Remote sensing is a valuable tool for reducing the environmental impact of agricultural practices by detecting crop nitrogen (N) and water status for site-specific N fertilization and irrigation. The interaction between N and water status may produce confounding effects in the acquired spectral reflectance, making it difficult to separate crop deficiencies. The objective of this study was to evaluate the potential of visible and infrared hyperspectral and thermal imaging sensors for N and water status assessment with reduced confounding effects. A winter wheat (Triticum aestivum L.) field experiment combining four N and two irrigation levels was conducted in Central Spain over 2 years. The Nitrogen Nutrition Index (NNI) was monitored (mid stem elongation, final stem elongation, flowering stage) and the crop water status was measured with a leaf porometer at flowering. Two hyperspectral sensors covering the visible and near infrared regions (400–850 nm) and part of the short-wave infrared (950–1750 nm) together with a thermal camera were installed on-board an aircraft to acquire images 300 m above the experiment. In addition, canopy reflectance (400−1000 nm) was measured with a handheld spectroradiometer at ground level. The relationship between the ground-based determination of N and water status with indicators based on remote sensors was analyzed. The planar domain Canopy Chlorophyll Content Index (CCCI) reduced soil background noise and correlated with the NNI in all cases (R² > 0.44; P < 0.001). Reliable assessment of water status was achieved by using the Water Deficit Index (WDI), which is calculated using the Vegetation Index-Temperature trapezoid. The CCCI distinguished between N levels reducing the confounding effect of the water status, in contrast to the WDI which was mostly affected by the water status. Combining the CCCI and WDI to assess the crop NNI reduced the root mean square error to 0.109, suggesting that the combination of spectral and thermal information could improve the adjustment of N fertilization and irrigation to crop requirements. However, the approach must be validated in other cultivars and environments before making N fertilization and irrigation recommendations.

遥感是通过针对特定地点的氮肥和灌溉检测农作物氮（N）和水状态来减少农业实践对环境的影响的宝贵工具。氮与水分状况之间的相互作用可能会在获得的光谱反射率中产生混杂效应，从而难以区分出作物亏缺。这项研究的目的是评估可见光和红外高光谱和热成像传感器在减少混杂影响的情况下对氮和水状态评估的潜力。冬小麦（小麦）L.）在西班牙中部进行了为期2年的结合了四个氮和两个灌溉水平的田间试验。监测氮营养指数（NNI）（茎中段伸长，最终茎伸长，开花期），并在开花时用叶栅仪测量作物的水分状况。在飞机上安装了两个覆盖可见和近红外区（400–850 nm）和部分短波红外区（950–1750 nm）的高光谱传感器以及一个热像仪，以获取实验上方300 m的图像。另外，用手持式光谱仪在地面上测量了树冠反射率（400-1000 nm）。分析了基于地面的氮素测定与水位状态之间的关系，该指标与基于遥感器的指示器有关。² > 0.44；P  <0.001）。通过使用植被指数-温度梯形计算得出的缺水指数（WDI），可以对水的状态进行可靠的评估。与主要受水状况影响的WDI相比，CCCI区分了N个水平以减少水状况的混杂影响。结合CCCI和WDI评估作物的NNI可以将均方根误差降低至0.109，这表明光谱和热信息的结合可以改善氮肥和灌溉对作物需求的调整。但是，在提出氮肥和灌溉建议之前，必须在其他品种和环境中对该方法进行验证。