Skip to main content
ARS Home » Research » Publications at this Location » Publication #119364
Title: AIRBORNE MULTISPECTRAL IMAGERY FOR MAPPING VARIABLE GROWING CONDITIONS AND YIELDS OF COTTON, GRAIN SORGHUM, AND CORN

Author
item YANG, CHENGHAI - TX A&M EXP STN-WESLACO,TX
item Bradford, Joe
item WIEGAND, CRAIG - RETIRED-ARS

Submitted to: Remote Sensing of Environment
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 8/20/2000
Publication Date: N/A
Citation: N/A

Interpretive Summary: Airborne remote sensing is becoming increasingly useful for mapping plant growth and yield variations in precision agriculture, but operational methodologies are not well developed nor tested. The main objective of this study was to integrate airborne multispectral imagery, ground observations, and yield monitoring for mapping spatial variations in plant growth and yields. A 30.4-ha dryland field that was divided into two conventional tillage strips and two conservation minimum tillage strips was planted to cotton in 1996, to grain sorghum in 1997, and to corn in 1998. Airborne digital imagery was acquired from the field three times each growing season and plant populations, height, and yield were made at 29 sites within the field. A yield monitor was also used to record yields at harvest for grain sorghum and corn. The images clearly revealed plant growth patterns within and across the three growing seasons as well as differences between the two tillage systems. Yields of cotton, grain sorghum, and corn were related to the image data extracted at the sampling sites. Statistical analyses showed that airborne imagery could explain 57, 59, and 76% of yield variability for cotton, grain sorghum, and corn, respectively. The yield maps generated from the image data corresponded well with those from the yield monitor data. These results illustrate practical ways to integrate airborne digital imagery with spatial information technology and ground observations to map plant growth conditions and yield variations within crop fields.

Technical Abstract: Airborne remote sensing is useful for mapping plant growth and yield variations in precision agriculture, but operational methodologies are not well developed. The objective of this study was to integrate airborne multispectral imagery, ground observations, global positioning systems (GPS), geographic information systems (GIS), and yield monitoring for mapping spatial variations in plant growth and yields. A 30.4-ha dryland field that was divided into two conventional tillage strips and two conservation minimum tillage strips was planted to cotton in 1996, to grain sorghum in 1997, and to corn in 1998. Airborne digital imagery was acquired from the field three times each growing season, and crop yields were made at 29 sites within the field. Yield monitor data were also collected for grain sorghum and corn. These data were integrated within a GIS to document, interpret, and map plant growth and yield variability. The images clearly revealed plant growth patterns within and across the three growing seasons as well as differences between the two tillage systems. Yields of cotton, grain sorghum, and corn were related to the image data for the three spectral bands and four vegetation indices extracted at the sampling sites. Best regression equations with R**2 values of 0.57, 0.59, and 0.76 for cotton, grain sorghum, and corn, respectively, were used to estimate the yields for each of the approximately 30,000 pixels. The yield maps generated from the image data corresponded well with those from the yield monitor data. These results illustrate practical ways to integrate airborne digital imagery with spatial information technology and ground observations to map plant growth conditions and yield variations within crop fields.