Trigonometric correction factors renders the fAPAR-NDVI relationship from active optical reflectance sensors insensitive to solar elevation angle
The normalized difference vegetation index (NDVI), derived from ground based or satellite borne, passive sensors is often used to estimate the fraction of absorbed photosynthetically active radiation (fAPAR) of a plant canopy. It is well documented that the measured NDVI from passive sensors is affected by the sun and/or view geometry due to the non-Lambertian properties of plant canopies. Despite this the fAPAR-NDVI relationships are often found to be independent of the solar elevation angle (ᶿs) because the ᶿs-dependent absorption of the Red wavelengths within the canopy, which dominates the fAPAR, cancels out the ᶿs-dependency of the NIR scattering which dominates the NDVI measurement. Active optical sensors (AOS), which have their own illuminating light source measure NDVI (NDVI AOS) without any interference of solar geometry. However as fAPAR of a plant canopy does change with solar elevation angle (ᶿs), the fAPAR-NDVIAOS relationship too changes with varying ᶿs. The objective of this study was to explore a correction factor which can eliminate the ᶿs-dependency in fAPAR-NDVIAOS relationship. Data were collected using LightScout quantum bar and CropCircle™ for Tall fescue ('Festuca arundinacea' var. Fletcher) at ᶿs ranging from 40° to 80°. A ᶿs-dependent vegetation index, NDVI*AOS that introduces simple trigonometric correction factors to the measured Red and NIR irradiance for nadir-viewing active optical sensor provides a fAPAR-NDVI relationship that is independent of ᶿs. When the solar elevation angle is introduced this way into the NDVIAOS the fAPAR can then be calculated from the NDVIAOS for any solar elevation angle within the range of 40-80°.
Using Active Optical Sensing for Determining Pasture Growth Rate Using a Light Use Efficiency Model
The ability to quantify pasture biomass and growth rate is of prime importance to the sustainability and profitability of extensive livestock industries, specifically as it relates to provide information for better farm management decisions. Assessment of pasture growth rate (PGR, kg/ha.day) using remote sensing has gained considerable interest to the farm managers for livestock grazing management. The context of this research is to investigate the use of in situ sensors and a light use efficiency (LUE) model to estimate PGR. A key parameter in this model is the light interception by the canopy, or fAPAR. Measuring fAPAR using active optical sensors (AOS) introduces new challenges hitherto not appreciated using traditional passive optical sensors and so a considerable portion of this work focusses on the derivation of fAPAR from a widely used optical reflectance index, the normalized difference vegetation index (NDVI). Therefore this research project comprises of two main components: (i) investigating an AOS to infer the fraction of absorbed photosynthetically active radiation (fAPAR) by the plant, a key variable in LUE model; and (ii) evaluating the LUE model using in situ sensors for estimating of PGR (kg/ha.day) at the sub field scale.
PA meets Lot - Integrating in-situ Sensor Data and Biomass Prediction Tools for Crops and Pastures
Monitoring pasture growth rate is an important component of managing grazing livestock production systems. In this study we demonstrate a pasture growth rate (PGR) model, initially designed for very large scale satellite imagery, can be operated at a scale of metres when incorporating in-situ sensor data. A light use efficiency (LUE)-based PGR model was combined with in-situ measurements from proximal weather (temperature), plant (fAPAR) and soil (relative moisture) sensors to calculate the growth rate of a tall fescue pasture. When incorporating in-situ measurements of temperature and moisture index, the model provided an accuracy (RMSE) of 1.68 kg/ha.day (R² = 0.96, p-value ≈ 0).
NDVI 'Depression' In Pastures Following Grazing
Pasture biomass estimation from normalized difference vegetation index (NDVI) using ground, air or space borne sensors is becoming more widely used in precision agriculture. Proximal active optical sensors (AOS) have the potential to eliminate the confounding effects of path radiance and target illumination conditions typically encountered using passive sensors. Any algorithm that infers the green fraction of pasture from NDVI must factor in plant morphology and live/dead plant ratio, irrespective of the senor used. Moreover, livestock grazing affects the morphology of pastures so the veracity of instrument calibration procedures applied under 'protected plot' conditions is questionable if the sensor is subsequently deploy as a 'calibrated sensor' into grazed fields. In this research we have simulated pasture grazing on establish plots of Tall fescue ('Festuca arundinacea') in a heavy clay (vertosol) soil and examined the effect of such grazing on the temporal NDVI values as derived using a Crop CircleTM sensor. Five plots with different soil moisture condition were maintained in the study period. Time domain reflectometer (TDR) was used to monitor volumetric soil moisture content (%) and NDVI measurements were taken on a daily basis. Following a grazing event (facilitated by uniformly mowing the grass to a height of 6 cm), biomass samples were collected on 3rd, 4th and 5th day along with coincident measures of the NDVI. For those plots with low soil moisture level (< ~37% of the full profile), the NDVI progressively decreased up to 2 or 3 days following the 'grazing' event, despite the plot biomass increasing due to regrowth. The NDVI values did not 'recover' until approximately 4 days after the 'grazing' event. However, for those plots of moderate to high soil moisture (>~37%) the NDVI-time curves monotonically increased with biomass re-growth immediately following 'grazing'. This has important ramifications for those intending to use NDVI as the basis for pasture assessment, particularly in situations involving short-term grazing rotation.