Purpose and Methods

Crop identification using remotely sensed imagery provides useful information to make management decisions about land use and crop health. This research used phonecams to acquire the Normalized Difference Vegetation Index (NDVI) of various crops for three crop seasons. NDVI time series from Sentinel (L121-L192) images was also acquired using Google Earth Engine (GEE) for the same period. The resolution of satellite data is low therefore gap filling and smoothening filters were applied to the time series data. The comparison of data from satellite images and phenocam provides useful insight into crop phenology. The results show that NDVI is generally underestimated when compared to phenocam data. The Savitzky-Golay (SG) and some other gap filling and smoothening methods are applied to NDVI time series based on satellite images. The smoothened NDVI curves are statistically compared with daily NDVI series based on phenocam images as a reference.

Results

The SG method has performed better than other methods like moving average. Furthermore, polynomial order has been found to be the most sensitive parameter in applying SG filter in GEE. Sentinel (L121-L192) image was used to identify wheat during the year 2022–2023 in Sargodha district where experimental fields were located. The Random Forest Machine Leaning algorithm was used in GEE as a classifier.

Conclusion

The classification accuracy has been found 97% using this algorithm which suggests its usefulness in applying to other areas with similar agro-climatic characteristics.

中文翻译：

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NDVI 田间验证以识别作物物候特征

 目的和方法

使用遥感图像进行作物识别可为做出有关土地利用和作物健康的管理决策提供有用的信息。这项研究使用电话摄像头获取了三个作物季节的各种作物的归一化植被指数（NDVI）。来自 Sentinel (L121-L192) 图像的 NDVI 时间序列也是使用 Google Earth Engine (GEE) 同期获取的。卫星数据的分辨率较低，因此对时间序列数据应用间隙填充和平滑滤波器。卫星图像和 phenocam 数据的比较为作物物候学提供了有用的见解。结果表明，与 Phenocam 数据相比，NDVI 通常被低估。 Savitzky-Golay (SG) 和其他一些间隙填充和平滑方法应用于基于卫星图像的 NDVI 时间序列。将平滑后的 NDVI 曲线与基于 Penocam 图像作为参考的每日 NDVI 系列进行统计比较。

 结果

SG 方法比移动平均线等其他方法表现更好。此外，多项式阶数被发现是在 GEE 中应用 SG 滤波器时最敏感的参数。 Sentinel (L121-L192) 图像用于识别试验田所在的 Sargodha 区 2022 年至 2023 年的小麦。 GEE 中使用随机森林机器学习算法作为分类器。

 结论

使用该算法的分类准确度为 97%，这表明其可用于具有类似农业气候特征的其他地区。