## 2 Materials and Methods
### UAV Field Campaign  
The UAV field campaign was carried out using a hexacopter (DJI M600 Pro, Dajiang, China) on November 14, 16, 19, and 21, 2018 before harvest. These days were selected for the field campaign for the following reasons: (1) These days were at the start of the dry season in the region and the biochar effects on soil water retention could be easily tested. (2) It was the phenology transition period during which the rice leaves changed from green at the beginning of the campaign to yellow (senescence) at the end of the campaign, likely creating large spatial and temporal variability in rice water use. (3) The LAI and GPP at the time of the campaign are expected to reflect the accumulated growth of the whole growing period and consequently, allowing the integrated effect of biochar to be quantified. (4) A field campaign spanning a whole rice growing period was logistically unfeasible. The Djiang DJI M600 Pro hexacopter has a payload capacity of 6 kg and a flight time of 35 minutes under minimal payload. In the campaign, the UAV was flying at a height of about 30 m above the ground. The UAV position was recorded by the three sets of GNSS and IMU systems inbuilt with the drone. A gimbal (Gremsy T7, Gremsy, Vietnam) was used to enable the camera to consistently viewing the nadir direction. We used a hyperspectral camera (FineHZYE 185 ST, Cubert, Germany) and a thermal camera (324\(\times\)256 Pixels, Tau2 324, FLIR, USA) with the attained ground resolutions of 4.5 mm and 2.25 cm respectively to investigate the plant productivity, soil, and surface properties. The Cubert hyperspectral camera has 50 \(\times\) 50 pixels, with each pixel spanning from 450 to 950 nm in 138 wavebands. The camera has an extra panchromatic band of 1000 \(\times\) 1000 pixels to facilitate sharpening the hyperspectral bands. It has been shown that the best spatial and spectral accuracy can be achieved from Cubert panchromatic sharpening at a flying height of 30 m [11]. In our application, we adopted the default pan-sharpening function in the Cubert-pilot software. During the flight, a spectrometer (Flame S VIS-NIR, Ocean Optics, Dunedin, FL, USA) was mounted on the drone to simultaneously collect spectral irradiance. It should be noted that the hyperspectral camera images had about 55% of side overlap and 40% of forward overlaps in the flight. The overlaps were smaller than the suggested  
Figure 1: Location of biochar plots at an upland rice experimental site in Costa Rica. The experiment consisted of three groups (BC1: bamboo biochar, BC2: sugarcane biochar, and C: control without biochar) in triplicate (Plot 1, 2, and 3). Each plot was divided into 3 sub-plots used in statistical analysis (indicated as dashed lines). The true-color image (4.5 mm resolution) is from a Cubert FirefIEYE 185 VNIR camera (band 56, 29, and 12) taken on 14 November 2018, orthorectified and mosaicked using Agisoft Metashape software. The star (\(\%\)) denotes the location of the weather station, and circles (\(\Circle\)) the locations of soil sensors. figures (60% of side overlap and 80% of forward overlap) in [30] due to the small field-of-view (FOV) of the Cubert hyperspectral camera (13\({}^{\circ}\)\(\times\) 13\({}^{\circ}\)). On the one hand, a small FOV camera with nadir viewing has a great advantage of minimizing the bi-directional reflectance distribution function (BRDF) effect [31], which otherwise needs to be adjusted carefully for quantitative remote sensing [32]. On the other hand, the small FOV creates difficulties in further camera alignment processing when the image overlap is not large enough as afore-mentioned in [30] and consequently the cloud points are not dense enough for estimating camera locations using Agisoft Metashape software. Otherwise, much more hyperspectral images should be collected, heavily increasing the field workload.