Global warming and climate change can potentially change not only rice production but also rice nutrient content. To adapt a rice-dependent country’s farming to the impacts of climate change, it is necessary to assess and monitor the potential risk that climate change poses to agriculture. The aim of this study was to clarify the relationship between rice grain protein content (GPC) and meteorological variables through unmanned aerial vehicle remote sensing and meteorological measurements. Furthermore, a method for GPC estimation that combines remote sensing data and meteorological variables was proposed. The conclusions of this study were as follows: (1) The accuracy and robustness of the GPC estimation model were improved by evaluating the nitrogen condition with the green normalized difference vegetation index at the heading stage (GNDVIheading) and evaluating photosynthesis with the average daily solar radiation during the grain-filling stage (SRgrain-filling). GPC estimation considering SRgrain-filling in addition to GNDVIheading was able to estimate the observed GPC under the different conditions. (2) Increased temperature from the transplantation date to the heading stage can affect increased GPC when extreme temperature does not cause the heat stress.

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SfM-MVS 3 NDVI UAV キーワード UAV SfM-MVS CSMs 濱 侃 1) 早崎 有香 2) 望月 篤 3) 鶴岡 康夫 3) 田中 圭 4) 近藤 昭彦 5) 1) 千葉大学 理学研究科 263-8522 1-33 2) 千葉大学 理学部(現在 株式会社 メイテツコム)

Use of information and communication technologies, as well as robotics, routinely saves labor and refines agricultural tasks; thus, innovative "smart farming" to maintain and enhance the quality of crops can improve the sustainability of agriculture. When managing crop growth using remote-sensing drones, the normalized difference vegetation index NDVI used to assess growth typically changes depending on sunlight conditions. In this study we have attempted to develop an empirical correction to correct for differences in sunlight conditions in drone NDVI images of paddy rice. Based on observations using a field sensor installed in a paddy field, and considering the effects of morning dew, we determined that 10:00 AM is the most appropriate time for NDVI observations in paddy rice, when the morning dew has largely evaporated. This observation time differs from that used in the radiative transmission models described in previous studies. In the drone observations, sections with lower NDVI were more strongly affected by solar altitude, and thus by time of day. Therefore, we found that when correcting NDVI according to sunlight conditions, it is necessary to adjust the correction parameters depending on the NDVI values. Based on the aforementioned results, we corrected the drone-observed NDVI and succeeded in mitigating the decline in NDVI value associated with changes in sunlight conditions, in terms of both NDVI values and NDVI images, within plots established in the experimental field.

The objectives of this study were to improve the yield estimation of paddy rice based on the unmanned aerial vehicle remote sensing (UAV-RS) and solar radiation data sets. The study used the UAV-RS-based normalized difference vegetation index (NDVI) at the heading stage, the solar radiation data of geostationary satellite Himawari-8 and the solar radiation data of polar orbiting satellite Aqua/ MODIS. A comparison of two satellite-based solar radiation data sets (Himawari-8 and MODIS PAR) showed that the coefficient of determination (R 2 ) of estimated yield based on Himawari-8 solar radiation was 0.7606 while the R 2 of estimated yield based on the MODIS PAR was 0.4749. Additionally, the root mean square error (RMSE) of Himawari-8 solar radiation was 26.5 g/m 2 while the RMSE of estimated yield based on the MODIS PAR was 39.2 g/m 2 (The average observed yield was 489.3 g/m 2 ). The Estimated yield based on Himawari-8 solar radiation, therefore, outperformed the MODIS PAR-based estimated yield. The improvement of the temporal resolution of the satellite-based dataset allowed by using the Himawari-8 data set contributed to the improvement of estimation accuracy. Satellite-based solar radiation data allow yield estimation based on remote sensing in regions where there are no ground observation data of solar radiation.