Title: Mapping intra-field yield variation using high resolution satellite imagery to integrate bioenergy and environmental stewardship in an agricultural watershed

Abstract

Biofuels derived from renewable biological materials are important alternatives for meeting our future energy needs. The Energy Independence and Security act (EISA) of 2007 mandated that 16 Billion gallons per year of total biofuel production come from cellulosic-based biofuels by 2022. Crop residues such as corn stover and dedicated bioenergy crops are expected to be available to satisfy this need (Billion ton study). However, uncertainty in environmental and socioeconomic impacts associated with removal of corn residue, additional allocation of land to bioenergy and increased use of fertilizer present barriers to accomplishing the national goal. In recent years, perennial bioenergy crops have been proposed as valuable cellulosic bioenergy feedstock which can support biodiversity and ecosystem sustainability. Successful integration of new cellulosic bioenergy crops into existing agricultural systems requires landscape design that maintains environmental sustainability and has minimal impacts on current net annual food, feed, and fiber production. The use of marginal agricultural land has potential for facilitating landscape designs that optimize outcomes for both commodity and bioenergy crops. The goal of this study was to demonstrate the utility of high resolution remote sensing for determining under-productive areas within a plot. Five spectral vegetation indies (SVIs; normalized difference vegetation index [NDVI], greenmore » NDVI, normalized difference red-edge index, visible atmospherically resistant index, and enhanced vegetation index 2) were computed using the RapidEye and National Agricultural Imagery Program (NAIP) images that were collected in the late August 2011. Of the five SVIs, the NDREI at 5 m resolution showed the highest correlation (R² = 0.56) with the corn yield estimated during harvest. The multiple linear regression model that was developed using the NDREI and spectral bands showed moderate yet positive correlation (R² = 0.58) with the estimated yield. This model was used to determine areas in a rural watershed which represent the bottom 1.4 – 14.4% corn yield locations. A calibrated Soil and Water Assessment Tool for a sample watershed in central Illinois was used to forecast the impact of growing switchgrass (Panicum virgatum) on the identified low corn yield areas under three threshold scenarios of yields less or equal to 3.1, 4.7, and 6.3 Mg ha^-1 (corresponding to ≈ 30%, 50%, and 70% of observed average corn productivity). The three thresholds resulted in conversion of 1.6%, 6.3%, and 14.4% of total area of the watershed. Relative to business as usual, the simulated conversions estimated reduced tile NO₃-N and sediment exports by 1.8 – 13.9% and 32.4 – 41.7%, respectively. Corresponding reductions in water yields ranged from 6.5 – 14.5%. Furthermore, the study demonstrates the integration of remotely sensed data and hydrologic modeling to quantify the multifunctional value of planned or projected future landscape patterns.« less

Authors:

Hamada, Yuki ^[1]; Ssegane, Herbert ^[1]; Negri, Maria Cristina ^[1]

---

+ Show Author Affiliations

1. Argonne National Lab. (ANL), Argonne, IL (United States)

Publication Date:

Fri Jul 31 00:00:00 EDT 2015

Research Org.:

Argonne National Laboratory (ANL), Argonne, IL (United States)

Sponsoring Org.:

USDOE Office of Energy Efficiency and Renewable Energy (EERE), Sustainable Transportation Office. Bioenergy Technologies Office (BETO); Office of Biomass

OSTI Identifier:

1209109

Alternate Identifier(s):

OSTI ID: 1344526

Grant/Contract Number:  

AC02-06CH11357

Resource Type:

Accepted Manuscript

Journal Name:

Remote Sensing

Additional Journal Information:

Journal Volume: 7; Journal Issue: 8; Journal ID: ISSN 2072-4292

Country of Publication:

United States

Language:

English

Subject:

09 BIOMASS FUELS; 54 ENVIRONMENTAL SCIENCES; predictive crop yield; red-edge; biofuel feedstock; sub-field scale; landscape design; future landscape patterns; hydrologic modeling; SWAT; water quality; 60 APPLIED LIFE SCIENCES