• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.182-196
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• 2023

Accurate and timely estimation of crop yields is crucial for various purposes, including global food security planning and agricultural policy development. Remote sensing techniques, particularly using vegetation indices (VIs), have show n promise in monitoring and predicting crop conditions. However, traditional VIs such as the normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) have limitations in capturing rapid changes in vegetation photosynthesis and may not accurately represent crop productivity. An alternative vegetation index, the near-infrared reflectance of vegetation (NIRv), has been proposed as a better predictor of crop yield due to its strong correlation with gross primary productivity (GPP) and its ability to untangle confounding effects in canopies. In this study, we investigated the potential of NIRv in estimating crop yield, specifically for corn and soybean crops in major crop-producing regions in 14 states of the United States. Our results demonstrated a significant correlation between the peak value of NIRv and crop yield/area for both corn and soybean. The correlation w as slightly stronger for soybean than for corn. Moreover, most of the target states exhibited a notable relationship between NIRv peak and yield, with consistent slopes across different states. Furthermore, we observed a distinct pattern in the yearly data, where most values were closely clustered together. However, the year 2012 stood out as an outlier in several states, suggesting unique crop conditions during that period. Based on the established relationships between NIRv peak and yield, we predicted crop yield data for 2022 and evaluated the accuracy of the predictions using the Root Mean Square Percentage Error (RMSPE). Our findings indicate the potential of NIRv peak in estimating crop yield at the county level, with varying accuracy across different counties.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.4
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• pp.340-348
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• 2021

Continuous monitoring of RGB (Red, Green, Blue) vegetation indices is important to apply remote sensing technology for the estimation of crop growth. In this study, we evaluated the performance of eight vegetation indices derived from soybean RGB images with various agronomic parameters under drought stress condition. Drought stress influenced the behavior of various RGB vegetation indices related soybean canopy architecture and leaf color. In particular, reported vegetation indices such as ExGR (Excessive green index minus excess red index), Ipca (Principal Component Analysis Index), NGRDI (Normalized Green Red Difference Index), VARI (Visible Atmospherically Resistance Index), SAVI (Soil Adjusted Vegetation Index) were effective tools in obtaining canopy coverage and leaf chlorophyll content in soybean field. In addition, the RGB vegetation indices related to leaf color responded more sensitively to drought stress than those related to canopy coverage. The PLS-DA (Partial Squares-Discriminant Analysis) results showed that the separation of RGB vegetation indices was distinct by drought stress. The results, yet preliminary, display the potential of applying vegetation indices based on RGB images as a tool for monitoring crop environmental stress.

• Journal of The Korean Society of Grassland and Forage Science
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• v.40 no.4
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• pp.196-202
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• 2020

Rye, whole-crop barley and Italian Ryegrass are major winter forage species in Korea, and yield monitoring of winter forage species is important to improve forage productivity by precision management of forage. Forage monitoring using Unmanned Aerial Vehicle (UAV) has offered cost effective and real-time applications for site-specific data collection. To monitor forage crop by multispectral camera with UAV, we tested four types of vegetation index (Normalized Difference Vegetation Index; NDVI, Green Normalized Difference Vegetation Index; GNDVI, Normalized Green Red Difference Index; NGRDI and Normalized Difference Red Edge Index; NDREI). Field measurements were conducted on paddy field at Naju City, Jeollanam-do, Korea between February to April 2019. Aerial photos were obtained by an UAV system and NDVI, GNDVI, NGRDI and NDREI were calculated from aerial photos. About rye, whole-crop barley and Italian Ryegrass, regression analysis showed that the correlation coefficients between dry matter and NDVI were 0.91~0.92, GNDVI were 0.92~0.94, NGRDI were 0.71~0.85 and NDREI were 0.84~0.91. Therefore, GNDVI were the best effective vegetation index to predict dry matter of rye, wholecrop barley and Italian Ryegrass by UAV system.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.151-154
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• 2008

Information on the area and spatial distribution of crop fields is needed for biomass production, arrangement of water resources, trace gas emission estimates, and food security. The present study aims to monitor crops status during the growing season by estimating its aboveground biomass and leaf area index (LAI) from field reflectance taken with a hand-held radiometer. Field reflectance values were collected over specific spectral bandwidths using a handheld radiometer(LI-1800). A methodology is described to use spectral reflectance as indicators of the vegetative status in crop cultures. Two vegetation indices were derived from these spectral measurements. In this paper, first we analyze each spectral reflectance characteristics of vegetation in the order of growth stage. Vegetation indices (NDVI, GNDVI) were calculated from crop reflectance. And assess the nature of relationships between LAI and VI, as measured by the in situ NDVI and GNDVI. Among the two VI, NDVI showed predictive ability across a wider range of LAI than did GNDVI. Specific objectives were to determine the relative accuracy of these two vegetation indices for predicting LAI. The results of this study indicated that the NDVI and GNDVI could potentially be applied to monitor crop agriculture on a timely and frequent basis.

• Korean Journal of Remote Sensing
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• v.22 no.3
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• pp.183-197
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• 2006

Combinations of visible and near-infrared (NIR) bands in an image are widely used for estimating vegetation vigor and productivity. Using this approach to understand within-field grain crop variability could allow pre-harvest estimates of yield, and might enable mapping of yield variations without use of a combine yield monitor. The objective of this study was to estimate within-field variations in crop yield using vegetation indices derived from hyperspectral images. Hyperspectral images were acquired using an aerial sensor on multiple dates during the 2003 and 2004 cropping seasons for corn and soybean fields in central Missouri. Vegetation indices, including intensity normalized red (NR), intensity normalized green (NG), normalized difference vegetation index (NDVI), green NDVI (gNDVI), and soil-adjusted vegetation index (SAVI), were derived from the images using wavelengths from 440 nm to 850 nm, with bands selected using an iterative procedure. Accuracy of yield estimation models based on these vegetation indices was assessed by comparison with combine yield monitor data. In 2003, late-season NG provided the best estimation of both corn $(r^2\;=\;0.632)$ and soybean $(r^2\;=\;0.467)$ yields. Stepwise multiple linear regression using multiple hyperspectral bands was also used to estimate yield, and explained similar amounts of yield variation. Corn yield variability was better modeled than was soybean yield variability. Remote sensing was better able to estimate yields in the 2003 season when crop growth was limited by water availability, especially on drought-prone portions of the fields. In 2004, when timely rains during the growing season provided adequate moisture across entire fields and yield variability was less, remote sensing estimates of yield were much poorer $(r^2<0.3)$.

• Korean Journal of Soil Science and Fertilizer
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• v.36 no.2
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• pp.92-103
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• 2003

Research and technological advances in the field of remote sensing have greatly enhanced the ability to detect and quantify physical and biological stresses that affect the productivity of agricultural crops. Reflectance in specific visible and near-infrared regions of the electromagnetic spectrum have proved useful in detection of nutrient deficiencies. Especially crop canopy sensors as a ground remote sensing measure the amount of light reflected from nearby surfaces such as leaf tissue or soil and is in contrast to aircraft or satellite platforms that generate photographs or various types of digital images. Multi-spectral vegetation indices derived from crop canopy reflectance in relatively wide wave band can be used to monitor the growth response of plants in relation to environmental factors. The normalized difference vegetation index (NDVI), where NDVI = (NIR-Red)/(NIR+Red), was originally proposed as a means of estimating green biomass. The basis of this relationship is the strong absorption (low reflectance) of red light by chlorophyll and low absorption (high reflectance and transmittance) in the near infrared (NIR) by green leaves. Thereafter many researchers have proposed the other indices for assessing crop vegetation due to confounding soil background effects in the measurement. The green normalized difference vegetation index (GNDVI), where the green band is substituted for the red band in the NDVI equation, was proved to be more useful for assessing canopy variation in green crop biomass related to nitrogen fertility in soils. Consequently ground remote sensing as a non destructive real-time assessment of nitrogen status in plant was thought to be useful tool for site specific crop nitrogen management providing both spatial and temporal information.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.60 no.3
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• pp.381-387
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• 2015

This study was firstly conducted to investigate changes of vegetation and soil characteristics in reclaimed land applied with solidified sewage sludge for the cultivation of bioenergy crops. Each vegetation survey site was approximately $15m{\times}3m$ on the inside of each experimental plot that consisted of 50% (A-1), 30% (A-2), 15% (A-3), and 5% (A-4) mixture of solidified sewage sludge, and original reclaimed soil (ORS). After the application of solidified sewage sludge, we monitored the changes of vegetation and soil properties for three years. In first year, soil pH, electrical conductivity (EC) and exchangeable $Ca^{2+}$ content was 9.4~10.8, $9.10{\sim}14.41\;dS\;m^{-1}$, and $62.1{\sim}204.2\;cmol\;kg^{-1}$, respectively, while three years later, it decreased to 8.1~8.4, $1.65{\sim}5.98\;dS\;m^{-1}$, and $21.9{\sim}43.1\;cmol\;kg^{-1}$, respectively. These results indicated that several of soil chemical elements which have nagative impacts on the plant growth in the plots of mixtures of solidified sewage sludge, steadily declined as the years go by. The vegetations in each survey site were recorded as 6 families and 12 species in 2014, while the vegetations were not occurred at all survey sites in 2012, and only halophytes as Phragmites australis and Suaeda asparagoides were observed in 2013. Diversity of vegetation, which was calculated by shannon index (H'), increased as the season progressed at each experimental plot applied with solidified sewage sludge. In original reclaimed soil, however, there was showed the high community similarity of vegetation due to the fact that P. australis and S. asparagoides were only occurred for survey periods.

• Proceedings of the Korean Society of Crop Science Conference
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• 2022.10a
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• pp.88-88
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• 2022

Detection of stress responses in crops is important to diagnose crop growth and evaluate yield. Also, the multi-spectral sensor is effectively known to evaluate stress caused by nutrient and moisture in crops or biological agents such as weeds or diseases. Therefore, in this experiment, multispectral images were taken by an unmanned aerial vehicle(UAV) under field condition. The experiment was conducted in the long-term fertilizer field in the National Institute of Crop Science, and experiment area was divided into different status of NPK(Control, N-deficiency, P-deficiency, K-deficiency, Non-fertilizer). Total 11 vegetation indices were created with RGB and NIR reflectance values using python. Variations in nutrient content in plants affect the amount of light reflected or absorbed for each wavelength band. Therefore, the objective of this experiment was to evaluate vegetation indices derived from multispectral reflectance data as input into machine learning algorithm for the classification of nutritional deficiency in rice. RandomForest model was used as a representative ensemble model, and parameters were adjusted through hyperparameter tuning such as RandomSearchCV. As a result, training accuracy was 0.95 and test accuracy was 0.80, and IPCA, NDRE, and EVI were included in the top three indices for feature importance. Also, precision, recall, and f1-score, which are indicators for evaluating the performance of the classification model, showed a distribution of 0.7-0.9 for each class.

• The Korean Journal of Ecology
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• v.6 no.3
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• pp.177-186
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• 1983

Vegetation types and their standing crop in the sand dune on the south coast of Korea was investigated by the method of Curtis, J.T. and McIntosh, R.P. (1951). The relationship between vegetation types and environmental factors was also analyzed. The dominant species in the vegetations of the south coast sand dune were Carex pumila, Calystegia soldamella, Imperata cylindrica var. koenigii, Vitex rotundifolia, Ixeris repens, Carex kobomugi, Zoysia macrostachya. The species density in the sand dune vegetation increased with the distance from the coast, psammophyte and rhizome psammophyte decreased with the distance from the coast but other plants increased. The standing crop of the sand dune vegetatiion was average $53.79g/m^2$. An individual standing crop of Vitex routundifolia and Carex kobomugi varied with the curve of secondary degree. The salt content of the sand dune soil from 2.95 to 11.78 mg %, and it was not significant differences among stands, but it was varied with the distance form the coast. Negative relationship between warmth index and aboveground standing crop was found and the formula y=283.8886 - 2.4910X could be estimated.

• Korean Journal of Medicinal Crop Science
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• v.14 no.6
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• pp.347-353
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• 2006

This study was conducted to evaluate the vegetation structure and ordination of the Peucedanum japonicum Thunb. community by the Phytosociological method, floristic composition table on east coast of South Korea. The vegetation of Peucedanum japonicum Thunb. community was classified into 2 communities such as the Aster Spathulifolius community and the calystegia soldanella-Artemisia princeps community. Vegetation of the Peucedanum japonicum Thunb. community with the Aster spathulifolius community was shown southward, whereas vegetation of Peucedanum japonicum Thunb. community with the calystegia soldanella-Artemisia princeps community was shown northward from the base line of Pohang at North latitude of 36" 05'. Accordingly, the Peucedanum japonicum Thunb. community was grown commonly with the Aster spathulifolius community southward of the base line of Pohang, whereas it was grown commonly with the calystegia soldanella-Artemisia princeps community northward on the east coast. All environmental conditions of habitat taken together, the optimum habitat of Peucedanum japonicum Thunb. was at Ulsan geographically and the middle region of sea cliff topography, and was suitable for alkali. sandyloam.