Soil Moisture Experiment in the Luan River (SMELR)

Brief Introduction: The Luan River is the second largest river in the North China region. It originates in northern Hebei, where its headstream is called as the Shandian River. It flows northwards into the province of Inner Mongolia, and then flows southeast back into Hebei, where it is joined by its tributary of the Xiaoluan River. The Luan River finally discharges into the Bohai Sea. The Luan River Basin has a typical temperate continental monsoon climate. Spring drought and flooding in summer are prone to occur in the basin, and the soil is alternately wetting and drying. The Luan River Basin is characterized by the interlaced zone of agriculture, pasture, and forestry with various land cover types. It is an important water conservation and ecological function zone for the Beijing-Tianjin-Hebei region, making the Luan River Basin a unique place for conducting soil moisture remote sensing experiments and land surface system related scientific research. The Soil Moisture Experiment in the Luan River (SMELR) is an experiment that integrates the space, airborne and ground based remote sensing technologies conducted from 2017 to 2018 under the framework of the “Comprehensive remote sensing experiment of carbon cycle, water cycle and energy balance”. It serves as an assessment tool and demonstration for a new Terrestrial Water Resources Satellite (TWRS) concept with one-dimensional synthetic aperture microwave techniques, for which soil moisture retrieval under variable satellite observing configurations (mainly in terms of incidence angels) is the greatest challenge.

Number of Datasets: 14

  • Multi-frequency and multi-angular ground-based microwave radiometer and radar cooperative experimental data for grassland in 2018

    Multi-frequency and multi-angular ground-based microwave radiometer and radar cooperative experimental data for grassland in 2018

    This data set was collected in 2018 during the ground-based microwave radiometry and radar cooperative experiment, which is part of the Soil Moisture Experiment in the Luan River (SMELR). The experiment site is located in Zhenglan Banner, Inner Mongolia (115.93° E, 42.04° N, at 1362 m in altitude). The data set contains four parts, namely brightness temperature data, radar backscatter coefficient, soil data and vegetation data. The microwave brightness temperature data was observed by a vehicle-mounted dual-polarized multi-frequency radiometer (RPG-6CH-DP), including the horizontal (H) and vertical (V) polarization brightness temperatures at L-, C- and X-bands. The brightness temperature data was acquired every 30 minutes from 30° to 65° with an interval of 2.5°. The active microwave data is obtained by ground-based synthetic aperture radar (GBSAR), including the L- and C-band backscattering coefficients under four polarization modes (VV, VH, HH, HV), and the incidence varies from 30° to 65° (2.5° interval). The soil data contains the surface roughness, soil moisture and temperature at six depths of layer (1 cm, 3 cm, 5 cm, 10 cm, 20 cm, 50 cm). The vegetation data is mainly the vegetation water content of the grassland. The experimental period lasted from August 18 to September 25, 2018, and it provided important data for the land surface microwave radiation modeling and validation, as well as the development of soil moisture retrieval algorithms.

    2021-08-23 1808 257

  • In-situ measurement data set (2019) of the soil moisture and temperature wireless sensor network within the Shandian River Basin

    In-situ measurement data set (2019) of the soil moisture and temperature wireless sensor network within the Shandian River Basin

    This data set contains in-situ measurements of soil moisture, soil temperature and precipitation at 34 stations from a wireless Soil Moisture Network within the ShanDian River basin (referred to as the SMN-SDR hereafter). The coverage of entire network is about 10,000 km2 (115.5-116.5°E, 41.5-42.5°N). The topography of the SMN-SDR is relatively flat, and land surfaces are typically dominated by grasslands and croplands. A total of 34 stations are set up in the network with three sampling scales including 100 km (large scale), 50 km (medium scale), and 10 km (small scale). The soil moisture sensors used are Decagon 5TM with five measuring depths (3, 5, 10, 20, and 50 cm) installed for each station. Of the 34 station, there are 20 stations equipped with HOBO rain gauges. Undisturbed soil samples at each layer of soil for each station was taken to analyze the gravimetric/volumetric water content, bulk density, and soil texture for a further specific calibration. The power supply is provided by solar panels and all data can be transmitted wirelessly to a server. The sampling interval of the data recording time is 10 (before June 2019) or 15 minutes (after June 2019). This network can improve the comprehensive observation capabilities of key water cycle parameters in the ShanDian River basin and provide long-term ground reference data for satellite- and model-based soil moisture products.

    2021-06-09 2511 334

  • Multi-frequency and multi-angular ground-based microwave radiometer and surface parameters experimental data for cropland in 2017

    Multi-frequency and multi-angular ground-based microwave radiometer and surface parameters experimental data for cropland in 2017

    This data set was collected in summer 2017 during the ground-based microwave radiometry experiment, which is part of the Soil Moisture Experiment in the Luan River (SMELR). The experiment site is located in Duolun County, Inner Mongolia (116.47°E, 42.18°N, at 1269 m in altitude). The data set contains three parts, namely brightness temperature data, soil data and vegetation data. The microwave brightness temperature data was observed by a vehicle-mounted dual-polarized multi-frequency radiometer (RPG-6CH-DP), including the horizontal (H) and vertical (V) polarization brightness temperatures at L-, C- and X-bands. The brightness temperature data were acquired from 30° to 65° with an interval of 2.5°, and the time resolution is 0.5 hours. Soil data contains 5 layers of soil moisture and soil temperature (2.5 cm, 10 cm, 20 cm, 30 cm, 50 cm) over three croplands (corn, oats, and buckwheat), with sampling intervals of 10 minutes. The soil data also contains soil surface roughness, rainfall, irrigation flags, and soil texture. Vegetation data contains leaf area index, plant height, vegetation water content, etc. The experimental period lasted from July 19 to August 30, 2017, and it provided important data for the land surface microwave radiation modeling and validation, as well as the development of soil moisture retrieval algorithms.

    2021-07-19 2177 250

  • In-situ measurement data set (2018) of the soil moisture and temperature wireless sensor network within the Shandian River Basin

    In-situ measurement data set (2018) of the soil moisture and temperature wireless sensor network within the Shandian River Basin

    This data set contains in-situ measurements of soil moisture, soil temperature and precipitation at 34 stations from a wireless Soil Moisture Network within the ShanDian River basin (referred to as the SMN-SDR hereafter). The coverage of entire network is about 10,000 km2 (115.5-116.5°E, 41.5-42.5°N). The topography of the SMN-SDR is relatively flat, and land surfaces are typically dominated by grasslands and croplands. A total of 34 stations are set up in the network with three sampling scales including 100 km (large scale), 50 km (medium scale), and 10 km (small scale). The soil moisture sensors used are Decagon 5TM with five measuring depths (3, 5, 10, 20, and 50 cm) installed for each station. Of the 34 station, there are 20 stations equipped with HOBO rain gauges. Undisturbed soil samples at each layer of soil for each station was taken to analyze the gravimetric/volumetric water content, bulk density, and soil texture for a further specific calibration. The power supply is provided by solar panels and all data can be transmitted wirelessly to a server. The sampling interval of the data recording time is 10 (before June 2019) or 15 minutes (after June 2019). This network can improve the comprehensive observation capabilities of key water cycle parameters in the ShanDian River basin and provide long-term ground reference data for satellite- and model-based soil moisture products.

    2021-06-09 2234 298

  • Surface spectra dataset during Soil Moisture Remote Sensing Experiment in Luanhe River Basin (2018)

    Surface spectra dataset during Soil Moisture Remote Sensing Experiment in Luanhe River Basin (2018)

    The surface coverage types of ground spectrum data set include bare soil, grassland and crops. The survey location is xiaoluanhe River Basin; The measurement time was from August to September 2018; Measurement method: using ASD spectrometer, each sample was observed three times. Spectral band range: 350 ~ 2500nm. Data processing software: viewspec Pro software. The data set consists of two parts, one is the spectrum of Xinyuan pasture and its surrounding objects (August 28, 2018 - September 12, 2018), the other is the Vegetation Spectrum of lightning River synchronous experiment (September 15, 2018 - September 26, 2018).

    2021-06-23 1856 234

  • A global daily soil moisture dataset derived from Chinese FengYun-3B Microwave Radiation Imager (MWRI) (2010-2019)

    A global daily soil moisture dataset derived from Chinese FengYun-3B Microwave Radiation Imager (MWRI) (2010-2019)

    This dataset contains 10 years (2010-2019) global daily surface soil moisture . The resolution is 36 km , the projection is EASE-Grid2, and the data unit is m3 / m3. This dataset adopts the soil moisture neural network retrieval algorithm developed by Yao et al. (2017,2021). This study transfers the merits of SMAP to FY-3B/MWRI through using an Artificial Neural Network (ANN) in which SMAP standard SSM products serve as training targets with FY-3B/MWRI brightness temperature (TB) as input. Finally, long term soil moisture data are output. The accuracy is about 5% volumetric water content,which is comparable with that of SMAP. (evaluation accuracy of 14 dense ground network globally.)

    2022-01-20 1720 85

  • The observation data set of L-band ground-based microwave radiometer within Shandian River Basin (2018)

    The observation data set of L-band ground-based microwave radiometer within Shandian River Basin (2018)

    The observation data set of L-band ground-based microwave radiometer within Shandian River Basin collects the experimental data of ground-based L-band mobile observation carried out by Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences in Shandian River Basin in September 2018. The L-band microwave radiometer is installed on the lifting platform of Changchun Jingyuetan remote sensing vehicle, and the platform is raised to 5M for dual polarization multi angle observation. The host system of the microwave radiometer system directly stores the data as a .dat file, which can be read and processed by Excel or MATLAB. The collected data has been sorted into excel form.This data can be used to study the inversion method of soil moisture.

    2021-09-06 1971 239

  • Synchronous observation data set of soil temperature and soil moisture in the upstream of Luan River (2018)

    Synchronous observation data set of soil temperature and soil moisture in the upstream of Luan River (2018)

    This data set contains the surface temperature, soil temperature, and soil moisture data measured simultaneously during the Soil Moisture Experiment in the Luan River (SMELR) in 2018, which is used as "true value" to validate the remote sensing retrieval. The dataset includes soil moisture (volumetric water content, %) of the surface layer (0-5cm), soil moisture of the deeper layers (5, 10, 20, 40 cm), temperature (℃) of shaded soil, illuminated soil, 5-cm soil, shaded and illuminated vegetation. The ground synchronous sampling quadrats were distributed in the upstream of Luan River (Shandian River Watershed and Xiaoluan River Watershed), and the sampling time was September in 2018. ML3 soil moisture sensor, TR-52i temperature sensor, soil ring sampler were used for measurement. The sampling scheme of Large Quadrat--Small Quadrat--Sampling Location was adopted to obtain data.

    2021-06-24 2063 331

  • A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002-2021)

    A long term global daily soil moisture dataset derived from AMSR-E and AMSR2 (2002-2021)

    This dataset contains 20 years (2002-2021) global spatio-temporal consistent surface soil moisture . The resolution is 36 km at daily scale, the projection is EASE-Grid2, and the data unit is m3 / m3. This dataset adopts the soil moisture neural network retrieval algorithm developed by Yao et al. (2017). This study transfers the merits of SMAP to AMSR-E/2 through using an Artificial Neural Network (ANN) in which SMAP standard SSM products serve as training targets with AMSR-E/2 brightness temperature (TB) as input. Finally, long term soil moisture data are output. The accuracy is about 5% volumetric water content. (evaluation accuracy of 14 dense ground network globally.)

    2022-01-20 10685 1076

  • Global daily 0.05 ° spatiotemporal continuous land surface temperature dataset (2002-2020)

    Global daily 0.05 ° spatiotemporal continuous land surface temperature dataset (2002-2020)

    Land surface temperature (LST) is a key parameter in the study of surface energy balance. It is widely used in the fields of meteorology, climate, hydrology, agriculture and ecology. As an important means to obtain global and regional scale LST information, satellite (thermal infrared) remote sensing is vulnerable to the influence of cloud cover and other atmospheric conditions, resulting in temporal and spatial discontinuity of LST remote sensing products, which greatly limits the application of LST remote sensing products in related research fields. The preparation of this data set is based on the empirical orthogonal function interpolation method, using Terra / Aqua MODIS surface temperature products to reconstruct the lst under ideal clear sky conditions, and then using the cumulative distribution function matching method to fuse era5 land reanalysis data to obtain the lst under all-weather conditions. This method makes full use of the spatio-temporal information of the original MODIS remote sensing products and the cloud impact information in the reanalysis data, alleviates the impact of cloud cover on LST estimation, and finally reconstructs the high-quality global 0.05 ° spatio-temporal continuous ideal clear sky and all-weather LST data set. This data set not only realizes the seamless coverage of space-time, but also has good verification accuracy. The reconstructed ideal clear sky LST data in the experimental areas of 17 land cover types in the world, the average correlation coefficient (R) is 0.971, the bias (bias) is -0.001 K to 0.049 K, and the root mean square error (RMSE) is 1.436 K to 2.688 K. The verification results of the reconstructed all-weather LST data and the measured data of ground stations: the average R is 0.895, the bias is 0.025 K to 2.599 K, and the RMSE is 4.503 K to 7.299 K. The time resolution of this data set is 4 times a day, the spatial resolution is 0.05 °, the time span is 2002-2020, and the spatial range covers the world.

    2022-04-15 9741 564

  • SMAP soil moisture and vegetation optical depth product using MCCA (2015-2021)

    SMAP soil moisture and vegetation optical depth product using MCCA (2015-2021)

    Soil moisture is an important boundary condition of earth-atmosphere exchanges, and it has been defined as an essential climate variable by GCOS. Vegetation optical depth is a physical variable to measure the attenuation of vegetation in microwave radiative transfer model, and it has been proved to be a good indicator of vegetation water content and biomass. This dataset uses the multi-channel collaborative algorithm (MCCA) to retrieve both soil moisture and polarized vegetation optical depth with SMAP brightness temperature. The algorithm uses a self-constraint relationship between land parameters and an analytical relationship between brightness temperature at different channels to perform the retrieval process. The MCCA does not depend on other auxiliary data on vegetation properties and can be applied to a variety of satellites. The soil moisture product from this dataset includes the soil moisture content in the unfrozen period and the liquid water content in the frozen period. Both horizontal- and vertical-polarization vegetation optical depth are retrieved. So far as we know, it is the first polarization-dependent vegetation optical depth product at L-band. This dataset was validated by 22 intensive soil moisture observation networks (9 core validation sites used by SMAP team and 13 sites not used by them). It was found that ubRMSE (unbiased root mean square error) of MCCA retrieved soil moisture is generally smaller than that of MTDCA and SMAP official products (DCA, SCA-H and SCA-V).

    2022-04-18 1000 42

  • Synchronous observation data set of soil surface roughness in the upstream of Luan River (2018)

    Synchronous observation data set of soil surface roughness in the upstream of Luan River (2018)

    The soil surface roughness data set measured simultaneously during the Soil Moisture Experiment in the Luan River (SMELR) in 2018, which covers (1) 30 quadrats in the north-south flight region of 70 km ×12 km typical experimental area, and (2) 8 quadrats in the northeast-southwest flight region of 165 km×5 km complex experimental area. The data were measured on September 17, September 18 and September 20, 2018 respectively. The soil surface roughness along the row (East-West) direction and cross the row (North-South) direction of typical features in each sample area were measured. The surface roughness of the dataset is described using three parameters; root mean square height (RMSH) and correlation length (CL). The root mean square height describes the random surface characteristics, while the correlation length and correlation function describe the periodicity of the surface. The surface roughness was calculated through the steps of soil surface height digitization, slope correction, periodic correction, and roughness calculation.

    2021-08-20 1911 248

  • Ground-based  dataset of vegetation water content in Shandian watershed (2018)

    Ground-based dataset of vegetation water content in Shandian watershed (2018)

    This vegetation water content data set is derived from the ground synchronous observation in the Luanhe River Basin soil moisture remote sensing experiment, including 55 sampled plots.The vegetation types involved in these sampled plots include grass, corn, potatoes, naked oats and carrots. The data measurement time is from September 13, 2018 to September 26, 2018.

    2021-09-02 1840 295

  • Observation dataset of soil temperature and moisture in Xiaoluan Watershed (2018-2019)

    Observation dataset of soil temperature and moisture in Xiaoluan Watershed (2018-2019)

    The dataset includes 29 soil temperature and moisture observation sites in Xiaoluan Watershed. The observation time is from August 28, 2018 to February 28, 2019, with an interval of 30 min. The depth of observation is 5 cm and 10 cm, totally 2 layers. The automatic observation network measures data both at the passive microwave pixel scale (e.g., SMAP, SMOS, AMSR2, and FY-3) and active microwave satellite pixel scale (e.g., Sentinel-1). The observation area of the active and passive microwave pixels is 0.1°×0.1° and 0.25°×0.25°, respectively. There are 12 sites (named A (Active)) in active microwave pixels and 17 sites (named P (Passive)) in passive microwave pixels.

    2021-06-10 1869 530