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Using the SCOPE model for potato growth, productivity and yield monitoring under different levels of nitrogen fertilization
International Journal of Applied Earth Observation and Geoinformation ( IF 7.5 ) Pub Date : 2022-09-21 , DOI: 10.1016/j.jag.2022.102997
Egor Prikaziuk, Georgios Ntakos, Tamara ten Den, Pytrik Reidsma, Tamme van der Wal, Christiaan van der Tol

Most applications of remote sensing in agricultural crop monitoring use multispectral imaging techniques, but with upcoming hyperspectral missions, the opportunity arises to better estimate pigment absorption and crop structure by exploiting the full solar reflective spectrum. In this study, we demonstrate how hyperspectral time series can be used with the Soil Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model to estimate crop yield variability among fields, crop varieties and nitrogen treatments generically, i.e. without a calibration with in situ, data. Field experiments were conducted in two potato fields in the Netherlands between May and September 2019. The fields were planted with five varieties of potato, under three nitrogen fertilization treatments. By fitting the model to the full VNIR-SWIR spectrum of measured hyperspectral reflectance, we retrieved the model input parameters of Leaf Area Index (LAI), leaf chlorophyll content (Cab) and leaf water content (Cw) and simulated the photosynthesis throughout the season using data of local Automatic Weather Stations (AWS). Statistical analysis of measured and retrieved traits of LAI, Cab and canopy water content showed that two fields responded differently to the treatments, exhibiting fewer classes than were expected based on the experimental design. Potato yield, which was estimated as the sum of photosynthesis flux multiplied by the harvest index of 0.64, correlated with the measured tuber dry weight with R2 0.36 and RMSE 2.5 t ha−1. This study demonstrates that even in the absence of crop or variety specific information, hyperspectral reflectance and local weather data ingested into SCOPE can explain a substantial part of the observed variability in yield among fields.



中文翻译:

使用 SCOPE 模型对不同施氮水平下马铃薯生长、生产力和产量进行监测

大多数遥感在农作物监测中的应用都使用多光谱成像技术,但随着即将到来的高光谱任务,有机会通过利用全太阳反射光谱来更好地估计色素吸收和作物结构。在这项研究中,我们展示了如何将高光谱时间序列与光化学和能量通量的土壤冠层观测 (SCOPE) 模型一起使用,以一般地估计农田、作物品种和氮处理之间的作物产量变异性,即无需原位校准, 数据。2019 年 5 月至 9 月期间,在荷兰的两个马铃薯田中进行了田间试验。在三个氮肥处理下,这些田地种植了五个马铃薯品种。通过将模型拟合到测量的高光谱反射率的完整 VNIR-SWIR 光谱,我们检索了叶面积指数 (LAI)、叶绿素含量 (Cab) 和叶含水量 (Cw) 的模型输入参数,并模拟了整个季节的光合作用使用本地自动气象站 (AWS) 的数据。对 LAI、Cab 和冠层含水量的测量和检索性状的统计分析表明,两个田地对处理的反应不同,表现出的类别少于基于实验设计的预期。马铃薯产量,20.36 和 RMSE 2.5 吨 ha -1。这项研究表明,即使在没有作物或品种特定信息的情况下,摄取到 SCOPE 中的高光谱反射率和当地天气数据也可以解释观察到的田间产量变化的很大一部分。

更新日期:2022-09-21
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