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Weather-related risks in crop production are not only crucial for farmers but also for market participants and policymakers since securing food supply is an important issue for society. Although crop growth condition and phenology represent essential information regarding such risks, extensive observations of these variables are virtually non-existent in many parts of the world. In this study, we developed an integrative approach to remotely monitor crop growth at a large scale. For corn and soybeans in Iowa and Illinois in the United States (2003–2015), we monitored crop growth and crop phenology with earth observation data and compared it against the United States Department of Agriculture National Agricultural Statistics Service (NASS) crop statistics. For crop phenology, we calculated three phenology metrics (i.e., start of season, end of season, and peak of season) at the pixel level from the MODIS 16-day Normalized Difference Vegetation Index (NDVI). For growth condition, we used two distinct approaches to acquire crop growth condition indicators: a process-based crop growth modeling and a satellite-NDVI-based method. Based on their pixel-wise historical distributions, we monitored relative growth strength and scaled-up that to the state-level. The estimates were compared with the crop progress and condition data of NASS. For the state-level phenology, the avg. root-mean-square-error (RMSE) of the estimates was 8.6 days for the all three metrics after bias correction. The absolute mean errors for the three metrics were smaller than 2.6 days after bias correction. For the condition, the state-level 10-day estimates showed moderate agreements with the observations (avg. RMSE = 10.02%). Notably, the condition estimates were sensitive to the severe degradation in 2003, 2012, and 2013 for both crops. In 2010, 2011 and 2013, unusually high errors occurred at the very beginning stage of growth (DOY 140–150), which attenuated over time. As the cumulative biomass and NDVI showed little change in comparison to the period mean biomass and NDVI for the spikes, this seems to be an error associated with variations in growth timing. Overall, the model using accumulated NDVI (S5) is preferable due to its performance and methodological simplicity. The proposed approach enables us to monitor crop growth for any given period and place where long-term statistics are available. It can be used to assist crop monitoring at large scales.

When sufficient time-series images and training data are unavailable for crop classification, features extracted from convolutional neural network (CNN)-based representative learning may not provide useful information to discriminate crops with similar spectral characteristics, leading to poor classification accuracy. In particular, limited input data are the main obstacles to obtain reliable classification results for early crop mapping. This study investigates the potential of a hybrid classification approach, i.e., CNN-random forest (CNN-RF), in the context of early crop mapping, that combines the automatic feature extraction capability of CNN with the superior discrimination capability of an RF classifier. Two experiments on incremental crop classification with unmanned aerial vehicle images were conducted to compare the performance of CNN-RF with that of CNN and RF with respect to the length of the time-series and training data sizes. When sufficient time-series images and training data were used for the classification, the accuracy of CNN-RF was slightly higher or comparable with that of CNN. In contrast, when fewer images and the smallest training data were used at the early crop growth stage, CNN-RF was substantially beneficial and the overall accuracy increased by maximum 6.7%p and 4.6%p in the two study areas, respectively, compared to CNN. This is attributed to its ability to discriminate crops from features with insufficient information using a more sophisticated classifier. The experimental results demonstrate that CNN-RF is an effective classifier for early crop mapping when only limited input images and training samples are available.

A crop model incorporating proximal sensing images from a remote-controlled aerial system (RAS) can serve as an enhanced alternative for monitoring field-based geospatial crop productivity. This study aimed to investigate wheat productivity for different cultivars and various nitrogen application regimes and determine the best management practice scenario. We simulated spatiotemporal wheat growth and yield by integrating RAS-based sensing images with a crop-modeling system to achieve the study objective. We conducted field experiments and proximal sensing campaigns to acquire the ground truth data and RAS images of wheat growth conditions and yields. These experiments were performed at Gyeongsang National University (GNU), Jinju, South Gyeongsang province, Republic of Korea (ROK), in 2018 and 2019 and at Chonnam National University (CNU), Gwangju, ROK, in 2018. During the calibration at GNU in 2018, the wheat yields simulated by the modeling system were in agreement with the corresponding measured yields without significant differences (p = 0.27–0.91), according to two-sample t-tests. Furthermore, the yields simulated via this approach were in agreement with the measured yields at CNU in 2018 and at GNU in 2019 without significant differences (p = 0.28–0.86), as evidenced by two-sample t-tests; this proved the validity of the proposed modeling system. This system, when integrated with remotely sensed images, could also accurately reproduce the geospatial variations in wheat yield and growth variables. Given the results of this study, we believe that the proposed crop-modeling approach is applicable for the practical monitoring of wheat growth and productivity at the field level.

It is important to be able to predict the yield and monitor the growth conditions of crops in the field to increase productivity. One way to assess field-based geospatial crop productivity is by integrating a crop model with a remote-controlled aerial system (RAS). The objective of this study was to simulate spatiotemporal barley growth and yield based on the development of a crop-modeling system integrated with RAS-based remote sensing images. We performed field experiments to obtain ground truth data and RAS images of crop growth conditions and yields at Chonnam National University (CNU), Gwangju, South Korea in 2018, and at Gyeongsang National University (GNU), Jinju, South Gyeongsang, South Korea in 2018 and 2019. In model calibration, there was no significant difference (p = 0.12) between the simulated barley yields and measured yields, based on a two-sample t-test at CNU in 2018. In model validation, there was no significant difference between simulated yields and measured yields at p = 0.98 and 0.76, according to two-sample t-tests at GNU in 2018 and 2019, respectively. The remote sensing-integrated crop model accurately reproduced geospatial variations in barley yield and growth variables. The results demonstrate that the crop modeling approach is useful for monitoring at-field barley conditions.

Recently, usage of UAV (Unmanned Aerial Vehicle) has increased in agricultural part. This study was conducted to classify onion and garlic using supervised classification of a fixed-wing UAV (Model : Ebee) images for evaluation of possibility about estimation of onion and garlic cultivation area using UAV images. Aerial images were obtained 11~12 times from study sites in Changryeng-gun and Hapcheon-gun during farming season from 2015 to 2016. The result for accuracy in onion and garlic image classification by R-G-B and R-G-NIR images showed highest Kappa coefficients for the maximum likelihood method. The result for accuracy in onion and garlic classification showed high Kappa coefficients of 0.75~0.97 from DOY 105 to DOY 141, implying that UAV images could be used to estimate onion and garlic cultivation area.

Recent technological advance in UAV (Unmanned Aerial Vehicle) technology offers new opportunities for assessing crop situation using UAV imagery. The objective of this study was to assess if reflectance and NDVI derived from consumer-grade cameras mounted on UAVs are useful for crop condition monitoring. This study was conducted using a fixed-wing UAV(Ebee) with Cannon S110 camera from March 2015 to March 2016 in the experiment field of National Institute of Agricultural Sciences. Results were compared with ground-based recordings obtained from consumer-grade cameras and ground multi-spectral sensors. The relationship between raw digital numbers (DNs) of UAV images and measured calibration tarp reflectance was quadratic. Surface (lawn grass, stairs, and soybean cultivation area) reflectance obtained from UAV images was not similar to reflectance measured by ground-based sensors. But NDVI based on UAV imagery was similar to NDVI calculated by ground-based sensors.

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.

Italian Ryegrass (IRG), which is known as high yielding and the highest quality winter annual forage crop, is grown in mid-south area in Korea. The objective of this study was to evaluate the use of unmanned aerial vehicle (UAV) for the monitoring IRG growth. Unmanned aerial vehicle imagery obtained from middle March to late May in Nonsan, Chungcheongnam-do. Unmanned aerial vehicle imagery corrected geometrically and atmospherically to calculate normalized difference vegetation index (NDVI). We analyzed the relationships between NDVIUAV of IRG and biophysical measurements such as plant height, fresh weight, and dry weight over an entire IRG growth period. The similar trend between NDVIUAV and growth parameters was shown. Correlation analysis between NDVIUAV and IRG growth parameters revealed that NDVIUAV was highly correlated with fresh weight (r=0.988), plant height (r=0.925), and dry weight (r=0.853). According to the relationship among growth parameters and NDVIUAV, the temporal variation of NDVIUAV was significant to interpret IRG growth. Four different regression models, such as (1) Linear regression function, (2) Linear regression through the origin, (3) Power function, and (4) Logistic function were developed to evaluate the relationship between temporal NDVIUAV and measured IRG growth parameters. The power function provided higher accurate results to predict growth parameters than linear or logistic functions using coefficient of determination. The spatial distribution map of IRG growth was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when NDVIUAV was applied to power function. From these results, NDVIUAV can be used as a new tool for monitoring IRG growth.

Kimchi cabbage is one of the most important vegetables in Korea and a target crop for market stabilization as well. In particular Kimchi cabbages in a highland area are very sensitive to the fluctuations in supply and demand. Yield variability due to growth conditions dictates the market fluctuations of Kimchi cabbage price. This study was carried out to understand the distribution of the highland Kimchi cabbage growth status in Anbandeok. Anbandeok area in Gangneung, Gangwon-do, Korea is one of the main producing districts of highland Kimchi cabbage. The highland Kimchi cabbage status map of each growth factor was obtained from unmanned aerial vehicle (UAV) imagery and field survey data. Six status maps include UAVRGB image map, normalized difference vegetation index (NDVI) distribution/anomaly map, Crop distribution map, Planting/Harvest distribution map, Growth parameter map and Growth disorder map. As a result, the highland Kimchi cabbage status maps from May 31 to Sep. 6 in 2016 were presented to show spatial variability in the field. The benefits of the highland Kimchi cabbage status map can be summarized as follows: crop growth monitoring, reference for field observations and survey, the relative comparison of the growth condition in field scale, evaluation of growth in comparison of average year, change detection of annual crops or planting areas, abandoned fields monitoring, prediction of harvest season etc.

2016Purpose: The overall objective of this study was to evaluate the vegetation fraction of soybeans, grown under different cropping conditions using an unmanned aerial vehicle (UAV) equipped with a red, green, and blue (RGB) camera. Methods: Test plots were prepared based on different cropping treatments, i.e., soybean single-cropping, with and without herbicide application and soybean and barley-cover cropping, with and without herbicide application. The UAV flights were manually controlled using a remote flight controller on the ground, with 2.4 GHz radio frequency communication. For image pre-processing, the acquired images were pre-treated and georeferenced using a fisheye distortion removal function, and ground control points were collected using Google Maps. Tarpaulin panels of different colors were used to calibrate the multi-temporal images by converting the RGB digital number values into the RGB reflectance spectrum, utilizing a linear regression method. Excess Green (ExG) vegetation indices for each of the test plots were compared with the M-statistic method in order to quantitatively evaluate the greenness of soybean fields under different cropping systems. Results: The reflectance calibration methods used in the study showed high coefficients of determination, ranging from 0.8 to 0.9, indicating the feasibility of a linear regression fitting method for monitoring multi-temporal RGB images of soybean fields. As expected, the ExG vegetation indices changed according to different soybean growth stages, showing clear differences among the test plots with different cropping treatments in the early season of 1. Conclusion: Therefore, multi-temporal images obtained with an UAV and a RGB camera could be applied for quantifying overall vegetation fractions and crop growth status, and this information could contribute to determine proper treatments for the vegetation fraction.Keywords: Barley cover cropping, Excess green, Image processing, M-statistic method, UAV, Vegetation index

Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because unmanned aerial vehicle imagery may be acquired quickly during critical periods of rapid crop growth. The objective of this study was to evaluate the use of unmanned aerial vehicle for the monitoring barley growth. Unmanned aerial vehicle imagery obtained from middle February to late June in Wanju, Jeollabuk-do. Unmanned aerial vehicle imagery corrected geometrically and atmospherically to calculate normalized difference vegetation index (NDVI). We analyzed the relationships between NDVI UAV of barley and biophysical measurements such as plant height, number of tiller and shoot dry weight over an entire barley growth period. The similar trend between NDVI UAV and growth parameters was shown. Correlation analysis between NDVI UAV and barley growth parameters revealed that NDVI UAV was highly correlated with shoot dry weight (r=0.932) and plant height (r=0.879). According to the relationship among growth parameters and NDVI UAV , the temporal variation of NDVIUAV was significant to interpret barley growth. The spatial distribution map of barley growth was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when NDVI UAV was applied to power function. From these results, NDVI UAV can be used as a new tool for monitoring barley growth.

For more than 50 years, satellite images have been used to monitor crop growth. Currently, unmanned aerial vehicle (UAV) imagery are 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 highland Kimchi cabbage growth prediction equation by using UAV derived normalized difference vegetation index (NDVI) and agro-meteorological factors. Anbandeok area in Gangneung, Gangwon-do, Korea is one of main producing district of highland Kimchi cabbage. UAV imagery was taken on the Anbandeok twelve times from early June to early September during the highland Kimchi cabbage growing season. Meanwhile, field reflectance spectra and three plant growth parameters, including plant height (P.H.), leaf height (L.H.) and leaf number (L.N.), were measured for about 40 plants (ten plants per plot) for each ground survey. The NDVIField for each of the 40 plants was measured using an active plant growth sensor (Crop CircleTM) at the same time. Six agro-meteorological factors include average temperature; maximum temperature; minimum temperature; accumulated temperature; rainfall and irradiation. The multiple linear regression models were suggested by using stepwise regression in the extraction of independent variables. Of the output independent variables of the stepwise regression, NDVIUAV performs best (r=0.933). And correlation coefficients of highland Kimchi cabbage growth parameters, accumulated temperature, irradiation and rainfall showed positive relationship; the results were respectively 0.931, 0.926, 0.893 (p

The NDVI (Normalized difference vegetation index) is used as indicators of crop growth situation in remote sensing. To measure or validate the NDVI, reliable NDVI sensors have been needed. We tested new fixed-field NDVI sensor, “SRS (Spectral Reflectance Sensor)” developed by Decagon Devices, during Kimchi cabbage growing season at the cultivation area located in Gochang, Gangneung and Taebaek in Korea from 2014 to 2015. The diurnal variation of NDVI measured by SRS (SRS NDVI) showed a slight ∩-profile shape and was affected by water on the sensor surface. This means that SRS NDVI around noontime is resonable, except rainy day. Comparisons were made between the SRS NDVI and NDVI of used widely mobile sensor (Cropcircle NDVI). The comparisons indicate that SRS NDVI are close to Cropcircle NDVI (R=0.99). SRS NDVI time series displayed change of the plant height and leaf width of Kimchi cabbage. An obvious exponential relationship is found between SRS NDVI and the plant height (R 2 ≥0.92) and leaf width(R 2 ≥0.92) of Kimchi cabbage. Thus, SRS NDVI will be used as indicator of crop growth situation and a very powerful tool for evaluation of remote sensing NDVI estimates and associated corrections.

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.

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.

This paper presents a classification method based on the combination of active learning with temporal contextual information extracted from past land-cover maps for the classification of crop cultivation areas. Iterative classification based on active learning is designed to extract reliable training data and cultivation rules from past land-cover maps are quantified as temporal contextual information to be used for not only assignment of training data but also relaxation of spectral ambiguity. To evaluate the applicability of the classification method proposed in this paper, a case study with MODIS time-series vegetation index data sets and past cropland data layers(CDLs) is carried out for the classification of corn and soybean in Illinois state, USA. Iterative classification based on active learning could reduce misclassification both between corn and soybean and between other crops and non crops. The combination of temporal contextual information also reduced the over-estimation results in major crops and led to the best classification accuracy. Thus, these case study results confirm that the proposed classification method can be effectively applied for crop cultivation areas where it is not easy to collect the sufficient number of reliable training data.

이 연구에서는 자료변환기법을 이용해 추출된 여러 특징과 다양한 분류방법론을 결합하여 다중시기 SAR 자료를 위한 새로운 토지피복 분류기법을 제안하였다. 먼저, 다중시기 SAR 자료로부터 원본자료와는 다른 새로운 정보를 추출하기 위해 주성분분석과 3차원 웨이블렛 변환을 이용한 자료변환을 수행하였다. 그리고 나서 최대우도법 분류자, 신경망, support vector machine을 포함한 세 가지 다른 분류자를 변환된 특징자료들과 원본 후방산란계수 자료를 포함한 세가지 자료에 적용하여 다양한 초기 분류 결과를 얻도록 한다. 이후 다수결규칙을 통해 모든 초기결과를 결합하여 최종 분류 결과를 생성하게 된다. 다중시기 ENVISAT ASAR 자료를 이용한 사례연구에서 모든 초기 결과는 사용한 특징자료와 분류자의 종류에 따라 매우 다양한 분류정확도를 보였다. 이러한 9개의 초기 분류 결과를 결합한 최종 분류 결과는 가장 높은 분류 정확도를 보여주고 있는데, 이는 각 초기 분류 결과가 토지피복을 결정하기 위한 상호 보완적인 정보를 제공하기 때문이다. 이 연구에서의 분류정확도 향상은 주로 자료변환을 통해 얻어진 각기 다른 특징자료와 다른 분류자를 결합에 의한 다양성 확보에서 기인한다. 그러므로 이 연구에서 제안한 토지피복 분류방법론은 다중시기 SAR자료의 분류에 효과적으로 적용가능하며, 또한 다중센서 원격탐사 자료융합으로 확장이 가능하다. 【In this study, a novel land-cover classification framework for multi-temporal SAR data is presented that can combine multiple features extracted through data transforms and multiple classifiers. At first, data transforms using principle component analysis (PCA) and 3D wavelet transform are applied to multi-temporal SAR dataset for extracting new features which were different from original dataset. Then, three different classifiers including maximum likelihood classifier (MLC), neural network (NN) and support vector machine (SVM) are applied to three different dataset including data transform based features and original backscattering coefficients, and as a result, the diverse preliminary classification results are generated. These results are combined via a majority voting rule to generate a final classification result. From an experiment with a multi-temporal ENVISAT ASAR dataset, every preliminary classification result showed very different classification accuracy according to the used feature and classifier. The final classification result combining nine preliminary classification results showed the best classification accuracy because each preliminary classification result provided complementary information on land-covers. The improvement of classification accuracy in this study was mainly attributed to the diversity from combining not only different features based on data transforms, but also different classifiers. Therefore, the land-cover classification framework presented in this study would be effectively applied to the classification of multi-temporal SAR data and also be extended to multi-sensor remote sensing data fusion.】

Stable production of green manure crop is one of the critical factor for successful implementation of environmental-friendly agriculture in South Korea. Most green manure crops including hairy vetch cultivated in South Korea must be passed cold winter season. This study was conducted to establishment of cultivation zones on green manure crops under paddy field in South Korea using on minimum temperature and drainage class. In order to build the spatial distribution map, hairy vetch, triticale, barley, rye, and clover as green manure crop were selected and mean of daily minimum temperature and drainage class in paddy field were considered as limiting factor for stable growth of green manure crop. And cultivation zone on green manure crops was divided into two categories; `Availability area` and `Safety area`. Low limit temperature on green manure crops in this study was the following. Temperature on `Availability area` was , , , and for hairy vetch (including triticale), barley, clover, and rye respectively. And temperature on `Safety area` was lower compared to that of `Availability area` exception rye which was the same degree. Rye was expected to safely cultivated the almost whole paddy field (99.71%) of South Korea. `Safety area` among the whole paddy field for hairy vetch, barley, and clover were 86.58, 53.06, and 15.76% respectively. The spatial distribution map of green manure crops reestablished in this study could be used the policy and agricultural management for environmental-friendly agriculture using green manure crops in South Korea.

Simulink, an extension of MATLAB, is a graphics-based model development environment for system modeling and simulation. Simulink’s user-friendly features, including block (data process) and arrow (data transfer) objects, a large number of existing blocks, no need to write codes, and a drag and drop interface, provide modelers with an easy development environment. In this study, a Tank model was developed using Simulink and applied to a rainfall-runoff simulation for a study watershed to demonstrate the potential of Simulink as a tool for hydrological analysis. In the example given here, the Tank model was extended by two sub-modules representing evapotranspiration and storage-runoff distribution. In addition, model pre- and post-processing, such as input data preparation and results plotting, was carried out in MATLAB. Moreover, model parameters were calibrated using MATLAB optimization tools without any additional programming for linking the calibration algorithms and the model. The graphical representation utilized in the Simulink version of the Tank model helped us to understand the hydrological interactions described in the model, and the modular structure of the program facilitated the addition of new modules and the modification of existing modules as needed. From the study, we found that Simulink could be a useful and convenient environment for hydrological analysis and model development.

Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because unmanned aerial vehicle imagery may be acquired quickly during critical periods of rapid crop growth. The objective of this study was to evaluate the use of unmanned aerial vehicle for the monitoring barley growth. Unmanned aerial vehicle imagery obtained from middle February to late June in Wanju, Jeollabuk-do. Unmanned aerial vehicle imagery corrected geometrically and atmospherically to calculate normalized difference vegetation index (NDVI). We analyzed the relationships between NDVI UAV of barley and biophysical measurements such as plant height, number of tiller and shoot dry weight over an entire barley growth period. The similar trend between NDVIUAV and growth parameters was shown. Correlation analysis between NDVIUAV and barley growth parameters revealed that NDVIUAV was highly correlated with shoot dry weight (r=0.932) and plant height (r=0.879). According to the relationship among growth parameters and NDVIUAV, the temporal variation of NDVIUAV was significant to interpret barley growth. The spatial distribution map of barley growth was in strong agreement with the field measurements in terms of geographical variation and relative numerical values when NDVIUAV was applied to power function. From these results, NDVIUAV can be used as a new tool for monitoring barley growth.

The remote monitoring system of water salinity was assessed in Wando reclaimed land lake during a farming season in 2009. Increasing of water salinity in this lake used to bring about salt damage on rice plant occasionally. At the early stage of the rice growing period, rice growth was not damaged due to enough rainfall with more than 120 mm from the mid-May to the first ten days of June. Data collection using on-site water salinity measuring sensors every 2 hours and real-time transmission in system were carried out for the experiment. We compared the transmitted values from the sensor system with water sample values collected and analyzed by a local technical office. Salt concentrations measured by sensor in real-time monitoring system were available data. The regression equation between rainfall and water salinity was presented as(water salinity after rainfall) = 0.621×(water salinity before rainfall)×exp(-0.0139×rainfall), (r 2 =0.579, p

This study investigates the prediction of soil chemical properties (organic matter (OM), pH, Ca, Mg, K, Na, total acidity, cation exchange capacity (CEC)) on 688 Korean soil samples using the visible-near infrared reflectance (VIS-NIR) spectroscopy. Reflectance from the visible to near-infrared spectrum (350 to 2500 nm) was acquired using the ASD Field Spec Pro. A total of 688 soil samples from 168 soil profiles were collected from 2009 to 2011. The spectra were resampled to 10 nm spacing and converted to the 1st derivative of absorbance (log (1/R)), which was used for predicting soil chemical properties. Principal components analysis (PCA), partial least squares regression (PLSR) and regression rules model (Cubist) were applied to predict soil chemical properties. The regression rules model (Cubist) showed the best results among these, with lower error on the calibration data. For quantitatively determining OM, total acidity, CEC, a VIS-NIR spectroscopy could be used as a routine method if the estimation quality is more improved.

This study was conducted to investigate the effect of green manure crops on soil fertilityand to provide informations for optimum cropping systems in Saemangeum reclaimed tidal land. Fieldtrial was established with summer [sesbania (Sesbania sesban), wild millet (Echinochloa crus-galli), andsorghum-sudangrass hybrid (Sorghum bicolor)] and winter [oat (Avena sativa L.), rye (Secale cereale L.),and barley (Hordeum vulgare L.)] green manure crops at Saemangeum reclaimed tidal land in Koreafrom 2007 to 2011. Biomass of the green manure crops were 9.7-15.0 ton/ha (dry weight), and sorghum-sudangrass hybrid produced consistently greater biomass than other crops across growing years. Culti-vation and application of green manure crops increased soil organic matter contents from 1.3 to 6.0 g/kgby 1.2 g/kg per year. Green manure crops also improved bulk density from 1.44 to 1.24 Mg/m3 by addi-tion of biomass residues. Sequential cultivation of sorghum-sudangrass hybrid followed by rye was mosteffective on decreasing bulk density of soil, and sesbania cultivation was most efficient on increasing soilnitrogen content. Overall, green manure crops can be probably useful for improvement soil fertility inSaemangeum reclaimed tidal 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.

This study investigated the runoff from rice paddy located on reclaimed fine sandy loam soil to provide data for the development of policies to protect water quality of estuaries. Total N (TN), Total P (TP) concentrations and runoff loads at outlet were monitored from 2006 to 2008. Soil phosphate adsorptivity was measured and compared with typical paddy soil in watersheds. TP concentration of the paddy water and TP runoff loads were much greater than those of typical paddy field in watershed because phosphate adsoptivity in reclaimed paddy field of fine sandy loam appeared to be a third of those of typical paddy soils by relatively low soil OM and high sand content of the reclaimed soil. Thus, nutrient runoff, particularly phosphate from the reclaimed paddy field needs to be managed more thoroughly to protect estuarine water quality.

In this study, land-cover classification was implemented using features extracted from multi-temporal SAR data through 3D wavelet transform and the applicability of the 3D wavelet transform as a feature extraction approach was evaluated. The feature extraction stage based on 3D wavelet transform was first carried out before the classification and the extracted features were used as input for land-cover classification. For a comparison purpose, original image data without the feature extraction stage and Principal Component Analysis (PCA) based features were also classified. Multi-temporal Radarsat-1 data acquired at Dangjin, Korea was used for this experiment and five land-cover classes including paddy fields, dry fields, forest, water, and built up areas were considered for classification. According to the discrimination capability analysis, the characteristics of dry field and forest were similar, so it was very difficult to distinguish these two classes. When using wavelet-based features, classification accuracy was generally improved except built-up class. Especially the improvement of accuracy for dry field and forest classes was achieved. This improvement may be attributed to the wavelet transform procedure decomposing multi-temporal data not only temporally but also spatially. This experiment result shows that 3D wavelet transform would be an effective tool for feature extraction from multi-temporal data although this procedure should be tested to other sensors or other areas through extensive experiments.