Crop rotation is one of the important decisions made independently by numerous farm managers, and is a critical variable in models of crop growth and soil carbon. In Iowa and much of the Midwestern United States (US), the typical management decision is to rotate corn and soybean crops for a single field; therefore, the land-cover changes each year even though the total area of agricultural land-use remains the same. The price for corn increased from 2001 to 2010, which increased corn production in Iowa. We tested the hypothesis that the production increase was the result of changes in crop rotation in Iowa using the annual remote sensing classification (the cropland data layer) produced by the United States Department of Agriculture, National Agricultural Statistics Service. It was found that the area planted in corn increased from 4.7 million hectares in 2001 to 5.7 million hectares in 2007, which was correlated with the market price for corn. At the county level, there were differences in how the increase in corn production was accomplished. Northern and central counties had little land to expand cultivation and generally increased corn production by converting to a corn-corn rotation from the standard corn-soybean rotation. Southern counties in Iowa increased corn production by expanding into land that was not under recent cultivation. These changes affect the amount of soil carbon sequestration.
Abstract. Using NOAA AVHRR or MODIS imagery to create land-use classifications has been attempted for many years. Unfortunately, most of these classifications do not differentiate crop types. Crop models require that vegetation characteristics extracted from an image be the correct crop type. This study compares four techniques to create land-use classifications using MODIS data. These classifications were compared to the ground data that had been set aside, to a mask where each MODIS sized pixel were at least 80% of a single land-used based on a Landsat TM classification for the same year, and to the Landsat TM classification. Using a decision tree method and comparing the classification to an 80% mask resulted in an accuracy of 73% which was the highest accuracy obtained in this study. The study showed that accuracies could range from 37% to 73% depending on the classification process and if segment data, an 80% mask, or a Landsat TM classification were used for accuracy assessment.
Numerous researchers have demonstrated the accuracy and utility of improved spatial resolution multispectral imagery by sharpening it with higher spatial resolution panchromatic imagery. A much more limited number of researchers have sharpened hyperspectral imagery with panchromatic imagery. In this research we have developed an algorithm that spatially sharpens specific ranges of hyperspectral bands with spectrally correlated multispectral bands of a higher spatial resolution to improve the spatial resolution of the hyperspectral imagery while maintaining or improving it's spectral fidelity. Preliminary validation of the algorithm has been conducted using a 7m AVIRIS scene of the Maryland Eastern Shore containing corn, soybean, and wheat fields. This data was used to simulate 28m HSI and 7m MSI that were used in the sharpening process. Initial analysis has verified the spectral accuracy of the sharpened data. In the next phase of the study, airborne spectral data from two different sensors will be used in the sharpening process with the results used as input for USDA/ARS crop yield and stress models.
Monitoring regional agricultural crop condition has traditionally been accomplished using NOAA AVHRR (1 km) data. New methods are developed for assessing crop yields by retrieving biophysical parameters from remotely sensed imagery and integrating with crop simulation models. The MODIS imagery with its 250 m resolution and a potentially daily coverage offers an opportunity for operational applications. The objective of this research was to assess the potential application of MODIS data for operational crop condition and yield estimates. A field study was conducted during the 2000 crop season in McLean county Illinois (IL), USA. Twenty corn and soybean fields were monitored with measurements for crop reflectance, Leaf area Index (LAI) and other crop growth parameters. A radiative transfer model was used to independently develop the LAI from the MODIS 250-m data. Crop growth parameters retrieved from the imagery were integrated in a crop yield simulation model. The magnitude and spatial variability of estimated LAI and the NASA product was partly due to differences in the classification of crop type and the pixel resolutions. A comparison with the NASA derived MODIS vegetation parameters and independently derived parameters are presented.
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