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Title: Corn response to climate stress detected with satellite-based NDVI time series

Abstract

Corn growth conditions and yield are closely dependent on climate variability. Leaf growth, measured as the leaf area index, can be used to identify changes in crop growth in response to climate stress. This research was conducted to capture patterns of spatial and temporal corn leaf growth under climate stress for the St. Joseph River watershed, in northeastern Indiana. Leaf growth is represented by the Normalized Difference Vegetative Index (NDVI) retrieved from multiple years (2000–2010) of Landsat 5 TM images. By comparing NDVI values for individual image dates with the derived normal curve, the response of crop growth to environmental factors is quantified as NDVI residuals. Regression analysis revealed a significant relationship between yield and NDVI residual during the pre-silking period, indicating that NDVI residuals reflect crop stress in the early growing period that impacts yield. Both the mean NDVI residuals and the percentage of image pixels where corn was under stress (risky pixel rate) are significantly correlated with water stress. Dry weather is prone to hamper potential crop growth, with stress affecting most of the observed corn pixels in the area. Oversupply of rainfall at the end of the growing season was not found to have a measurable effectmore » on crop growth, while above normal precipitation earlier in the growing season reduces the risk of yield loss at the watershed scale. Furthermore, the spatial extent of stress is much lower when precipitation is above normal than under dry conditions, masking the impact of small areas of yield loss at the watershed scale.« less

Authors:
 [1]; ORCiD logo [1];  [1]
  1. Purdue Univ., West Lafayette, IN (United States)
Publication Date:
Research Org.:
Purdue Univ., West Lafayette, IN (United States)
Sponsoring Org.:
USDOE
OSTI Identifier:
1257970
Grant/Contract Number:  
EE0004396
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Remote Sensing
Additional Journal Information:
Journal Volume: 8; Journal Issue: 4; Journal ID: ISSN 2072-4292
Publisher:
MDPI
Country of Publication:
United States
Language:
English
Subject:
54 ENVIRONMENTAL SCIENCES; 47 OTHER INSTRUMENTATION; temporal and spatial corn growth variability; corn NDVI-climate stress relation; risky cell detection

Citation Formats

Wang, Ruoyu, Cherkauer, Keith, and Bowling, Laura. Corn response to climate stress detected with satellite-based NDVI time series. United States: N. p., 2016. Web. doi:10.3390/rs8040269.
Wang, Ruoyu, Cherkauer, Keith, & Bowling, Laura. Corn response to climate stress detected with satellite-based NDVI time series. United States. doi:10.3390/rs8040269.
Wang, Ruoyu, Cherkauer, Keith, and Bowling, Laura. Wed . "Corn response to climate stress detected with satellite-based NDVI time series". United States. doi:10.3390/rs8040269. https://www.osti.gov/servlets/purl/1257970.
@article{osti_1257970,
title = {Corn response to climate stress detected with satellite-based NDVI time series},
author = {Wang, Ruoyu and Cherkauer, Keith and Bowling, Laura},
abstractNote = {Corn growth conditions and yield are closely dependent on climate variability. Leaf growth, measured as the leaf area index, can be used to identify changes in crop growth in response to climate stress. This research was conducted to capture patterns of spatial and temporal corn leaf growth under climate stress for the St. Joseph River watershed, in northeastern Indiana. Leaf growth is represented by the Normalized Difference Vegetative Index (NDVI) retrieved from multiple years (2000–2010) of Landsat 5 TM images. By comparing NDVI values for individual image dates with the derived normal curve, the response of crop growth to environmental factors is quantified as NDVI residuals. Regression analysis revealed a significant relationship between yield and NDVI residual during the pre-silking period, indicating that NDVI residuals reflect crop stress in the early growing period that impacts yield. Both the mean NDVI residuals and the percentage of image pixels where corn was under stress (risky pixel rate) are significantly correlated with water stress. Dry weather is prone to hamper potential crop growth, with stress affecting most of the observed corn pixels in the area. Oversupply of rainfall at the end of the growing season was not found to have a measurable effect on crop growth, while above normal precipitation earlier in the growing season reduces the risk of yield loss at the watershed scale. Furthermore, the spatial extent of stress is much lower when precipitation is above normal than under dry conditions, masking the impact of small areas of yield loss at the watershed scale.},
doi = {10.3390/rs8040269},
journal = {Remote Sensing},
issn = {2072-4292},
number = 4,
volume = 8,
place = {United States},
year = {2016},
month = {3}
}

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Cited by: 9 works
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