Soil Moisture Retrieval Using Microwave Remote Sensing: Review of Techniques and Applications

Laachrate, Hibatoullah; Fadil, Abdelhamid; Ghafiri, Abdessamad

doi:10.1007/978-3-030-24974-8_3

Cited by 5 publications

(3 citation statements)

References 123 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Microwave remote sensing has a significant association with the soil dielectric constant, and satellite data can be utilized to provide SM from regional to global scales with daily or hourly temporal resolution [7], providing SM datasets with high information content, large observation range, high speed, and relatively high accuracy. The Soil Moisture Active Passive (SMAP) mission [1,8,9] was launched in 2015 and received the highest ranking of mission priorities from the National Research Council [10].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Forecast of SMAP L3 Soil Moisture Using Spatial–Temporal Deep Learning Model with Data Integration

Zhang

Huang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

Soil moisture (SM) has significant impacts on the Earth’s energy and water cycle system. Remote sensing, such as the Soil Moisture Active Passive (SMAP) mission, has delivered valuable estimations of global surface soil moisture. However, it has a 2~3 days revisit time leading to gaps between SMAP areas. To achieve accurate and comprehensive real-time forecast of SM, we propose a spatial–temporal deep learning model based on the Convolutional Gated Recursive Units with Data Integration (DI_ConvGRU) to capture the spatial and temporal variation in SM simultaneously by modeling the influence of adjacent SM values in space and time. Experiments show that the DI_ConvGRU outperforms the ConvGRU with Linear Interpolation (interp_ConvGRU) and the Long Short-Term Memory with Data Integration (DI_LSTM). The best performance (Bias = 0.0132 m3/m3, ubRMSE = 0.022 m3/m3, R = 0.977) has been achieved through the use of spatial–temporal deep learning model and Data Integration term. In comparison with interp_ConvGRU and DI_LSTM, DI_ConvGRU has improved the model performance in 74.88% and 68.99% of the regions according to RMSE, respectively. The predictability of SM depends highly on SM memory characteristics. DI_ConvGRU can provide accurate spatial–temporal forecast for SM with missing data, making them potentially useful for applications such as filling observational gaps in satellite data.

show abstract

Section: Introductionmentioning

confidence: 99%

Real-Time Forecast of SMAP L3 Soil Moisture Using Spatial–Temporal Deep Learning Model with Data Integration

Zhang

Huang

et al. 2023

Remote Sensing

View full text Add to dashboard Cite

show abstract

“…It not only plays a critical role in the hydrological process, vegetation and crop growth, and material and energy cycle of ecosystem [1][2][3][4], but it is also essential for understanding the dynamics of the earth system and predicting climate and land changes in the future [5]. The current rapid development of satellite remote sensing technology, including optical, radar, and microwave remote sensing, provides an effect way to achieve soil moisture observation at different scales [6][7][8][9]. Especially for passive microwave satellite remote sensing [10], due to the lessened influence of the atmosphere, deeper detection depth, and more direct physical relationship between remote sensing information and soil moisture [11][12][13], it has become the mainstream method to obtain soil moisture observation data at global and local scales, using C band (4-8 GHz) at higher frequencies and L band (0.5-1.5 GHz) at lower frequencies [14][15][16].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network-Based Microwave Satellite Soil Moisture Reconstruction over the Qinghai–Tibet Plateau, China

Wang

2021

Remote Sensing

View full text Add to dashboard Cite

Soil moisture is a key parameter for land-atmosphere interaction system; however, fewer existing spatial-temporally continuous and high-quality observation records impose great limitations on the application of soil moisture on long term climate change monitoring and predicting. Therefore, this study selected the Qinghai–Tibet Plateau (QTP) of China as research region, and explored the feasibility of using Artificial Neural Network (ANN) to reconstruct soil moisture product based on AMSR-2/AMSR-E brightness temperature and SMAP satellite data by introducing auxiliary variables, specifically considering the sensitivity of different combination of input variables, number of neurons in hidden layer, sample ratio, and precipitation threshold in model building. The results showed that the ANN model had the highest accuracy when all variables were used as inputs, it had a network containing 12 neurons in a hidden layer, it had a sample ratio 80%-10%-10% (training-validation-testing), and had a precipitation threshold of 8.75 mm, respectively. Furthermore, validation of the reconstructed soil moisture product (named ANN-SM) in other period were conducted by comparing with SMAP (April 2019 to July 2021) for all grid cells and in situ soil moisture sites (August 2010 to March 2015) of QTP, which achieved an ideal accuracy. In general, the proposed method is capable of rebuilding soil moisture products by adopting different satellite data and our soil moisture product is promising for serving the studies of long-term global and regional dynamics in water cycle and climate.

show abstract

“…

…”

mentioning

confidence: 99%

Modeling and Assessment of Vegetation Water Content on Soil Moisture Retrieval via the Synergistic Use of Sentinel‐1 and Sentinel‐2

Wang

Jin

et al. 2022

Earth and Space Science

View full text Add to dashboard Cite

As one of the important parameters of the fifty "basic climate variables", soil moisture plays an important role in ecology, hydrology, climatology, and meteorology (Attarzadeh et al., 2018;Laachrate et al., 2020). From an ecological point of view, soil moisture is an important water element absorbed by plant roots, which directly affects plant growth, soil respiration, and plant function (Blyth et al., 2011). For the hydrology, soil moisture controls surface infiltration and redistribution of surface runoff and is an important part of the global water cycle (Kornelsen & Coulibaly, 2013). From a climatological perspective, soil moisture regulates the distribution and circulation of water and energy on the land surface, which in turn affects the global climate pattern (Seneviratne et al., 2010).Studies on carbon and water cycles, climate change, and energy balance with large-scale and spatial-temporal characteristics often require the participation of global soil moisture products such as Soil Moisture Active and Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), etc (Carrera et al., 2017;Jones et al., 2017). However, these products with low spatial resolution cannot be used for research studies on crop yield evaluation, drought monitoring, and agricultural irrigation management (Carrao et al., 2016;Rivers et al., 2015). Ground measurements based on this point is often expensive and laborious. Therefore, it is extremely necessary and important to achieve high spatial and temporal resolutions of soil moisture retrieval. Optical and thermal infrared remote sensing are susceptible to surface coverings and severely affected by weather clouds and fog, and the accuracy of soil moisture retrieval is greatly reduced. High-resolution Synthetic Aperture Radar (SAR) data with microwave bands of band C, band X, and band L (e.g., COSMO-SkyMed, Sentinel-1, and ALOS), which have the advantage of being unaffected by weather, low frequency band, and being sensitive to soil moisture, and thus

show abstract

Soil Moisture Retrieval Using Microwave Remote Sensing: Review of Techniques and Applications

Cited by 5 publications

References 123 publications

Real-Time Forecast of SMAP L3 Soil Moisture Using Spatial–Temporal Deep Learning Model with Data Integration

Real-Time Forecast of SMAP L3 Soil Moisture Using Spatial–Temporal Deep Learning Model with Data Integration

Artificial Neural Network-Based Microwave Satellite Soil Moisture Reconstruction over the Qinghai–Tibet Plateau, China

Modeling and Assessment of Vegetation Water Content on Soil Moisture Retrieval via the Synergistic Use of Sentinel‐1 and Sentinel‐2

Contact Info

Product

Resources

About