Agriculture is one of the most important markets in the world. For the agriculture production efficiency and cost reduction, the modern agriculture no longer exists in farm fields only, but expands quickly in information fields as well. The recent trend of agriculture is moving towards precision farming, which gives rise to great demands for IT supports. The future of precision agriculture is considered highly promising, and lots of solution packages will be developed to support farming activities during the entire farming cycle.
Accurate estimates are emerging with technological advances in remote sensing, and the triangle method has demonstrated to be a useful tool for the estimation of evaporative fraction (EF). The purpose of this study was to estimate the EF using the triangle method at the regional level. We used data from the Moderate Resolution Imaging Spectroradiometer orbital sensor, referring to indices of surface temperature and vegetation index for a 10-year period (2002/2003 to 2011/2012) of cropping seasons in the state of Paraná, Brazil. The triangle method has shown considerable results for the EF, and the validation of the estimates, as compared to observed data of climatological water balance, showed values >0.8 for modified “d” of Wilmott and R2 values between 0.6 and 0.7 for some counties. The errors were low for all years analyzed, and the test showed that the estimated data are very close to the observed data. Based on statistical validation, we can say that the triangle method is a consistent tool, is useful as it uses only images of remote sensing as variables, and can provide support for monitoring large-scale agroclimatic, specially for countries of great territorial dimensions, such as Brazil, which lacks a more dense network of meteorological ground stations, i.e., the country does not appear to cover a large field for data.
Sugarcane, as one of the most mainstay crop in Brazil, plays an essential role in ethanol production. To monitor sugarcane crop growth and predict sugarcane sucrose content, remote sensing technology plays an essential role while accurate and timely crop growth information is significant, in particularly for large scale farming. We focused on the issues of sugarcane sucrose content estimation using time-series satellite image. Firstly, we calculated the spectral features and vegetation indices to make them be correspondence to the sucrose accumulation biological mechanism. Secondly, we improved the statistical regression model considering more other factors. The evaluation was performed and we got precision of 90% which is about 20% higher than the conventional method. The validation results showed that prediction accuracy using our sugarcane growth modeling and improved mix model is satisfied.
The main goal of this study was to evaluate the environmental condition of two watersheds, called Ribeirao das Cabras and Ribeirao Piracicamirim, both part of the 'Piracicaba' River Watershed (Sao Paulo State - Brazil), using Remote Sensing and Geographic Information System (GIS), in order to provide subsidies to land use planning. Tendencies and potentialities were identified in each area in order to study the land use adequacy using different scenarios and propose a land use planning for both regions using environmental planning concepts. All information layers were integrated and analysed to generate land use capacity maps. These maps, together with maps of possible protected areas, according to the environmental laws, were compared to the actual land use, leading to maps of conflict areas for both watersheds, which were studied and compared through methods and specifics criteria to identify tendencies and potentialities for both areas. The natural and cultural attributes, main degradation processes, development tendencies and potentialities identified in each area, and the available legal instruments, were the basis for a guideline for conservation and protection of these areas through environmental planning concepts, which showed to be very important and adequate to the occupation planning process.