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This study aimed to map, separate, and estimate soya bean and corn crop areas in Paraná State, Brazil, in the harvest years 2012/13 and 2013/14, using the enhanced vegetation index (EVI) images from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor. Thus, two methodologies were integrated, the first considering heterogeneity on the dates of crop cycles, the scenes required to generate images of minimum and maximum vegetation indexes, creating a colour composite red, green, and blue (RGB), and identifying two cultures simultaneously. In the second methodology, soya bean and corn were identified and mapped using the selection of pure pixels and the supervised classification algorithm Spectral Angle Mapper (SAM). In order to avoid overlapping areas, we multiplied the results from the first and second methodologies to obtain the final separation. The final validation of the mapping was compared to official data, identifying high correlation to crops. Based on Medium-Resolution Linear Imaging Self-Scanner (LISS-III) and Land Remote Sensing Satellite (Landsat-8) images, the similarity of global accuracy (GA) and kappa accuracy indices was determined, being classified as good and excellent, respectively. It showed that the use of the two consortium methodologies for separation and overlap elimination of these crops in the state of Paraná was efficient. [ABSTRACT FROM AUTHOR]

Vegetation Indices are important for agricultural monitoring and the assessment of biophysical attributes of different crops. In this context, the objective of this study was to evaluate the relationships between normalized difference vegetation indices (NDVIs) obtained by different terrestrial remote sensors (active and passive), with respect to biophysical aspects of soybeans and beans. With the NDVI GS (GreenSeeker 505 handheld active sensor), it was possible to obtain a better leaf area index and height estimate for both crops, as well chlorophyll index for soybeans. The NDVI FS (FieldSpec4 model Standard-Res passive sensor) is highlight in being able to estimate values for intercepted photosynthetically active radiation, mainly for soybeans. By using NDVIs with active and passive terrestrial remote sensors, it was possible to create models to estimate the biophysical parameters for soybeans and beans. [ABSTRACT FROM AUTHOR]

The objective of this research was to distinguish and estimate cultivated areas of soybean and corn in Paraná State, Brazil, in the 2014/2015 crop season. The main obstacle in mapping summer crops using vegetation index images is to separate the cultivated areas with soybean and corn. These crops planted in a similar period present similar spectral signatures. Thus, with the use of Data Mining techniques (DM) and Artificial Neural Network (ANN) it was possible to carry out the crop mapping, even for those that present similarities in spectral-temporal profile of vegetation indexes. The accuracy obtained in the mappings resulted in a KI (Kappa Index) of 0.78 and 89% of OA (overall accuracy) indicating a high accuracy in the separation of soybean and corn summer crops. [ABSTRACT FROM AUTHOR]

The state of Paraná is one of the largest corn producers in Brazil, cultivating in two growing seasons. This study evaluated bivariate correlations between precipitation and the second-crop corn yield between the first 10 days of April and the first 10 days of October using spatial statistics. The spatial analysis of yield was evaluated using geostatistical models and validated by cross-validation. Spatial analysis was performed using the local indicator of spatial association and a cross-semivariogram, and the co-dispersion coefficient was used to validate the correlation between variables. Cross-validation identified the exponential model as the best fit, indicating a spatial dependence of 317.26 km. Spatial associations between municipalities were significant throughout the state of Paraná, Brazil. The cross-semivariogram allowed fitting the wave, spherical, Gaussian, and exponential models, with a spatial correlation of 37.04 to 570.38 km between the precipitations and yield. Moreover, the co-dispersion coefficient validated the correlation between the two variables for all 10-day periods. [ABSTRACT FROM AUTHOR]

cInterdisciplinary Centre for Energy Planning, State University of Campinas, Campinas, SP CEP 13083-896, Brazil (Received 14 June 2014; accepted 21 January 2015) The use of remote-sensing technology has been studied as a way to make the monitoring of agricultural crops more efficient, dynamic, and reliable. The use of data from the Moderate Resolution Imaging Spectroradiometer (MODIS) has proved to be an interesting tool regarding the mapping of large areas, however, some challenges still need to be addressed. One of these is the identification of specific types of crops, especially when they have similar phenologies. The purpose of this study was to perform discrimination and mapping of soya bean and corn crops in the state of Paraná, Brazil, for the 2010/2011 and 2011/2012 crop years. A methodology using spectro-temporal profile information of the crops derived from vegetation indices (VIs), the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and the wide dynamic range vegetation index (WDRVI) based on MODIS data was appraised. This method generated a series of maps of the respective crops that were later qualitatively or quantitatively appraised. Some of the maps drawn showed a global accuracy rate above 80% and a kappa coefficient (κ) of over 0.7. The data areas showed an average difference of 6% for the cultivation of soya beans, and 11% for corn when compared to official data. The WDRVI and EVI were similar and showed better performance when compared to the NDVI in the assessments made. The results demonstrate that the soya bean crop was better mapped compared to corn, particularly in terms of the size of the crop area. The use of spectro-temporal profiles of the VIs assisted in obtaining important information, enabling better identification of crops from regional scale mapping using the MODIS data. [ABSTRACT FROM AUTHOR]

The knowledge of Sowing Dates (SD), Maximum Vegetative Development Dates (MVDD), and Harvest Dates (HD) of crops is important for estimating and forecasting large-scale productivity. Prior information on harvest date of crops is also useful in optimizing grain reception logistics and improving business decision-making in agro-industrial companies. This study aimed to develop a method to predict the HD of soybeans from MVDD, which was obtained from crop maps in the state of Paraná between 2011/2012 and 2013/2014 crop years. TIMESAT software was used to perform a seasonal trend analysis of enhanced vegetation index (EVI) time series from Moderate Resolution Imaging Spectroradiometer (MODIS) satellite imagery. After estimating time intervals between MVDD and HD (MH), applied to each mapped pixel, we calculated the 3 year MH average (average of the interval between MVDD and HD for the 3 crop years) for mesoregion and state levels, respectively. The MH by mesoregion was added to MVDD at a regional level, generating HDFORECAST, which is a prediction of the HD. Our results were compared with information from 44 monitored farms in the state in the 2013/2014 crop year. The findings showed that, for soybeans, MH by mesoregion can be used to predict HD with a Mean Error (ME) of 4.84 days, Mean Absolute Error (MAE) of 6.84 days, Root Mean Square Error (RMSE) of 8.12 days, Willmott modified coefficient (d) of 0.9, and Pearson correlation coefficient (r) of 0.88. Our study has produced valuable information for crop productivity models, as well as for logistics of grain transportation and storage in agro-companies, which can anticipate operations knowing when grains will be harvested and delivered to the centres. [ABSTRACT FROM AUTHOR]

Brazilian farming influences directly the worldwide economy. Thus, fast and reliable information on areas sown with the main crops is essential for planning logistics and public or private commodity market policies. Recent farming practices have embraced remote sensing to provide fast and reliable information on commodity dynamics. Medium-to-low resolution free orbital images, such as those from Landsat 8 and Sentinel 2, have been used for crop mapping; however, satellite image processing requires high computing power, especially when monitoring vast areas. Therefore, cloud data processing has been the only feasible option to deal with a large amount of orbital data and its processing and analysis. Thus, our goal was to develop a method to map the two main crops (soybeans and corn) in Paraná, one of the major Brazilian state producers. Landsat-8, Sentinel-2, SRTM+, and field data from 2016 to 2018 were used with the Simple Non-Iterative Clustering segmentation method and the Continuous Naive Bayes classifier, to identify cropped areas. A minimum global accuracy of 90% was found for both crops. Comparison with field data showed correlations of 0.96 and agreement coefficients no lower than 0.86. This ensures mapping quality when using Sentinel and/or Landsat imagery on the GEE platform. [ABSTRACT FROM AUTHOR]