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

• Main Authors: Bagher Bayat, Christiaan van der Tol, Wouter Verhoef
• Format: Article
• Language: English
• Published: MDPI AG 2016-07-01
• Series: Remote Sensing
• Subjects: soil moisture deficit; drought stress; canopy reflectance; grass response; spectroscopic techniques; vegetation indices; RTMo model inversion; remote sensing
• Online Access: http://www.mdpi.com/2072-4292/8/7/557

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.