Remote Sensing Update 2011

Tree phenology encompasses the complex biological processes of growth and the cycles of flowering and fruit production. Developing a model of tree phenology involves monitoring processes occurring in the soil-plant-atmosphere continuum, such as evapotranspiration and CO2 assimilation. Data collection involves frequent soil, leaf and canopy measurements. The model must also incorporate weather and spectral data, and cultural inputs such as irrigation and fertilization.

Essential Parameters

Leaf area index (LAI) is a parameter essential to model development. LAI is defined as the total, one-sided area of leaves per unit ground surface area. Many plant conditions are evident by its variation because, as plants endure long term nutrients, water, and pest stress, the amount of leaf canopy decreases. Water consumption and nutrient status are linked to LAI.

Measurement of sunlight interception by the tree canopy is also essential to model development because this data is linked to LAI and demonstrated to be directly proportional to crop yield in almond and walnut tree crops (Lampinen et al. 2009).

We are using the following ground measurements to calculate LAI: Fisheye photographs acquired from beneath the canopy in several plots from 2008 – 2010; Lampinen Mule Light Bar canopy light interception data from several orchards.

Using Airborne Measurements & Satellite Images

Direct methods for LAI calculation and canopy light interception are time consuming, though essential for model development. Airborne and satellite images, when correlated with these direct methods, can provide estimates of canopy reflectance that could be used to estimate light interception and even LAI over large areas.

Initial plant measurements recorded by the other Co-PIs provide the basis in developing modeling parameters. That is, their recorded yield, nutrient and water status data and spectral measurements from the experimental orchards will be linked to airborne and satellite remote sensing of canopy and leaf conditions.

We used airborne remote sensing imaging systems (color infrared digital photography, and NASA's MASTER and AVRIS sensors) to collect data with orchard overflights in 2009 and 2010. We also used imagery from the Landsat satellite for 2000 – 2010. Each 5 to 30 meter pixel in the images is associated with the location of ground measurements to generate models of growth, water stress and canopy density. These aerial and satellite sensors measure the variation in reflectance among the wavelengths of light associated with plant constituents, such as water and pigments. The concentration of these constituents indicates the vegetation health and other biophysical conditions.

Our Progress

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  Layout of sampling points in a fanjet irrigated almond orchard (USDA collaboration)
  We are using data from experiments conducted from 2008 – 2010 for developing the parameters to estimate nitrogen and water status and demand, and predict yield through scale up from ground measurements to airborne and satellite imagery.
• We are associating leaf sampling data for water content and stem water
  potential with airborne imagery acquisition.
• We are tracking phenological changes in the tree canopy by matching tree-sampled yield and other plant measurements associated with Landsat images, extrapolating across many orchards.
• Collaboration with USDA-ARS during MASTER overflights: they used RADAR to measure water content from the surface soil to the top of the vegetation. While we share our vegetation water content measurements with them, they will share their soil moisture measurements, and RADAR evaluation.