Using MODIS imagery, climate and soil data to estimate pasture growth rates on farms in the south-west of Western Australia

Abstract

Remote sensing of vegetation and its monitoring using the normalised difference vegetation index (NDVI) offers the opportunity to provide a coverage of agricultural land at a large scale. The availability of MODIS NDVI at a resolution of 250 m provided the opportunity to evaluate the hypothesis that pasture growth rate (PGR) of individual paddocks can be accurately predicted using a model based on MODIS NDVI in combination with climate and soil data and a light-use efficiency model. Model estimates of PGR were compared with field measurements of PGR recorded in grazing enclosure cages collected over 3 years from six farms located across the south-west region of Western Australia. The estimates attained from the model explained 70% of the variation in PGR for individual paddocks on farms over the 3 years of the study, with an average error at the paddock scale of 10.4 kg DM/ha.day over all growing seasons and years. Across all farms studied, there was generally good agreement between satellite-derived PGR and ground-based measurements, although estimates of PGR varied between years and farms. The model explained 47% of the variation in pasture growth early in the season (from break of season to end of July), compared with 62% late in the season (from August to pasture senescence). The present study demonstrated that PGR for individual paddocks can be predicted at weekly intervals from MODIS imagery, climate and soil data and a light-use efficiency model at an accuracy sufficient to facilitate on-farm pasture and livestock management.

Evaluation of vegetation indices for rangeland biomass estimation in the Kimberley area of Western Australia

“…Although the NDVI has been useful in many studies, it has clear limitations. It is most sensitive to ground coverage with chlorophyll containing leaves (Purevdorj et al 1998), as present in the vegetative growth phase (Donald et al, 2010), but the response saturates when the ground is completely covered, typically at a LAI of 2 to 3 (Baret and Guyot, 1991).…”

“…In the Australian context, relationships between biomass and vegetation indices have been successfully developed for the Mediterranean and temperate zones (Donald et al, 2010). However, little information is available for the North Western tropical zone as many vegetation indices have not been extensively tested.…”

Pasture Monitoring Using SAR with COSMO-SkyMed, ENVISAT ASAR, and ALOS PALSAR in Otway, Australia

“…NDVI has been widely used for estimation of grass biomass [4,5]. The "Pastures from Space" project in Western Australia was aimed to provide biomass information from Moderate Resolution Imaging Spectroradiometer (MODIS) data at the paddock scale [6,7]. However, with the 250 m spatial resolution, it cannot provide biomass information for a whole paddock because of unavailable mixed pixels in between paddocks.…”

Ground truthing protocols for biomass estimation in rangeland environments

“…Optimising grazing pressure and timely de-stocking is the key element in achieving this and accurate forage assessment is a vital component in this decision making. Recent studies in remote and proximal sensing-based biomass estimation methods and agro-meteorologic models which make use of plant growth and climate variables in combination with remote sensing have shown potential [2], [3]. Remote sensing based approaches offer an advantage in that they are spatially explicit and dynamic but require calibration with ground data [6].…”