Thermal imaging and Hyperspectral remote sensing for crop water deficit stress monitoring
Updated: Jun 8, 2019
Water deficit in crops induces a stress that may ultimately result in low production. Identification of response of genotypes towards water deficit stress is very crucial for plant phenotyping. The study was carried out with the objective to identify the response of different rice genotypes to water deficit stress. Ten rice genotypes were grown each under water deficit stress and well watered or nonstress conditions. Thermal images coupled with visible images were recorded to quantify the stress and response of genotypes towards stress, and relative water content (RWC) synchronized with image acquisition was also measured in the lab for rice leaves. Synced with thermal imaging, Canopy reflectance spectra from same genotype fields were also recorded. For quantification of water deficit stress, Crop Water Stress Index (CWSI) was computed and its mode values were extracted from processed thermal imageries. It was ascertained from observations that APO and Pusa Sugandha-5 genotypes exhibited the highest resistance to the water deficit stress or drought whereas CR-143, MTU-1010, and Pusa Basmati-1 genotypes ascertained the highest sensitiveness to the drought. The study reveals that there is an effectual relationship (R sq = 0.63) between RWC and CWSI. The relationship between canopy reflectance spectra and CWSI was also established through partial least square regression technique. A very efficient relationship (calibration R sq = 0.94 and cross-validation R sq = 0.71) was ascertained and 10 most optimal wavebands related to water deficit stress were evoked from hyperspectral data resampled at 5 nm wavelength gap. The identified ten most optimum wavebands can contribute in the quick detection of water deficit stress in crops. This study positively contributes towards the identification of drought tolerant and drought resistant genotypes of rice and may provide valuable input for the development of drought-tolerant rice genotypes in future.
The research work has been published in Geocarto International journal. Get the link of full paper in bottom of this article.
Representative thermal imagery and synchronized visible image is given in the figure below. The methodological approach has also been given in the figure below-
Frequency distribution of CWSI values for each genotype in both the stressed and non stressed conditions, were plotted. Non stressed condition frequency was plotted with stressed condition frequency for each genotype, to monitor the response of particular genotype in drought condition.
Relationship b/w crop hyperspectral reflectance spectra and Plant Water content
The partial least squares (PLS) regression technique was exploited to establish a relationship between canopy spectral reflectance and CWSI values for all genotypes. The PLS regression was executed using kernel PLS algorithm. The number of components was decided using variance values plotted against RMSEP. A strong coefficient of determination (R2) for calibration as 0.94 and for validation as 0.71
Ten Most optimum water sensitive spectral wave bands were extracted from this analysis.
The research work has been published in Geocarto International journal. Click here to get full paper.
New Delhi, India