Spatial information on crop nutrient status is central for monitoring vegetation health, plant productivity and managing nutrient optimization programs in agricultural systems. This study maps the spatial variability of leaf chlorophyll content within fields with differing quantities of nitrogen fertilizer application, using multispectral Landsat-8 OLI data (30 m). Leaf chlorophyll content and leaf area index measurements were collected at 15 wheat ( Triticum aestivum ) sites and 13 corn ( Zea mays ) sites approximately every 10 days during the growing season between May and September 2013 near Stratford, Ontario. Of the 28 sites, 9 sites were within controlled areas of zero nitrogen fertilizer application. Hyperspectral leaf reflectance measurements were also sampled using an Analytical Spectral Devices FieldSpecPro spectroradiometer (400–2500 nm). A two-step inversion process was developed to estimate leaf chlorophyll content from Landsat-8 satellite data at the sub-field scale, using linked canopy and leaf radiative transfer models. Firstly, at the leaf-level, leaf chlorophyll content was modelled using the PROSPECT model, using both hyperspectral and simulated mulitspectral Landsat-8 bands from the same leaf sample. Hyperspectral and multispectral validation results were both strong (R 2 = 0.79, RMSE = 13.62 μg/cm 2 and R 2 = 0.81, RMSE = 9.45 μg/cm 2 , respectively). Secondly, leaf chlorophyll content was estimated from Landsat-8 satellite imagery for 7 dates within the growing season, using PROSPECT linked to the 4-Scale canopy model. The Landsat-8 derived estimates of leaf chlorophyll content demonstrated a strong relationship with measured leaf chlorophyll values (R 2 = 0.64, RMSE = 16.18 μg/cm 2 ), and compared favourably to correlations between leaf chlorophyll and the best performing tested spectral vegetation index (Green Normalised Difference Vegetation Index, GNDVI; R 2 = 0.59). This research provides an operational basis for modelling within-field variations in leaf chlorophyll content as an indicator of plant nitrogen stress, using a physically-based modelling approach, and opens up the possibility of exploiting a wealth of multispectral satellite data and UAV-mounted multispectral imaging systems.

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使用 Landsat-8 图像绘制用于氮管理的农作物田内叶绿素含量图

作物养分状况的空间信息对于监测植被健康、植物生产力和管理农业系统中的养分优化计划至关重要。本研究使用多光谱 Landsat-8 OLI 数据 (30 m) 绘制了不同施氮量田间叶叶绿素含量的空间变异性。在 2013 年 5 月至 2013 年 9 月的生长季节期间，大约每 10 天在安大略省斯特拉特福附近的 15 个小麦（普通小麦）地点和 13 个玉米（玉米）地点收集叶叶绿素含量和叶面积指数测量值。在这 28 个地点中，有 9 个地点在零氮肥施用控制区内。还使用 Analytical Spectral Devices FieldSpecPro 光谱辐射计 (400–2500 nm) 对高光谱叶片反射率测量值进行采样。开发了一个两步反演过程，以使用关联的冠层和叶片辐射传递模型从 Landsat-8 卫星数据在子场尺度上估计叶片叶绿素含量。首先，在叶片级别，使用 PROSPECT 模型对叶片叶绿素含量进行建模，使用来自同一叶片样本的高光谱和模拟多光谱 Landsat-8 波段。高光谱和多光谱验证结果都很强（分别为 R 2 = 0.79，RMSE = 13.62 μg/cm 2 和 R 2 = 0.81，RMSE = 9.45 μg/cm 2 ）。其次，使用与 4 比例冠层模型相关联的 PROSPECT，根据 Landsat-8 卫星图像估计生长季节内 7 个日期的叶叶绿素含量。Landsat-8 得出的叶片叶绿素含量估计值表明与测量的叶片叶绿素值 (R 2 = 0.64, RMSE = 16.18 μg/cm 2 ) 密切相关，并且与叶片叶绿素和性能最佳的测试光谱植被指数之间的相关性比较有利（绿色归一化差异植被指数，GNDVI；R 2 = 0.59）。这项研究为使用基于物理的建模方法对作为植物氮胁迫指标的叶片叶绿素含量的田间变化进行建模提供了操作基础，并开辟了利用丰富的多光谱卫星数据和无人机安装的多光谱数据的可能性。成像系统。并与叶叶绿素和表现最佳的测试光谱植被指数（绿色标准化差异植被指数，GNDVI；R 2 = 0.59）之间的相关性相比具有优势。这项研究为使用基于物理的建模方法对作为植物氮胁迫指标的叶片叶绿素含量的田间变化进行建模提供了操作基础，并开辟了利用丰富的多光谱卫星数据和无人机安装的多光谱数据的可能性。成像系统。并与叶叶绿素和表现最佳的测试光谱植被指数（绿色标准化差异植被指数，GNDVI；R 2 = 0.59）之间的相关性相比具有优势。这项研究为使用基于物理的建模方法对作为植物氮胁迫指标的叶片叶绿素含量的田间变化进行建模提供了操作基础，并开辟了利用丰富的多光谱卫星数据和无人机安装的多光谱数据的可能性。成像系统。