Sang-Il Na

Development of Korean Paddy Rice Yield Prediction Model (KRPM) using Meteorological Element and MODIS NDVI

Food policy is considered as the most basic and central issue for all countries, while making efforts to keep each country`s food sovereignty and enhance food self-sufficiency. In the case of Korea where the staple food is rice, the rice yield prediction is regarded as a very important task to cope with unstable food supply at a national level. In this study, Korean paddy Rice yield Prediction Model (KRPM) developed to predict the paddy rice yield using meteorological element and MODIS NDVI. A multiple linear regression analysis was carried out by using the NDVI extracted from satellite image. Six meteorological elements include average temperature; maximum temperature; minimum temperature; rainfall; accumulated rainfall and duration of sunshine. Concerning the evaluation for the applicability of the KRPM, the accuracy assessment was carried out through correlation analysis between predicted and provided data by the National Statistical Office of paddy rice yield in 2011. The 2011 predicted yield of paddy rice by KRPM was 505 kg/10a at whole country level and 487 kg/10a by agroclimatic zones using stepwise regression while the predicted value by KOrea Statistical Information Service was 532 kg/10a. The characteristics of changes in paddy rice yield according to NDVI and other meteorological elements were well reflected by the KRPM.

Estimation of Highland Kimchi Cabbage Growth using UAV NDVI and Agro-meteorological Factors

For more than 50 years, satellite images have been used to monitor crop growth. Currently, unmanned aerial vehicle (UAV) imagery is being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study refers to the derivation of growth estimating equation for highland Kimchi cabbage using UAV derived normalized difference vegetation index (NDVI) and agro-meteorological factors. Anbandeok area in Gangneung, Gangwon-do, Korea is one of main districts producing highland Kimchi cabbage. UAV imagery was taken in the Anbandeok ten times from early June to early September. Meanwhile, three plant growth parameters, plant height (P.H.), leaf length (L.L.) and outer leaf number (L.N.), were measured for about 40 plants (ten plants per plot) for each ground survey. Six agro-meteorological factors include average temperature; maximum temperature; minimum temperature; accumulated temperature; rainfall and irradiation during growth period. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. As a result, NDVIUAV and rainfall in the model explain 93% of the P.H. and L.L. with a root mean square error (RMSE) of 2.22, 1.90 cm. And NDVIUAV and accumulated temperature in the model explain 86% of the L.N. with a RMSE of 4.29. These lead to the result that the characteristics of variations in highland Kimchi cabbage growth according to NDVIUAV and other agro-meteorological factors were well reflected in the model.

Estimating the Amount of Nitrogen in Hairy Vetch on Paddy Fields using Unmaned Aerial Vehicle Imagery

Remote sensing can be used to provide information about the monitoring of crop situation. This study was conducted to estimate the amount of nitrogen present in paddy fields by measuring the amount of nitrogen in hairy vetch using an UAV (Unmaned Aerial Vehicle). NDVIs (Normalized Difference Vegetation Index) were calculated using UAV images obtained from paddy fields in Seocheon on May 14 th 2015. There was strong relationship between UAV NDVI and the amount of nitrogen in hairy vetch (R 2 =0.79). Spatial distribution maps of green manure nitrogen were generated on each paddy field using the nitrogen-vegetation index relations to help farmers determine the amount of N fertilizers added to their rice fields after the application of green manure such as hairy vetch.

Analysis of runoff in the Han River basin by SSARR model considering agricultural water

This study begins to address the need for a runoff model that is able to simulate runoffs at control points in a dam’s upper and lower stream during the seasons of high and low water levels. We need to establish a synthetic management plan on water resources considering the runoff at the upper and lower streams to effectively manage the limited water resources in Korea. For this reason, we classified the Han River Basin into 24 sub-basins and arranged a great amount of rainfall data using 151 rainfall observation stations so as to prepare for the spatial distribution of precipitation. We chose several dams as subjects for this study, which includes the Chungju Regulating Reservoir, Soyang, Chungju, Hoengseong, Hwacheon, Chuncheon, Euiam, Cheongpyeong, and Paldang Dams as main controlling points. Also, we made up input data of this model, selecting the Streamflow Synthesis and Reservoir Regulation (SSARR) model as a runoff model in the Han River Basin. We performed a sensitivity analysis of parameters using hydrological data from the year 2002. And as a result, the findings of this study showed that, among many parameters related to the basin runoff, the following have revealed greater sensitivity than any other parameters: soil moisture index-runoff percent, baseflow infiltration index-baseflow percent, and surface-subsurface separation. On the basis of the above sensitivity analysis, we have used hydrological data between 2001 and 2002 when drafts and floods broke out in Korea to verify and calibrate the parameters of the SSARR model. Furthermore, we verified and calibrated the 2000 data using corrected parameters and performed an analysis of annual water balance in the Han River Basin from 1996 to 2005 considering agricultural water.

Estimation of Corn and Soybean Yields Based on MODIS Data and CASA Model in Iowa and Illinois, USA

The crop growing conditions make accurate predictions of yield ahead of harvest time difficult. Such predictions are needed by the government to estimate, ahead of time, the amount of crop required to be imported to meet the expected domestic shortfall. Corn and soybean especially are widely cultivated throughout the world and a staple food in many regions of the world. On the other hand, the CASA (Carnegie-Ames-Stanford Approach) model is a process-based model to estimate the land plant NPP (Net Primary Productivity) based on the plant growing mechanism. In this paper, therefore, a methodology for the estimation of corn/soybean yield ahead of harvest time is developed specifically for the growing conditions particular to Iowa and Illinois. The method is based on CASA model using MODIS data, and uses Net Primary Productivity (NPP) to predict corn/soybean yield. As a result, NPP at DOY 217 (in Illinois) and DOY 241 (in Iowa) tend to have high correlation with corn/soybean yields. The corn/soybean yields of Iowa in 2013 was estimated to be 11.24/3.55 ton/ha and Illinois was estimated to be 10.09/3.06 ton/ha. Errors were 6.06/17.58% and -10.64/-7.07%, respectively, compared with the yield forecast of the USDA. Crop yield distributions in 2013 were presented to show spatial variability in the state. This leads to the conclusion that NPP changes in the crop field were well reflected crop yield in this study.

Daily global solar radiation estimate in the South Korea based on geostationary satellite remote sensing

Available solar radiation is an important crop growth monitoring and water balance that determines South Koreas crop productivity but has been poorly characterized. This paper studied the method of estimates for the available solar energy and crop growth monitoring based on data of Satellite imagery and meteorological observed data between 1982 and 2005 in South Korea. The objectives of this study are: a) assess of daily and monthly global radiation which most closely associated with rice growth; b) estimates for the available solar energy of the South Korea. Results from the study not only monitor the solar radiation in investigation area, but also illustrate the powerful potential to provide information about solar radiation based geostationary satellite imagery data.

Quantification of the Relationship Between Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) in Arable Land

The survey of Landsat satellite image is effective in the continuous monitoring of a vast area during long periods of time. It is increasingly being used to derive and analyze spatial distribution data of both the Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature (LST) that are major indicators for an analysis of vegetation-environment. Likewise, NDVI and LST are essential in order to detect, as well as to monitor, the environmental changes in arable land. Therefore, the relationship between NDVI and LST should be quantified for the accuracy improvement of agricultural statistical data based on Remote Sensing. This study has intended to analyze the characteristics of NDVI and LST using Landsat imagery of arable land in Cheongju City, to quantify the relationship between NDVI and LST. The results indicated that time seasonal change of raster data for four times of the highest group of LST and the lowest group of vegetation located in the Cheongju city, Chungcheongbuk-do, Korea, are observed and analyzed their correlations for the change detection of land cover. This experiment, based on proposed algorithms, detected a strong and proportional correlation relationship between the highest group of LST and the lowest group of vegetation index which exceeded R=(+)0.9. Therefore, the proposed Correlation Analysis Model between the highest group of LST and the lowest group of vegetation index will be able to give proof of an effective suitability to the land cover change detection and monitoring.

Estimation of Chinese Cabbage Growth by RapidEye Imagery and Field Investigation Data

Chinese cabbage is one of the most important vegetables in Korea and a target crop for market stabilization as well. Remote sensing has long been used as a tool to extract plant growth, cultivated area and yield information for many crops, but little research has been conducted on Chinese cabbage. This study refers to the derivation of simple Chinese cabbage growth prediction equation by using RapidEye derived vegetation index. Daesan-myeon area in Gochang-gun, Jeollabuk-do, Korea is one of main producing district of Chinese cabbage. RapidEye multi–spectral imagery was taken on the Daesan-myeon five times from early September to late October during the Chinese cabbage growing season. Meanwhile, field reflectance spectra and five plant growth parameters, including plant height (P.H.), plant diameter (P.D.), leaf height (L.H.), leaf length (L.L.) and leaf number (L.N.), were measured for about 20 plants (ten plants per plot) for each ground survey. The normalized difference vegetation index (NDVI) for each of the 20 plants was measured using an active plant growth sensor (Crop Circle TM ) at the same time. The results of correlation analysis between the vegetation indices and Chinese cabbage growth data showed that NDVI was the most suited for monitoring the L.H. (r=0.958~0.978), L.L. (r=0.950~0.971), P.H. (r=0.887~0.982), P.D. (r=0.855~0.932) and L.N. (r=0.718~0.968). Retrieval equations were developed for estimating Chinese cabbage growth parameters using NDVI. These results obtained using the NDVI is effective provided a basis for establishing retrieval algorithm for the biophysical properties of Chinese cabbage. These results will also be useful in determining the RapidEye multi-spectral imagery necessary to estimate parameters of Chinese cabbage.

Classification of Soil Desalination Areas Using High Resolution Satellite Imagery in Saemangeum Reclaimed Land

This study was aimed to classify soil desalination area for cultivation using NDVI (Normalized difference vegetation index) of high-resolution satellite image because the soil salinity affects the change of plant community in reclaimed lands. We measured the soil salinity and NDVI at 28 sites in the Saemangeum reclaimed land in June 2013. In halophyte and non-vegetation sites, no relation was found between NDVI and soil salinity. In glycophyte sites, however, we found that the soil salinity was below 0.1% and NDVI ranged from 0.11 to 0.57 which was greater than the other sites. So, we could distinguish the glycophyte sites from the halophyte sites and non-vegetation, and classify the area that soil salinty was below 0.1%. This technique could save the time and labor to measure the soil salinity in large area for agricultural utilization.

Status of Rice Paddy Field and Weather Anomaly in the Spring of 2015 in DPRK

To understand the impact of 2015 spring drought on crop production of DPRK (Democratic People’s Republic of Korea), we analyzed satellite and weather data to produce 2015 spring outlook of rice paddy field and rice growth in relation to weather anomaly. We defined anomaly of 2015 for weather and NDVI in comparison to past 5 year-average data. Weather anomaly layers for rainfall and mean temperature were calculated based on 27 weather station data. Rainfall in late April, early May, and late May in 2015 was much lower than those in average years. NDVI values as an indicator of rice growth in early June of 2015 was much lower than in 2014 and the average years. RapidEye and Radarsat-2 images were used to monitor status of rice paddy irrigation and transplanting. Due to rainfall shortage from late April to May, rice paddy irrigation was not favorable and rice planting was not progressed in large portion of paddy fields until early June near Pyongyang. Satellite images taken in late June showed rice paddy fields which were not irrigated until early June were flooded, assuming that rice was transplanted after rainfall in June. Weather and NDVI anomaly data in regular basis and timely acquired satellite data can be useful for grasping the crop and land status of DPRK, which is in high demand.