Abstract
The aim of this study was to use small unmanned aerial vehicles (UAVs) for determining high-resolution normalized difference vegetation index (NDVI) values. Subsequently, these results were used to assess their correlations with fertilizer application levels and the yields of rice and wheat crops. For multispectral sensing, we flew two types of small UAVs (DJI Phantom 4 and DJI Phantom 4 Pro)—each equipped with a compact multispectral sensor (Parrot Sequoia). The information collected was composed of numerous RGB orthomosaic images as well as reflectance maps with spatial resolution greater than a ground sampling distance of 10.5 cm. From 223 UAV flight campaigns over 120 fields with a total area coverage of 77.48 ha, we determined that the highest efficiency for the UAV-based remote sensing measurement was approximately 19.8 ha per 10 min while flying 100 m above ground level. During image processing, we developed and used a batch image alignment algorithm—a program written in Python language–to calculate the NDVI values in experimental plots or fields in a batch of NDVI index maps. The color NDVI distribution maps of wide rice fields identified differences in stages of ripening and lodging-injury areas, which accorded with practical crop growth status from aboveground observation. For direct-seeded rice, variation in the grain yield was most closely related to that in the NDVI at the early reproductive and late ripening stages. For wheat, the NDVI values were highly correlated with the yield ( R 2 = 0.601–0.809) from the middle reproductive to the early ripening stages. Furthermore, using the NDVI values, it was possible to differentiate the levels of fertilizer application for both rice and wheat. These results indicate that the small UAV-derived NDVI values are effective for predicting yield and detecting fertilizer application levels during rice and wheat production.
Highlights
Small commercial unmanned aerial vehicles (UAVs)–often referred to as drones–are conspicuous newcomers providing easy cost-efficient measurements for use in remote sensing science
We examined the feasibility of using small UAVs for remote sensing in order to support the rice and wheat growers in the double cropping region
We report on the feasibility of normalized difference vegetation index (NDVI) remote sensing by using small UAV in rice and wheat fields
Summary
Small commercial UAVs–often referred to as drones–are conspicuous newcomers providing easy cost-efficient measurements for use in remote sensing science. Most small commercial UAVs on the market have the ability to fly autonomously and to survey crops from overhead for detailed observation. For unobstructed areas free of interference, such as in sparsely populated agricultural and forestry land, UAVs on autopilot can fly much more widely and safely for acquiring significant sensing imagery. The acquired high-resolution orthomosaic imagery gives researchers and growers enormous advantages for accurate observation and measurement in agricultural and environmental applications. In a few recent studies, UAV-based high-resolution imagery was proposed for estimating the plant density of wheat (Triticum aestivum L.) [1], ascertaining the canopy height and aboveground biomass of maize (Zea mays L.) [2] or grassland [3], predicting the yield of rice (Oryza sativa L.) [4,5] or wheat [6], quality assessment of digital surface models (DSMs) [7], and land cover classification [8]. Many more applications of UAVs for agroforestry and environmental monitoring can be found in recent review papers [9,10,11]
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