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

REMOTE SENSING OF ENVIRONMENT

卷:194

Article

Boschetti, Mirco¹  Busetto, Lorenzo¹  Manfron, Giacinto¹  Laborte, Alice²  Asilo, Sonia^2,3  Pazhanivelan, Sellaperumal⁴  Nelson, Andrew³ 

[1] Italian Natl Res Council, Inst Electromagnet Sensing Environm, Via Bassini 15, I-20133 Milan, Italy

[2] Int Rice Res Inst, Los Banos 4031, Philippines

[3] Univ Twente, ITC Fac Geoinformat Sci & Earth Observat, Dept Nat Resources, POB 217, NL-7500 AE Enschede, Netherlands

[4] Tamil Nadu Agr Univ, Dept Remote Sensing & GIS, Coimbatore 641003, Tamil Nadu, India

关键词: MODIS;    Agriculture;    Rice;    Time series;    Phenology;   

DOI  :  10.1016/j.rse.2017.03.029

来源: Elsevier

【 摘 要 】

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 x E x 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 (r(2) between 0.75 and 0.92) and crop establishment dates were in close agreement with the reference data (r(2) = 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. (C) 2017 Elsevier Inc. All rights reserved.

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