Next Article in Journal
Operational Drought Monitoring in Kenya Using MODIS NDVI Time Series
Next Article in Special Issue
Early Drought Detection by Spectral Analysis of Satellite Time Series of Precipitation and Normalized Difference Vegetation Index (NDVI)
Previous Article in Journal
Novel Approach to Unsupervised Change Detection Based on a Robust Semi-Supervised FCM Clustering Algorithm
Previous Article in Special Issue
Blending Satellite Observed, Model Simulated, and in Situ Measured Soil Moisture over Tibetan Plateau
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(4), 269; doi:10.3390/rs8040269

Corn Response to Climate Stress Detected with Satellite-Based NDVI Time Series

1
Department of Agricultural and Biological Engineering, Purdue University, West Lafayette, IN 47907, USA
2
Department of Agronomy, Purdue University, West Lafayette, IN 47907, USA
*
Author to whom correspondence should be addressed.
Academic Editors: Zhongbo Su, Yijian Zeng, Zoltan Vekerdy, Parth Sarathi Roy and Prasad S. Thenkabail
Received: 22 January 2016 / Revised: 23 February 2016 / Accepted: 17 March 2016 / Published: 23 March 2016
View Full-Text   |   Download PDF [3316 KB, uploaded 23 March 2016]   |  

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 effect on crop growth, while above normal precipitation earlier in the growing season reduces the risk of yield loss at the watershed scale. 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. View Full-Text
Keywords: temporal and spatial corn growth variability; corn NDVI-climate stress relation; risky cell detection temporal and spatial corn growth variability; corn NDVI-climate stress relation; risky cell detection
Figures

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Wang, R.; Cherkauer, K.; Bowling, L. Corn Response to Climate Stress Detected with Satellite-Based NDVI Time Series. Remote Sens. 2016, 8, 269.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Remote Sens. EISSN 2072-4292 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top