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Climate variability has been viewed as a prime field of interest among the environmental scientific communities, especially in the agriculture planning field. Variable climatic condition has a direct impact toward the agricultural productivity. This variability, especially change in temperature, could be due to the expansion of urbanization within the region. Rapid growth of urbanization due to population...
Numerous mixed pixels from coarse resolution data and missing date from moderate resolution data are major concern in cropland identification. Therefore, estimation using combining the coarse time series data and moderate resolution data has become the popular tendency for cultivated area identification. We proposed a novel method which integrated the coarse time series and moderate resolution data...
Air temperature is one of important environmental phenomena with both spatial and temporal characteristics. In order to realize spatial-temporal interpolation at any point in space-time field, a kind of practical product-sum covariance for spatial-temporal modeling is chosen for monthly average air temperature in the three provinces (Heilongjiang, Jilin and Liaoning Province) of Northeast China from...
FY-3A is a polar-orbiting meteorological satellite launched by China in 2008 which carries Medium Resolution Spectral Imaging (MERSI). This paper used 250m FY-3A/MERSI 10-day composite Normalized Differential Vegetation Index (NDVI) time series to estimate the winter wheat acreage in 10 provinces in northern China. Eleven time series from October 2010 to January 2011 are collected and reconstructed...
The normalized difference vegetation index (NDVI) time-series data, derived from satellite sensors, has been used to support land cover change detection and monitor crops successfully, but further applications are hindered by residual noise in the NDVI data. Methods for reducing noise and constructing high-quality NDVI time-series data sets can be broadly grouped into three general types, including...
Spatial information of crop area statistics is of great significance for study of global change, population, resources, environment, ecology and food security. In this paper, based on MODIS NDVI time series data and global optimization algorithm SCE-UA (Shuffled Complex Evolution-University of Arizona), the research of spatialization of crop area statistical data was carried out in 13 counties which...
Understanding change in climate and temperature is extremely important to carry out any sort agricultural decision. These fluctuations of climate could be cause by some global reason such as tropical atmospheric circulation. Expansion in the tropical atmospheric circulation brings significant temperature changes and precipitation especially in the equatorial region such as the Caribbean. For this...
Irrigated agriculture in China is of national and global significance. Official statistical data on agricultural water withdrawal might be more relevant to the actual water consumption in an agriculture sector than the irrigation requirements estimated from modeling studies. However, agricultural water withdrawal is always reported on the basis of geopolitical units or river basins, without spatial...
Crop development information is critical to U.S. agricultural economy and decision making. In this paper, a general framework of Hidden Markov Models (HMMs) based corn progress percents esitmation method has been presented. Multivariate time series involving mean NDVI, fractal dimension, and Accumulated Growing Degree Days (AGDDs) are embedded into the modified HMM. Features of mean NDVI and fractal...
The length of the crop season is a major determinant of yields in Sahel. Based on green-up onset detection derived from MODIS time series, this paper proposes a new methodology for the estimation of the sowing dates. It builds upon a novel stochastic model that translates vegetation onset detections around villages into sowing probabilities. Results for Niger show that this approach outperforms the...
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