A Study on Estimating Rice Yield in DPRK Using MODIS NDVI and Rainfall Data

Hong, Suk Young; Na, Sang-Il; Lee，Kyung-Do,; Kim, Yong-Seok; Baek, Shin-Chul

doi:10.7780/kjrs.2015.31.5.8

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…These yield data were consistent with the results of VCI3 in the cropland ( Figure 6). The lower NDVI value was highly associated with the smaller yield by the damage on crop growth [61]. In the summer of the first meteorological drought year 2014, the VCI3s in Paju, Cheorwon, Gaesong, and Pyonggang did not indicate severe drought conditions.…”

Section: Discussionmentioning

confidence: 91%

Different Agricultural Responses to Extreme Drought Events in Neighboring Counties of South and North Korea

et al. 2019

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Satellite-based remote sensing techniques have been widely used to monitor droughts spanning large areas. Various agricultural drought indices have been developed to assess the intensity of agricultural drought and to detect damaged crop areas. However, to better understand the responses of agricultural drought to meteorological drought, agricultural management practices should be taken into consideration. This study aims to evaluate the responses to drought under different forms of agricultural management for the extreme drought that occurred on the Korean Peninsula in 2014 and 2015. The 3-month standardized precipitation index (SPI3) and the 3-month vegetation health index (VHI3) were selected as a meteorological drought index and an agricultural drought index, respectively. VHI3, which comprises the 3-month temperature condition index (TCI3) and the 3-month vegetation condition index (VCI3), differed significantly in the study area during the extreme drought. VCI3 had a different response to the lack of precipitation in South and North Korea because it was affected by irrigation. However, the time series of TCI3 were similar in South and North Korea. These results meant that each drought index has different characteristics and should be utilized with caution. Our results are expected to help comprehend the responses of the agricultural drought index on meteorological drought depending on agricultural management.

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Section: Discussionmentioning

confidence: 91%

Different Agricultural Responses to Extreme Drought Events in Neighboring Counties of South and North Korea

et al. 2019

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“…Kim et al (2014) estimated corn and soybean yields using several MODIS products and climatic variables for Midwestern United States (US) and represented prediction errors of about 10 %. Hong et al (2015) built multiple regression models using MODIS NDVI and weather data to estimate rice yields in North Korea and showed the RMSE of 0.27 ton/ha. Most of the previous studies are based on the multivariate regression analysis using the relationship between crop yields and agro-environmental factors such as vegetation index, climate variables and soil properties.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Approaches to Corn Yield Estimation Using Satellite Images and Climate Data: A Case of Iowa State

Kim¹,

Lee²

2016

Journal of the Korean Society of Surveying, Geodesy, Photogramm

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Remote sensing data has been widely used in the estimation of crop yields by employing statistical methods such as regression model. Machine learning, which is an efficient empirical method for classification and prediction, is another approach to crop yield estimation. This paper described the corn yield estimation in Iowa State using four machine learning approaches such as SVM (Support Vector Machine), RF (Random Forest), ERT (Extremely Randomized Trees) and DL (Deep Learning). Also, comparisons of the validation statistics among them were presented. To examine the seasonal sensitivities of the corn yields, three period groups were set up: (1) MJJAS (May to September), (2) JA (July and August) and (3) OC (optimal combination of month). In overall, the DL method showed the highest accuracies in terms of the correlation coefficient for the three period groups. The accuracies were relatively favorable in the OC group, which indicates the optimal combination of month can be significant in statistical modeling of crop yields. The differences between our predictions and USDA (United States Department of Agriculture) statistics were about 6-8 %, which shows the machine learning approaches can be a viable option for crop yield modeling. In particular, the DL showed more stable results by overcoming the overfitting problem of generic machine learning methods. This is an Open Access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http:// creativecommons.org/licenses/by-nc/3.0) which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

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“…Satellite time series of the MODIS NDVI 16-day global 250-m data covering the period from 2014 to 2018 were requested from the University of Natural Resources and Applied Life Sciences (BOKU) (http://ivfl-info.boku.ac.at (accessed on 7 August 2020)). The monthly NDVI products from 2014 to 2018 were rebuilt based on the maximum value using a cell statistic tool in ArcGIS, since MODIS vegetation indices produced on 16-day intervals, having at least 2 tiles of NDVI image in the month [28,29]. Annual maximum NDVIs (2014 to 2018) were masked by the extent of the Kyrgyzstan boundary, respectively.…”

Section: Time-series Ndvi Datamentioning

confidence: 99%

Assessing Climate Change Impact on Cropland Suitability in Kyrgyzstan: Where Are Potential High-Quality Cropland and the Way to the Future

Park

Lim

Kim³

et al. 2021

Agronomy

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Climate change is one of the greatest challenges in Kyrgyzstan. There have been negative spillover effects in agriculture. This study aims to assess the climate change impacts on cropland suitability in Kyrgyzstan. We used the random forest algorithm to develop a model that captures the effects of multiple climate and environment factors at a spatial resolution of 1 km2. The model was then applied in the scenario analysis for an understanding of how climate change affects cropland distribution. The potential high-quality cropland was found to be included in existing croplands, while the remaining were distributed around the Chu-Talas valley, the Issyk-kul area, and the Fergana valley. These potential high-quality croplands comprise grasslands (47.1%) and croplands (43.7%). In the future, the potential high-quality cropland exhibited inland trends at the periphery of original cropland category, with grassland and cropland as the primary land components. Due to climate change, potential high-quality cropland is expected to gradually reduce from the 2050s to the 2070s, exhibiting the largest reduction in potential high-quality areas for the Representative Concentration Pathway 8.5 scenario. Therefore, the short- and long-term adaptation strategies are needed for prioritizing the croplands to ensure food security and agricultural resilience.

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A Study on Estimating Rice Yield in DPRK Using MODIS NDVI and Rainfall Data

Cited by 6 publications

References 5 publications

Different Agricultural Responses to Extreme Drought Events in Neighboring Counties of South and North Korea

Different Agricultural Responses to Extreme Drought Events in Neighboring Counties of South and North Korea

Machine Learning Approaches to Corn Yield Estimation Using Satellite Images and Climate Data: A Case of Iowa State

Assessing Climate Change Impact on Cropland Suitability in Kyrgyzstan: Where Are Potential High-Quality Cropland and the Way to the Future

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