Applying the NDVI from satellite images in delimiting management zones for annual crops

Júnior Melo Damian,Antônio Luis Santi,Maurício Roberto Cherubin,Alencar Zachi Da Fonseca,Osmar Henrique De Castro Pias,Ezequiel Zibetti Fornari

doi:10.1590/1678-992x-2018-0055

Abstract

The utilization of Normalized Difference Vegetation Index (NDVI) data obtained through satellite images can technically improve the process of delimiting management zones (MZ) for annual crops, resulting in socio-economic and environmental benefits. The aim of this study was to compare delimited MZ, using crop productivity data, with delimited MZ using the NDVI obtained from satellite images in areas under a no-tillage system. The study was carried out in three areas located in the state of Rio Grande do Sul, Brazil. Three crop productivity maps, from 2009 to 2015, were used for each area, whereby the NDVI was calculated for each crop productivity map using images from the Landsat series of satellites. Descriptive and geostatistical analysis were conducted to determine the productivity and NDVI data. The MZ were then delimited using the fuzzy c-means algorithm. Spearman’s correlation matrix was used to compare the methodologies used for delimiting the MZ. The MZ based on NDVI calculated from the satellite images correlated with the MZ based on crop productivity data (0.48 < r< 0.61), suggesting that the NDVI can replace or be complementary to productivity data in delimiting MZ for annual cropping systems.

Highlights

Increasing the productivity of an agricultural system, coupled with attractive economic return and minimal environment impacts, is one of the main challenges of the 21st century (Godfray et al, 2010)
Our results indicated that there is no universal pattern of variation per crop, confirming that productivity data of different crops need to be included in delimiting management zones (MZ) so as to cover the spatio-temporal variation of productivity within diversified cropping systems
For the two indices under test, the results showed that out of the eight management zones tested, two zones would be ideal for representing the set of three productivity maps (Figure 4A-F)

Summary

Introduction

Increasing the productivity of an agricultural system, coupled with attractive economic return and minimal environment impacts, is one of the main challenges of the 21st century (Godfray et al, 2010). Under this scenario, it is imperative that decision makers (e.g., farmers, consultants, extension agents) have access to specific information about the soil, cropping system and climate that directly and indirectly support the adoption of better management practice choices in each agricultural production system (Lee and Ehsani, 2015; Srbinovska et al, 2015). Crop productivity maps are created from the yield data collected by a set of sensors coupled to the harvesters together with positioning information systems (Blackmore and Moore, 1999). High resolution satellite images can be acquired at a low price, which allows for mapping large areas with low investments

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Conclusion