Multispectral remote sensing for yield estimation using high-resolution imagery from an unmanned aerial vehicle

Satellites and autonomous unmanned aerial vehicles (UAVs) are two major platforms for acquiring remotely-sensed information of the earth’s surface. Due to the limitations of satellite-based imagery, such as coarse spatial resolution and fixed schedules, applications of UAVs as low-cost remote sensing systems are rapidly expanding in many research areas, particularly precision agriculture. UAVs can provide imagery with high spatial resolution (finer than 1 meter) and acquire information in visible, near infrared, and even thermal bands. In agriculture, vegetation characteristics such as health, water stress, and the amount of biomass, can be estimated using UAV imagery. In this study, three sets of high-resolution aerial imagery have been used for yield estimation based on vegetation indices. These images were captured by the Utah State University AggieAir™ UAV system flown in June 2017, August 2017, and October 2017 over a field experiment pasture site located in northern Utah. The pasture study area is primarily tall fescue. The field experiment includes 20 50 x 20-m plots, with 4 replications of 5 irrigation levels. Approximately 60 yield samples were harvested after each flight. Sample locations were recorded with high-accuracy real-time kinematic (RTK) GPS. In addition, the leaf area index (LAI) for each sample plot was measured using an optical sensor (LAI2200C) before harvesting. The relationship of yield for each sample versus vegetation indices (VIs) was explored. The VIs include the normalized difference vegetation index (NDVI), calculated using AggieAir imagery, and LAI measured using a ground-based sensor. The results of this study reveal the correlation between vegetation indices and the amount of biomass.

[1]  Hirofumi Hashimoto,et al.  Exploring Simple Algorithms for Estimating Gross Primary Production in Forested Areas from Satellite Data , 2012, Remote. Sens..

[2]  T. Hwang,et al.  Optical remote sensing of terrestrial ecosystem primary productivity , 2013 .

[3]  Y. Miao,et al.  Very high resolution crop surface models (CSMs) from UAV-based stereo images for rice growth monitoring In Northeast China , 2013 .

[4]  Johanna Link,et al.  Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System , 2014, Remote. Sens..

[5]  G. Asrar,et al.  Estimating Absorbed Photosynthetic Radiation and Leaf Area Index from Spectral Reflectance in Wheat1 , 1984 .

[6]  J. Peñuelas,et al.  Remote sensing of biomass and yield of winter wheat under different nitrogen supplies , 2000 .

[7]  C. Rebella,et al.  Remote sensing capabilities to estimate pasture production in France , 2004 .

[8]  Raj S. Chhikara,et al.  Use of satellite spectral data in crop yield estimation surveys , 1992 .

[9]  C. Tucker,et al.  North American vegetation patterns observed with the NOAA-7 advanced very high resolution radiometer , 1985, Vegetatio.

[10]  P. Curran,et al.  Technical Note Grass chlorophyll and the reflectance red edge , 1996 .

[11]  Mac McKee,et al.  Estimating chlorophyll with thermal and broadband multispectral high resolution imagery from an unmanned aerial system using relevance vector machines for precision agriculture , 2015, Int. J. Appl. Earth Obs. Geoinformation.

[12]  Liang Tian-gang,et al.  Estimating grassland yields using remote sensing and GIS technologies in China , 1998 .

[13]  C. Tucker,et al.  Satellite remote sensing of primary production , 1986 .