ABSTRACT: Sugarcane (saccharum spp.) in Brazil is managed on the basis of “production environments”. These “production environments” are used for many purposes, such as variety allocation, application of fertilizers and definition of the planting and harvesting periods. A quality classification is essential to ensure high economic returns. However, the classification is carried out by few and, most of the time, non-representative soil samples, showing unreal local conditions of soil spatial variability and resulting in classifications that are imprecise. One of the important tools in the precision agriculture technological package is the apparent electrical conductivity (ECa) sensors that can quickly map soil spatial variability with high-resolution and at low-cost. The aim of the present work was to show that soil ECa maps are able to assist classification of the “production environments” in sugarcane fields and rapidly and accurately reflect the yield potential. Two sugarcane fields (35 and 100 ha) were mapped with an electromagnetic induction sensor to measure soil ECa and were sampled by a dense sampling grid. The results showed that the ECa technique was able to reflect mainly the spatial variability of the clay content, evidencing regions with different yield potentials, guiding soil sampling to soil classification that is both more secure and more accurate. Furthermore, ECa allowed for more precise classification, where new “production environments”, different from those previously defined by the traditional sampling methods, were revealed. Thus, sugarcane growers will be able to allocate suitable varieties and fertilize their agricultural fields in a coherent way with higher quality, guaranteeing greater sustainability and economic return on their production.

Resumo: O objetivo deste trabalho foi avaliar o potencial de diversos índices, calculados com o uso de imagens do sensor Modis (“moderate resolution imaging spectroradiometer”), em identificar eventos de seca na cana-de-açúcar. As imagens dos satélites Terra e Aqua foram utilizadas para calcular os índices espectrais, com bandas na região do visível (vermelho), infravermelho próximo e infravermelho médio, e oito índices foram selecionados: NDVI, EVI2, GVMI, NDI6, NDI7, NDWI, SRWI e MSI. Os índices foram calculados com base em imagens de outubro a abril de quatro anos agrícolas: 2007/08, 2008/09, 2009/10 e 2013/14. Esses índices foram correlacionados com o índice de seca meteorológica SPEI, calculado para 1, 3 e 6 meses. Quatro deles tiveram correlação significativa com o índice SPEI: GVMI, MSI, NDI7 e NDWI. Os índices espectrais derivados do sensor Modis a bordo do satélite Aqua (MYD) são mais adequados para o reconhecimento de eventos de seca, e março proporcionou os índices mais relevantes para esse propósito. Índices de seca calculados com base em dados Modis são efetivos em detectar eventos de seca em cana-de-açúcar, além de serem capazes de apontar flutuações sazonais.

Abstract: The objective of this work was to evaluate the potential of several spectral indices, used on moderate resolution imaging spectroradiometer (Modis) images, in identifying drought events in sugarcane. Images of Terra and Aqua satellites were used to calculate the spectral indices, using visible (red), near infrared, and shortwave infrared bands, and eight indices were selected: NDVI, EVI2, GVMI, NDI6, NDI7, NDWI, SRWI, and MSI. The indices were calculated using images between October and April of the crop years 2007/08, 2008/09, 2009/10, and 2013/14. These indices were then correlated with the standardized precipitation-evapotranspiration index (SPEI), calculated for 1, 3, and 6 months. Four of them had significant correlations with SPEI: GVMI, MSI, NDI7, and NDWI. Spectral indices from Modis sensor on board the Aqua satellite (MYD) were more suited for drought detection, and March provided the most relevant indices for that purpose. Drought indices calculated from Modis sensor data are effective for detecting sugarcane drought events, besides being able to indicate seasonal fluctuations.

O objetivo deste trabalho foi estimar e mapear as áreas com as culturas de soja e milho, no Paraná, com uso de imagens multitemporais EVI/Modis. Foram avaliados os anos‑safra de 2004/2005 a 2007/2008. Em razão da alta dinâmica temporal e da heterogeneidade de datas de semeadura das culturas no estado, foram utilizadas cenas que contemplavam as fases de pré‑plantio e de desenvolvimento inicial das culturas, para gerar a imagem de mínimo EVI (IMIE), e cenas que consideravam o pico vegetativo das culturas, para gerar a imagem de máximo EVI (IMAE). Estas imagens foram utilizadas para gerar a composição colorida RGB (R, IMAE; GB, IMIE), o que permitiu a confecção de máscara das áreas com soja e milho. As estimativas das áreas de máscara por município foram comparadas com dados oficiais de produção agrícola municipal, tendo-se observado bons ajustes (R²>0,84, d>0,95, c>0,85) entre os dados. Para a avaliação da exatidão espacial das máscaras, imagens Landsat‑5/TM e AWiFS/IRS foram usadas como referência para construção da matriz de erros. Os resultados obtidos são indicativos de que a metodologia proposta é altamente eficiente e pode ser utilizada para mapeamento dessas culturas.

The objective of this work was to estimate and map crop areas with soybean and corn in the state of Paraná, Brazil, using EVI/Modis images. The crop seasons from 2004/2005 to 2007/2008 were evaluated. Due to the high temporal dynamics and difference in sowing dates of the cultures within the state, scenes containing the pre‑planting and initial crop development phases were used to obtain the minimum EVI image (IMIE), and scenes at the peak of the crop cycle were used to obtain the maximum EVI image (IMAE). These images were used to generate the RGB color composition (R, IMAE; GB, IMIE), which allowed for the creation of masks of the areas planted with soybean and corn. The estimation of masked areas by municipality was compared with the municipal agricultural production official data, and good fits (R²>0.84, d>0.95, c>0.85) were observed between data. For spatial accuracy assessment, Landsat‑5/TM and AWiFS/IRS images were used as references to build the error matrix. The obtained results indicate that the proposed methodology is highly efficient and may be used as a model for cropland mapping.