Timely and accurate estimates of crop parameters are crucial for agriculture management. Unmanned aerial vehicles (UAVs) carrying sophisticated cameras are very pertinent for this work because they can obtain remote-sensing images with higher temporal, spatial, and ground resolution than satellites. In this study, we evaluated (i) the performance of crop parameters estimates using a near-surface spectroscopy (350~2500 nm, 3 nm at 700 nm, 8.5 nm at 1400 nm, 6.5 nm at 2100 nm), a UAV-mounted snapshot hyperspectral sensor (450~950 nm, 8 nm at 532 nm) and a high-definition digital camera (Visible, R, G, B); (ii) the crop surface models (CSMs), RGB-based vegetation indices (VIs), hyperspectral-based VIs, and methods combined therefrom to make multi-temporal estimates of crop parameters and to map the parameters. The estimated leaf area index (LAI) and above-ground biomass (AGB) are obtained by using linear and exponential equations, random forest (RF) regression, and partial least squares regression (PLSR) to combine the UAV based spectral VIs and crop heights (from the CSMs). The results show that: (i) spectral VIs correlate strongly with LAI and AGB over single growing stages when crop height correlates positively with AGB over multiple growth stages; (ii) the correlation between the VIs multiplying crop height and AGB is greater than that between a single VI and crop height; (iii) the AGB estimate from the UAV-mounted snapshot hyperspectral sensor and high-definition digital camera is similar to the results from the ground spectrometer when using the combined methods (i.e., using VIs multiplying crop height, RF and PLSR to combine VIs and crop heights); and (iv) the spectral performance of the sensors is crucial in LAI estimates (the wheat LAI cannot be accurately estimated over multiple growing stages when using only crop height). The LAI estimates ranked from best to worst are ground spectrometer, UAV snapshot hyperspectral sensor, and UAV high-definition digital camera.

Black carbon (BC), brown carbon (BrC), and mineral dust (DU) are three major light absorbing aerosols, playing important roles in climate change. Better knowledge of their concentrations is necessary for more accurate estimates of their radiative forcing effects of climate. We present a method to retrieve columnar contents of BC, BrC, and DU simultaneously from spectral refractive indices and spectral single scattering albedo obtained from the sun‐sky radiometer measurements. Then, this method is applied to investigate the columnar volume fractions and mass concentrations of BC, BrC, and DU in Beijing, China, based on measurements obtained from 2009 to 2010. Results show that among the three absorbing aerosols, DU dominates the largest volume fraction in the total aerosol volume (20–45%), followed by BrC (5–25%), and BC (< 5%). The retrieved monthly mean content of each absorbing component exhibits clear seasonal variation. BrC dominates in late fall and winter (40–92.5 mg/m²), whereas is extremely low in summer (< 10 mg/m²). DU dominates in spring, ranging from 270 to 405 mg/m² (with volume fraction >30%), while during June–September, the DU fraction is generally lower than 30%. BC is characterized by low levels throughout the year. The monthly mean BC columnar mass concentration ([BC]) ranges from 2.7 to 7.3 mg/m² with winter slightly higher than other seasons. As a preliminary validation, we compare our retrieved [BC] with in situ measurements. Similar day‐to‐day variation trends and good correlations are found between the retrieved [BC] and in situ measurements.