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Sources of Variation in Assessing Canopy Reflectance of Processing Tomato by Means of Multispectral Radiometry

机译:用多光谱辐射计评估加工番茄冠层反射率的变化源

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摘要

Canopy reflectance sensors are a viable technology to optimize the fertilization management of crops. In this research, canopy reflectance was measured through a passive sensor to evaluate the effects of either crop features (N fertilization, soil mulching, appearance of red fruits, and cultivars) or sampling methods (sampling size, sensor position, and hour of sampling) on the reliability of vegetation indices (VIs). Sixteen VIs were derived, including seven simple wavelength reflectance ratios (NIR/R460, NIR/R510, NIR/R560, NIR/R610, NIR/R660, NIR/R710, NIR/R760), seven normalized indices (NDVI, G-NDVI, MCARISAVI, OSAVI, TSAVI, TCARI), and two combined indices (TCARI/OSAVI; MCARI/OSAVI). NIR/560 and G-NDVI (Normalized Difference Vegetation Index on Greenness) were the most reliable in discriminating among fertilization rates, with results unaffected by the appearance of maturing fruits, and the most stable in response to different cultivars. Black mulching film did not affect NIR/560 and G-NDVI behavior at the beginning of the growing season, when the crop is more responsive to N management. Due to a moderate variability of NIR/560 and G-NDVI, a small sample size (5–10 observations) is sufficient to obtain reliable measurements. Performing the measurements at 11:00 and 14:00 and maintaining a greater distance (1.8 m) between plants and instrument enhanced measurement consistency. Accordingly, NIR/560 and G-NDVI resulted in the most reliable VIs.
机译:冠层反射率传感器是一种用于优化农作物施肥管理的可行技术。在这项研究中,通过无源传感器测量了冠层反射率,以评估作物特征(氮肥,土壤覆盖,红色水果的外观和品种)或采样方法(采样大小,传感器位置和采样时间)的影响植被指数(VIs)的可靠性。导出了16个VI,包括七个简单的波长反射率比(NIR / R460,NIR / R510,NIR / R560,NIR / R610,NIR / R660,NIR / R710,NIR / R760),七个归一化指标(NDVI,G-NDVI ,MCARISAVI,OSAVI,TSAVI,TCARI)和两个组合索引(TCARI / OSAVI; MCARI / OSAVI)。 NIR / 560和G-NDVI(绿色标准化归一化植被指数)在区分施肥率方面最可靠,结果不受成熟果实外观的影响,并且对不同品种的反应最稳定。在农作物生长季节开始时,当农作物对氮的管理更加敏感时,黑色地膜不会影响NIR / 560和G-NDVI的行为。由于NIR / 560和G-NDVI的适度变化,小样本量(5-10次观测)就足以获得可靠的测量结果。在11:00和14:00进行测量并在工厂和仪器之间保持更大的距离(1.8 m),可以提高测量的一致性。因此,NIR / 560和G-NDVI产生了最可靠的VI。

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