• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.5
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• pp.89-99
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• 2019

In this study, we estimated the spatially-distributed soil moisture at the high resolution ($10m{\times}10m$) using the satellite-based Sentinel-1A/B SAR (Synthetic Aperture Radar) sensor images. The Sentinel-1A/B raw data were pre-processed using the SNAP (Sentinel Application Platform) tool provided from ESA (European Space Agency), and then the pre-processed data were converted to the backscatter coefficients. The regression equations were derived based on the relationships between the TDR (Time Domain Reflectometry)-based soil moisture measurements and the converted backscatter coefficients. The TDR measurements from the 51 RDA (Rural Development Administration) monitoring sites were used to derive the regression equations. Then, the soil moisture values were estimated using the derived regression equations with the input data of Sentinel-1A/B based backscatter coefficients. Overall, the soil moisture estimates showed the linear trends compared to the TDR measurements with the high Pearson's correlations (more than 0.7). The Sentinel-1A/B based soil moisture values matched well with the TDR measurements with various land surface conditions (bare soil, crop, forest, and urban), especially for bare soil (R: 0.885~0.910 and RMSE: 3.162~4.609). However, the Mandae-ri (forest) and Taean-eup (urban) sites showed the negative correlations with the TDR measurements. These uncertainties might be due to limitations of soil surface penetration depths of SAR sensors and complicated land surface conditions (artificial constructions near the TDR site) at urban regions. These results may infer that qualities of Sentinel-1A/B based soil moisture products are dependent on land surface conditions. Although uncertainties exist, the Sentinel-1A/B based high-resolution soil moisture products could be useful in various areas (hydrology, agriculture, drought, flood, wild fire, etc.).

• Korean Journal of Remote Sensing
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• v.37 no.4
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• pp.789-802
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• 2021

Synthetic Aperture Radar (SAR) at C-band is an ideal remote sensing system for crop monitoring owing to its short wavelength, which interacts with the upper parts of the crop canopy. This study evaluated the potential of dual polarimetric Sentinel-1 at C-band for monitoring rice phenology. Rice phenological variations occur in a short period. Hence, the short revisit time of Sentinel-1 SAR system can facilitate the tracking of short-term temporal morphological variations in rice crop growth. The sensitivity of SAR backscattering coefficients, backscattering ratio, and polarimetric decomposition parameters on rice phenological stages were investigated through a time-series analysis of 33 Sentinel-1 Single Look Complex images collected from 10th April to 25th October 2020 in Gimhae, South Korea. Based on the observed temporal variations in SAR parameters, we could identify and distinguish the phenological stages of the Gimhae rice growth cycle. The backscattering coefficient in VH polarisation and polarimetric decomposition parameters showed high sensitivity to rice growth. However, amongst SAR parameters estimated in this study, the VH backscattering coefficient realistically identifies all phenological stages, and its temporal variation patterns are preserved in both Sentinel-1A (S1A) and Sentinel-1B (S1B). Polarimetric decomposition parameters exhibited some offsets in successive acquisitions from S1A and S1B. Further studies with data collected from various incidence angles are crucial to determine the impact of different incidence angles on polarimetric decomposition parameters in rice paddy fields.

• Journal of The Korean Society of Agricultural Engineers
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• v.62 no.6
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• pp.11-20
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• 2020

We estimated the spatio-temporally distributed soil moisture using Sentinel-1A/B SAR (Synthetic Aperture Radar) sensor images and soil moisture data assimilation technique in South Korea. Soil moisture data assimilation technique can extract the hydraulic parameters of soils using observed soil moisture and GA (Genetic Algorithm). The SWAP (Soil Water Atmosphere Plant) model associated with a soil moisture assimilation technique simulates the soil moisture using the soil hydraulic parameters and meteorological data as input data. The soil moisture based on Sentinel-1A/B was validated and evaluated using the pearson correlation and RMSE (Root Mean Square Error) analysis between estimated soil moisture and TDR soil moisture. The soil moisture data assimilation technique derived the soil hydraulic parameters using Sentinel-1A/B based soil moisture images, ASOS (Automated Synoptic Observing System) weather data and TRMM (Tropical Rainfall Measuring Mission)/GPM (Global Precipitation Measurement) rainfall data. The derived soil hydrological parameters as the input data to SWAP were used to simulate the daily soil moisture values at the spatial domain from 2001 to 2018 using the TRMM/GPM satellite rainfall data. Overall, the simulated soil moisture estimates matched well with the TDR measurements and Sentinel-1A/B based soil moisture under various land surface conditions (bare soil, crop, forest, and urban).

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.274-274
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• 2021

토양수분은 가뭄, 홍수, 산불 및 산사태 등 자연재해 발생에 직간접적으로 영향을 미치기 때문에, 시·공간적으로 연속적인 토양수분 관측이 필요하다. 과거에는 TDR (Time Domain Reflectometry) 관측 장비를 설치하여 토양수분의 변화를 관측하였으나, 이러한 지점관측의 경우 하나의 관측지점에서 토양수분을 관측하기 때문에 공간적인 토양수분 변화를 나타내지 못한다. 최근 이러한 문제를 해결하기 위하여 인공위성 이미지 자료를 이용한 토양수분 산정에 관한 연구가 활발히 수행되고 있다. 그러나 SMOS (Soil Moisture and Ocean Salinity), SMAP (Soil Moisture Active Passive)와 같은 다양한 위성에서 관측된 토양수분은 낮은 공간해상도로 인한 불확실성이 커지는 단점이 있다. 최근 이러한 한계를 극복하기 위하여 광학위성영상과 달리 날씨의 영향을 받지 않으며 고해상도 이미지자료를 제공하는 Sentinel-1A/B 위성을 활용하여 토양수분을 관측하는 연구가 진행되고 있다. Sentinel-1은 10m의 높은 공간해상도를 제공하지만, 1~2주 주기로 영상취득이 가능하기 때문에 재방문시기와 같은 시간해상도 문제가 발생한다. 따라서 본 연구에서는 Sentinel-1A/B SAR 기반 후방산란계수와 농촌진흥청에서 제공하는 TDR 기반 토양수분 실측값을 이용하여 우리나라 토양수분 공간분포를 산정하였다. 산정된 Sentinel-1A/B 기반 토양수분과 토양수분자료동화기법을 연계하여 토양의 수리학적 매개변수를 추출하였으며, 추출된 매개변수와 기상자료를 이용하여 장기간(2001~2018) 일별 토양수분 공간분포를 산정하였다.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1545-1557
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• 2021

Crops show sensitive spectral characteristics according to their species and growth conditions and although frequent observation is required especially in summer, it is difficult to utilize optical satellite images due to the rainy season. To solve this problem, Constrained Cloud-Maximum Normalized difference vegetation index Composite (CC-MNC) algorithm was developed to generate periodic composite images with minimal cloud effect. In thisstudy, using this method, monthly Sentinel-2A/B Normalized Difference Vegetation Index (NDVI) composite images were produced for paddies and high-latitude cabbage fields from 2019 to 2021. In August 2020, which received 200mm more precipitation than other periods, the effect of clouds, was also significant in MODIS NDVI 16-day composite product. Except for this period, the CC-MNC method was able to reduce the cloud ratio of 45.4% of the original daily image to 14.9%. In the case of rice paddy, there was no significant difference between Sentinel-2A/B and MODIS NDVI values. In addition, it was possible to monitor the rice growth cycle well even with a revisit cycle 5 days. In the case of high-latitude cabbage fields, Sentinel-2A/B showed the short growth cycle of cabbage well, but MODIS showed limitations in spatial resolution. In addition, the CC-MNC method showed that cloud pixels were used for compositing at the harvest time, suggesting that the View Zenith Angle (VZA) threshold needsto be adjusted according to the domestic region.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.141-141
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• 2019

토양수분은 수문 분석에 있어 매우 중요한 인자 중 하나이며 최근 기후변화로 인한 가뭄, 홍수 및 산불발생과 같은 물 관련 재해 발생에 직 간접적으로 영향을 미치기 때문에 지표 토양수분산정은 매우 중요하다. Sentinel-1 SAR(Synthetic Aperture Radar)는 능동형 위성으로 10m의 공간해상도로 제공되기 때문에 기존의 토양수분 전용위성인 SMOS(Soil Moisure and Ocean Salinity), SMAP(Soil Moisture Active Passive) 및 GCOM-W1(Global Change Observation Mission Water) 등 다르게 고해상도 토양수분 산정이 가능하다. 그러나 Sentinel-1 SAR 센서에서는 고해상도 지표 관측 이미지 자료만 제공하며, 토양수분 자료를 직접적으로 제공하지 않는다. 따라서 본 연구에서는 2018년도 Sentinel-1 A/B IW(Interferometric Wide swath) 모드의 VH(Vertical Transmit - Horizontal Receive) 편파 영상과 Sentinel-1 SAR 위성자료 전처리 도구인 SNAP(Sentinel Application Platform)을 이용하여 후방산란계수를 산정하였으며, 산정된 후 방산란계수와 농촌진흥청에서 제공하는 65개 지점의 실측 TDR(Time Domain Reflectrometry) 토양수분의 관계를 이용하여 회귀모형을 도출 및 토양수분 공간분포를 산정하였다. 비록 불확실성은 어느정도 발생 하였으나, 전체적으로 TDR 관측값과 $10m{\times}10m$ 해상도의 Sentinel-1 SAR 기반 토양수분이 일치하는 경향을 보였다. 본 연구 결과는 수문, 농업, 산림, 재해 등 다양한 분야에 활용될 수 있을 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.117-117
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• 2021

본 연구에서는 Sentinel-1A/B C-band SAR(Synthetic Aperture Radar) 위성영상을 기반으로 인공신경망(Artificial Neural Network, ANN) 모형을 활용해 금강 유역 상류 40×50 km² 면적에 대한 토양수분을 산정하였다. 10 m 공간 해상도의 Sentinel-1A/B SAR 영상은 8일 간격으로 2015년부터 2019년까지 5년 동안 구축하였고, SNAP(SentiNel Application Platform)을 통해 기하 보정, 방사 보정 및 잡음(Noise) 보정을 수행하고 VV 및 VH 편파 후방산란계수로 변환하였다. ANN 모형 검증자료로 TDR(Time Domain Reflectometry)로 측정된 9개 지점의 실측 토양수분 자료를 구축하였으며, 수문학적 개념인 선행강우를 고려하기 위해 동지점에 대한 강수량 자료를 구축하였다. ANN은 각 지점에 해당하는 토양 속성별로 모델링하고, 전체 기간 및 계절별로 나누어 모의하였으며, 전체 자료의 60%와 40%를 각각 훈련 및 테스트 데이터로 사용하였다. 산정된 토양수분은 상관계수(Correlation Coefficient, R)와 평균제곱근오차(Root Mean Square Error, RMSE)를 활용하여 검증을 수행할 예정이다.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.989-998
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• 2021

In the utilization of optical satellite imagery, which is greatly affected by clouds, periodic composite technique is a useful method to minimize the influence of clouds. Recently, a technique for selecting the optimal pixel that is least affected by the cloud and shadow during a certain period by directly inputting cloud and cloud shadow information during period compositing has been proposed. Accurate extraction of clouds and cloud shadowsis essential in order to derive optimal composite results. Also, in the case of an surface targets where spectral information is important, such as crops, the loss of spectral information should be minimized during cloud-free compositing. In thisstudy, clouds using two spectral indicators (Haze Optimized Tranformation and MeanVis) were used to derive a detection technique with low loss ofspectral information while maintaining high detection accuracy of clouds and cloud shadowsfor cabbage fieldsin the highlands of Gangwon-do. These detection results were compared and analyzed with cloud and cloud shadow information provided by Sentinel-2A/B. As a result of analyzing data from 2019 to 2021, cloud information from Sentinel-2A/B satellites showed detection accuracy with an F1 value of 0.91, but bright artifacts were falsely detected as clouds. On the other hand, the cloud detection result obtained by applying the threshold (=0.05) to the HOT showed relatively low detection accuracy (F1=0.72), but the loss ofspectral information was minimized due to the small number of false positives. In the case of cloud shadows, only minimal shadows were detected in the Sentinel-2A/B additional layer, but when a threshold (= 0.015) was applied to MeanVis, cloud shadowsthat could be distinguished from the topographically generated shadows could be detected. By inputting spectral indicators-based cloud and shadow information,stable monthly cloud-free composited vegetation index results were obtained, and in the future, high-accuracy cloud information of Sentinel-2A/B will be input to periodic cloud-free composite for comparison.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.56-56
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• 2023

남양호는 농업용수 공급을 위해 건설된 하구 담수호로 과도한 영양물질 축적으로 인해 매년 여름 녹조류가 번성한다. 따라서 본 연구에서는 조류발생 특성을 분석하고자 식물성 플랑크톤 및 관련 분해 산물에 의해 고유 광학특성을 가지고 있는 Chlorophyll-a(Chl-a)의 추정을 통한 녹조 발생을 파악하고자 Sentinel-2 Multi Spectral Image(MSI)의 원격 반사율 광학 스펙트럼을 사용하였다. Chl-a 추정알고리즘 개발을 위하여 Sentinel-2 A, B의 교차 방문주기인 5일 간격에 맞추어 현장수질자료(2022년: 27회 2023년: 27회)를 측정하였다. Chl-a 농도는 EXO-YSI를이용하여 측정하였으며 해당기간동안 9.4 ~ 127.1 mg/L의 범위를 보였으며, Sentine-2 자료는 A, B자료에서 B1(443 nm) ~ B8A(865 nm)파장의 값을 기상조건(구름, 안개, 강수)을 고려하여 현장수질측정 위치에서 반사도를 추출하였다. 입력자료는 대기 및 방사영향을 고려해 반사도 간의 비율자료와 선행연구에서 활용된 반사도를 활용하였으며 알고리즘은 다중선형회귀분석(Multi Linear Regression Model)과 Random Forest를 활용하였다. MLR의 경우 결정계수(R2)가 학습 및 검증에서 각각 0.68, 0.59의 성능을 보였으며, RF의 경우 각각 0.94, 0.85의 성능을 보였다. 해당알고리즘으로 생성된 Chl-a 시공간농도 자료는 담수호내 조류발생 특성을 분석하고 효율적 조류관리 및 대처에 활용될 것으로 판단된다.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.609-620
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• 2023

In this study, we used Sentinel-1 C-band synthetic aperture radar to calculate the surface state factor (SSF) for distinguishing the frozen-thawed state of soil. The accuracy of SSF classification was analyzed through comparison with air temperature (AT), grass temperature (GT), and underground temperature (UT). For the analysis, 116 Sentinel-1B Descending nodes observed over a period of 4 years from 2017 to 2020 were established for the central region of South Korea. AT, GT, and UT data were obtained from 23 soil moisture observation points of the Rural Development Administration during the same period, and analyzed using the 06:00 am data adjacent to the shooting time of the Sentinel-1B images. The average accuracy and F1-score for all stations were 0.63 and 0.47 for AT, 0.63 and 0.48 for GT, and 0.57 and 0.21 for UT, respectively. For winter (December-February) data, the average accuracy and F1-score were 0.66 and 0.76 for AT, 0.67 and 0.76 for GT, and 0.47 and 0.44 for UT, respectively. The increase in accuracy during winter data may be attributed to the fact that errors occurring in other seasons are not included.