'Sugar from Space': Using Satellite Imagery to Predict Cane Yield and Variability
Satellite imagery has been demonstrated to be an effective technology for producing accurate pre-harvest estimates in many agricultural crops. For Australian sugarcane, yield forecasting models have been developed from a single date SPOT satellite image acquired around peak crop growth. However, a failure to acquire a SPOT image at this critical growth stage, from continued cloud cover or from competition for the satellite, can prevent an image being captured and therefore a forecast being made for that season. In order to reduce the reliance on a single image capture and to improve the accuracies of the forecasts themselves, time series yield prediction models have been developed for eight sugarcane growing regions using multiple years of free Landsat satellite images. In addition to the forecasting of average regional yield, an automated computational and programming procedure enabling the derivation of crop vigour variability (GNDVI) maps from the freely available Sentinel 2 satellite imagery was developed. These maps, produced for 15 sugarcane growing regions during the 2017 growing season, identify both variations in crop vigour across regions and within every individual crop. These outputs were made available to collaborating mills within each growing region. This paper presents the accuracies achieved from the time series yield forecasting models versus actual 2017 yields for the respective regions, as well as provides an example of the derived mapping outputs.
Evaluating remote sensing technologies for improved yield forecasting and for the measurement of foliar nitrogen concentration in sugarcane
AN ANALYSIS OF time series Landsat imagery acquired over the Bundaberg region between 2010 and 2015 identified variations in annual crop vigour trends, as determined by greenness normalised difference vegetation index (GNDVI). On average, early to mid-April was identified as the crucial period where crops achieved their maximum vigour and as such indicated when single image captures should be acquired for future regional yield forecasting. Additionally, the regional crop GNDVI averaged from Landsat images between February to April, produced a higher coefficient of determination to final yield (R2 = 0.91) than the average crop GNDVI value from a single mid-season SPOT5 image capture (R2 = 0.52). This result indicates that the time series method may be more appropriate for future regional yield forecasting. For improved prediction accuracies at the individual crop level, a univariate model using only crop GNDVI values (SPOT5) and corresponding yield (t/ha) produced a higher prediction accuracy for the 2014 Bundaberg harvest than a multivariate model that included additional historic spectral and crop attribute data. For Condong, a multivariate model improved the prediction accuracy of individual crops harvested in 2014 by 41.8% for one-year-old cane (Y1), and 46.2% for two-year-old cane (Y2). For the non-invasive measure of foliar nitrogen (N%), the specific wavelengths 615 nm, 737 nm and 933 nm (Airborne hyperspectral), and 634 nm, 750 nm and 880 nm (ground based field spectroscopy) were found to be the most significant. These results were supported by satellite imagery (Worldview-2 and Worldview-3) acquired over three replicated field trials in Mackay (2014 and 2015) and Tully (2015), where the vegetation index (VI) REN2NDVIWV, a ratio of the rededge band (705-745 nm) and the Near-IR2 band (860-1040 nm), produced a higher correlation to nitrogen concentration (%) than NDVI.
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.