REMOTE SENSING FOR CROP AND WATER MANAGEMENT IN IRRIGATED AGRICULTURE
Location: Water Management and Conservation Research
Title: USING AERIAL HYPERSPECTRAL REMOTE SENSING IMAGERY TO ESTIMATE CORN PLANT STAND DENSITY
| Steward, Brian - IOWA STATE, AMES, IA |
| Kaleita, Amy - IOWA STATE, AMES, IA |
| Batchelor, William - MIS STATE, STARKVILLE, MS |
Submitted to: Transactions of the ASABE
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: November 15, 2007
Publication Date: March 1, 2008
Citation: Thorp, K.R., Steward, B.L., Kaleita, A.L., Batchelor, W.D. 2008. Using aerial hyperspectral remote sensing imagery to estimate corn plant stand density. Transactions of the ASABE. 51(1):311-320.
Interpretive Summary: Corn yield is heavily influenced by the population of plants per unit area, but deviations from the optimum population can occur as a result of planter performance issues, emergence delays or failure, or early-season plant death due to stress. In an effort to better understand the factors that cause variability in plant population, researchers have recently developed ground-based sensing technologies to automatically measure the number of plants per unit area. Our objective was to use data from a ground-based sensing system to evaluate the potential of using remote sensing imagery for quick estimation corn plant population variability from the air. We were able to demonstrate that remote sensing imagery can be useful for estimating corn plant population variability if 1) substantial population variability exists in the field, and 2) the remote sensing imagery is not affect by variability in any other crop growth factors such as nitrogen stress. Development of strategies for using remote sensing to evaluate the status of crop growth is important for companies that want to market remote sensing products to crop producers. These types of products should ultimately allow crop producers to make management decisions that increase the agricultural productivity of our nation and/or reduce the impact of agricultural production on the environment. Results of this research will also be useful for researchers who aim to develop remote sensing technology as an information tool for agriculture.
Since corn plant stand density is important for optimizing crop yield, several researchers have recently developed ground-based systems for automatic measurement of this crop growth parameter. Our objective was to use data from such a system to assess the potential for estimation of corn plant stand density using remote sensing images. Aerial hyperspectral remote sensing imagery was collected on three dates over three plots of corn in central Iowa during the 2004 growing season. The imagery had a spatial resolution of 1 m and a spectral resolution of 3 nm between 498 nm and 855 nm. A machine vision system for early-season measurement of corn plant stand density was also used to map every row of corn within the three plots, and a complete inventory of corn plants was generated as a rich ground reference dataset. A principal component regression analysis was used to assess relationships between plant stand density measurements and principal components of hyperspectral reflectance for each plot, on each image collection date, and at three different spatial resolutions (2, 6, and 10 m). The maximum R2 for regressions was 0.79. Estimates of corn plant stand density were best when using imagery collected at the later vegetative and early reproductive corn growth stages. Quantization effects due to row width complicated corn plant stand density estimates at 2 m spatial resolution, and better estimations were typically seen at resolutions of 6 m and 10 m. Among the different cases of plot, image date, and spatial resolution, the principal components of reflectance most highly correlated with plant stand density were able to be classified into four distinct types, denoted as Types A, B, C, and D. Type A principal components contrasted all available visible red wavelengths with all available near-infrared wavelengths. Type B principal components contrasted green wavelengths (531 to 552 nm) plus shorter wave near-infrared (759 nm) with red wavelengths (675 to 693 nm) plus longer wave near-infrared (852 nm). Type C principal components summed green wavelengths (528 to 546 nm) and near-infrared wavelengths (717 to 855 nm). Type D principal components contrasted blue/green wavelengths (498 to 507 nm) with the red edge (717 nm). Remote sensing can be best used to estimate corn plant stand density at mid-season as long as plant stand variability exists and variability due to other factors is minimal.