Next Article in Journal
Ground-Penetrating Radar Mapping Using Multiple Processing and Interpretation Methods
Next Article in Special Issue
Surface Energy Balance of Fresh and Saline Waters: AquaSEBS
Previous Article in Journal
Estimation of Forest Topsoil Properties Using Airborne LiDAR-Derived Intensity and Topographic Factors
Previous Article in Special Issue
Crop Monitoring Based on SPOT-5 Take-5 and Sentinel-1A Data for the Estimation of Crop Water Requirements
Article Menu

Export Article

Open AccessArticle
Remote Sens. 2016, 8(7), 557; doi:10.3390/rs8070557

Remote Sensing of Grass Response to Drought Stress Using Spectroscopic Techniques and Canopy Reflectance Model Inversion

Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, P.O. Box 217, 7500 AE Enschede, The Netherlands
*
Author to whom correspondence should be addressed.
Academic Editors: Zhongbo Su, Yijian Zeng, Zoltan Vekerdy, Clement Atzberger and Prasad S. Thenkabail
Received: 14 April 2016 / Revised: 23 June 2016 / Accepted: 29 June 2016 / Published: 1 July 2016
View Full-Text   |   Download PDF [3583 KB, uploaded 1 July 2016]   |  

Abstract

The aim of this study was to follow the response to drought stress in a Poa pratensis canopy exposed to various levels of soil moisture deficit. We tracked the changes in the canopy reflectance (450–2450 nm) and retrieved vegetation properties (Leaf Area Index (LAI), leaf chlorophyll content (Cab), leaf water content (Cw), leaf dry matter content (Cdm) and senescent material (Cs)) during a drought episode. Spectroscopic techniques and radiative transfer model (RTM) inversion were employed to monitor the gradual manifestation of drought effects in a laboratory setting. Plots of 21 cm × 14.5 cm surface area with Poa pratensis plants that formed a closed canopy were divided into a well-watered control group and a group subjected to water stress for 36 days. In a regular weekly schedule, canopy reflectance and destructive measurements of LAI and Cab were taken. Spectral analysis indicated the first sign of stress after 4–5 days from the start of the experiment near the water absorption bands (at 1930 nm, 1440 nm) and in the red (at 675 nm). Spectroscopic techniques revealed plant stress up to 6 days earlier than visual inspection. Of the water stress-related vegetation indices, the response of Normalized Difference Water Index (NDWI_1241) and Normalized Photochemical Reflectance Index (PRI_norm) were significantly stronger in the stressed group than the control. To observe the effects of stress on grass properties during the drought episode, we used the RTMo (RTM of solar and sky radiation) model inversion by means of an iterative optimization approach. The performance of the model inversion was assessed by calculating R2 and the Normalized Root Mean Square Error (RMSE) between retrieved and measured LAI (R2 = 0.87, NRMSE = 0.18) and Cab (R2 = 0.74, NRMSE = 0.15). All parameters retrieved by model inversion co-varied with soil moisture deficit. However, the first strong sign of water stress on the retrieved grass properties was detected as a change of Cw followed by Cab and Cdm in the earlier stages. The results from this study indicate that the spectroscopic techniques and RTMo model inversion have a promising potential of detecting stress on the spectral reflectance and grass properties before they become visibly apparent. View Full-Text
Keywords: soil moisture deficit; drought stress; canopy reflectance; grass response; spectroscopic techniques; vegetation indices; RTMo model inversion; remote sensing soil moisture deficit; drought stress; canopy reflectance; grass response; spectroscopic techniques; vegetation indices; RTMo model inversion; remote sensing
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

Bayat, B.; van der Tol, C.; Verhoef, W. Remote Sensing of Grass Response to Drought Stress Using Spectroscopic Techniques and Canopy Reflectance Model Inversion. Remote Sens. 2016, 8, 557.

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