Evaluating Spectral Resolution Effects on Crop Monitoring: A Comparison of UAV-based Multispectral, Hyperspectral and handheld Greenseeker sensor

Spectral sensors have become an integral part of modern precision agriculture. It enables fast, non-destructive and map-based crop monitoring of key crop physiological parameters. The spectral resolution of a sensor plays an important role in determining its ability to detect subtle changes in the crop, nutrient status and canopy development. Sensor’s comparison based on spectral resolution remains limited particularly in the context of field-level agronomic monitoring. This study aims to address this gap by using three sensors UAV-based multispectral (MS), UAV-based hyperspectral (HS) and handheld Greenseeker (GS) NDVI measurements. Hyperspectral sensors provide continuous high-resolution data from visible to near infrared; MS use fewer broad bands; GS limits to two bands for quick NDVI field checks. The experimental study was conducted in the arid region of Uttar Pradesh, India. The experimental setup consisted of plots with same irrigation (100% ETc) and varying nitrogen dosage i.e. 150,120 and 90 kg/ha (Plot 1, Plot 2 and Plot 3, respectively) with three replications. Plots 4 and 5, representing farmer-field conditions with 120 kg ha⁻¹ nitrogen and no nitrogen respectively, followed regional irrigation practices, whereas Plot 6 (rainfed) was irrigated only once initially. A series of UAV-flights were conducted across critical phenological stages, and the reflectance was used to generate Normalized Difference Vegetation Index (NDVI) representing canopy density.

The results showed that NDVI rapidly increased during early vegetative stage (61-75) DAS, saturated around (75-85) DAS, followed by a decline during (101-117) DAS.  NDVI peaked around flowering stage for all the sensors. GS-NDVI varied between (0.46-0.78), MS-NDVI displayed (0.52-0.86), whereas HS- NDVI varied between (0.55-0.90). The mean NDVI values were (0.570 ± 0.085) for GS, (0.608 ± 0.075) for MS, and (0.664 ± 0.087) for the HS, with HS exceeding others by 16.5% (vs. GS) and 9.3% (vs. MS). Pearson correlation coefficients confirm strong inter-sensor agreement: Greenseeker-Hyperspectral r = 0.96, Multispectral-Hyperspectral r = 0.91, Greenseeker-Multispectral r = 0.87 (all p<0.001), indicating consistent vegetation health trends despite spectral resolution variances. Across days 61-117, fully irrigated plots with varying nitrogen dosage (Plot 1-3) maintained higher vegetation indices (0.60-0.90) than stressed plots. For Plot 4 (0.57-0.84), Plot 5(0.49-0.79) and Plot 6(0.46-0.76), the decline accelerated under water and nitrogen deficit. Water-stressed and nitrogen deficit plots show greater NDVI drops, indicating higher stress levels leading to early senescence, thus affecting the grain yield.

Overall, the three sensors show strong agreement in NDVI trends. For precision agriculture, HS optimized subtle changes, followed by MS; statistical trends aligned with established NDVI comparison protocols using correlation and regression. Hyperspectral sensor offered the highest diagnostic capability, multispectral provided spatial characteristics and greenseeker served as an efficient tool for rapid monitoring of field. These combined observations emphasize the importance of selecting sensors based on the required level of detail, operational constraints, and monitoring objectives in precision agriculture. Integrating data from multiple sensor types can further enhance crop assessment accuracy and support more informed decision-making in precision agriculture.