%0 Journal Article
%4 sid.inpe.br/mtc-m21c/2019/
%2 sid.inpe.br/mtc-m21c/2019/
%@issn 0303-2434
%T Detailed agricultural land classification in the Brazilian cerrado based on phenological information from dense satellite image time series
%D 2019
%8 Oct.
%9 journal article
%A Bendini, Hugo do Nascimento,
%A Fonseca, Leila Maria Garcia,
%A Schwieder, Marcel,
%A Körting, Thales Sehn,
%A Rufin, Philippe,
%A Sanches, Ieda Del'Arco,
%A Leitão, Pedro J.,
%A Hostert, Patrick,
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Humboldt-Universität zu Berlin
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Humboldt-Universität zu Berlin
%@affiliation Instituto Nacional de Pesquisas Espaciais (INPE)
%@affiliation Humboldt-Universität zu Berlin
%@affiliation Humboldt-Universität zu Berlin
%@electronicmailaddress hugo.bendini@inpe.br
%@electronicmailaddress leila.fonseca@inpe.br
%@electronicmailaddress marcel.schwieder@geo.hu-berlin.de
%@electronicmailaddress thales.korting@inpe.br
%@electronicmailaddress philippe.rufin@geo.hu-berlin.de
%@electronicmailaddress ieda.sanches@inpe.br
%@electronicmailaddress p.leitao@geo.hu-berlin.de
%@electronicmailaddress patrick.hostert@geo.hu-berlin.de
%B International Journal of Applied Earth Observation and Geoinformation
%V 82
%P UNSP 101872
%K Big data, Time-Series mining, Random forest algorithm, Land use and Land cover mapping (LULC), Multi-Sensor.
%X The paradox between environmental conservation and economic development is a challenge for Brazil, where there is a complex and dynamic agricultural scenario. This reinforces the need for effective methods for the detailed mapping of agriculture. In this work, we employed land surface phenological metrics derived from dense satellite image time series to classify agricultural land in the Cerrado biome. We used all available Landsat images between April 2013 and April 2017, applying a weighted ensemble of Radial Basis Function (RBF) convolution filters as a kernel smoother to fill data gaps such as cloud cover and Scan Line Corrector (SLC)-off data. Through this approach, we created a dense Enhanced Vegetation Index (EVI) data cube with an 8-day temporal resolution and derived phenometrics for a Random Forest (RF) classification. We used a hierarchical classification with four levels, from land cover to crop rotation classes. Most of the classes showed accuracies higher than 90%. Single crop and Non-commercial crop classes presented lower accuracies. However, we showed that phenometrics derived from dense Landsat-like image time series, in a hierarchical classification scheme, has a great potential for detailed agricultural mapping. The results are promising and show that the method is consistent and robust, being applicable to mapping agricultural land throughout the entire Cerrado.
%@language en
%3 bendini_detailed.pdf