Detection of diurnal variation in orchard canopy water content using MODIS/ASTER airborne simulator (MASTER) data

Abstract

Retrievals of vegetation canopy water content (CWC) from remotely sensed imagery can improve our understanding of the water cycle and help manage irrigation of agricultural crops. Optical remote sensing data can be used to detect seasonal CWC variation but whether they are sensitive enough for detecting diurnal CWC variation remains unknown. This paper investigates whether MODIS/ASTER airborne simulator (MASTER) data can be used to detect diurnal variation in CWC over well irrigated almond and pistachio orchards in the southern San Joaquin Valley of California, USA. MASTER images were first corrected for the Bi-directional Reflectance Distribution Function (BRDF) effect to remove cross-track variation in reflectance amplitude. Two spectral indexes, the Normalized Difference Infrared Index (NDII) and the Normalized Difference Vegetation Index (NDVI), were derived from corrected morning and afternoon MASTER imagery and related to the field-measured CWC. At the ground level, a significant decrease (~9%) in CWC occurred from morning to afternoon (p<0.0001). The field-measured CWC was positively correlated with MASTER-derived NDII and NDVI for both morning (NDII: r2=0.67, NDVI: r2=0.56, p<0.0001) and afternoon (NDII: r2=0.42, NDVI: r2=0.39, p<0.001) data. The diurnal change in CWC also led to a statistically significant spectral change that was observed as a 4% decline in NDII (p<0.005) or 2% decline in NDVI (p<0.0005). Our results show that the diurnal variation in CWC can be detected for the irrigated orchards using simple spectral indexes derived from MASTER data, with higher sensitivity for NDII than for NDVI as expected. The results also demonstrate the potential for remote sensing to improve crop management and better understand plant physiological changes at field to regional scales.