Semi-Supervised Support Vector Rainfall Estimation Using Satellite Images

In this paper we introduce the use of semi-supervised support vector machines for rainfall estimation using images obtained from visible and infrared NOAA satellite channels. Two experiments were performed, one involving traditional SVM and other using semi-supervised SVM (S 3 VM). The S 3 VM approach outperforms SVM in our experiments, with can be seen as a good methodology for rainfall satellite estimation, due to the large amount of unlabeled data. 1

Extended time weather forecasts contributes to agricultural productivity estimates

Weather conditions in critical periods of the vegetative crop development influence crop productivity, thus being a basic parameter for crop forecast. Reliable extended period weather forecasts may contribute to improve the estimation of agricultural productivity. The production of soybean plays an important role in the Brazilian economy, because this country is ranked among the largest producers of soybeans in the world. This culture can be significantly affected by water conditions, depending on the intensity of water deficit. This work explores the role of extended period weather forecasts for estimating soybean productivity in the southern part of Brazil, Passo Fundo, and Londrina (State of Rio Grande do Sul and Paraná, respectively) in the 2005/2006 harvest. The goal was to investigate the possible contribution of precipitation forecasts as a substitute for the use of climatological data on crop forecasts. The results suggest that the use of meteorological forecasts generate more reliable productivity estimates during the growth period than those generated only through climatological information102343350CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO - CNPQFUNDAÇÃO DE AMPARO À PESQUISA DO ESTADO DE SÃO PAULO - FAPESPnão temnão te

Análise temporal de municípios produtores de cana-de-açúcar no estado de São Paulo por meio de agrupamento do NDVI (AVHRR/NOAA) e dados de produtividade e área

Sugar cane has become strategic to the Brazilian economy, especially due to its importance in the replacement of fossil fuels. In recent years, the development of bi-fuel car engines leaded to a fast increase in the demand for ethanol, one of the most important derivatives of sugar cane. Therefore, it is very important to monitor the crop seasons in order to estimate the yield of sugar cane as well as to evaluate the expansion of this crop in the country. There are several models to estimate the sugar cane production involving physiological, meteorological and pedological variables. However, the use of satellite images to aid in the monitoring of sugar cane production has increased greatly in recent years. In this context, this paper presents an assessment of the distribution of sugar cane regions in the last eight crop seasons, according to three different clusters. The test area was the most important region of sugar cane production in the São Paulo state, Brazil. In the experiments, we have used annual variables of NDVI (from AVHRR/NOAA), planted area and productivity values. These variables were used to define the three clusters by a clustering technique. Results showed that there was an increasing in the number of regions in the cluster where the planted area is greater over the years, confirming the sugar cane expansion to other areas.Pages: 591-59