Classification of Crops, Pastures, and Tree Plantations along the Season with Multi-Sensor Image Time Series in a Subtropical Agricultural Region

Author(s)

Melo de Oliveira Santos, Cecilia Lira ^[1] ; Camargo Lamparelli, Rubens Augusto ^[2] ; Dantas Araujo Figueiredo, Gleyce Kelly ^[1] ; Dupuy, Stephane ^[3] ; Boury, Julie ^[4] ; dos Santos Luciano, Ana Claudia ^{[1, 5]} ; Torres, Ricardo da Silva ^[6] ; le Maire, Guerric ^{[7, 5, 2, 8]}

Affiliation

^[1] Univ Estadual Campinas, UNICAMP, FEAGRI, Sch Agr Engn, BR-13083875 Campinas, SP - Brazil

^[2] Univ Estadual Campinas, UNICAMP, Interdisciplinary Ctr Energy Planning, NIPE, BR-13083896 Campinas, SP - Brazil

^[3] CIRAD, UMR TETIS, F-34398 Montpellier - France

^[4] AgroParisTech, Paris Inst Technol Life Food & Environm Sci, F-75231 Paris - France

^[5] CNPEM, Brazilian Ctr Res Energy & Mat, CTBE, Brazilian Bioethanol Sci & Technol Lab, BR-13083970 Campinas, SP - Brazil

^[6] Univ Estadual Campinas, UNICAMP, Inst Comp, BR-13083852 Campinas, SP - Brazil

^[7] CIRAD, UMR Eco&Sols, BR-13083896 Campinas, SP - Brazil

^[8] Univ Montpellier, Montpellier SupAgro, INRA, CIRAD, IRD, Eco&Sols, F-34000 Montpellier - France

• Document type: Journal article
• Source: REMOTE SENSING; v. 11, n. 3 FEB 1 2019.
• Web of Science Citations: 3

Abstract

Timely and efficient land-cover mapping is of high interest, especially in agricultural landscapes. Classification based on satellite images over the season, while important for cropland monitoring, remains challenging in subtropical agricultural areas due to the high diversity of management systems and seasonal cloud cover variations. This work presents supervised object-based classifications over the year at 2-month time-steps in a heterogeneous region of 12,000 km(2) in the Sao Paulo region of Brazil. Different methods and remote-sensing datasets were tested with the random forest algorithm, including optical and radar data, time series of images, and cloud gap-filling methods. The final selected method demonstrated an overall accuracy of approximately 0.84, which was stable throughout the year, at the more detailed level of classification; confusion mainly occurred among annual crop classes and soil classes. We showed in this study that the use of time series was useful in this context, mainly by including a small number of highly discriminant images. Such important images were eventually distant in time from the prediction date, and they corresponded to a high-quality image with low cloud cover. Consequently, the final classification accuracy was not sensitive to the cloud gap-filling method, and simple median gap-filling or linear interpolations with time were sufficient. Sentinel-1 images did not improve the classification results in this context. For within-season dynamic classes, such as annual crops, which were more difficult to classify, field measurement efforts should be densified and planned during the most discriminant window, which may not occur during the crop vegetation peak. (AU)

• FAPESP's process: 14/12236-1 - AnImaLS: Annotation of Images in Large Scale: what can machines and specialists learn from interaction?
• Grantee: Alexandre Xavier Falcão
• Support type: Research Projects - Thematic Grants
• FAPESP's process: 13/50155-0 - Combining new technologies to monitor phenology from leaves to ecosystems
• Grantee: Leonor Patricia Cerdeira Morellato
• Support type: Research Program on Global Climate Change - University-Industry Cooperative Research (PITE)
• FAPESP's process: 15/24494-8 - Communications and processing of big data in cloud and fog computing
• Grantee: Nelson Luis Saldanha da Fonseca
• Support type: Research Projects - Thematic Grants
• FAPESP's process: 14/50715-9 - Characterizing and predicting biomass production in sugarcane and eucalyptus plantations in Brazil
• Grantee: Rubens Augusto Camargo Lamparelli
• Support type: Research Grants - Research Partnership for Technological Innovation - PITE