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The present study focused on the combination of biphasic calcium phosphate (BCP) nanoparticles into the modified hyaluronic acid based injectable hydrogels for bone tissue engineering. Self-cross-l...

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.

The present study focused on the combination of biphasic calcium phosphate (BCP) nanoparticles into the modified hyaluronic acid based injectable hydrogels for bone tissue engineering. Self-cross-linked thiolated hyaluronic acid (HA-HS) injectable hydrogels loaded with biphasic calcium phosphate (BCP) nanoparticles were prepared by disulfide cross-linking to mimic the extracellular matrix as a potential material for bone treatment. Varying concentration of HA-HS ranging between 1 and 5w/v% was tested to optimize the optimum concentration and were further modified with varying BCP concentrations for final optimization. Physico-chemical characterizations of the prepared hydrogel such as SEM, EDS, FT-IR, and XRD confirmed that the BCP has effectively loaded and distributed homogeneously in the HA-HS hydrogel. The results showed that the 3% (w/v) HA-HS hydrogel exhibits the appropriate properties for injectable hydrogel system such as gelation times, swelling rate and

Various methods for assessing soil total nitrogen (TN) and inorganic N content have been developed to manage nutrient and to understand N cycle in soil. This paper address the technical procedures in arable soil samples to conduct soil sampling, sample preparation, and measuring total N and inorganic N. Among various methods for measuring soil total nitrogen contents, Kjeldahl distillation and Indophenol blue method have widely used due to reliability and economic advances. Also, two methods can analyze more samples at the same time compared with other nitrogen measuring methods. For evaluating inorganic N content, mainly in forms of nitrate-N (NO₃ - -N) and ammonium-N (NH₄ + -N), extraction with a single reagent such as 2M KCl has been employed, followed by Kjeldahl distillation or indophenol blue methods.

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.

With increasing social concerns for healthy food, the studies on the cultivation of crops to increase accumulation of functional metabolites in crops have been investigated. Accumulation of the metabolites in crops is highly affected by various types of stress, such as nutrient deficiency, water deficit (WD), extreme temperature and UV-B radiation as well as their own life cycle. This review summarizes the previous studies on the effects of environmental stresses, especially WD and UV-B radiation, on accumulation of functional metabolites in crops. UV-B radiation and WD during specific period (mainly at maturation stage) activates the adaptation and/or defense system in crops, thereby increasing biosynthesis of the metabolites. Although WD and UV-B radiation tend to decrease in crop yield, the decrease can be compensated by the production of high value crops having high content of functional metabolites.

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

There is a growing body of knowledge on the spatial distribution of soil properties. Fewer studies have investigated temporal trends in soil properties whereas such information is essential for understanding soil productivity and long-term sustainability of agro-ecosystems. We have investigated temporal trends of soil chemical properties in paddy soils of South Korea using data from over two million topsoil samples (0–15 cm) from soil test laboratories collected between 2000 and 2012. The soil pH increased from 5.6 prior to 2000, to 5.9 after 2009, and the rate of increases was about 0.3 pH units per decade. Based on the confidence interval of spatial prediction, 35% of the paddy area (4180 km 2 ) likely has a pH increase (likelihood >66%), and 20% (2350 km 2 ) was very likely to have an increased soil pH (likelihood >90%). The rate of soil pH increase was higher in more acid soils. In addition to the soil pH increase, soil silicate (SiO 2 ) content increased from a mean of 81 mg kg −1 prior 2000 to 153 mg kg −1 after 2009. This is the result of programs that recommend and subsidise the application of silicate fertilizers that has also caused higher levels of soil exchangeable Ca. The soil test data quantified soil changes over time and demonstrated the long-term effects of soil management on soil chemical properties, which is crucial to develop sustainable soil management systems.

Soil moisture content is generally accepted as an important factor to understand the process of crop growth and is the basis of earth system models for analysis and prediction of the crop condition. To continuously monitor soil moisture changes at kilometer scale, it is demanded to create high resolution data from the current, several tens of kilometers. In this paper we described a downscaling method for Advanced Microwave Scanning Radiometer 2 (AMSR2) Soil Moisture Content (SMC) from 10 km to 30 m resolution using a soil texture and field measurements that have a high correlation with the SMC. As a result, the soil moisture variations of both data (before and after downscaling) were identical, and the Root Mean Square Error (RMSE) of SMC exhibited the low values. Also, time series analyses showed that three kinds of SMC data (field measurement, original AMSR2, and downscaled AMSR2) had very similar temporal variations. Our method can be applied to downscaling of other soil variables and can contribute to monitoring small-scale changes of soil moisture by roviding high resolution data.

Temporal and spatial variation of soil moisture is important for understanding patterns of climate change, for developing and evaluating land surface models, for designing surface soil moisture observation networks, and for determining the appropriate resolution for satellite-based remote sensing instruments for soil moisture. In this study, we measured several soil moistures in upland soil using Advanced Microwave Scanning Radiometer 2 (AMSR2) Soil Moisture Content (SMC) during eight-month period in Chungbuk province. The upland soil moisture properties were expressed by simple statistical methods (average, standard deviation and coefficient of variation) from the monthly context. Supplementary studies were also performed about the effect of top soil texture on the soil moisture responses. If the results from this study were utilized well in specific cities and counties in Korea, it would be helpful to establish the countermeasures and action plans for preventing disasters because it was possible to compare with the relationship between soil moisture and top soil texture of each region. And it would be the fundamental data for estimating the effect of future agricultural plan.

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.

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.H.), plant diameter (P.D.), leaf height (L.H.), leaf length (L.L.) and leaf number (L.N.), were measured for about 20 plants (ten plants per plot) for each ground survey. The normalized difference vegetation index (NDVI) for each of the 20 plants was measured using an active plant growth sensor (Crop Circle TM ) at the same time. The results of correlation analysis between the vegetation indices and Chinese cabbage growth data showed that NDVI was the most suited for monitoring the L.H. (r=0.958~0.978), L.L. (r=0.950~0.971), P.H. (r=0.887~0.982), P.D. (r=0.855~0.932) and L.N. (r=0.718~0.968). 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.

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.

In developed countries such as USA and Europe, agricultural monitoring system is developed and utilized in various fields in order to predict crop yield, observe weather conditions and anomaly, categorize crop fields, and calculate areas for each crop. These system is Web Map Service(WMS) which utilizes open source and commercial softwares, and various information collected from remote sensing data are provided. This study will utilize tools such as Geoserver, ArcGIS server, which are widely used to monitor agricultural production, to publish Map Server and Web Application Server. This enables performance test study for future agricultural production monitoring system by making use of response time and data transfer test. When tested in identical condition Geoserver showed a better result in response time and data transfer for performance test.

Soil carbon is a key component of functional ecosystems and crucial for food, soil, water and energy security. Climate change and altered land-use are having a great impact on soils. The influence of these factors creates a dynamic feedback between soil and the environment. There is a crucial need to evaluate the responses of soil to global environmental change at large spatial scales that occur along natural environmental gradients over decadal timescales. This work provides a suite of new data on global soil change which will uniquely utilize the world’s prior investment in soil data infrastructure. Here we attempt a comprehensive global space–time assessment of soil carbon dynamics in different ecoregions of the world accounting for impacts of climate change and other environmental factors.

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.】

원격탐사 자료 기반의 식생지수 시계열 자료를 이용함에 있어서 가장 중요한 것은 구름이나 에어로졸에 의한 자료의 품질저하 문제이다. 이 연구에서는 MODIS09 지표 분광반사도 자료를 이용하여 구름영향에 의한 저품질 자료를 제거한 뒤 결손자료를 내삽, 평활하여 연속적인 Normalized Difference Vegetation Index (NDVI) 시계열 자료를 생산하였다. 구름에 의한 영향을 제거하기 위한 방법으로 MODIS 분광반사도 자료를 이용한 5가지의 구름탐지기법을 선정하여 비교, 평가하였다. 위성자료에서 제공하는 품질관리정보 (Quality Assessment, QA)에서 구름이라고 판단한 경우, MODIS09 Band 3 반사도가 10% 이상인 경우와 20% 이상인 경우, Cloud Detection Index (CDI)가 임계값 이상인 경우, 센서 천정각이 $32.25^{\circ}$ 이상인 경우를 각각 구름으로 판단하였다. 구름탐지로 인해 발생한 자료의 결손은 선형적 내삽 기법을 이용하여 보정한 뒤 Savitzky-Golay (S-G) 필터와 웨이브렛 변환을 각각 적용하여 평활하였다. 구름 탐지 기법은 10% 이상 Band 3 반사도 제거 기법(85%), Quality Control (QC) (82%), 20% 이상 Band 3 반사도 제거 기법(81%)의 순으로 높은 구름탐지율을 보였다. 웨이브렛 변환은 선형의 시계열 패턴을 얻을 수 있지만 원 자료의 최대값을 반영하지 못하는 반면 S-G 필터는 구름에 의한 신뢰도 낮은 값은 제거하면서도 NDVI 원 자료의 최대값을 유지하여 시계열 자료의 계절적 특성을 잘 보여주는 것을 확인하였다. 이 연구에서는 구름의 탐지, 결손 내삽, 평활 기법의 순차적인 자료처리기법을 적용하여 구름 영향을 제거한 고품질의 시계열 자료의 생산이 가능함을 확인하였다. 【A comparative study was conducted for alternative consecutive procedures of detection of cloud-contaminated pixels and gap-filling and smoothing of time-series data to produce high-quality gapless satellite vegetation index (i.e. Normalized Difference Vegetation Index, NDVI). Performances of five alternative methods for detecting cloud contaminations were tested with ground-observed cloudiness data. The data gap was filled with a simple linear interpolation and then, it was applied two alternative smoothing methods (i.e. Savitzky-Golay and Wavelet transform). Moderate resolution imaging spectroradiometer (MODIS) data were used in this study. Among the alternative cloud detection methods, a criterion of MODIS Band 3 reflectance over 10% showed best accuracy with an agreement rate of 85%, which was followed by criteria of MODIS Quality assessment (82%) and Band 3 reflectance over 20% (81%), respectively. In smoothing process, the Savitzky-Golay filter was better performed to retain original NDVI patterns than the wavelet transform. This study demonstrated an operational framework of gapdetection, filling, and smoothing to produce high-quality satellite vegetation index.】

This study was implemented as a part of the experiment to develop two kinds of soil-based Benzo[a]pyrene (BaP) proficiency testing materials (PTMs) for soil analysis. A test was carried out for the check of solubility of the reference material (high purity reagent) using several solvents. Another test was also conducted for the evaluation of homogeneity and stability of two kinds of candidate soil reference materials. The test analysis of BaP in terms of the candidate materials was conducted according to the Standard Soil Analytical Methods by Ministry of Environment. Dissolution of the reference material was shown to vary depending on solvent type and was higher in the order of Dichloromethane > Acetone > Acetone/MeOH (9 : 1) > Nhexane. In addition, the slope on calibration curve for BaP standard solutions was largest on BaP standard solutions prepared with dichloromethane of the tested solvents. Such tendency appeared egually in the commercial BaP standard solution. Therefore, it is thought to be reasonable to use dichloromethane as the solvent in case of the standard stock solution that is used for the measurement of BaP concentration in soil. ISO 13528 and IUPAC protocol were used for verification of homogeneity on the two kinds of soil candidate materials, Both candidate materials were sufficiently homogeneous. Stability assessment of the two candidate materials was made according to ISO Guide 35 and the result showed that both batches did not have any long-term and short term stability issues that might occur during shipping. However, monitoring results of BaP concentration in soil showed that BaP concentration of the two batches measured at 15 days after the sample preparation was reduced by about 24~37% compared with that of the samples measured on 0 day of the sample preparation. Identification was done with several treatments such as irradiation and sterilization etc. The major cause was shown to be irradiation to the samples.

최근 기후변화로 인하여 농작물 생산에 영향을 주는 기온, 강수량 등 환경요인의 변화가 급격하게 진행되고 있다. 이에 따른 농작물에 대한 생산량 예측은 전세계적인 식량 안보 문제의 해결, 국가차원의 농작물 수급정책 결정, 농가단위의 농가 소득 보전 등 지속가능한 농업경제 발전을 위한 중요한 요소가 되고 있다. 미국, 유럽, 중국, 캐나다, FAO, USDA, NASA 등 여러 국가들 및 기관들은 협력체계를 구축하여 넓게는 전세계 단위에서 국가단위까지 작물 모니터링 시스템을 운영하고 있다. 본 연구에서는 다양한 스케일로 운영되고 있는 국외 작황모니터링 시스템의 전반적인 현황을 파악하고 제공되고 있는 정보의 수준, 활용되고 있는 위성영상, 기후, 토양 습도 등 다양한 생물리변수의 활용 여부를 판단하여 향후 위성영상을 활용한 작황모니터링 시스템의 발전방향을 제시하고자 한다.

Moderate Resolution Imaging Spectroradiometer (MODIS) provides for the air temperature (Tair) and dew point temperature (Tdew) profiles at a global scale with 5 km spatial resolution. Recently, the MODIS07_L2 atmosphere profile algorithm was improved to Collection 6 with various algorithm modifications. This study was evaluated the Tair and Tdew at the surface level derived from Collection 5 (C005) and Collection 6 (C006) Aqua MODIS07_L2 (MYD07_L2) products using the measurements from 77 National Weather Stations (NWS). Furthermore saturated and actual vapor pressures were calculated using air and dew point temperature data. The mean error (ME) and root mean square error (RMSE) for the C005 Tair were -2.01K and 4.15K, respectively. For the C006, those errors were decreased 57.7% for ME and 18.3% for RMSE. For the Tdew, RMSE of C006 was increased 10% rather than C005. The application of ambient lapse rate for the Tair showed the improvements of estimation accuracy for both of C005 and C006 at the surface level, though the RMSE for C006 Tdew was slightly increased (1.4%). The vapor pressure datasets calculated by C006 showed generally enhanced accuracies rather than C005. The results produced in this study indicate the accuracies of C006 MYD07_L2 product have been improved in South Korea.

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.

Radar remote sensing is appropriate for rice monitoring because the areas where this crop is cultivated are often cloudy and rainy. Especially, Synthetic Aperture Radar (SAR) can acquire remote sensing information with a high temporal resolution in tropical and subtropical regions due to its all-weather capability. This paper analyzes the relationships between backscattering coefficients of rice measured by RADARSAT-2 SAR and growth parameters during a rice growth period. And we applied the relationships to crop monitoring of paddy rice in North Korea. As a result, plant height and Leaf Area Index (LAI) increased until Day Of Year (DOY) 234 and then decreased, while fresh weight and dry weight increased until DOY 253. Correlation coefficients revealed that Horizontal transmit and Horizontal receive polarization (HH)-polarization backscattering coefficients were correlated highly with plant height (r

Satellite optical-infrared remote sensing from the Moderate Resolution Imaging Spectroradiometer (MODIS) provides effective air temperature (T-a) retrieval at a spatial resolution of 5 km. However, frequent cloud cover can result in substantial signal loss and remote sensing retrieval error in MODIS T-a. We presented a simple pixel-wise empirical regression method combining synergistic information from MODIS T-a and 37 GHz frequency brightness temperature (T-b) retrievals from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E) for estimating surface level T-a under both clear and cloudy sky conditions in the United States for 2006. The instantaneous T-a retrievals showed favorable agreement with in situ air temperature records from 40 AmeriFlux tower sites; mean R-2 correspondence was 86.5 and 82.7 percent, while root mean square errors (RMSE) for the T-a retrievals were 4.58 K and 4.99 K for clear and cloudy sky conditions, respectively. Daily mean T-a was estimated using the instantaneous T-a retrievals from day/night overpasses, and showed favorable agreement with local tower measurements (R-2 = 0.88; RMSE = 3.48 K). The results of this study indicate that the combination of MODIS and AMSR-E sensor data can produce T-a retrievals with reasonable accuracy and relatively fine spatial resolution (similar to 5 km) for clear and cloudy sky conditions.

A ground-based microwave scatterometer has an advantage for monitoring soil moisture content using multi-polarization, multi-frequencies and various incidence angles. In this paper, ground-based multi-frequency (L-, C-, and X-band) polarimetric scatterometer system capable of making observations every 10 min was used to monitor the soil moisture conditions in a corn field over an entire growth cycle. Measurements of volumetric soil moisture were obtained and their relationships to the backscatter observations were examined. Time series of soil moisture content was not corresponding with backscattering coefficient pattern over the whole growth stage, although it increased until early July (Day Of Year, DOY 160). We examined the relationship between the backscattering coefficients from each band and soil moisture content of the field. Backscattering coefficients for all bands were not correlated with soil moisture content when considered over the entire stage (r≤0.48). However, L-band Horizontal transmit and Horizontal receive polarization (HH) had a good correlation with soil moisture (r=0.85**) when LAI was lower than 2. Prediction equations for soil moisture were developed using the L-HH data. Relation between L-HH and soil moisture shows linear pattern and related with soil moisture content (R²=0.77). Results from this study show that backscattering coefficients of microwave scatterometer appear to be effective to estimate soil moisture content in the field level.

Ground-based polarimetric scatterometers have been effective tools to monitor the growth of crop with multi-polarization and frequencies and various incident angles. An important advantage of these systems that can be exploited is temporal observation of a specific crop target. Polarimetric backscatter data at L-, C- and X-bands were acquired every 10 minutes. We analyzed the relationships between L-, C- and X-band signatures, biophysical measurements over the whole corn growth period. The Vertical transmit and Vertical receive polarization (VV) backscattering coefficients for all bands were greater than those of the Horizontal transmit and Horizontal receive polarization (HH) until early-July, and then thereafter HH-polarization was greater than VV-polarization or Horizontal transmit and Vertical receive polarization (HV) until the harvesting stage (Day Of Year, DOY 240). The results of correlation analysis between the backscattering coefficients for all bands and corn growth data showed that L-band HH-polarization (L-HH) was the most suited for monitoring the fresh weight (r=0.95***), dry weight (r=0.95***), leaf area index (r=0.86**), and vegetation water content (r=0.93***). Retrieval equations were developed for estimating corn growth parameters using L-HH. The results indicated that L-HH could be used for estimating the vegetation biophysical parameters considered here with high accuracy. Those results can be useful in determining frequency and polarization of satellite Synthetic Aperture Radar stem and in designing a future ground-based microwave system for a long-term monitoring of corn.

The crop growing conditions make accurate predictions of yield ahead of harvest time difficult. Such predictions are needed by the government to estimate, ahead of time, the amount of crop required to be imported to meet the expected domestic shortfall. Corn and soybean especially are widely cultivated throughout the world and a staple food in many regions of the world. On the other hand, the CASA (Carnegie-Ames-Stanford Approach) model is a process-based model to estimate the land plant NPP (Net Primary Productivity) based on the plant growing mechanism. In this paper, therefore, a methodology for the estimation of corn/soybean yield ahead of harvest time is developed specifically for the growing conditions particular to Iowa and Illinois. The method is based on CASA model using MODIS data, and uses Net Primary Productivity (NPP) to predict corn/soybean yield. As a result, NPP at DOY 217 (in Illinois) and DOY 241 (in Iowa) tend to have high correlation with corn/soybean yields. The corn/soybean yields of Iowa in 2013 was estimated to be 11.24/3.55 ton/ha and Illinois was estimated to be 10.09/3.06 ton/ha. Errors were 6.06/17.58% and -10.64/-7.07%, respectively, compared with the yield forecast of the USDA. Crop yield distributions in 2013 were presented to show spatial variability in the state. This leads to the conclusion that NPP changes in the crop field were well reflected crop yield in this study.

The radar vegetation index (RVI) has low sensitivity to changes in environmental conditions and has the potential as a tool to monitor vegetation growth. In this letter, we expand on previous research by investigating the radar response over a wheat canopy. RVI was computed using observations made with a ground-based multifrequency polarimetric scatterometer system over an entire wheat growth cycle. We analyzed the temporal variations of backscattering coefficients for L-, C-, and X-bands; RVI; vegetation water content (VWC); and fresh weight. We found that the L-band RVI was highly correlated with both VWC (r = 0.98) and fresh weight (r = 0.98). Based upon these analyses, linear equations were developed for estimation of VWC (root-mean-square error (RMSE = 0.126 kg m-2)) and fresh weight (RMSE = 0.12 kg m-2). In addition, the results of the wheat study were combined with previous investigations with other crops (e.g., rice and soybean). We found that a single linear relationship between L-band RVI and VWC can be used for all crop types (RMSE = 0.47 kg m-2). These results clearly demonstrate the potential of RVI as a robust method for characterizing vegetation canopies. VWC is a key input requirement for retrieving soil moisture from microwave remote sensing observations. The results of this investigation will be useful for the Soil Moisture Active and Passive mission (2014), which is designed to measure global soil moisture.