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Optimal season for discriminating C3 and C4 grass functional types using multi-date Sentinel 2 data

机译：使用多日期Sentinel 2数据区分C3和C4草功能类型的最佳季节

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The ability of remote sensing systems to optimally discriminate and map C3 and C4 grass species varies over time, due to environmental changes, which influence their phenological, physiological and morphological characteristics. In this regard, the discrimination of C3 and C4 grasses is insufficient when using a single image acquired at a specific period. In this study, multi-date Sentinel 2A MultiSpectral Instrument (MSI) data were explored to determine the optimal period for classifying and mapping Festuca costata, C3 and Themeda Triandra, C4 grasses in the montane grasslands of South Africa. The study further assessed how seasonal variations in species classification can be explained by climatic variability (rainfall and temperature). Results showed that image acquisition dates influence the discrimination accuracy, spatial representation of the two grass species, as well as the performance of spectral bands. The winter period also presents a better temporal window for discriminating C3 and C4 target grass species, with higher overall classification accuracies (between 91.8% and 95.3%), than summer (between 81.4% and 90.3%). Lower omission (between 2.8% and 11.6%) and commission (between 2.5% and 14.2%) errors were also observed when discriminating using winter images, as compared to those acquired in summer. Summer images showed large grass species areal coverage (e.g. in November and March, C3 and C4 covered ?25%), whereas in winter (mainly August), a notable decrease was observed. Overall, findings of the study have demonstrated the relevance of multi-date Sentinel data in discriminating C3 and C4 grass species. There is, however, a need to explore the classification ability of Sentinel 2 derivatives, especially during early summer and winter fall.
机译：由于环境变化，遥感系统最佳区分和绘制C3和C4草种的能力随时间而变化，这影响了它们的物候，生理和形态特征。在这方面，当使用在特定时间段获取的单个图像时，对C3和C4草的判别是不够的。在这项研究中，探索了多日期的Sentinel 2A多光谱仪器（MSI）数据，以确定在南非山地草原上对Festuca costata C3草和Themeda Triandra C4草进行分类和制图的最佳时期。该研究进一步评估了如何通过气候变化（降雨和温度）来解释物种分类的季节性变化。结果表明，图像采集日期会影响两种草种的判别精度，空间表示以及光谱带的性能。与冬季（81.4％至90.3％）相比，冬季也为区分C3和C4目标草种类提供了更好的时间窗口，具有更高的总体分类准确度（介于91.8％和95.3％之间）。与冬季获得的图像相比，使用冬季图像进行判别时，还可以发现较低的遗漏（2.8％至11.6％）和佣金（2.5％至14.2％）错误。夏季图像显示大面积草种覆盖（例如，11月和3月，C3和C4覆盖了25％），而冬季（主要是8月）则观察到显着的减少。总体而言，研究结果表明，多日期前哨数据在区分C3和C4草种方面具有相关性。但是，有必要探索Sentinel 2衍生物的分类能力，尤其是在初夏和秋冬期间。

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