Satellite remote sensing applications for surface soil moisture monitoring: A review

2009 ◽  
Vol 3 (2) ◽  
pp. 237-247 ◽  
Author(s):  
Lingli Wang ◽  
John J. Qu
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Satellite Remote Sensing ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Sensing Applications ◽  
Remote Sensing Applications ◽  
Soil Moisture Monitoring

scholarly journals ADVANCES IN SOIL MOISTURE RETRIEVAL FROM NEAR-SURFACE MEASUREMENTS USING SATELLITE REMOTE SENSING

2018 ◽  
Vol XLII-5 ◽  
pp. 861-869
Author(s):  
R. Prajapati ◽  
D. Chakraborty ◽  
V. Kumar
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Moisture Content ◽  
Spatial Resolution ◽  
Satellite Remote Sensing ◽  
Soil Moisture Content ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Generate Surface ◽  
Fine Resolution

<p><strong>Abstract.</strong> Soil moisture influences numerous environmental processes occurring over large spatial and temporal scales. It profoundly influences the hydrological and meteorological activity together with climate predictions and hazard analysis. Space-borne sensors are capable of retrieving the surface soil moisture over a region on a regular basis. Latent heat measurements of soil, reflectance based methods, microwave measurements and synergistic approaches are some of the techniques used since long for providing soil moisture estimates over regional and global scales. Due to the dynamic interaction of soil with crops, retrieval of surface soil moisture is always challenging. This paper gives a brief overview of advance in soil moisture retrieval techniques, and an attempt to generate surface soil moisture from fine-resolution satellite remote sensing data. The optical remote sensing explores the linear relationship between land surface reflectance and soil moisture content, and through development of empirical spectral vegetation indices. Another way to estimate soil moisture emerged by measuring amplitude of diurnal temperature, which is closely related to thermal conductivity and heat capacity of soil. Emergence of radiometric satellite measurements at fine resolution has reached at a higher level of technology these days. Microwave remote sensing techniques have a long legacy of providing surface soil moisture estimates with reasonable accuracy. The SMOS (Soil Moisture and Ocean Salinity) and SMAP (Soil Moisture Passive and Active) missions launched in 2009 and 2015 respectively, are completely dedicated for providing soil moisture at global scale with a spatial resolution of 35<span class="thinspace"></span>km &amp; 3&amp;ndash;40<span class="thinspace"></span>km. These soil moisture products, however, provides data at highly coarser spatial resolution. The launch of Sentinels gave insight by providing active radar and optical data at higher resolution (&amp;sim;10<span class="thinspace"></span>m). Sentinel-1 is the first SAR (Synthetic Aperture Radar) constellation having 6-day revisit time providing data in C-band with dual polarisations. However, no algorithm or methodology is available to generate surface soil moisture product at a finer resolution from dual polarisations. Sentinel-1 data has been used to generate regional surface soil moisture image through modelling. The same has been also used for generating surface soil moisture map of IARI farm at New Delhi. Dubois, a bare surface model, was tested for its suitability for surface soil moisture retrieval of the farm. In addition, radar- based Soil moisture (SM) proxy method was used over Sentinel-1 data for the month of July 2018, and validated through actual surface soil moisture (gravimetric) measurements. Results were satisfactory for a range of 4&amp;ndash;16<span class="thinspace"></span>m<sup>3</sup><span class="thinspace"></span>m<sup>&amp;minus;3</sup> of soil moisture, with coefficient of determination (R<sup>2</sup>) as 0.45, RMSE of 2.35 and a p-value of 0.005. However, over a higher range of soil moisture (21&amp;ndash;33<span class="thinspace"></span>m<sup>3</sup><span class="thinspace"></span>m<sup>&amp;minus;3</sup>), which occurred after the rainfall, the R<sup>2</sup> value reduced to 0.22 with larger RMSE. Results suggested that SM-proxy approach might work well for a limited range (drier part) of soil moisture content, and not for the wet soil.</p>

Integrative soil moisture monitoring in Switzerland for a better preparedness for projected drying trends

2021 ◽  
Author(s):  
Dominik Michel ◽  
Martin Hirschi ◽  
Sonia I. Seneviratne
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Real Time ◽  
Surface Soil ◽  
Microwave Remote Sensing ◽  
Data Availability ◽  
Climate Projections ◽  
Surface Soil Moisture ◽  
Soil Moisture Monitoring

&lt;p&gt;Climate projections indicate an increasing risk of dry and hot episodes in Central Europe, including in Switzerland. However, models display a large spread in projections of changes in summer drying, highlighting the importance of related observations to evaluate climate models and constrain projections. Land hydrological variables play an essential role for these projections. This is particularly the case for soil moisture and land evaporation, which are directly affecting the development of droughts and heatwaves in both present and future.&lt;/p&gt;&lt;p&gt;The recent 2020 spring as well as 2015 and 2018 summer droughts in Switzerland have highlighted the importance of monitoring and assessing changes of soil moisture and land evaporation, which are strongly related to drought impacts on agriculture, forestry, and ecosystems. The country was affected by major drought and heatwave conditions in 2015 and 2018. While the meteorological conditions started to recover at the end of the summer, the soil moisture conditions (and runoff) continued to be anomalously low for most of the fall. This illustrates the decoupling between meteorological drought and soil moisture drought conditions related to the intrinsic memory of the soil.&lt;/p&gt;&lt;p&gt;The only Switzerland-wide soil moisture monitoring programme currently in place is the SwissSMEX (Swiss Soil Moisture Experiment) measurement network. It was initiated in 2008 and comprises 19 soil moisture measurement profiles at 17 different sites (grassland, forest and arable land). Since 2017, seven grassland SwissSMEX sites were complemented with land evaporation measurements from mini-lysimeters.&lt;/p&gt;&lt;p&gt;First, a quality assessment and inter-comparison of the in-situ soil moisture and land evaporation observations at 12 grassland sites revealed substantial discrepancies between different sensor types in terms of absolute values and data availability. A standard procedure for processing and interpreting the SwissSMEX data is thus being established. Second, analyses have been carried out comparing the SwissSMEX measurements with gridded remote-sensing and reanalysis products that provide near real time soil moisture data. In particular, the European Space Agency (ESA) Climate Change Initiative (CCI) surface soil moisture product (ESA-CCI soil moisture) as well as the new ECMWF reanalysis ERA5 are considered. The seasonal evolution of the soil moisture anomalies (with respect to the long-term mean) show for 2020 two pronounced phases of dryness. These are consistently represented in SwissSMEX in-situ observations and ERA5. Also the other recent drought events of 2015 and 2018 show a similar temporal evolution in both datasets. The response of ESA-CCI surface soil moisture is less pronounced, more variable and also dependent on the measurement methodology, i.e., active or passive microwave remote sensing.&lt;/p&gt;&lt;p&gt;These first analyses provide useful insights in order to provide near-real time monitoring, enhance process understanding at the national scale and a better preparedness for future droughts.&lt;/p&gt;

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.

scholarly journals Establishing an Empirical Model for Surface Soil Moisture Retrieval at the U.S. Climate Reference Network Using Sentinel-1 Backscatter and Ancillary Data

2020 ◽  
Vol 12 (8) ◽  
pp. 1242 ◽  
Author(s):  
Sumanta Chatterjee ◽  
Jingyi Huang ◽  
Alfred E. Hartemink
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Soil Properties ◽  
Soil Water ◽  
Surface Soil ◽  
Reference Network ◽  
Surface Soil Moisture ◽  
Ancillary Data ◽  
Terrain Parameters ◽  
The U.S

Progress in sensor technologies has allowed real-time monitoring of soil water. It is a challenge to model soil water content based on remote sensing data. Here, we retrieved and modeled surface soil moisture (SSM) at the U.S. Climate Reference Network (USCRN) stations using Sentinel-1 backscatter data from 2016 to 2018 and ancillary data. Empirical machine learning models were established between soil water content measured at the USCRN stations with Sentinel-1 data from 2016 to 2017, the National Land Cover Dataset, terrain parameters, and Polaris soil data, and were evaluated in 2018 at the same USCRN stations. The Cubist model performed better than the multiple linear regression (MLR) and Random Forest (RF) model (R2 = 0.68 and RMSE = 0.06 m3 m-3 for validation). The Cubist model performed best in Shrub/Scrub, followed by Herbaceous and Cultivated Crops but poorly in Hay/Pasture. The success of SSM retrieval was mostly attributed to soil properties, followed by Sentinel-1 backscatter data, terrain parameters, and land cover. The approach shows the potential for retrieving SSM using Sentinel-1 data in a combination of high-resolution ancillary data across the conterminous United States (CONUS). Future work is required to improve the model performance by including more SSM network measurements, assimilating Sentinel-1 data with other microwave, optical and thermal remote sensing products. There is also a need to improve the spatial resolution and accuracy of land surface parameter products (e.g., soil properties and terrain parameters) at the regional and global scales.

scholarly journals Machine Learning to Estimate Surface Soil Moisture from Remote Sensing Data

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3223
Author(s):  
Hamed Adab ◽  
Renato Morbidelli ◽  
Carla Saltalippi ◽  
Mahmoud Moradian ◽  
Gholam Abbas Fallah Ghalhari
Keyword(s):  
Machine Learning ◽  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Soil ◽  
Remote Sensing Data ◽  
Surface Soil Moisture ◽  
Machine Learning Methods ◽  
Land Use Types ◽  
Semi Arid Region ◽  
Semi Arid

Soil moisture is an integral quantity parameter in hydrology and agriculture practices. Satellite remote sensing has been widely applied to estimate surface soil moisture. However, it is still a challenge to retrieve surface soil moisture content (SMC) data in the heterogeneous catchment at high spatial resolution. Therefore, it is necessary to improve the retrieval of SMC from remote sensing data, which is important in the planning and efficient use of land resources. Many methods based on satellite-derived vegetation indices have already been developed to estimate SMC in various climatic and geographic conditions. Soil moisture retrievals were performed using statistical and machine learning methods as well as physical modeling techniques. In this study, an important experiment of soil moisture retrieval for investigating the capability of the machine learning methods was conducted in the early spring season in a semi-arid region of Iran. We applied random forest (RF), support vector machine (SVM), artificial neural network (ANN), and elastic net regression (EN) algorithms to soil moisture retrieval by optical and thermal sensors of Landsat 8 and knowledge of land-use types on previously untested conditions in a semi-arid region of Iran. The statistical comparisons show that RF method provided the highest Nash–Sutcliffe efficiency value (0.73) for soil moisture retrieval covered by the different land-use types. Combinations of surface reflectance and auxiliary geospatial data can provide more valuable information for SMC estimation, which shows promise for precision agriculture applications.

Sign in / Sign up

Export Citation Format

Share Document