Remote sensing, normalized difference vegetation index (NDVI), and crop yield forecasting
Lv, Xijie
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Xijie_Lv.pdf
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https://hdl.handle.net/2142/46590
Description
Title
Remote sensing, normalized difference vegetation index (NDVI), and crop yield forecasting
Author(s)
Lv, Xijie
Issue Date
2014-01-16T17:55:21Z
Director of Research (if dissertation) or Advisor (if thesis)
Paulson, Nicholas D.
Department of Study
Agr & Consumer Economics
Discipline
Agricultural & Applied Econ
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2014-01-16T17:55:21Z
Keyword(s)
Remote Sensing
Normalized Difference Vegetation Index (NDVI)
Crop Yield Assessment
Forecasting
Abstract
The thesis explored the feasibility of using remotely sensed image and its derived products, Normalized Difference Vegetation Index (NDVI), to assess and quantify corn and soybean yield potential. Fixed-effect panel and ordinary least squares NDVI regression models were developed for different level of spatial aggregation. Through the regression analysis, the thesis identified the relationship between the accumulation of crops’ “greenness” over the growing season and the final crops yield. The ultimate goal of the thesis is to examine whether the NDVI model can produce accurate and timely yield forecasts. Due to the unique features of the spatial data (e.g. global coverage, frequent repeat cycle and etc.), the model can provide significant value to developing countries where the meteorological network is scarce and official crop production estimates are either inaccurate or nonexistent. Therefore, to evaluate the NDIV model’s predictive power, the model’s out-of-sample forecasts were compared to the predictions of a weather-based regression model (modified Thompson model) as well as August, September, and October USDA estimates.
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Xijie_Lv.pdf
