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INTEGRATING REMOTE SENSING TO IMPROVE CROP GRAIN YIELD ESTIMATES FOR ASSESSING WITHIN-FIELD SPATIAL AND TEMPORAL VARIABILITY

Bhatta, Aman
http://rave.ohiolink.edu/etdc/view?acc_num=osu1594060903495952

Abstract Details

2020, Master of Science, Ohio State University, Environmental Science.
Understanding of within-field spatial and temporal variability of crop yield and the potential drivers for such variability is critical for site-specific crop management (a.k.a precision agriculture) from both economic and environmental perspectives. The objectives of this study are to 1) improve crop yield estimates at a field scale by integrating remote sensing data to assess spatial and temporal within-field yield variability, and 2) evaluate how the design of management zones varies using crop yield data of various spatial resolutions. To meet these objectives, yield monitor data of three fields (~32.5 hectares) that are in corn-soybean and corn-wheat rotations over the period 2016-2019 in the Molly Caren Agriculture Center at London, Ohio were used. Crop grain yield data collected from yield monitor was integrated with topographic variables derived from digital elevation model (DEM) (0.76 m) and vegetation indices derived from high- and medium-resolution remotely sensed imagery (0.3 m to 3 m) using linear regression (LR) and random forest (RF) algorithms to create high-and medium-resolution crop yield maps at a field scale. Yield monitor data were cleaned using Yield Editor software. Topographic variables, such as slope, elevation and wetness index, were calculated using DEM data. Remotely sensed imagery were preprocessed and analyzed, and various vegetation indices (e.g., normalized difference vegetation index (NDVI), Green NDVI (GNDVI), Excess Greenness (ExG)) were calculated. Using high-and medium-resolution yield maps, temporal and spatial standard deviations (SD) of crop yields were calculated. Based on SD and average crop yield, areas within a field were classified into four zones (z), with z1 and z2 having consistently higher and lower yield than average yield, respectively; z3 with variable but below average yield, and z4 with variable but above average yield. DEM derived topographic variables were used to assess their impact on yield variability within the four zones. RF models consistently estimated crop yields with higher accuracy than LR models, and the models were able to explain up to 83% within-field yield variability. Model based yield maps demonstrated within-field yield differences better than yield monitor data. Spatial variability of crop yield was generally lower than temporal variability. Topographic properties were found to play significant role in within-field yield differences. Areas with lower slope were found to have higher yield suggesting the need to consider topographic variabilities in implementation of agricultural practices. Improved understanding on processes underlying spatial and temporal variability of crop yield can help develop management practices for optimal productivity with improved environmental quality.
Sami Khanal, Dr. (Committee Chair)
45 p.
Environmental Science
Precision Agriculture, Interpolation, Spatial and Temporal Yield Variability

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Citations

  • Bhatta, A. (2020). INTEGRATING REMOTE SENSING TO IMPROVE CROP GRAIN YIELD ESTIMATES FOR ASSESSING WITHIN-FIELD SPATIAL AND TEMPORAL VARIABILITY [Master's thesis, Ohio State University]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=osu1594060903495952

    APA Style (7th edition)

  • Bhatta, Aman. INTEGRATING REMOTE SENSING TO IMPROVE CROP GRAIN YIELD ESTIMATES FOR ASSESSING WITHIN-FIELD SPATIAL AND TEMPORAL VARIABILITY. 2020. Ohio State University, Master's thesis. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=osu1594060903495952.

    MLA Style (8th edition)

  • Bhatta, Aman. "INTEGRATING REMOTE SENSING TO IMPROVE CROP GRAIN YIELD ESTIMATES FOR ASSESSING WITHIN-FIELD SPATIAL AND TEMPORAL VARIABILITY." Master's thesis, Ohio State University, 2020. http://rave.ohiolink.edu/etdc/view?acc_num=osu1594060903495952

    Chicago Manual of Style (17th edition)

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This open access ETD is published by The Ohio State University and OhioLINK.