Imaging spectroscopy provides the opportunity to monitor nutrient status of vegetation. In crops, prior studies have generally been limited in scope, either to a small wavelength range (e.g., 400–1300 nm), a small number of crop cultivars, a single growth stage or single growing season. Methods that are not time- or site-specific are needed to use imaging spectroscopy for routine monitoring of crop status. Using data from four cultivars of potatoes (Solanum tuberosum L.), three growth stages and two growing seasons, we demonstrate the capacity of full-range (400–2350 nm) imaging spectroscopy to quantify nutrient status (petiole nitrate, whole leaf and vine total nitrogen) and predict tuber yield in potatoes across cultivars, growth stages and growing seasons. We specifically tested the capabilities of: (1) ordinary least-squares regression (OLSR) using traditional hyperspectral vegetation indices (VIs); (2) partial least-squares regression (PLSR) using full spectrum (400–2350 nm), VNIR- (visible-to-near infrared: 400–1300 nm) or SWIR-only (shortwave infrared: 1400–2350 nm) wavelengths; (3) predictive models developed for one potato type or planting season on withheld data from a different type or season. Our results show that OLSR models produced poor predictions with data from all dates pooled together (validation R² < 0.01). Single-date OLSR models performed better (R² = 0.20–0.60, relative RMSE = 15–30%). PLSR models performed well and were comparable using different spectral regions (full-spectrum, VNIR-only and SWIR-only), with validation R² = 0.68–0.82 and RRMSE = 12–25%. Testing across potato types, models produced reliable predictions (R² = 0.45–0.75, RRMSE = 13–30%), but with some bias. Cross-season models had validation R² = 0.46–0.75 and RRMSE = 17–100%, with a more significant bias than the cross-potato type models. To achieve models that are generalizable and robust, we recommend: (1) obtaining ground measurements that capture the full range of plant growth conditions and developmental stages, and (2) ensuring that image processing approaches minimize spectral discrepancies among dates.

成像光谱学提供了监测植被营养状况的机会。在作物中，先前的研究通常局限于范围较小的波长范围（例如400-1300 nm），少量的作物品种，单一的生长阶段或单一的生长季节。需要使用非时间或地点特定的方法来使用成像光谱法常规监测作物状态。从马铃薯的4个品种（使用数据马铃薯大号。），三个生长阶段和两个生长季节，我们证明了全范围（400–2350 nm）成像光谱技术能够量化营养状况（硝酸盐叶柄，全叶和葡萄藤总氮）并预测不同品种马铃薯的块茎产量，生长阶段和生长季节。我们专门测试了以下功能：（1）使用传统的高光谱植被指数（VI）进行普通最小二乘回归（OLSR）；（2）使用全光谱（400–2350 nm），VNIR-（可见至近红外：400–1300 nm）或仅SWIR（短波红外：1400–2350 nm）波长的偏最小二乘回归（PLSR） ; （3）根据不同类型或季节的隐含数据为一种马铃薯类型或种植季节开发的预测模型。R ²  <0.01）。单日期OLSR模型的效果更好（R ²  = 0.20–0.60，相对RMSE = 15–30％）。PLSR模型表现良好，并且在不同的光谱区域（全光谱，仅VNIR和仅SWIR）具有 可比性，验证的R ² = 0.68–0.82和RRMSE = 12–25％。对马铃薯类型进行测试后，模型 得出了可靠的预测值（R ² = 0.45–0.75，RRMSE = 13–30％），但存在一定偏差。跨季节模型的验证值为R ² = 0.46-0.75，RRMSE = 17-100％，比交叉马铃薯类型的模型有更大的偏差。为了获得可推广使用的强大模型，我们建议：（1）获得能够涵盖植物生长条件和发育阶段的全部范围的地面测量值，以及（2）确保图像处理方法将日期之间的光谱差异最小化。