Spatial Downscaling of the FY3B Soil Moisture Using Random Forest Regression

Sheng, Jiahui; Rao, Peng; Ma, Hongliang

doi:10.1109/agro-geoinformatics.2019.8820253

Cited by 5 publications

(3 citation statements)

References 7 publications

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“…The dataset was available at 1 km spatial resolution. In addition to this, literature reported that, black and white sky albedo data has strong association with SM (Sheng et al, 2019). The data from MCD43A3 version 6 product on black and white sky albedo available at 500 m spatial resolution was used in this study (Wan, 2015).…”

Section: Modis Based Indicesmentioning

confidence: 99%

“…Modeling-based downscaling approaches mainly involve statistical downscaling (Kaheil et al, 2008) or physical process based models (Ines and Droogers, 2002). Due to the limited availability of field level sensor observations needed for physical models, many researchers have developed the machine learning based models (Liu et al, 2017, Sheng et al, 2019.…”

Section: Introduction and State Of The Artmentioning

confidence: 99%

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Spatial Downscaling of Smap Soil Moisture Using the Modis and SRTM Observations

Mohite

Sawant²,

Pandit³

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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Abstract. The main objective of this study is the spatial downscaling of Soil Moisture Active Passive (SMAP) soil moisture (36 km) using the Moderate Resolution Imaging Spectroradiometer (MODIS) and Shuttle Radar Topography Mission (SRTM) products. The study was conducted over India during the post-monsoon (i.e., Rabi) season Daily SMAP soil moisture (SM) data was composited to 3 days to cover the entire study area. MODIS data for the Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Albedo, and Land Surface Temperature (LST) were similarly obtained by constructing a three day composite. SMAP soil moisture was used as a dependent variable, whereas, MODIS NDVI, NDWI, Albedo, LST, and SRTM elevation were used as independent variables in a regression analysis for downscaling of SMAP soil moisture. The coefficient of determination (R2) was used to evaluate the performance of multi-variate linear regression (MLR), support vector regression (SVR), and random forest regression (RFR). Each method was used to test the performance of monthly and seasonal models. RFR outperformed MLR and SVR for monthly and seasonal models. Furthermore, a comparison of monthly and seasonal models revealed that the model created on Jan. data performed best (R2=0.80), while R2 of 0.73, 0.61, 0.75, and 0.76 were attained using RFR for seasonal, Dec., Feb., and Mar. models, respectively. In addition, in-situ soil moisture data was used to validate downscaled soil moisture (1 km). Comparison between downscaled soil moisture and in-situ soil moisture showed good agreement with a difference ranging between −9.3 to 7.4 %.

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Section: Modis Based Indicesmentioning

confidence: 99%

Section: Introduction and State Of The Artmentioning

confidence: 99%

Spatial Downscaling of Smap Soil Moisture Using the Modis and SRTM Observations

Mohite

Sawant²,

Pandit³

et al. 2022

Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci.

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“…Recently, the ISMN was recognised as validation source for testing algorithms to derive soil moisture from Global Navigation Satellite Systems (GNSS; e.g., ; Chew and Small (2020). Similarly, there is an increasing trend in the use of ISMN data for the validation of novel high-resolution satellite soil moisture products, which either downscalfe coarse-resolution products through the use of other finer resolution satellite or ancillary data (e.g., Sheng et al (2019); Helgert and Khodayar (2020)), or directly derive soil moisture from high-resolution Synthetic Aperture Aperture satellites like Sentinel-1 (e.g., Rodionova (2019b); Foucras et al (2020)). It should be noted that the ISMN and its contributing networks are mostly designed for analysing time series, thus lacking reference data to assess spatial patterns in the data, particularly in high-resolution products (de Jeu and Dorigo, 2016).…”

Section: Scientific Studiesmentioning

confidence: 99%

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Dorigo¹

2021

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In 2009, the International Soil Moisture Network (ISMN) was initiated as a community effort, funded by the European Space Agency, to serve as a centralised data hosting facility for globally available in situ soil moisture measurements (Dorigo et al., 2011b, a). The ISMN brings together in situ soil moisture measurements collected and freely shared by a multitude of organisations, harmonizes them in terms of units and sampling rates, applies advanced quality control, and stores them in a Ground-based soil moisture measurements of land surface variables are indispensable in the process of developing, validating, and advancing spatially contiguous data sets derived from satellites or models (Loew et al., 2017;Gruber et al., 2020). Although the first systematic measurements of soil moisture started well before the satellite era in the former Soviet Union to support agricultural decision making (Robock et al., 2000), it was not until the early 2000s that soil moisture monitoring networks started being widely established as part of hydrological and meteorological observing capacities. Particularly, the launch of the Soil Moisture Ocean Salinity (SMOS) mission of the European Space Agency (ESA) in 2009 (Kerr et al., 2016), and the launch of the Soil Moisture Active Passive (SMAP) mission of the National Aeronautics and Space Administration (NASA) in 2015 (Entekhabi et al., 2010), boosted the establishment of new research networks (Colliander et al., 2017).While all networks are a valuable asset for assessing the skill of soil moisture products under various conditions and scales, their usage is hampered by the diversity of sensors, data formats, quality control, and accessibility mechanisms. The need of bringing together and harmonising available soil moisture data was recognised by the international soil moisture community and expedited by the Global Energy and Water cycle Exchanges (GEWEX) project of the World Climate Research Programme (WCRP) with support of the Commission on Earth Observation Satellites (CEOS), the Global Climate Observing System (GCOS), and the Group on Earth Observation (GEO). In the advent of the SMOS mission, ESA decided to provide the financial impetus to establish a global reference database of in situ soil moisture measurements for the purpose of satellite product development and validation. As a result, the International Soil Moisture Network (ISMN) went online in 2010 (Dorigo et al., 2011b, a).The primary objective of the ISMN is to collect in situ soil moisture data sets shared by various data organisations on a voluntary basis and make them available in a harmonized format through a centralised free and open web portal (ismn.earth).While 10 years after its launch the core objective of the ISMN remains valid, its functionality has expanded since then. This new functionality includes the integration of advanced quality control methods (Dorigo et al., 2013), the provision of additional metadata and ancillary variables (e.g., precipitation, soil and air temperature), ongoing automation, the ...

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