Jong-Min Yeom

Sensitivity of vegetation indices to spatial degradation of RapidEye imagery for paddy rice detection: a case study of South Korea

Satellite remote sensing is an essential tool for crop monitoring over large areas. One of the most practical issues is defining the appropriate spatial resolution level in terms of technical aspects such as orbit path, swath width, or revisit rate, particularly in South Korea where the major agricultural activity of rice cultivation is conducted mostly by private farmers on small parcels of land. This study is an experimental approach to examine the sensitivity of vegetation indices of three paddy rice crops at various spatial resolutions during two seasons (July, September) using RapidEye multi-spectral image data. The results showed that lower spatial resolutions (beyond 26 m) had higher uncertainty for reflecting homogeneous field conditions and differentiating crop species. We stress, however, that the appropriate resolution might be dependent on the actual paddy size in the fields. Regarding the phenology of rice plants, the spectral difference of vegetation indices was more dependent on the same spatial resolution in July than in September. In addition, the July imagery, in which the vegetative and reproductive growth stages of the various rice cultivars were mixed, was slightly more effective for differentiating paddy rice crop classes. As an additional benefit, provision by RapidEye of red-edge spectral information ensured that a transformed vegetation index that made use of the red-edge band, which was edgNDVI in this study, was more applicable for differentiating three paddy rice crops in homogeneous rice cultivation.

Effect of red-edge and texture features for object-based paddy rice crop classification using RapidEye multi-spectral satellite image data

Recent satellite missions have provided new perspectives by offering high spatial resolution, a variety of spectral properties, and fast revisit rates to the same regions. In this study, we examined the utility of both broadband red-edge spectral information and texture features for classifying paddy rice crops in South Korea into three different growth stages. The rice grown in South Korea can be grouped into early-maturing, medium-maturing, and medium-late-maturing cultivars, and each cultivar is known to have a minimum and maximum productivity. Therefore, the accurate classification of paddy rice crops into a certain time line enables pre-estimation of the expected rice yields. For the analysis, two seasons of RapidEye satellite image data were used. The results showed that the broadband red-edge information slightly improved the classification accuracy of the paddy rice crops, particularly when single-season image data were used. In contrast, texture information resulted in only minor improvement or even a slight decline in accuracy, although it is known to be advantageous for object-based classification. This was due to the homogeneous nature of paddy rice fields, as different rice cultivars are similar in terms of their morphology. Based on these results, we conclude that the additional spectral information such as the red-edge band is more useful than the texture features to detect different crop conditions in relatively homogeneous rice paddy environments. We therefore confirm the potential of broadband red-edge information to improve the classification of paddy rice crops. However, there is still a need to examine the relationship between textural properties and paddy rice crop parameters in greater depth.

Feasibility of using Geostationary Ocean Colour Imager GOCI data for land applications after atmospheric correction and bidirectional reflectance distribution function modelling

Koreas Geostationary Ocean Colour Imager (GOCI) has very high temporal resolution as well as wide spatial coverage. There is thus great interest in testing its applicability for monitoring land areas in addition to ocean areas. GOCI has eight spectral bands, from blue to near-infrared. These bands can be sensitive to vegetation change, but their wavelength ranges are slightly different from those of the extensively studied Moderate Resolution Imaging Spectroradiometer (MODIS). This study examines whether GOCI data can be applied for land monitoring and how GOCI data should be processed so as to reflect the spectral characteristics of land surfaces as detected by polar-orbit satellite sensors. Several image processing steps were performed for the GOCI data, including atmospheric correction and semi-empirical bidirectional reflectance distribution function modelling, before the results were compared with the MODIS land-surface product. Among the four GOCI normalized difference vegetation index (NDVI) products tested in this study, the GOCI NDVI with viewing-angle-adjusted reflectance showed the best agreement with MODIS NDVI calculated from normalized reflectance, with the lowest root mean square error of 0.126. Additionally, its temporal trends over forest and mixed vegetation areas were similar to those of MODIS NDVI during the study period from September to December.

Evaluation on penetration rate of cloud for incoming solar radiation using geostationary satellite data

Solar surface insolation (SSI) represents how much solar radiance reaches the Earth’s surface in a specified area and is an important parameter in various fields such as surface energy research, meteorology, and climate change. This study calculates insolation using Multi-functional Transport Satellite (MTSAT-1R) data with a simplified cloud factor over Northeast Asia. For SSI retrieval from the geostationary satellite data, the physical model of Kawamura is modified to improve insolation estimation by considering various atmospheric constituents, such as Rayleigh scattering, water vapor, ozone, aerosols, and clouds. For more accurate atmospheric parameterization, satellite-based atmospheric constituents are used instead of constant values when estimating insolation. Cloud effects are a key problem in insolation estimation because of their complicated optical characteristics and high temporal and spatial variation. The accuracy of insolation data from satellites depends on how well cloud attenuation as a function of geostationary channels and angle can be inferred. This study uses a simplified cloud factor that depends on the reflectance and solar zenith angle. Empirical criteria to select reference data for fitting to the ground station data are applied to suggest simplified cloud factor methods. Insolation estimated using the cloud factor is compared with results of the unmodified physical model and with observations by ground-based pyranometers located in the Korean peninsula. The modified model results show far better agreement with ground truth data compared to estimates using the conventional method under overcast conditions.

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.

Comparison of NDVIs from GOCI and MODIS Data towards Improved Assessment of Crop Temporal Dynamics in the Case of Paddy Rice

The monitoring of crop development can benefit from the increased frequency of observation provided by modern geostationary satellites. This paper describes a four-year testing period from 2010 to 2014, during which satellite images from the worlds first Geostationary Ocean Color Imager (GOCI) were used for spectral analyses of paddy rice in South Korea. A vegetation index was calculated from GOCI data based on the bidirectional reflectance distribution function (BRDF)-adjusted reflectance, which was then used to visually analyze the seasonal crop dynamics. These vegetation indices were then compared with those calculated using the Moderate-resolution Imaging Spectroradiometer (MODIS)-normalized difference vegetation index (NDVI) based on Nadir BRDF-adjusted reflectance. The results show clear advantages of GOCI, which provided four times better temporal resolution than the combined MODIS sensors, interpreting subtle characteristics of the vegetation development. Particularly in the rainy season, when data acquisition under clear weather conditions was very limited, it was possible to find cloudless pixels within the study sites by compiling GOCI images obtained from eight acquisition periods per day, from which the vegetation index could be calculated. In this study, ground spectral measurements from CROPSCAN were also compared with satellite-based vegetation products, despite their different index magnitude, according to systematic discrepancy, showing a similar crop development pattern to the GOCI products. Consequently, we conclude that the very high temporal resolution of GOCI is very beneficial for monitoring crop development, and has potential for providing improved information on phenology.

Solar Radiation Received by Slopes Using COMS Imagery, a Physically Based Radiation Model, and GLOBE

This study mapped the solar radiation received by slopes for all of Korea, including areas that are not measured by ground station measurements, through using satellites and topographical data. When estimating insolation with satellite, we used a physical model to measure the amount of hourly based solar surface insolation. Furthermore, we also considered the effects of topography using the Global Land One-Kilometer Base Elevation (GLOBE) digital elevation model (DEM) for the actual amount of incident solar radiation according to solar geometry. The surface insolation mapping, by integrating a physical model with the Communication, Ocean, and Meteorological Satellite (COMS) Meteorological Imager (MI) image, was performed through a comparative analysis with ground-based observation data (pyranometer). Original and topographically corrected solar radiation maps were created and their characteristics analyzed. Both the original and the topographically corrected solar energy resource maps captured the temporal variations in atmospheric conditions, such as the movement of seasonal rain fronts during summer. In contrast, although the original solar radiation map had a low insolation value over mountain areas with a high rate of cloudiness, the topographically corrected solar radiation map provided a better description of the actual surface geometric characteristics.

Mapping heatwave vulnerability in Korea

Due to the inevitable increase in temperatures that are attributable to significant climate variability and notable shifts in climate under increasing greenhouse gases, heat waves are becoming a major natural disaster. They lead to elevating incidences of human mortality, health risks, and damage to the economy, agriculture, and natural ecosystems. As a precautionary foresight to mitigate detrimental impacts of this disaster, a better understanding of regional vulnerability to heat waves is essential. This study has investigated the cumulative role of the social and climatic factors on heatwave-related deaths across the 232 administrative counties in Korea. Correlation and clustering analyses performed on heatwave-related deaths and social and climatic factors indicated that the number of heatwave days, tropical nights, elderly living alone, and agricultural workers had a significant relationship with the number of heatwave-related deaths. In order to demonstrate the practicality of this approach for heatwave analysis, a spatial heatwave vulnerability map was created to identify the distribution of heatwave risk by compositing the four most significant vulnerability factors identified with regression method. Among the several available regression methods that are applied on countable data, this study has utilized zero-inflated Poisson regression because the available data on heatwave-related deaths included many zeros. The heatwave vulnerability map depicted well the actual distribution of heatwave-related deaths, particularly for counties with a large number of heatwave deaths. In light of this evidence, it is postulated that the heatwave vulnerability map can be used as a useful decision-making tool that can help facilitate efficient utilization of various disaster management resources at the national level and also to identify emphatically the heatwave-related risk over spatial scales to aid in the establishment of customized health risk precautionary measures.

Application of GOCI-derived vegetation index profiles to estimation of paddy rice yield using the GRAMI rice model

Feasibility study of the GOCI satellite for rice yield simulation with GRAMI model.Advantages of GOCI with high temporal resolution to simulate rice growth and development.Method to calculate rice phenology based on the BRDF-adjusted reflectance with GOCI and MODIS. In this paper, satellite remote sensing was used as the input parameter of the GRAMI rice model to evaluate its applicability for simulating paddy rice crop condition and yield assessment at the field scale. Especially, the worlds first Geostationary Ocean Color Imager (GOCI), which provides better temporal resolution than does MODIS, was applied to evaluate the estimation of intuitive paddy rice growth and development and to examine the feasibility for vegetation index profiles of the GRAMI rice model. Moderate Resolution Imaging Spectroradiometer (MODIS) satellite data at 500-m resolution were used as reference data to validate the quality of the crop growth and development data derived from GOCI. Field measurements of paddy rice at Chonnam National University, Gwangju, South Korea, were performed to determine initial parameters of the GRAMI rice model, which is used to optimize biophysical processes in the soil-crop-atmosphere system. For angular-dependent vegetation products, daily rolling time series of vegetation indices of GOCI and MODIS were estimated using semi-empirical BRDF modeling based on 16-day composite procedures. The observed temporal variation in GOCI vegetation indices (VIs) based on BAR (bidirectional reflectance distribution function adjusted reflectance) showed a similar growing pattern to the simulated VIs of the crop model, but MODIS showed a difference between measured and simulated VIs during the cloudy monsoon season. The rice yields predicted by integrating satellite data and the GRAMI rice model were compared with field measurements and showed reasonable agreement with reference to paddy rice productivity in the study area.

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.