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Remote Sensing

Temporal and Spatial Relationships between Within-Field Yield Variability in Cotton and High-Spatial Hyperspectral Remote Sensing Imagery

Cent. for Spatial Technol. and Remote Sensing (CSTARS), Dep. of Land, Air, and Water Resour. (LAWR), One Shields Ave., The Barn, Univ. of California, Davis, CA 95616-8671, USA

^* Corresponding author (pzarco{at}ias.csic.es)

Received for publication October 20, 2003. Traditional remote sensing methods for yield estimation rely on broadband vegetation indices, such as the Normalized Difference Vegetation Index, NDVI. Despite demonstrated relationships between such traditional indices and yield, NDVI saturates at larger leaf area index (LAI) values, and it is affected by soil background. We present results obtained with several new narrow-band hyperspectral indices calculated from the Airborne Visible and Near Infrared (AVNIR) hyperspectral sensor flown over a cotton (Gossypium hirsutum L.) field in California (USA) collected over an entire growing season at 1-m spatial resolution. Within-field variability of yield monitor spatial data collected during harvest was correlated with hyperspectral indices related to crop growth and canopy structure, chlorophyll concentration, and water content. The time-series of indices calculated from the imagery were assessed to understand within-field yield variability in cotton at different growth stages. A K means clustering method was used to perform field segmentation on hyperspectral indices in classes of low, medium, and high yield, and confusion matrices were used to calculate the kappa () coefficient and overall accuracy. Structural indices related to LAI [Renormalized Difference Vegetation Index (RDVI), Modified Triangular Vegetation Index (MTVI), and Optimized Soil-Adjusted Vegetation Index (OSAVI)] obtained the best relationships with yield and field segmentation at early growth stages. Hyperspectral indices related to crop physiological status [Modified Chlorophyll Absorption Index (MCARI) and Transformed Chlorophyll Absorption Index (TCARI)] were superior at later growth stages, close to harvest. From confusion matrices and class analyses, the overall accuracy (and kappa) of RDVI at early stages was 61% ( = 0.39), dropping to 39% ( = 0.08) before harvest. The MCARI chlorophyll index remained sensitive to within-field yield variability at late preharvest stage, obtaining overall accuracy of 51% ( = 0.22).

Abbreviations: AVNIR, Airborne Visible and Near Infrared • C_a+b, chlorophyll a and b • CARI, Chlorophyll Absorption in Reflectance Index • DGPS, differential global positioning system • LAI, leaf area index • MCARI, Modified Chlorophyll Absorption Index • MSAVI, Improved Soil-Adjusted Vegetation Index • MSR, Modified Simple Ratio • MTVI, Modified Triangular Vegetation Index • NDVI, Normalized Difference Vegetation Index • NDWI, Normalized Difference Water Index • NIR, near infrared • OSAVI, Optimized Soil-Adjusted Vegetation Index • PWI, Plant Water Index • RDVI, Renormalized Difference Vegetation Index • SAVI, Soil-Adjusted Vegetation Index • SRWI, Simple Ratio Water Index • TCARI, Transformed Chlorophyll Absorption Index • TVI, Triangular Vegetation Index

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