A.D. Nelson

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Abstract

Agricultural monitoring systems require spatio-temporal information on widely cultivated staple crops like rice. More emphasis has been made on area estimation and crop detection than on the temporal aspects of crop cultivation, but seasonal and temporal information such as i) crop duration, ii) date of crop establishment and iii) cropping intensity are as important as area for understanding crop production. Rice cropping systems are diverse because genetic, environmental and management factors (G × E × M combinations) influence the spatio-temporal patterns of cultivation. We present a rule based algorithm called PhenoRice for automatic extraction of temporal information on the rice crop using moderate resolution hypertemporal optical imagery from MODIS. Performance of PhenoRice against spatially and temporally explicit reference information was tested in three diverse sites: rice-fallow (Italy), rice-other crop (India) and rice-rice (Philippines) systems. Regional product accuracy assessments showed that PhenoRice made a conservative, spatially representative and robust detection of rice cultivation in all sites (r2 between 0.75 and 0.92) and crop establishment dates were in close agreement with the reference data (r2 = 0.98, Mean Error = 4.07 days, Mean Absolute Error = 9.95 days, p < 0.01). Variability in algorithm performance in different conditions in each site (irrigated vs rainfed, direct seeding vs transplanting, fragmented vs clustered rice landscapes and the impact of cloud contamination) was analysed and discussed. Analysis of the maps revealed that cropping intensity and season length per site matched well with local information on agro-practices and cultivated varieties. The results show that PhenoRice is robust for deriving essential temporal descriptions of rice systems in both temperate and tropical regions at a level of spatial and temporal detail that is suitable for regional crop monitoring on a seasonal basis.
Original languageEnglish
Pages (from-to)347-365
JournalRemote sensing of environment
Volume194
DOIs
Publication statusPublished - 2017

Fingerprint

Crops
remote sensing
satellite data
Time series
time series analysis
rice
Satellites
time series
crop
crops
cropping practice
methodology
plant establishment
method
accuracy assessment
irrigated conditions
Monitoring
monitoring
direct seeding
moderate resolution imaging spectroradiometer

Keywords

  • METIS-322122
  • ITC-ISI-JOURNAL-ARTICLE

Cite this

Boschetti, Mirco ; Busetto, Lorenzo ; Manfron, Giancinto ; Laborte, Alice ; Asilo, S. ; Pazhanivelan, S. ; Nelson, A.D. / PhenoRice : A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series. In: Remote sensing of environment. 2017 ; Vol. 194. pp. 347-365.
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abstract = "Agricultural monitoring systems require spatio-temporal information on widely cultivated staple crops like rice. More emphasis has been made on area estimation and crop detection than on the temporal aspects of crop cultivation, but seasonal and temporal information such as i) crop duration, ii) date of crop establishment and iii) cropping intensity are as important as area for understanding crop production. Rice cropping systems are diverse because genetic, environmental and management factors (G × E × M combinations) influence the spatio-temporal patterns of cultivation. We present a rule based algorithm called PhenoRice for automatic extraction of temporal information on the rice crop using moderate resolution hypertemporal optical imagery from MODIS. Performance of PhenoRice against spatially and temporally explicit reference information was tested in three diverse sites: rice-fallow (Italy), rice-other crop (India) and rice-rice (Philippines) systems. Regional product accuracy assessments showed that PhenoRice made a conservative, spatially representative and robust detection of rice cultivation in all sites (r2 between 0.75 and 0.92) and crop establishment dates were in close agreement with the reference data (r2 = 0.98, Mean Error = 4.07 days, Mean Absolute Error = 9.95 days, p < 0.01). Variability in algorithm performance in different conditions in each site (irrigated vs rainfed, direct seeding vs transplanting, fragmented vs clustered rice landscapes and the impact of cloud contamination) was analysed and discussed. Analysis of the maps revealed that cropping intensity and season length per site matched well with local information on agro-practices and cultivated varieties. The results show that PhenoRice is robust for deriving essential temporal descriptions of rice systems in both temperate and tropical regions at a level of spatial and temporal detail that is suitable for regional crop monitoring on a seasonal basis.",
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PhenoRice : A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series. / Boschetti, Mirco (Corresponding Author); Busetto, Lorenzo; Manfron, Giancinto; Laborte, Alice; Asilo, S.; Pazhanivelan, S.; Nelson, A.D.

In: Remote sensing of environment, Vol. 194, 2017, p. 347-365.

Research output: Contribution to journalArticleAcademicpeer-review

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T2 - A method for automatic extraction of spatio-temporal information on rice crops using satellite data time series

AU - Boschetti, Mirco

AU - Busetto, Lorenzo

AU - Manfron, Giancinto

AU - Laborte, Alice

AU - Asilo, S.

AU - Pazhanivelan, S.

AU - Nelson, A.D.

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