Abstract
Miniature hyperspectral and thermal cameras onboard lightweight unmanned aerial vehicles (UAV) bring new opportunities for monitoring land surface variables at unprecedented fine spatial resolution with acceptable accuracy. This research applies hyperspectral and thermal imagery from a drone to quantify upland rice productivity and water use efficiency (WUE) after biochar application in Costa Rica. The field flights were conducted over two experimental groups with bamboo biochar (BC1) and sugarcane biochar (BC2) amendments and one control (C) group without biochar application. Rice canopy biophysical variables were estimated by inverting a canopy radiative transfer model on hyperspectral reflectance. Variations in gross primary productivity (GPP) and WUE across treatments were estimated using light-use efficiency and WUE models respectively from the normalized difference vegetation index (NDVI), canopy chlorophyll content (CCC), and evapotranspiration rate. We found that GPP was increased by 41.9 ± 3.4% in BC1 and 17.5 ± 3.4% in BC2 versus C, which may be explained by higher soil moisture after biochar application, and consequently significantly higher WUEs by 40.8 ± 3.5% in BC1 and 13.4 ± 3.5% in BC2 compared to C. This study demonstrated the use of hyperspectral and thermal imagery from a drone to quantify biochar effects on dry cropland by integrating ground measurements and physical models.
Highlights
Land surface variables are required for modeling of carbon, water, and energy exchanges between land and atmosphere
We investigate the effect of biochar in a field experiment with upland rice (Oryza sativa L.) in the North Pacific of Costa Rica to assess changes associated with energy, productivity, soil water availability, ET, and water use efficiency (WUE)
We found that rice gross primary productivity (GPP), normalized difference vegetation index (NDVI), and canopychlorophyll chlorophyll content (CCC) were higher in both biochar applications than in the control group (Figure 6)
Summary
Land surface variables are required for modeling of carbon, water, and energy exchanges between land and atmosphere. 2021, 13, 1866 onboard 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]. Kandylakis 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. Chen et al [5] used data from UAV-borne multispectral and thermal cameras to evaluate cotton canopy water stress. The recent advances in miniature hyperspectral snapshot imaging technology bring new opportunities for low-cost UAV-borne remote sensing [11]
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