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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},
number = 4,
volume = 8,
place = {United States},
year = {Wed Mar 23 00:00:00 EDT 2016},
month = {Wed Mar 23 00:00:00 EDT 2016}
}

Journal Article:
Free Publicly Available Full Text
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Citation Metrics:
Cited by: 9works
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  • Time-integrated normalized difference vegetation index (TI NDVI) derived from the multitemporal satellite imagery (1989--1993) was used as a surrogate for primary production to investigate climate impacts on grassland performance for central and northern Great Plains grasslands. Results suggest that spatial and temporal variability in growing season precipitation, potential evapotranspiration, and growing degree days are the most important controls on grassland performance and productivity. When TI NDVI and climate data of all grassland land cover classes were examined as a whole, a statistical model showed significant positive correlation between the TI NDVI and accumulated spring and summer precipitation, and a negativemore » correlation between TI NDVI and spring potential evapotranspiration. The coefficient of determination (R{sup 2}) of the general model was 0.45. When the TI NDVI-climate relationship was examined by individual land cover type, the relationship was generally better defined in terms of the variance accounted for by class-specific models.« less
  • Two studies were conducted in 1988 to examine the effects of simulated acid rain in combination with various levels of drought stress on the grain yield of field grown corn (Zea mays L., B73 {times} Mol17 and FS854). In both studies corn was treated with twice weekly applications of simulated rainfall of pH 5.6 or 3.0 at amounts that totaled 100% (30 cm), 50% (15 cm), and 25% (7.5 cm) of the seasonal average for Champaign-Urbana, Il. In addition to those treatments, in one of the studies the plants were subjected to daily wetting with the appropriate simulated rain frommore » tassel emergence through pollination and fertilization. In both studies, reduced moisture levels resulted in significant reduction in grain yield but simulated rain of pH 3.0 had no effect on yield at any of the moisture levels studied. For both cultivars in both studies, reducing rainfall application from seasonal average to one-half of the normal decreased yields by approximately 30%. When only one-fourth of the seasonal rainfall amount was applied, yields were decreased between 40 and 55% compared to the yield for plants receiving the seasonal average rainfall. Results from these studies suggest that application of simulated acid rain of pH 3.0 has little or not negative effect on grain yield of the corn cultivars evaluated, even when relatively severe moisture stress was present, and when plants were subjected to daily wetting from tassel emergence through fertilization.« less
  • Here, recent studies have shown that global Penman-Monteith equation based (PM-based) models poorly simulate water stress when estimating evapotranspiration (ET) in areas having a Mediterranean climate (AMC). In this study, we propose a novel approach using precipitation, vertical root distribution (VRD), and satellite-retrieved vegetation information to simulate water stress in a PM-based model (RS-WBPM) to address this issue. A multilayer water balance module is employed to simulate the soil water stress factor (SWSF) of multiple soil layers at different depths. The water stress factor (WSF) for surface evapotranspiration is determined by VRD information and SWSF in each layer. Additionally, fourmore » older PM-based models (PMOV) are evaluated at 27 flux sites in AMC. Results show that PMOV fails to estimate the magnitude or capture the variation of ET in summer at most sites, whereas RS-WBPM is successful. The daily ET resulting from RS-WBPM incorporating recommended VI (NDVI for shrub and EVI for other biomes) agrees well with observations, with R2 = 0.60 ( RMSE = 18.72 W m-2) for all 27 sites and R2=0.62 ( RMSE 5 18.21 W m-2) for 25 nonagricultural sites. However, combined results from the optimum older PM-based models at specific sites show R2 values of only 0.50 ( RMSE 5 20.74 W m-2) for all 27 sites. RS-WBPM is also found to outperform other ET models that also incorporate a soil water balance module. As all inputs of RS-WBPM are globally available, the results from RS-WBPM are encouraging and imply the potential of its implementation on a regional and global scale.« less
  • It is critically meaningful to accurately predict NDVI (Normalized Difference Vegetation Index), which helps guide regional ecological remediation and environmental managements. In this study, a combination forecasting model (CFM) was proposed to improve the performance of NDVI predictions in the Yellow River Basin (YRB) based on three individual forecasting models, i.e., the Multiple Linear Regression (MLR), Artificial Neural Network (ANN), and Support Vector Machine (SVM) models. The entropy weight method was employed to determine the weight coefficient for each individual model depending on its predictive performance. Results showed that: (1) ANN exhibits the highest fitting capability among the four orecastingmore » models in the calibration period, whilst its generalization ability becomes weak in the validation period; MLR has a poor performance in both calibration and validation periods; the predicted results of CFM in the calibration period have the highest stability; (2) CFM generally outperforms all individual models in the validation period, and can improve the reliability and stability of predicted results through combining the strengths while reducing the weaknesses of individual models; (3) the performances of all forecasting models are better in dense vegetation areas than in sparse vegetation areas.« less
  • Changes in plant phenology affect the carbon flux of terrestrial forest ecosystems due to the link between the growing season length and vegetation productivity. Digital camera imagery, which can be acquired frequently, has been used to monitor seasonal and annual changes in forest canopy phenology and track critical phenological events. However, quantitative assessment of the structural and biochemical controls of the phenological patterns in camera images has rarely been done. In this study, we used an NDVI (Normalized Difference Vegetation Index) camera to monitor daily variations of vegetation reflectance at visible and near-infrared (NIR) bands with high spatial and temporalmore » resolutions, and found that the infrared camera based NDVI (camera-NDVI) agreed well with the leaf expansion process that was measured by independent manual observations at Harvard Forest, Massachusetts, USA. We also measured the seasonality of canopy structural (leaf area index, LAI) and biochemical properties (leaf chlorophyll and nitrogen content). Here we found significant linear relationships between camera-NDVI and leaf chlorophyll concentration, and between camera-NDVI and leaf nitrogen content, though weaker relationships between camera-NDVI and LAI. Therefore, we recommend ground-based camera-NDVI as a powerful tool for long-term, near surface observations to monitor canopy development and to estimate leaf chlorophyll, nitrogen status, and LAI.« less