USING REMOTE SENSING AND SOIL PHYSICAL PROPERTIES FOR PREDICTING THE SPATIAL DISTRIBUTION OF COTTON LINT YIELD

Abstract

This field crop research study addresses the potential of image based remote sensing to provide spatially and temporally distributed information on timely basis for site-specific cotton crop management. Universal applicability of site-specific crop management is hampered by lack of timely distributed and economically feasible information on soils and crop conditions in the field and their interaction. The objectives of this study were to demonstrate (1) how site-specific lint yield and associated soil physical properties in a cotton (Gossypium hirsutum L.) production field are related to changes in NDVI across the growing season, and (2) when multispectral images should be collected to optimize the cost and efficiency of remote sensing as a tool for site-specific management of the cotton crop. Temporal multispectral images data acquired comprised 10 dates (1998) and 17 dates (1999) during growing seasons, respectively with analysis focused on 24 areas of interest (AOI) (each 2 x 8 m) located in two transects on a 162-ha farm field. Along each transect, soil textural classification ranged from sandy loam to silt loam. At an early growth stage [~300-600-degree days (DDs) after emergence], low NDVI and plant density were associated with soils having low saturated hydraulic conductivity (ks) and characterized as drainage ways. Among the AOI’s, maximal NDVI was reached at approximately 1565 DD in 1998 and 1350 DD in 1999. A strong range of Pearson correlation (r2=0.65 – 0.83) between lint yield and NDVI during flowering stage (~800-1500 DDs) supports the utility of NDVI maps for site-specific application. However, values for NDVI did not correlate well with lint yields beyond 1500 DDs [fruit (boll) opening stage] and decreased sharply on sites with sandy soil texture. Visual separation of seasonal trends in the NDVI vs. DD relationship was also related to sandy soil vs. silt loam soil texture and seasonal rainfall difference between years. Based on the statistical relationship between NDVI vs. DD it was concluded that acquisition of a single imagery during peak bloom period would be sufficient for predicting the spatial distribution of lint yield and will also be economically feasible. Results of this study indicate that spatial variability in soil physical properties induced variability in crop growth and yield. Similar methodology could be adopted for site-specific management of other crops.