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15 September 2011 Remote detection of water stress in orchard canopies using MODIS/ASTER airborne simulator (MASTER) data
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Abstract
Vegetation canopy water content (CWC) is an important parameter for monitoring natural and agricultural ecosystems. Previous studies focused on the observation of annual or monthly variations in CWC but lacked temporal details to study vegetation physiological activities within a diurnal cycle. This study provides an evaluation of detecting vegetation diurnal water stress using airborne data acquired with the MASTER instrument. Concurrent with the morning and afternoon acquisitions of MASTER data, an extensive field campaign was conducted over almond and pistachio orchards in southern San Joaquin Valley of California to collect CWC measurements. Statistical analysis of the field measurements indicated a significant decrease of CWC from morning to afternoon. Field measured CWC was linearly correlated to the normalized difference infrared index (NDII) calculated with atmospherically corrected MASTER reflectance data using either FLAASH or empirical line (EL). Our regression analysis demonstrated that both atmospheric corrections led to a root mean square error (RMSE) of approximately 0.035 kg/m2 for the estimation of CWC (R2=0.42 for FLAASH images and R2=0.45 for EL images). Remote detection of the subtle decline in CWC awaits an improved prediction of CWC. Diurnal CWC maps revealed the spatial patterns of vegetation water status in response to variations in irrigation treatment.
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Tao Cheng, David Riaño, Alexander Koltunov, Michael L. Whiting, and Susan L. Ustin "Remote detection of water stress in orchard canopies using MODIS/ASTER airborne simulator (MASTER) data", Proc. SPIE 8156, Remote Sensing and Modeling of Ecosystems for Sustainability VIII, 815605 (15 September 2011); doi: 10.1117/12.892889; https://doi.org/10.1117/12.892889
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