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.

中文翻译：

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使用基于Landsat影像时间序列的基于现象学的度量标准绘制季节内的棉田

基于物候学 进行了作物类型制图，以绘制澳大利亚东部整个棉花种植​​区的棉花田图。该工作流程是在Google Earth Engine（GEE）平台中实现的，因为它节省了时间，并且不需要在多个平台上进行处理即可完成分类步骤。从Landsat 8表面反射层1（L8SR）生成了时序差异植被指数（NDVI）图像的时间序列，并使用傅里叶变换对其进行了处理。这用于从原始NDVI产生协调的NDVI（H-NDVI），然后从H-NDVI生成相位和幅度值以可视化目标区域中的活性棉。建立了随机森林（RF）模型来对生长早期，中期和晚期的棉花进行分类，以评估该模型随着季节的进展对棉花进行分类的能力，相位，振幅和其他各个波段作为预测变量。从一季休假交叉验证（LOSOCV）获得的结果表明，在模型中添加幅度和相位作为预测变量时，总体准确性（OA），Kappa，生产者的准确性（PA）和用户的准确性（UA）显着增加。 ，而不是仅使用H-NDVI或原始频段进行的预测。随着季节的进行，佣金和遗漏错误大大减少，并且可以获得更多的季节图像。在这项研究中提出的方法可以基于随时间变化的独特棉花反射率轨迹的精确绘制棉花作物图。这项研究证实了物候指标在改善澳大利亚东部地区季节性棉田制图方面的重要性。