Browse > Article
http://dx.doi.org/10.7780/kjrs.2017.33.5.2.1

Monitoring on Crop Condition using Remote Sensing and Model  

Lee, Kyung-do (National Institute of Agricultural Science, Rural Development Administration)
Park, Chan-won (National Institute of Agricultural Science, Rural Development Administration)
Na, Sang-il (National Institute of Agricultural Science, Rural Development Administration)
Jung, Myung-Pyo (National Institute of Agricultural Science, Rural Development Administration)
Kim, Junhwan (National Institute of Agricultural Science, Rural Development Administration)
Publication Information
Korean Journal of Remote Sensing / v.33, no.5_2, 2017 , pp. 617-620 More about this Journal
Abstract
The periodic monitoring of crop conditions and timely estimation of crop yield are of great importance for supporting agricultural decision-makings, as well as for effectively coping with food security issues. Remote sensing has been regarded as one of effective tools for crop condition monitoring and crop type classification. Since 2010, RDA (Rural Development Administration) has been developing technology for monitoring on crop condition using remote sensing and model. These special papers address recent state-of-the-art of remote sensing and geospatial technologies for providing operational agricultural information, such as, crop yield estimation methods using remote sensing data and process-oriented model, crop classification algorithm, monitoring and prediction of weather and climate based on remote sensing data,system design and architecture of crop monitoring system, history on rice yield forecasting method.
Keywords
remote sensing; crop condition; crop monitoring; crop model; agrometeorology;
Citations & Related Records
Times Cited By KSCI : 13  (Citation Analysis)
연도 인용수 순위
1 Ban, H.-Y., D.-H. Choi, J.-B. Ahn, and B.-W. Lee, 2017. Predicting Regional Soybean Yield using Crop Growth Simulation Model, Korean Journal of Remote Sensing, 33(5-2): 699-708 (in Korean with English abstract).   DOI
2 Jung, M.-P., H.-J. Park, and J.-B. Ahn, 2017. Distribution of Agro-climatic Indices in Agroclimatic Zones of Northeast China Area between 2011 and 2016, Korean Journal of Remote Sensing, 33(5-2): 641-645 (in Korean with English abstract).   DOI
3 Kim, J.-H., C.-K. Lee, W.-G. Sang, P. Shin, H.-S. Cho, and M.-C. Seo, 2017. Introduction to Empirical Approach to Estimate Rice Yield and Comparison with Remote Sensing Approach, Korean Journal of Remote Sensing, 33(5-2): 733-740 (in Korean with English abstract).   DOI
4 Kwak, K.-H., N.-W. Park, K.-D. Lee, and K.-Y. Choi, 2017. Crop Classification for Inaccessible Areas using Semi-Supervised Learning and Spatial Similarity : A Case Study in the Daehongdan region, North Korea, Korean Journal of Remote Sensing, 33(5-2): 689-698 (in Korean with English abstract).   DOI
5 Lee, J.-H., B.-S. Seo, and S.-K Kang, 2017a. Development of a Biophysical Rice Yield Model using All-Weather climate data, Korean Journal of Remote Sensing, 33(5-2): 721-732 (in Korean with English abstract).   DOI
6 Na, S.-I., C.-W. Park, K.-H. So, J.-M. Park, and K.-D. Lee, 2017a. Satellite Imagery based Winter Crop Classification Mapping using Hierarchical Classification, Korean Journal of Remote Sensing, 33(5-2): 647-659 (in Korean with English abstract).   DOI
7 Lee, J.-L., J.-B. Ahn, and M.-P. Jung, and K.-M. Shim, 2017b. A Study on the Method of Producing the 1 km Resolution Seasonal Prediction of Temperature over South Korea for Boreal Winter using Genetic Algorithm and Global Elevation Data Based on Remote Sensing, Korean Journal of Remote Sensing, 33(5-2): 661-676 (in Korean with English abstract).   DOI
8 Lee, K.-D., S.-I. Na, S.-Y. Hong, C.-W. Park, K.-H. So, and J.-M. Park, 2017c. Estimating Corn and Soybean Yield Using MODIS NDVI and Meteorological Data in Illinois and Iowa, USA, Korean Journal of Remote Sensing, 33(5-2): 741-750 (in Korean with English abstract).   DOI
9 Ma, J.-W., K.-D. Lee, K.-Y. Choi, and J. Heo, 2017. Rice Yield Estimation of South Korea from Year 2003-2016 Using Stacked Sparse AutoEncoder, Korean Journal of Remote Sensing, 33(5-2): 631-640 (in Korean with English abstract).   DOI
10 Na, S.-I., C.-W. Park, K.-H. So, J.-M. Park, and K.-D. Lee, 2017b. Development of Garlic & Onion Yield Prediction Model on Major Cultivation Regions considering MODIS NDVI and Meteorological Elements, Korean Journal of Remote Sensing, 33(5-2): 677-687 (in Korean with English abstract).   DOI
11 Yoo, H.-Y., K.-D. Lee, S.-I., Na, C.-W. Park, and N.-W. Park, 2017. Field Crop Classification Using Multi-Temporal High-Resolution Satellite Imagery: A Case Study on Garlic/Onion Field, Korean Journal of Remote Sensing, 33(5-2): 621-630 (in Korean with English abstract).   DOI
12 Nguyen, M.-H., J.-W. Ma, K.-D. Lee, and J. Heo, 2017. The Design of Web-based Crop Information System Using Open-Source Framework and Remotely Sensed Data, Korean Journal of Remote Sensing, 33(5-2): 751-762 (in Korean with English abstract).   DOI
13 Park, H.-J., J.-B. Ahn, and M.-P. Jung, 2017. Correlation between the Maize Yield and Satellite-based Vegetation Index and Agricultural Climate Factors in the Three Provinces of Northeast China, Korean Journal of Remote Sensing, 33(5-2): 709-720 (in Korean with English abstract).   DOI