Abstract
A phenology-based crop type mapping approach was carried out to map cotton fields throughout the cotton-growing areas of eastern Australia. The workflow was implemented in the Google Earth Engine (GEE) platform, as it is time efficient and does not require processing in multiple platforms to complete the classification steps. A time series of Normalised Difference Vegetation Index (NDVI) imagery were generated from Landsat 8 Surface Reflectance Tier 1 (L8SR) and processed using Fourier transformation. This was used to produce the harmonised-NDVI (H-NDVI) from the original NDVI, and then phase and amplitude values were generated from the H-NDVI to visualise active cotton in the targeted fields. Random Forest (RF) models were built to classify cotton at early, mid and late growth stages to assess the ability of the model to classify cotton as the season progresses, with phase, amplitude and other individual bands as predictors. Results obtained from leave-one-season-out cross validation (LOSOCV) indicated that Overall Accuracy (OA), Kappa, Producer’s Accuracies (PA) and User’s Accuracy (UA), increased significantly when adding amplitude and phase as predictor variables to the model, than prediction using H-NDVI or raw bands only. Commission and omission errors were reduced significantly as the season progressed and more in-season imagery was available. The methodology proposed in this study can map cotton crops accurately based on the reconstruction of the unique cotton reflectance trajectory through time. This study confirms the importance of phenological metrics in improving in-season cotton fields mapping across eastern Australia. This model can be used in conjunction with other datasets to forecast yield based on the mapped crop type for improved decision making related to supply chain logistics and seasonal outlooks for production.
Highlights
Remote identification of crop type is widely studied for many agricultural applications
The entire study area is shown in the left side of Figure 4, but a small region is highlighted to show the different model mapping results due to the large extent of the study area
The aim of this study was to demonstrate the potential of mapping cotton fields in NSW and QLD within season, by applying a simple method using phenology-based indices derived from a time series of imagery
Summary
Remote identification of crop type is widely studied for many agricultural applications In most cases, this is done retrospectively after the growing season [1], in-season mapping is required for better decisions for policymakers and others in the agribusiness sector [2]. The conventional way of collecting land-use information is mostly achieved by surveying farmers or conducting land surveys This is an accurate way of mapping crop type, but it is relatively expensive and time-consuming [6]. Remote sensing technologies offer great potential for collecting temporal and spatial data to support decisions required for crop monitoring and management [7]. This is usually cost and time-efficient at the farm-scale, and for large areas [8]
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