scholarly journals A new dataset of satellite observation-based global surface soil moisture covering 2003–2018

2020 ◽  
Author(s):  
Yongzhe Chen ◽  
Xiaoming Feng ◽  
Bojie Fu
Keyword(s):  
Neural Networks ◽  
Soil Moisture ◽  
Surface Soil ◽  
Microwave Remote Sensing ◽  
Impact Factors ◽  
Satellite Monitoring ◽  
Surface Soil Moisture ◽  
Spatial And Temporal Patterns ◽  
Fusion Algorithm ◽  
Neural Network Approach

Abstract. Soil moisture is an important variable linking the atmosphere and the terrestrial ecosystems. However, long-term satellite monitoring of surface soil moisture is still lacking at global scale. In this study, we conducted data calibration and fusion of 11 well-acknowledged microwave remote sensing-based soil moisture products since 2003 through neural network approach, with SMAP soil moisture data applied as the fundamental training target. The training efficiency proves to be high (R2 = 0.95) due to the selection of 9 quality impact factors of microwave soil moisture products and the elaborate organization structure of multiple various neural networks(5 rounds of simulation; 8 substeps; 74 independent neural networks; and > 106 regional subnetworks). We achieved global satellite monitoring of surface soil moisture during 2003–2018 at 0.1° resolution. This new dataset, once validated against the International Soil Moisture Network (ISMN) records, is supposed to be superior to the existing products (ASCAT-SWI, GLDAS Noah, ERA5-Land, CCI/ECV and GLEAM), and is applicable to studying both the spatial and temporal patterns. It suggests an increase in global mean surface soil moisture, and reveals that the surface moisture decline on rainless days is highest in summers over the low-latitudes but highest in winters over most mid-latitude areas. Notably, the error propagation with the extension of the simulation period to the past is well controlled, indicating that the fusion algorithm will be more meaningful in future when more advanced sensors are in operation. The dataset can be accessed at https://doi.pangaea.de/10.1594/PANGAEA.912597 (Chen, 2020).

scholarly journals An improved global remote-sensing-based surface soil moisture (RSSSM) dataset covering 2003–2018

2021 ◽  
Vol 13 (1) ◽  
pp. 1-31
Author(s):  
Yongzhe Chen ◽  
Xiaoming Feng ◽  
Bojie Fu
Keyword(s):  
Remote Sensing ◽  
Neural Networks ◽  
Soil Moisture ◽  
Data Fusion ◽  
Surface Soil ◽  
Microwave Remote Sensing ◽  
Satellite Monitoring ◽  
Surface Soil Moisture ◽  
Spatial Coverage

Abstract. Soil moisture is an important variable linking the atmosphere and terrestrial ecosystems. However, long-term satellite monitoring of surface soil moisture at the global scale needs improvement. In this study, we conducted data calibration and data fusion of 11 well-acknowledged microwave remote-sensing soil moisture products since 2003 through a neural network approach, with Soil Moisture Active Passive (SMAP) soil moisture data applied as the primary training target. The training efficiency was high (R2=0.95) due to the selection of nine quality impact factors of microwave soil moisture products and the complicated organizational structure of multiple neural networks (five rounds of iterative simulations, eight substeps, 67 independent neural networks, and more than 1 million localized subnetworks). Then, we developed the global remote-sensing-based surface soil moisture dataset (RSSSM) covering 2003–2018 at 0.1∘ resolution. The temporal resolution is approximately 10 d, meaning that three data records are obtained within a month, for days 1–10, 11–20, and from the 21st to the last day of that month. RSSSM is proven comparable to the in situ surface soil moisture measurements of the International Soil Moisture Network sites (overall R2 and RMSE values of 0.42 and 0.087 m3 m−3), while the overall R2 and RMSE values for the existing popular similar products are usually within the ranges of 0.31–0.41 and 0.095–0.142 m3 m−3), respectively. RSSSM generally presents advantages over other products in arid and relatively cold areas, which is probably because of the difficulty in simulating the impacts of thawing and transient precipitation on soil moisture, and during the growing seasons. Moreover, the persistent high quality during 2003–2018 as well as the complete spatial coverage ensure the applicability of RSSSM to studies on both the spatial and temporal patterns (e.g. long-term trend). RSSSM data suggest an increase in the global mean surface soil moisture. Moreover, without considering the deserts and rainforests, the surface soil moisture loss on consecutive rainless days is highest in summer over the low latitudes (30∘ S–30∘ N) but mostly in winter over the mid-latitudes (30–60∘ N, 30–60∘ S). Notably, the error propagation is well controlled with the extension of the simulation period to the past, indicating that the data fusion algorithm proposed here will be more meaningful in the future when more advanced microwave sensors become operational. RSSSM data can be accessed at https://doi.org/10.1594/PANGAEA.912597 (Chen, 2020).

scholarly journals Estimating annual water storage variations in medium-scale (2000–10 000 km<sup>2</sup>) basins using microwave-based soil moisture retrievals

2017 ◽  
Vol 21 (3) ◽  
pp. 1849-1862 ◽  
Author(s):  
Wade T. Crow ◽  
Eunjin Han ◽  
Dongryeol Ryu ◽  
Christopher R. Hain ◽  
Martha C. Anderson
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Water Budget ◽  
Surface Soil ◽  
Water Storage ◽  
Microwave Remote Sensing ◽  
The United States ◽  
Surface Soil Moisture ◽  
Medium Scale ◽  
Annual Water

Abstract. Due to their shallow vertical support, remotely sensed surface soil moisture retrievals are commonly regarded as being of limited value for water budget applications requiring the characterization of temporal variations in total terrestrial water storage (dS ∕ dt). However, advances in our ability to estimate evapotranspiration remotely now allow for the direct evaluation of approaches for quantifying dS ∕ dt via water budget closure considerations. By applying an annual water budget analysis within a series of medium-scale (2000–10 000 km2) basins within the United States, we demonstrate that, despite their clear theoretical limitations, surface soil moisture retrievals derived from passive microwave remote sensing contain statistically significant information concerning dS ∕ dt. This suggests the possibility of using (relatively) higher-resolution microwave remote sensing products to enhance the spatial resolution of dS ∕ dt estimates acquired from gravity remote sensing.

Mapping surface soil moisture over wheat crops in southern Mediterranean regions using the backscattering coefficient and the interferometric coherence derived from Sentinel-1

2020 ◽  
Author(s):  
Nadia Ouaadi ◽  
Lionel Jarlan ◽  
Jamal Ezzahar ◽  
Saïd Khabba ◽  
Mehrez Zribi ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Temporal Resolution ◽  
Surface Soil ◽  
Cloud Model ◽  
Microwave Remote Sensing ◽  
Radar Data ◽  
Optical Data ◽  
Surface Soil Moisture ◽  
Backscattering Coefficient ◽  
New Approach

&lt;p&gt;High spatial and temporal resolution products of Sentinel-1 are used for surface soil moisture (SSM) mapping over wheat fields in semi-arid areas. Within these regions, monitoring the water-use is a critical aspect for optimizing the management of the limited water resources via irrigation monitoring. SSM is one of the principal quantities affecting microwave remote sensing. This sensitivity has been exploited to estimate SSM from radar data, which has the advantages of providing data independent of illumination and weather conditions. In addition, with the use of Sentinel-1 products, the spatial and temporal resolution is greatly improved. Within this context, the main objective of this work is estimate SSM over wheat fields using an approach based on the use of C-band Sentinel-1 radar data only. Over the study site, field measurement are collected during 2016-2017 and 2017-2018 growing seasons over two fields of winter wheat with drip irrigation located in the Haouz plain in the center of Morocco. Data of other sites in Morocco and Tunisia are taken for validation purposes. The validation database contains a total number of 20 plots divided between irrigated and rainfed wheat plots. Two different information extracted from Sentinel-1 products are used: the backscattering coefficient and the interferometric coherence. A total number of 408 GRD and 419 SLC images were processed for computing the backscattering coefficient and the interferometric coherence, respectively. The analysis of Sentinel-1 time series over the study site show that coherence is sensitive to the development of wheat, while the backscatter coefficient is widely linked to changes in surface soil moisture. Later on, the Water Cloud Model coupled with the Oh et al, 1992 model were used for better understand the backscattering mechanism of wheat canopies. The coupled model is calibrated and validated over the study site and it proved to goodly enough reproduce the Sentinel-1 backscatter with RMSE ranging from 1.5 to 2.52 dB for VV and VH using biomass as a descriptor of wheat. On the other side, the analysis show that coherence is well correlated to biomass. Thus, the calibrated model is used in an inversion algorithm to retrieve SSM using the Sentinel-1 backscatter and coherence as inputs. The results of inversion show that the proposed new approach is able to retrieve the surface soil moisture at 35.2&amp;#176; for VV, with R=0.82, RMSE=0.05m&lt;sup&gt;3/&lt;/sup&gt;m&lt;sup&gt;3 &lt;/sup&gt;and no bias. Using the validation database of Morocco and Tunisia, R is always greater than 0.7 and RMSE and bias are less than 0.008 m&lt;sup&gt;3/&lt;/sup&gt;m&lt;sup&gt;3&lt;/sup&gt; and 0.03 m&lt;sup&gt;3/&lt;/sup&gt;m&lt;sup&gt;3&lt;/sup&gt;, respectively even that the incidence angle is higher (40&amp;#176;). In order to assess its quality, the approach is compared to four SSM retrieval methods that use radar and optical data in empirical and semi-empirical approaches. Results indicate that the proposed approach shows an improvement of SSM retrieval between 17% and 42% compared to other methods. Finally, the validated new approach is used for SSM mapping, with a spatial resolution of 10*10 m, over irrigated perimeters of wheat in Morocco.&lt;/p&gt;

Microwave Observations of Soil Hydrology

1999 ◽  
Author(s):  
T. J. Jackson ◽  
E. T. Engman
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Radiant Energy ◽  
Microwave Remote Sensing ◽  
Heat Fluxes ◽  
Soil Parameters ◽  
Soil Hydrology ◽  
Surface Soil Moisture ◽  
Spatially Distributed

The upper few centimeters of the soil are extremely important because they are the interface between soil science and land-atmosphere research and are also the region of the greatest amount of organic material and biological activity (Wei, 1995). Passive microwave remote sensing can provide a measurement of the surface soil moisture for a range of cover conditions within reasonable error bounds (Jackson and Schmugge, 1989). Since spatially distributed and multitemporal observations of surface soil moisture are rare, the use of these data in hydrology and other disciplines has not been fully explored or developed. The ability to observe soil moisture frequently over large regions could significantly improve our ability to predict runoff and to partition incoming radiant energy into latent and sensible heat fluxes at a variety of scales up to those used in global circulation models. Temporal observation of surface soil moisture may also provide the information needed to determine key soil parameters, such as saturated conductivity (Ahuja et al., 1993). These sensors provide a spatially integrated measurement that may aid in understanding the upscaling of essential soil parameters from point observations. Some specific issues in soil hydrology that could be addressed with remotely sensed observations as described above include (Wei, 1995): (1) criteria for soil mapping based on spatial and temporal variance structures of state variables, (2) identifying scales of observation, (3) determining soil physical properties within profiles based on surface observations, (4) quantifying correlation lengths of soil moisture in time and space relative to precipitation and evaporation, (5) examining the covariance structure between soil water properties and those associated with water and heat fluxes at the land-atmosphere boundary at various scales, and (6) determining if vertical and horizontal fluxes of energy and matter below the surface can be ascertained from surface soil moisture distributions. In this chapter, the basis of microwave remote sensing of soil moisture will be presented along with the advantages and disadvantages of different techniques. Currently available sensor systems will be described.

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