Seungtaek Jeong

Simulation and mapping of rice growth and yield based on remote sensing

Abstract. The GRAMI crop growth model uses remote sensing data and thus has the potential to produce maps of crop growth and yield. A pixel-based crop information delivery system (CIDS) to simulate and map rice (Oryza sativa) growth and yield was developed using GRAMI. The GRAMI-rice model was parameterized using field data obtained at Chonnam National University, Gwangju, Republic of Korea, in 2011 and 2012. The model was separately validated using field data obtained at the same research site in 2009 and 2010. The model was then integrated into the CIDS to produce two-dimensional (2-D) maps of crop growth and yield. Simulated values of rice growth and yield agreed well with the corresponding measurements in both parameterization and evaluation. The simulated yields were in statistical agreement with the corresponding measured yields according to paired t tests (p=0.415 for parameterization and p=0.939 for validation). The CIDS accurately produced 2-D maps of rice growth and yield. The GRAMI-rice CIDS has simple input requirements and will be useful for regional rice growth monitoring and yield mapping projects.

Potential impacts on climate change on paddy rice yield in mountainous highland terrains

Crop models are suitable tools to assess the potential impacts of climate change on crop productivity. While the associated assessment reports have been focused on major rice production regions, there is little information on how climate change will impact the future rice crop production in mountainous highland regions. This study investigated effects of climate change on yield of paddy rice (Oryza sativa) in mountainous highland terrains of Korea using the CERES-Rice 4.0 crop model. The model was first calibrated and validated based on observed data and then applied to simulations for the future projections of rice yield in a typical mountainous terrain which borders North and South Korea, the Haean Basin in Kangwon Province, Republic of Korea. Rice yield in the highland terrain was projected to increase by 2050 and 2100 primarily due to elevated CO2 concentration. This effect of CO2 fertilization on yield (+10.9% in 2050 and +20.0% in 2100) was also responsible for increases in water-use efficiency and nitrogen-use efficiency. With management options, such as planting date shift and increasing nitrogen application, additional yield gains were predicted in response to the future climate in this area. We also found that improving genetic traits should be another option to get further yield increases. All in all, climate change in mountainous highland areas should positively influence on paddy rice productivity.

Monitoring canopy growth and grain yield of paddy rice in South Korea by using the GRAMI model and high spatial resolution imagery

Monitoring crop conditions and forecasting crop yields are both important for assessing crop production and for determining appropriate agricultural management practices; however, remote sensing is limited by the resolution, timing, and coverage of satellite images, and crop modeling is limited in its application at regional scales. To resolve these issues, the Gramineae (GRAMI)-rice model, which utilizes remote sensing data, was used in an effort to combine the complementary techniques of remote sensing and crop modeling. The model was then investigated for its capability to monitor canopy growth and estimate the grain yield of rice (Oryza sativa), at both the field and the regional scales, by using remote sensing images with high spatial resolution. The field scale investigation was performed using unmanned aerial vehicle (UAV) images, and the regional-scale investigation was performed using RapidEye satellite images. Simulated grain yields at the field scale were not significantly different (p = 0.45, p = 0.27, and p = 0.52) from the corresponding measured grain yields according to paired t-tests (α = 0.05). The model’s projections of grain yield at the regional scale represented the spatial grain yield variation of the corresponding field conditions to within ±1 standard deviation. Therefore, based on mapping the growth and grain yield of rice at both field and regional scales of interest within coverages of a UAV or the RapidEye satellite, our results demonstrate the applicability of the GRAMI-rice model to the monitoring and prediction of rice growth and grain yield at different spatial scales. In addition, the GRAMI-rice model is capable of reproducing seasonal variations in rice growth and grain yield at different spatial scales.

Construction of an unmanned aerial vehicle remote sensing system for crop monitoring

Abstract. We constructed a lightweight unmanned aerial vehicle (UAV) remote sensing system and determined the ideal method for equipment setup, image acquisition, and image processing. Fields of rice paddy (Oryza sativa cv. Unkwang) grown under three different nitrogen (N) treatments of 0, 50, or 115  kg/ha were monitored at Chonnam National University, Gwangju, Republic of Korea, in 2013. A multispectral camera was used to acquire UAV images from the study site. Atmospheric correction of these images was completed using the empirical line method, and three-point (black, gray, and white) calibration boards were used as pseudo references. Evaluation of our corrected UAV-based remote sensing data revealed that correction efficiency and root mean square errors ranged from 0.77 to 0.95 and 0.01 to 0.05, respectively. The time series maps of simulated normalized difference vegetation index (NDVI) produced using the UAV images reproduced field variations of NDVI reasonably well, both within and between the different N treatments. We concluded that the UAV-based remote sensing technology utilized in this study is potentially an easy and simple way to quantitatively obtain reliable two-dimensional remote sensing information on crop growth.

Application of an unmanned aerial system for monitoring paddy productivity using the GRAMI-rice model

ABSTRACT Recent developments in unmanned aerial system (UAS) require an urgent introduction to monitoring technologies of crop diagnostic information because of their advantage in manoeuvering tasks at a high-spatial resolutions and low costs in a user-friendly manner. In this study, an advanced application method of an UAS remote sensing system was performed using the grid GRAMI-rice model such that it can be driven using weather and remote sensing data to monitor the spatiotemporal productivities of rice (Oryza sativa). Remotely sensed data for the model were supplied, along with normalized difference vegetation index images obtained using the UAS remote sensing system. The model was first evaluated using paddy data from experimental fields (treated with two nitrogen (N) applications) at Chonnam National University, Gwangju, Republic of Korea (ROK). Practical application was then performed using paddy data from farm fields under conventional farm management practices at the Gimje plain in ROK. The grid GRAMI-rice model statistically well reproduces the field conditions of spatiotemporal rice productivities, showing an acceptable statistical accuracy in the comparison of growth between the simulated and observed values, using a Nash–Sutcliffe efficiency range of 0.113–0.955. According to t-tests (α = 0.05), there were no significant differences between the simulated and observed grain yields from both the evaluation and practical applications. The scientific approach adopted here is unique, advanced, and practical, in a way that UAS remote sensing methods were effectively incorporated with crop modelling techniques. Therefore, it was concluded that the UAS-based remote sensing techniques proposed in this study could represent an innovative way of projecting reliable spatiotemporal crop productivities for precision agriculture.

Updating Absolute Radiometric Characteristics for KOMPSAT-3 and KOMPSAT-3A Multispectral Imaging Sensors Using Well-Characterized Pseudo-Invariant Tarps and Microtops II

Radiometric calibration of satellite imaging sensors should be performed periodically to account for the effect of sensor degradation in the space environment on image accuracy. In this study, we performed vicarious radiometric calibrations (relying on in situ data) of multispectral imaging sensors on the Korea multi-purpose satellite-3 and -3A (KOMPSAT-3 and -3A) to adjust the existing radiometric conversion coefficients according to time delay integration (TDI) adjustments and sensor degradation over time. The Second Simulation of a Satellite Signal in the Solar Spectrum (6S) radiative transfer model was used to obtain theoretical top of atmosphere radiances for both satellites. As input parameters for the 6S model, surface reflectance values of well-characterized pseudo-invariant tarps were measured using dual ASD FieldSpec® 3 hyperspectral radiometers, and atmospheric conditions were measured using Microtops II® Sunphotometer and Ozonometer. We updated the digital number (DN) of the radiance coefficients of the satellites; these had been used to calibrate the sensors during in-orbit test periods in 2013 and 2015. The coefficients of determination, R2, values between observed DNs of the sensors, and simulated radiances for the tarps were more than 0.999. The calibration errors were approximately 5.7% based on manifested error sources. We expect that the updated coefficients will be an important reference for KOMPSAT-3 and -3A users.

Geospatial delineation of South Korea for adjusted barley cultivation under changing climate

Determining effective measures to alleviate the impact of climate change on crops under various regional environments is one of the most urgent issues facing agriculture. In this study, geographic regions of South Korea for future-adjusted barley cultivation were outlined and the impact of climate change on barley production in the next 100 years was evaluated under two greenhouse gas concentration trajectory scenarios: the representative concentration pathway (RCP) 4.5 and RCP 8.5. To achieve our intended study goals, a geospatial crop simulation modeling (GCSM) scheme was formulated using CERES-barley model of Decision Support System for Agricultural Technology (DSSAT) crop model package version 4.6 to simulate grid-based geospatial crop yields. Two experiments were carried out at an open field to obtain model coefficients for the nation and at temperature gradient field chambers to evaluate the performance of the CERES-barley model under elevated temperature conditions. Suitable cultivation regions for three different types of barley (naked, hooded, and malting) under changing climate were projected to expand to the northern regions under both RCP 8.5 and RCP 4.5. However, they were projected to expand more rapidly under RCP 8.5 than those under RCP 4.5. Projected yields of four barley varieties were increased with a slow phase as year progressed under RCP 4.5 scenario. However, they were rapidly increased under RCP 8.5 scenario. It appears that geospatial variation in barley yield under changing climate can be effectively outlined. Therefore, GCSM system might be useful for determining impacts of climate change on geospatial variations of crops, potentially providing means to impede food insecurity.