The aims of this study were to: (i) evaluate the relationships between vegetation indices (VIs) derived from Sentinel-2 imagery and grain yield (GY) and the number of spikes per square meter (SN) of winter wheat and triticale; (ii) determine the dates and plant growth stages when the above relationships were the strongest at individual field scale, thus allowing for accurate yield prediction. Observations of GY and SN were performed at harvest on six fields (three locations in two seasons: 2017 and 2018) in three regions of Poland, i.e., northeastern (A—Brożówka), central (B—Zdziechów) and southeastern Poland (C—Kryłów). Vegetation indices (Normalized Difference Vegetation Index (NDVI), Soil-Adjusted Vegetation Index (SAVI), modified SAVI (mSAVI), modified SAVI 2 (mSAVI2), Infrared Percentage Vegetation Index (IPVI), Global Environmental Monitoring Index (GEMI), and Ratio Vegetation Index (RVI)) calculated for sampling points from mid-March until mid-July, covering within-field soil and topographical variability, were included in the analysis. Depending on the location, the highest correlation coefficients (of about 0.6–0.9) for most of VIs with GY and SN were obtained about 4–6 weeks before harvest (from the beginning of shooting to milk maturity). Therefore, satellite-derived VIs are useful for the prediction of within-field cereal GY as well as SN variability. Information on GY, predicted together with the results for soil nutrient availability, is the basis for the formulation of variable fertilize rates in precision agriculture. All examined VIs were similarly correlated with GY and SN via the commonly used NDVI. The increase in NDVI by 0.1 unit was related to an average increase in GY by about 2 t ha⁻¹.

中文翻译：

---

卫星获取的植被指数，冬小麦和黑小麦的籽粒产量和穗数之间的场内关系

这项研究的目的是：（i）评估Sentinel-2影像得出的植被指数（VIs）与谷物产量（GY）以及冬小麦和黑小麦的每平方米穗数（SN）之间的关系；（ii）确定上述关系在单个田间尺度上最强的日期和植物生长阶段，从而可以进行准确的产量预测。GY和SN的观测是在波兰三个地区的六个田地（分别在两个季节：2017年和2018年在三个地点）进行收获的，这三个地区分别是波兰东北部（A-Brożówka），波兰中部（B-Zdziechów）和东南部（C- Kryłów）。植被指数（归一化植被指数（NDVI），土壤校正植被指数（SAVI），改良SAVI（mSAVI），改良SAVI 2（mSAVI2），红外植被百分比指数（IPVI），分析中包括了从3月中旬到7月中旬的采样点计算出的全球环境监测指数（GEMI）和比率植被指数（RVI），其中包括田间土壤和地形变异性。根据位置的不同，大多数具有GY和SN的VI的最高相关系数（约0.6-0.9）是在收获前约4-6周（从开始射击到牛奶成熟）获得的。因此，卫星衍生的VI对于预测田间谷物GY以及SN变异性很有用。有关GY的信息以及土壤养分有效性的预测结果，是精确农业中可变施肥量公式的基础。通过常用的NDVI，所有检查的VI与GY和SN类似地相关。NDVI增加0。^-1。