: Lack of agricultural information for food supply and demand in Democratic People's republic Korea(DPRK) make people sometimes confused for right and timely decision for policy support. We carried out a study to estimate paddy rice yield in DPRK using MODIS NDVI reflecting rice growth and climate data. Mean of MODIS NDVI max in paddy rice over the country acquired and processed from 2002 to 2014 and accumulated rainfall collected from 27 weather stations in September from 2002 to 2014 were used to estimated paddy rice yield in DPRK. Coefficient of determination of the multiple regression model was 0.44 and Root Mean Square Error(RMSE) was 0.27 ton/ha. Two-way analysis of variance resulted in 3.0983 of F ratio and 0.1008 of p value. Estimated milled rice yield showed the lowest value as 2.71 ton/ha in 2007, which was consistent with RDA rice yield statistics and the highest value as 3.54 ton/ha in 2006, which was not consistent with the statistics. Scatter plot of estimated rice yield and the rice yield statistics implied that estimated rice yield was higher when the rice yield statistics was less than 3.3 ton/ha and lower when the rice yield statistics was greater than 3.3 ton/ha. Limitation of rice yield model was due to lower quality of climate and statistics data, possible cloud contamination of time-series NDVI data, and crop mask for rice paddy, and coarse spatial resolution of MODIS satellite data. Selection of representative areas for paddy rice consisting of homogeneous pixels and utilization of satellite-based weather information can improve the input parameters for rice yield model in DPRK in the future.

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.

Turbid water of agricultural reservoir and downstream is getting worse and worse because the soil flows out from current residential land development and road construction. Sediment loads, which fill the water bodies (lakes, agricultural reservoir, dams, and aquatic ecosystems) are one of the most important environmental problems throughout the world. Water turbidity is a commonly used index of the factors that determine light penetration in the water column. Consistent estimation of water turbidity is crucial to design environmental and restoration management plans, to predict fate of possible pollutants, and to estimate sedimentary fluxes into the ocean. Traditional methods monitoring fixed geographical locations at fixed intervals may not be representative of the mean water turbidity in estuaries between intervals, and can be expensive and time consuming. Although remote sensing offers a good solution to this limitation, it is still not widely used due in part to required complex processing of imagery. The aims of this study were two folds: to map water turbidity and estimate water turbidity level based on Landsat imagery. Based on field measurements and principal component analysis (PCA), was examined the spatial variability of water turbidity in Lake Paldang by using the Landsat satellite imagery collected on 2001~2007. The result of this study is that when we carried out PCA using Landsat imagery, water turbidity had contributed at PC 2 which was similar to the in-situ data. A correlation formula (water turbidity = 0.3169 × PC2 -21.477, R 2 = 0.6319) between the in-situ data and PC2. And we can now use formula to map the water turbidity distribution from the synchronously acquired Landsat imagery, and continue the discussion on the inverse water turbidity results of the Landsat imagery. Because results from this type of study vary with season and time of day, it is necessary to monitor continuously in-situ data as well as radiance feature of reflectance in order to determine accurately the environmental factors of water quality.

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. 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.

No abstract