Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer

doi:10.1016/S2095-3119(16)61502-2

Journal of Integrative Agriculture ›› 2017, Vol. 16 ›› Issue (02): 398-407.DOI: 10.1016/S2095-3119(16)61502-2

收稿日期:2016-06-07 修回日期:2016-10-17 出版日期:2017-02-20 发布日期:2017-01-31

Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer

Ranjay Shrestha^{1, 2}, Liping Di^{1, 2}, Eugene G. Yu¹, Lingjun Kang^{1, 2}, SHAO Yuan-zheng³, BAI Yu-qi⁴

¹Center for Spatial Information Science and Systems (CSISS), George Mason University, VA 22030, USA
²Department of Geography and Geoinformation Science, George Mason University, VA 22030, USA
³State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, P.R.China
⁴Key Laboratory for Earth System Modelling, Ministry of Education/Department of Earth System Science (DESS), Tsinghua University, Beijing 100084, P.R.China

Received:2016-06-07 Revised:2016-10-17 Online:2017-02-20 Published:2017-01-31
Contact: Liping Di, E-mail: ldi@gmu.edu
Supported by:
This research has been supported by grants from the National Aeronautics and Space Administration (NASA) of the United States (NNX12AQ31G and NNX12AQ31G NNX14AP91G, PI: Dr. Liping Di).

摘要/Abstract

Abstract: Flood events and their impact on crops are extremely significant scientific research issues; however, flood monitoring is an exceedingly complicated process. Flood damages on crops are directly related to yield change, which requires accurate assessment to quantify the damages. Various remote sensing products and indices have been used in the past for this purpose. This paper utilizes the moderate resolution imaging spectroradiometer (MODIS) weekly normalized difference vegetation index (NDVI) product to detect and further quantify flood damages on corn within the major corn producing states in the Midwest region of the US. County-level analyses were performed by taking weighted average of all pure corn pixels (>90%) masked by the United States Department of Agriculture (USDA) Cropland Data Layer (CDL). The NDVI-based time-series difference between flood years and normal year (median of years 2000–2014) was used to detect flood occurrences. To further measure the impact of the flood on corn yield, regression analysis between change in NDVI and change in corn yield as independent and dependent variables respectively was performed for 30 different flooding events within growing seasons of the corn. With the R² value of 0.85, the model indicates statistically significant linear relation between the NDVI and corn yield. Testing the predictability of the model with 10 new cases, the average relative error of the model was only 4.47%. Furthermore, small error (4.8%) of leave-one-out cross validation (LOOCV) along with smaller statistical error indicators (root mean square error (RMSE), mean absolute error (MAE), and mean bias error (MBE)), further validated the accuracy of the model. Utilizing the linear regression approach, change in NDVI during the growing season of corn appeared to be a good indicator to quantify the yield loss due to flood. Additionally, with the 250 m MODIS-based NDVI, these yield losses can be estimated up to field level.

Key words: NDVI, MODIS, agriculture, corn yield, remote sensing, regression

Ranjay Shrestha, Liping Di, Eugene G. Yu, Lingjun Kang, SHAO Yuan-zheng, BAI Yu-qi. [J]. Journal of Integrative Agriculture, 2017, 16(02): 398-407.

Ranjay Shrestha, Liping Di, Eugene G. Yu, Lingjun Kang, SHAO Yuan-zheng, BAI Yu-qi. Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer[J]. Journal of Integrative Agriculture, 2017, 16(02): 398-407.

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Regression model to estimate flood impact on corn yield using MODIS NDVI and USDA cropland data layer

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