Mapping Intra-Field Yield Variation Using High Resolution Satellite Imagery to Integrate Bioenergy and Environmental Stewardship in an Agricultural Watershed

Abstract

Biofuels are important alternatives for meeting our future energy needs. Successful bioenergy crop production requires maintaining environmental sustainability and minimum impacts on current net annual food, feed, and fiber production. The objectives of this study were to: (1) determine under-productive areas within an agricultural field in a watershed using a single date; high resolution remote sensing and (2) examine impacts of growing bioenergy crops in the under-productive areas using hydrologic modeling in order to facilitate sustainable landscape design. Normalized difference indices (NDIs) were computed based on the ratio of all possible two-band combinations using the RapidEye and the National Agricultural Imagery Program images collected in summer 2011. A multiple regression analysis was performed using 10 NDIs and five RapidEye spectral bands. The regression analysis suggested that the red and near infrared bands and NDI using red-edge and near infrared that is known as the red-edge normalized difference vegetation index (RENDVI) had the highest correlation (R 2 = 0.524) with the reference yield. Although predictive yield map showed striking similarity to the reference yield map, the model had modest correlation; thus, further research is needed to improve predictive capability for absolute yields. Forecasted impact using the Soil and Water Assessment Tool model of growing switchgrass (Panicum virgatum) on under-productive areas based on corn yield thresholds of 3.1, 4.7, and 6.3 Mg· ha −1 showed reduction of tile NO3-N and sediment

Mid-Season High-Resolution Satellite Imagery for Forecasting Site-Specific Corn Yield

“…Yield and imagery data of each field showed a significant autocorrelation indicating significant within-field variability and dependence of values that are closer in space than values at a further distance. Our result agrees with previous research results on field level spatial variation and autocorrelation on corn yield [46][47][48] and VI [27,49,50]. An in depth study of the main reason behind a significant variation in yield and growth will follow this paper, but based on literature, soil nutrient and other resource variation, slope, production and management history of each area, drainage, and level of competition between neighboring plants can be cited.…”

“…Application of PA is crucial for fields that inherently have variability and, therefore, use of high-resolution satellite imagery can be critical for site-specific management. Previous investigations reflected significant correlation between mid-season VIs and final yield at the regional-scale [23,27] but there is scarce information for application of high-resolution satellite imagery data for intra-field level [13] management.…”

“…Previous studies portrayed the benefits of using high-resolution satellite imagery for identifying within-field variation [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27] and provided a solid foundation for developing and extending this procedure to multiple sites and evaluation years. The current research project provides novel points in the evaluation of multiple fields in diverse site-years and in exploring the utilization of econometric models for accounting for the spatial variation in corn production.…”

The economics of growing shrub willow as a bioenergy buffer on agricultural fields: A case study in the Midwest Corn Belt

“…The study area is located in the Indian Creek watershed in central Illinois (USA). The watershed characteristics (climate, major crop rotations, and soils) were described by Hamada et al . Briefly, the watershed is a high productivity grains landscape in the heart of the US Corn Belt, with Drummer silty clay loam, Reddick clay loam, and Saybrook silt loam as the most prevalent soils.…”

Mapping Maize Evapotranspiration at Field Scale Using SEBAL: A Comparison with the FAO Method and Soil-Plant Model Simulations

“…In addition, it is difficult to predict the correct growth stage dates for large populations of crops and fields [15].A viable alternative for mapping evaporation at field and regional scales is the use of satellite images that can provide an excellent tool to detect the spatial and temporal structure of ET [16]. Remote sensing (RS) is a reliable and cost-effective method to forecast crop ET and yield over large areas [17,18] and the integrated use of remote-sensing data and crop modeling for yield prediction has been applied for many years [18]. Applications can be found in the literature for different crop types and regions using various data assimilation schemes [19][20][21].…”