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REMOTE SENSING

Estimating Ground Cover of Field Crops Using Medium-Resolution Multispectral Satellite Imagery

Dep. of Plant and Soil Science, Texas Tech Univ., Lubbock, TX 79409

^* Corresponding author (stephen.maas{at}ttu.edu).

Remote sensing is useful for estimating plant canopy characteristics, such as leaf area index (LAI) and ground cover (GC). When the source of remote sensing data is medium-resolution satellite imagery, plant canopy characteristics can be estimated for numerous fields within an agricultural region. In this study, a procedure was developed to estimate GC of field crops from medium-resolution satellite image data in the red and near-infrared (NIR) spectral bands. In the procedure, GC is estimated from the ratio of the perpendicular vegetation index (PVI) value calculated for an image pixel to the PVI value corresponding to full vegetation canopy. Two main advantages of this procedure are that it does not rely on empirical relationships, and that it can use raw satellite digital count data without conversion to surface reflectance or normalization for scene-to-scene differences in brightness. A field study was conducted in 2006 in the Texas High Plains to collect ground-based observations of GC for 31 agricultural fields containing various crops for testing the procedure. The GC for these fields was estimated using the procedure from Landsat-5 TM image data acquired on four dates during the growing season. Statistical analysis of the linear regression between satellite-based estimates of GC and corresponding ground-based observations of GC indicated that the regression was not different from a 1:1 relationship. Statistical analysis also indicated that the average of the satellite-based estimates of GC was not significantly different from the average of the ground-based observations of GC. The results suggest that, on average, estimates of GC determined using this procedure should be within 6% of their true values. The relative simplicity of this procedure should facilitate the quantification of vegetation resources in agricultural regions.

Abbreviations: AAE, average absolute error • CRP, Conservation Reserve Program • DC, digital count • FAPAR, fractional absorbed photosynthetically active radiation • GC, ground cover • LAI, leaf area index • LMM, Linear Mixture Model • NIR, near-infrared • PVI, perpendicular vegetation index • TM, Thematic Mapper • VI, vegetation index

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Received for publication April 15, 2007.