UNDERSTANDING THE DYNAMIC OF TROPICAL AGRICULTURE FOR REMOTE SENSING APPLICATIONS: A CASE STUDY OF SOUTHEASTERN BRAZIL

• Main Authors: I. D. Sanches, A. J. B. Luiz, B. Montibeller, B. Schultz, K. Trabaquini, I. D. R. Eberhardt, A. R. Formaggio, L. E. Maurano
• Format: Article
• Language: English
• Published: Copernicus Publications 2019-07-01
• Series: The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
• Online Access: https://www.int-arch-photogramm-remote-sens-spatial-inf-sci.net/XLII-3-W6/149/2019/isprs-archives-XLII-3-W6-149-2019.pdf

The agricultural activity can greatly benefit from remote sensing technology (RS). Optical passive RS has been vastly explored for agricultural mapping and monitoring, in despite of cloud cover issue. This is observed even in the tropics, where frequency of clouds is very high. However, more studies are needed to better understand the high dynamism of tropical agriculture and its impact on the use of passive RS. In tropical countries, such as in Brazil, the use of current agricultural technologies, associated with favourable climate, allow the planting period to be wide and to have plants of varying phenological cycles. In this context, the main objective of the current study is to better understand the dynamics of a selected area in Southeast of São Paulo state, and its impact on the use of orbital passive RS. For that purpose, data (from field and satellite) from 55 agricultural fields, including annual, semi-perennial and perennial crops and silviculture, were acquired between July 2014 and December 2016. Field campaigns were conducted in a monthly base to gather information about the condition of the crops along their development (data available in a website). Field data corresponding to the 2014–2015 crop year were associated with a time series of Landsat-8/OLI RGB false-colour compositions images and MODIS/Terra NDVI profiles. The type of information that can be extracted (such as specie identification, crop management practices adopted, date of harvest, type o production system used etc) by combining passive remote sensing data with field data is discussed in the paper.