Unmanned aerial vehicle (UAV) equipped with multispectral cameras for remote sensing (RS) has provided new opportunities for ecological and agricultural related applications for modelling, mapping, and monitoring. However, when the multispectral images are used for the quantitative study, they should be radiometrically calibrated, which accounts for atmospheric and solar conditions by converting the digital number into a unit of scene reflectance that can be directly used in quantitative remote sensing (QRS). Indeed, some of the present applications using multispectral images are processed without precise calibration or with coarse calibration. The radiometric calibration of images from the UAV platform is quite difficult to perform, as the imaging condition is different for every single image. Thus, a standard procedure is necessary for a systematical radiometric calibration method to generate multispectral images with unit reflectance. Further, these images can be used to calculate vegetation indices, which are useful in monitoring vegetation phenology. These vegetation indices are considered as a potential screening tool to know the plant status, such as nitrogen, chlorophyll content, green leaf biomass, etc. This study focuses on a series of radiometric calibrations for multispectral images acquired from different flight altitudes, time instants, and weather conditions. Radiometric calibration for multispectral images is performed using the linear regression method (LRM). The main contribution involves (1) affirming the optimal calibration targets and assessing the atmospheric effects of different flights using the single scene of images; (2) to evaluate the effects of mosaic images with the LRM; (3) to propose and validate a universal calibration equation for the Mini Multiple Camera Array (MCA) 6 camera. The obtained results show that the three calibration targets, such as the dark, moderate, and white, are better for the Mini MCA 6 camera. The atmospheric effects increase with the increase of flight altitudes for each band, and the camera effect is of a fixed number. However, the camera effect and atmospheric attenuation to reflectance from different altitudes were relatively low considering the accuracy assessment. The performance measures namely, mean absolute deviation (indicated as V) and root mean square error (RMSE) between single and mosaic images show that the mosaic will not influence too much reflectance. The LRM performs well in all weather conditions. The universal calibration equation is suitable to apply to the images acquired during a sunny day and even with a little cloud.

In this study, the potential climate change impacts on rice growth and rice yield under 1.5 and 2.0 °C warming scenarios, respectively, are simulated using the Ceres-Rice Model based on high-quality, agricultural, experimental, meteorological and soil data, and the incorporation of future climate data generated by four Global Climate Models (GCMs) in the Pearl River Delta, China. The climatic data is extracted from four Global Climate Models (GCMs) namely: The Community Atmosphere Model 4 (CAM4), The European Centre for Medium-Range Weather Forecasts-Hamburg 6 (ECHAM6), Model for Interdisciplinary Research On Climate 5 (MIROC5) and the Norwegian Earth System Model 1 (NorESM1). The modeling results show that climate change has major negative impacts on both rice growth and rice yields at all study sites. More specifically, the average of flowering durations decreases by 2.8 days (3.9 days), and the maturity date decreases by 11.0 days (14.7 days) under the 1.5 °C and (2.0 °C) warming scenarios, respectively. The yield for early mature rice and late mature rice are reduced by 292.5 kg/ha (558.9 kg/ha) and 151.8 kg/ha (380.0 kg/ha) under the 1.5 °C (2.0 °C) warming scenarios, respectively. Adjusting the planting dates of eight days later and 15 days earlier for early mature rice and late mature rice are simulated to be adaptively effective, respectively. The simulated optimum fertilizer amount is about 240 kg/ha, with different industrial fertilizer and organic matter being applied.

Increasing temperatures, greater carbon dioxide concentrations, and changes in related climatic variables will continue to affect the growth and yields of agricultural crops. Rice (Oryza sativa L.) is extremely vulnerable to these climatic changes. Therefore, investigating the degree to which climate changes could influence rice yields and what effective adaptive strategies could be taken to mitigate the potential adverse impacts is of vital importance. In this article, the impacts of climate change on rice yields in Zhejiang province, China, were simulated under the Representative Concentration Pathway (RCP) 4.5 and 8.5 scenarios. The impacts of climate change, with and without CO₂ fertilization effects, were evaluated and the three most effective adaptive measures were examined. Compared with the yield for the baseline time of 1981–2010, the simulated average yields of all cultivars were inevitably projected to decrease under both RCPs when the CO₂ fertilization effects were not considered during the three periods of the 2020s (2011–2040), 2050s (2041–2070), and 2080s (2071–2099), respectively. Declines in rice yields were able to be alleviated when the CO₂ fertilization effects were accounted for, but the yields were still lower than those of the baseline. Therefore, the three adaptive measures of advancing planting dates, switching to high-temperature-tolerant cultivars, and breeding new cultivars were simulated. The results indicated that adaptive measures could effectively mitigate the adverse effects of climate change. Although the simulation had uncertainties and limitations, the results provide useful insights into the potential impacts of climate change in Zhejiang province while also proposing adaptive measures.

Quantifying the contributing and limiting factors of yield potential is of vital importance, and the closure of existing yield gaps on currently available agricultural land is regarded as the most effective measure to meet future food demands. In this study, the CERES-Rice model and long-term rice yield records of 12 sites from 1981 to 2010 were combined together to investigate the spatial and temporal distributions of yield potential, yield attainable, yield actual, and yield gaps for double cropping rice in the Pearl River Delta (PRD), China. The evaluated yield potential of all the sites ranged from 7500 to 14,900 kg/ha, while yield attainable was from 6400 to 12,665 kg/ha, and yield actual was from 4000 to 7000 kg/ha. The yield gaps between yield potential and yield actual, yield potential and yield attainable, and yield attainable and yield actual were projected to be 3500 kg/ha, 1400 kg/ha, and 2100 kg/ha, respectively. The decrease of yield potential was due to the increasing temperature for early mature rice and the prolonged sunshine hours for the yield potential of late mature rice, respectively. The social–economic impacts of yield actual were also assessed, and adaptive measures were simulated so that the yield would certainly increase.