## 1 Introduction  
Land surface variables are required for modeling of carbon, water, and energy exchanges between land and atmosphere. Miniature multispectral and thermal camerasonboard lightweight unmanned aerial vehicle (UAV) have been widely used for providing spatially explicit land surface variables and as an efficient tool supporting precision farming and crop management e.g., [1; 2; 3]. For example, [PERSON] et al. [4] used UAV-borne multispectral and shortwave infrared cameras to estimate LAI of vineyards, and assess water stress conditions using individual relations gained from UAV data and in-situ measurements via statistical regression. [PERSON] et al. [5] used data from UAV-borne multispectral and thermal cameras to evaluate cotton canopy water stress. Infrared thermography has been used to diagnose crop water deficit and drought conditions [6; 7; 8]. Besides multispectral, hyperspectral imaging onboard an aircraft with dozens or even 100+ continuous narrow bands of high spectral resolution is also attracting research interests. However, the previously high-cost and heavyweight hyperspectral imager using motion scanning mechanics limited the hyperspectral technique to few studies on expensive airborne sensing (e.g., [9; 10]). The recent advances in miniature hyperspectral snapshot imaging technology bring new opportunities for low-cost UAV-borne remote sensing [11]. These snapshot imagers use non-moving parts such as image slicer mirror or filter-on-chip technologies [12], and minimize moving artifacts during sensing to gain satisfactory spectral and spatial accuracy. The current miniature hyperspectral snapshot cameras are promising for monitoring crop growth and retrieving crop biophysical variables for precision agriculture from UAVs. Hyper- or multiple spectral data from UAV sensing are usually analyzed in three approaches to estimate land surface properties. (1) Vegetation indices simply combining different band reflectance are often used to estimate a certain land surface variable based on empirical relations. For example, the normalized difference vegetation index (NDVI) formulated using red and near-infrared (NIR) bands is widely used to indicate land surface greenness [13]. Other narrow hyperspectral band combinations are used for leaf chlorophyll estimation [10]. (2) Machine learning (ML) techniques. ML is increasingly used to estimate land surface variables based on large field measurements, including for example partial least squared regression [14], artificial neural network [15], and random forest [16]. These empirical methods need site-specific land surface variables for model training and the trained model may not be suitable for other sites. (3) Physical model inversion. A radiative transfer model, such as PRO4 SAL [17], can simulate hyperspectral reflectance close to the field measured reflectance with optimal inputs. The inverse of the radiative transfer model based on field hyperspectral measurements can obtain leaf and canopy variables such as pigment content and leaf area index (LAI) by minimizing the difference between the modelled and the measured reflectance in all spectral wavelengths in model optimization process. Such a physically based model does not necessarily require field-measured land surface variables for parameter calibration, especially for common crops like rice, being more robust than vegetation index methods and avoiding overfitting issues in ML methods to retrieve plant and soil parameters. These techniques can be used to assess crop-atmosphere exchanges when manipulating soil conditions, for example, using biochar for soil amendment. Biochar is an old technique used for soil amendment in the Amazon region centuries ago [18; 19]. The porous charcoal has a large surface area that can bind and retain soil water and nutrients, and reduce nutrient leaching and volatilization loss, thereby helping to increase long-term plant water availability and soil fertility [20; 21].