Unmanned aerial system (UAS)-based remote sensing is one promising technique for precision crop management, but few studies have reported the applications of such systems on nitrogen (N) estimation with multiple sensors in rice (Oryza sativa L.). This study aims to evaluate three sensors (RGB, color-infrared (CIR) and multispectral (MS) cameras) onboard UAS for the estimation of N status at individual stages and their combination with the field data collected from a two-year rice experiment. The experiments were conducted in 2015 and 2016, involving different N rates, planting densities and rice cultivars, with three replicates. An Oktokopter UAS was used to acquire aerial photography at early growth stages (from tillering to booting) and field samplings were taken at a near date. Two color indices (normalized excess green index (NExG), and normalized green red difference index (NGRDI)), two near infrared vegetation indices (green normalized difference vegetation index (GNDVI), and enhanced NDVI (ENDVI)) and two red edge vegetation indices (red edge chlorophyll index (CI_{red edge}), and DATT) were used to evaluate the capability of these three sensors in estimating leaf nitrogen accumulation (LNA) and plant nitrogen accumulation (PNA) in rice. The results demonstrated that the red edge vegetation indices derived from MS images produced the highest estimation accuracy for LNA (R²: 0.79–0.81, root mean squared error (RMSE): 1.43–1.45 g m⁻²) and PNA (R²: 0.81–0.84, RMSE: 2.27–2.38 g m⁻²). The GNDVI from CIR images yielded a moderate estimation accuracy with an all-stage model. Color indices from RGB images exhibited satisfactory performance for the pooled dataset of the tillering and jointing stages. Compared with the counterpart indices from the RGB and CIR images, the indices from the MS images performed better in most cases. These results may set strong foundations for the development of UAS-based rice growth monitoring systems, providing useful information for the real-time decision making on crop N management.

Crop biomass is a critical variable for characterizing crop growth development, understanding dry matter partitioning, and predicting grain yield. Previous studies on the spectroscopic estimation of crop biomass focused on the use of various spectral indices based on chlorophyll absorption features and found that they often became saturated at high biomass levels. Given that crop biomass is commonly expressed as the dry weight of canopy components per unit ground area, it may be better estimated using the spectral indices that directly characterize dry matter absorption. This study aims to evaluate a group of four dry matter indices (DMIs) by comparison with a group of four chlorophyll indices (CIs) for estimating the biomass of individual components (e.g., leaves, stems) and their combinations with the field data collected from a two-year rice cultivation experiment. The Red-edge Chlorophyll Index (CI_Red-edge) of the CI group exhibited the best relationship with leaf biomass (R² = 0.82) for the whole growing season and with total biomass (R² = 0.81), but only for the growth stages before heading. However, the Normalized Difference Index for Leaf Mass per Area (ND_LMA) of the DMI group showed the best relationships with both stem biomass (R² = 0.81) and total biomass (R² = 0.81) for the whole season. This research demonstrated the suitability of dry matter indices and provided physical explanations for the superior performance of dry matter indices over chlorophyll indices for the estimation of whole-season total biomass.