• Korean Journal of Soil Science and Fertilizer
• /
• v.48 no.6
• /
• pp.571-581
• /
• 2015

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 providing high resolution data.

• Journal of Korea Water Resources Association
• /
• v.37 no.1
• /
• pp.21-29
• /
• 2004

The growth of water resources engineering associated with stable supply, management, development is essential to overcome the coming water deficit of our country. Large scale remote sensing and the analysis of sub-pixel variability of soil moisture fields are necessary in order to understand water cycle and to develop appropriate hydrologic model. The target resolution of coming Global monitoring of soil moisture field is about 10km which is not appropriate for the regional scale hydrologic model. Therefore, we need a downscaling scheme to generate hydrologic variables which are suitable for the regional hydrologic model. The results of the analysis of sub-pixel soil moisture variability show that the relationship between ancillary data and soil moisture fields shows there is very weak linear relationship. A downscaling scheme was developed using physically-based classification scheme and Neural Networks which are able to link the nonlinear relationship between ancillary data and soil moisture fields. The model is demonstrated by downscaling soil moisture fields from 4km to 0.2km resolution using remotely-sensed data from the Washita'92 experiment.

• Korean Journal of Remote Sensing
• /
• v.36 no.4
• /
• pp.587-607
• /
• 2020

Soil moisture is essential information for meteorological and hydrological analyses. To date, many efforts have been made to achieve the two goals for soil moisture data, i.e., the improvement of accuracy and resolution, which is very challenging. We presented an ensemble downscaling method for quality improvement of gridded soil moisture data in terms of the accuracy and the spatial resolution by the integration of BMA (Bayesian model averaging) and ATPRK (area-to-point regression kriging). In the experiments, the BMA ensemble showed a 22% better accuracy than the data sets from ESA CCI (European Space Agency-Climate Change Initiative), ERA5 (ECMWF Reanalysis 5), and GLDAS (Global Land Data Assimilation System) in terms of RMSE (root mean square error). Also, the ATPRK downscaling could enhance the spatial resolution from 0.25° to 0.05° while preserving the improved accuracy and the spatial pattern of the BMA ensemble, without under- or over-estimation. The quality-improved data sets can contribute to a variety of local and regional applications related to soil moisture, such as agriculture, forest, hydrology, and meteorology. Because the ensemble downscaling method can be applied to the other land surface variables such as temperature, humidity, precipitation, and evapotranspiration, it can be a viable option to complement the accuracy and the spatial resolution of satellite images and numerical models.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2002.05b
• /
• pp.1173-1178
• /
• 2002

• Journal of The Korean Society of Agricultural Engineers
• /
• v.60 no.4
• /
• pp.49-54
• /
• 2018

With increasing satellite-based RS(Remotely Sensed) techniques, RS soil moisture footprints have been providing for various purposes at the spatio-temporal scales in hydrology, agriculture, etc. However, their coarse resolutions still limit the applicability of RS soil moisture to field regions. To overcome these drawbacks, the LDA(Landsat-based Downscaling Algorithm) was developed to downscale RS soil moisture footprints from the coarse- to finer-scales. LDA estimates Landsat-based soil moisture($30m{\times}30m$) values in a spatial domain, and then the weighting values based on the Landsat-based soil moisture estimates were derived at the finer-scale. Then, the coarse-scale RS soil moisture footprints can be downscaled based on the derived weighting values. The LW21(Little Washita) site in Oklahoma(USA) was selected to validate the LDA scheme. In-situ soil moisture data measured at the multiple sampling locations that can reprent the airborne sensing ESTAR(Electronically Scanned Thinned Array Radiometer, $800m{\times}800m$) scale were available at the LW21 site. LDA downscaled the ESTAR soil moisture products, and the downscaled values were validated with the in-situ measurements. The soil moisture values downscaled from ESTAR were identified well with the in-situ measurements, although uncertainties exist. Furthermore, the SMAP(Soil Moisture Active & Passive, $9km{\times}9km$) soil moisture products were downscaled by the LDA. Although the validation works have limitations at the SMAP scale, the downscaled soil moisture values can represent the land surface condition. Thus, the LDA scheme can downscale RS soil moisture products with easy application and be helpful for efficient water management plans in hydrology, agriculture, environment, etc. at field regions.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2011.05a
• /
• pp.349-352
• /
• 2011

토양수분은 지표와 대기에서 물과 에너지를 교환하는 중요한 수문기상 인자임에도 불구하고 토양수분에 대한 중요성이 부족한 실정이다. 최근에는 위성기술의 발달로 Aqua위성에 탑재된 Advanced Microwave Scanning Radiometer E (AMSR-E)를 이용하여 토양수분을 측정하고 있다. 이는 토양수분을 측정하고 있는 가장 유용한 기기로서 25km의 낮은 공간 해상도를 가지고 있어 토양수분의 변화를 나타내는데 한계점을 가지고 있다. 본 연구에서는 AMSR-E의 공간 해상도를 높이고자 비교적 높은 해상도를 (1km) 가지고 있는 Moderate Resolution Imaging Spectroradiometer (MODIS)를 연동하였으며, MODIS의 산출물 중 Albedo, LST, NDVI 인자를 이용하였다. 이를 바탕으로 1km의 고해상도 일 별 토양수분 지도를 작성하였으며, 이 지도를 각각 관측 토양수분과 비교 검증하였다. 향후 일별 고해상도 토양수분 지도를 작성하면 우리나라에 대한 토양수분 데이터베이스를 구축해 나갈 수 있을 것이다.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2015.05a
• /
• pp.31-31
• /
• 2015

토양수분은 일반적으로 시료를 채취하거나 현장에 설치된 다양한 센서를 통해 추정하지만 이는 시간과 비용이 많이 소모되기 ?문에 유역내의 공간적인 토양수분 분포를 추정하는데 상당한 어려움이 따른다. 토양수분뿐만 아니라 공간적인 대기현상, 토양수분, 식생현황 등을 관측하는데 대중적으로 사용되는 것이 위성 관측이며, 기본적으로는 위성에 탑재된 센서가 각 주파수대역에 따라 영상을 생성하면 이를 특정 알고리듬을 적용하여 원하는 값을 도출하게 된다. 토양수분 산정에 사용되는 대표적인 위성영상으로는 SMOS (Soil Moisture and Ocean Salinity), ARMS-E(Advanced Microwave Scanning Radiometer - Earth Observing System), ARMS2 (ARMS ver.2) 영상 등이 있으며, 이러한 위성은 해상도가 약 10 km ~ 40 km로 상당이 낮기 때문에 우리나라와 같이 면적이 좁고 지형이 복잡하며 다양한 토지피복이 밀집되어있는 곳에서는 기존 수문 연구에 응용할 수 있는 토양수분 공간지도 산정을 위해 상세화(Downscaling)과정이 필요하다고 판단된다. 따라서 본 연구에서는 ARMS2 토양수분 영상을 MODIS 영상의 식생지수(NDVI, Normalized Difference Vegetation Index), 알베도 및 온도를 활용하여 공간적으로 상세화된 토양 수분 지도를 작성하였고, 유역 내에서 실제 측정되고 있는 토양수분 관측값을 활용하여 상세화기법의 적용성을 검토하였다.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.26 no.4B
• /
• pp.329-334
• /
• 2006

The spatial variability of the L- and C- band large scale remotely sensed soil moisture data, obtained during the Southern Great Plain 1999 Experiment (SGP'99), was characterized. The results demonstrate that soil moisture data using L-band show the break in statistical symmetry (multiscaling behavior) with the variation of scale of observation, which is similar to that of the soil property such as sand content. Also, soil moisture data using C-band show single scaling behavior with the variation of scale of observation, which is similar to that of the vegetation condition. The results should be considered during downscaling the Global soil moisture data using AMSR instrument.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.30 no.5B
• /
• pp.437-444
• /
• 2010

Soil moisture has been recognized as the essential parameter when understanding the complicated relationship between land surface and atmosphere in water and energy recycling system. It has been generally known that it is related with the temperature, wind, evaporation dependent on soil properties, transpiration due to vegetations and other constituents. There is, however, little research concerned about the relationship between soil moisture and these constitutes, thus it is needed to investigate it in detail. We estimated the soil moisture and then compared with field data using the hydrometerological data such as atmospheric temperature, specific humidity, and wind obtained from the Flux tower in Selmacheon, Korea. In the winter season, subterranean temperature showed highly positive correlation with soil moisture while it was negatively correlated from the spring to the fall. Estimation of seasonal soil moisture was compared with field measurements with the correlation of determination, R=0.82, 0.81, 0.82, and 0.96 for spring, summer, fall, and winter, respectively. Comprehensive relationship from this study can supply useful information about the downscaling of soil moisture with relatively large spatial resolutions, and will help to deepen the understanding of the water and energy recycling on the earth's surface.

• Korean Journal of Remote Sensing
• /
• v.35 no.6_3
• /
• pp.1285-1298
• /
• 2019

Drought is one of the factors that can cause wildfires. Drought is related to not only the occurrence of wildfires but also their frequency, extent and severity. In South Korea, most wildfires occur in dry seasons (i.e. spring and autumn), which are highly correlated to drought events. In this study, we examined the relationship between wildfire occurrence and drought factors, and developed satellite-based new drought indices for assessing wildfire risk over South Korea. Drought factors used in this study were high-resolution downscaled soil moisture, Normalized Different Water Index (NDWI), Normalized Multi-band Drought Index (NMDI), Normalized Different Drought Index (NDDI), Temperature Condition Index (TCI), Precipitation Condition Index (PCI) and Vegetation Condition Index (VCI). Drought indices were then proposed through weighted linear combination and one-class support vector machine (One-class SVM) using the drought factors. We found that most drought factors, in particular, soil moisture, NDWI, and PCI were linked well to wildfire occurrence. The validation results using wildfire cases in 2018 showed that all five linear combinations produced consistently good performance (> 88% in occurrence match). In particular, the combination of soil moisture and NDWI, and the combination of soil moisture, NDWI, and precipitation were found to be appropriate for representing wildfire risk.