scholarly journals A Machine Learning-Based Approach for Surface Soil Moisture Estimations with Google Earth Engine

2021 ◽  
Vol 13 (11) ◽  
pp. 2099
Author(s):  
Felix Greifeneder ◽  
Claudia Notarnicola ◽  
Wolfgang Wagner
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Moisture Content ◽  
Spatial Resolution ◽  
Soil Moisture Content ◽  
Surface Soil ◽  
High Spatial Resolution ◽  
Google Earth ◽  
Surface Soil Moisture ◽  
Google Earth Engine

Due to its relation to the Earth’s climate and weather and phenomena like drought, flooding, or landslides, knowledge of the soil moisture content is valuable to many scientific and professional users. Remote-sensing offers the unique possibility for continuous measurements of this variable. Especially for agriculture, there is a strong demand for high spatial resolution mapping. However, operationally available soil moisture products exist with medium to coarse spatial resolution only (≥1 km). This study introduces a machine learning (ML)—based approach for the high spatial resolution (50 m) mapping of soil moisture based on the integration of Landsat-8 optical and thermal images, Copernicus Sentinel-1 C-Band SAR images, and modelled data, executable in the Google Earth Engine. The novelty of this approach lies in applying an entirely data-driven ML concept for global estimation of the surface soil moisture content. Globally distributed in situ data from the International Soil Moisture Network acted as an input for model training. Based on the independent validation dataset, the resulting overall estimation accuracy, in terms of Root-Mean-Squared-Error and R², was 0.04 m3·m−3 and 0.81, respectively. Beyond the retrieval model itself, this article introduces a framework for collecting training data and a stand-alone Python package for soil moisture mapping. The Google Earth Engine Python API facilitates the execution of data collection and retrieval which is entirely cloud-based. For soil moisture retrieval, it eliminates the requirement to download or preprocess any input datasets.

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>

Development of a deep learning-based method for estimating surface soil moisture at high spatial resolution from Sentinel-1 satellite data

2021 ◽  
Author(s):  
Nicklas Simonsen ◽  
Zheng Duan
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Deep Learning ◽  
High Resolution ◽  
Moisture Content ◽  
Spatial Resolution ◽  
Soil Moisture Content ◽  
Surface Soil ◽  
Surface Soil Moisture

&lt;p&gt;Soil moisture content is an important hydrological and climatic variable with applications in a wide range of domains. The high spatial variability of soil moisture cannot be well captured from conventional point-based in-situ measurements. Remote sensing offers a feasible way to observe spatial pattern of soil moisture from regional to global scales. Microwave remote sensing has long been used to estimate Surface Soil Moisture Content (SSMC) at lower spatial resolutions (&gt;1km), but few accurate options exist in the higher spatial resolution (&lt;1km) domain. This study explores the capabilities of deep learning in the high-resolution domain of remotely sensed SSMC by using a Convolutional Neural Network (CNN) to estimate SSMC from Sentinel-1 acquired Synthetic Aperture Radar (SAR) imagery. The developed model incorporates additional SSMC predictors such as Normalized Difference Vegetation Index (NDVI), temperature, precipitation, and soil type to yield a more accurate estimation than traditional empirical formulas that focus solely on the conversion of backscatter signals to relative soil moisture. This also makes the developed model less sensitive to site-specific conditions and increases the model applicability outside the training domain. The model is developed and tested with in-situ soil moisture measurements in Denmark from a dense network maintained by HOBE (Danish Hydrological Observatory). The unique advantage of the developed model is its transferability across climate zones, which has been historically absent in many prior models. This would open up opportunities for high-resolution soil moisture mapping through remote sensing in areas with relatively few soil moisture gauges. A reliable high-resolution soil moisture platform at good temporal resolution would allow for more precise erosion modelling, flood forecasting, drought monitoring, and precision agriculture.&lt;/p&gt;

Generation of spatially complete and daily continuous surface soil moisture of high spatial resolution

2019 ◽  
Vol 233 ◽  
pp. 111364 ◽  
Author(s):  
Di Long ◽  
Liangliang Bai ◽  
La Yan ◽  
Caijin Zhang ◽  
Wenting Yang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Spatial Resolution ◽  
Surface Soil ◽  
High Spatial Resolution ◽  
Surface Soil Moisture ◽  
Continuous Surface

Exploring the use of machine-learning techniques to integrate ground- and remote sensing-based observations for efficient near-surface soil moisture mapping

2020 ◽  
Author(s):  
Sarah Schönbrodt-Stitt ◽  
Paolo Nasta ◽  
Nima Ahmadian ◽  
Markus Kurtenbach ◽  
Christopher Conrad ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Spatial Resolution ◽  
Surface Soil ◽  
Bare Soil ◽  
Small Scale ◽  
Surface Soil Moisture ◽  
Near Surface ◽  
Campania Region

&lt;p&gt;Mapping near-surface soil moisture (&lt;em&gt;&amp;#952;&lt;/em&gt;) is of tremendous relevance for a broad range of environment-related disciplines and meteorological, ecological, hydrological and agricultural applications. Globally available products offer the opportunity to address &lt;em&gt;&amp;#952;&lt;/em&gt; in large-scale modelling with coarse spatial resolution such as at the landscape level. However, &lt;em&gt;&amp;#952;&lt;/em&gt; estimation at higher spatial resolution is of vital importance for many small-scale applications. Therefore, we focus our study on a small-scale catchment (MFC2) belonging to the &amp;#8220;Alento&amp;#8221; hydrological observatory, located in southern Italy (Campania Region). The goal of this study is to develop new machine-learning approaches to estimate high grid-resolution (about 17 m cell size) &lt;em&gt;&amp;#952;&lt;/em&gt; maps from mainly backscatter measurements retrieved from C-band Synthetic Aperture Radar (SAR) based on Sentinel-1 (S1) images and from gridded terrain attributes. Thus, a workflow comprising a total of 48 SAR-based &lt;em&gt;&amp;#952;&lt;/em&gt; patterns estimated for 24 satellite overpass dates (revisit time of 6 days) each with ascendant and descendent orbits will be presented. To enable for the mapping, SAR-based &lt;em&gt;&amp;#952;&lt;/em&gt; data was calibrated with in-situ measurements carried out with a portable device during eight measurement campaigns at time of satellite overpasses (four overpass days in total with each ascendant and descendent satellite overpasses per day in November 2018). After the calibration procedure, data validation was executed from November 10, 2018 till March 28, 2019 by using two stationary sensors monitoring &lt;em&gt;&amp;#952;&lt;/em&gt; at high-temporal (1-min recording time). The specific sensor locations reflected two contrasting field conditions, one bare soil plot (frequently kept clear, without disturbance of vegetation cover) and one non-bare soil plot (real-world condition). Point-scale ground observations of &lt;em&gt;&amp;#952;&lt;/em&gt; were compared to pixel-scale (17 m &amp;#215; 17 m), SAR-based &lt;em&gt;&amp;#952;&lt;/em&gt; estimated for those pixels corresponding to the specific positions of the stationary sensors. Mapping performance was estimated through the root mean squared error (RMSE). For a short-term time series of &lt;em&gt;&amp;#952;&lt;/em&gt; (Nov 2018) integrating 136 in situ, sensor-based &lt;em&gt;&amp;#952;&lt;/em&gt; (&lt;em&gt;&amp;#952;&lt;/em&gt;&lt;sub&gt;insitu&lt;/sub&gt;) and 74 gravimetric-based &lt;em&gt;&amp;#952;&lt;/em&gt; (&lt;em&gt;&amp;#952;&lt;/em&gt;&lt;sub&gt;gravimetric&lt;/sub&gt;) measurements during a total of eight S1 overpasses, mapping performance already proved to be satisfactory with RMSE=0.039 m&amp;#179;m&lt;sup&gt;-&lt;/sup&gt;&amp;#179; and R&amp;#178;=0.92, respectively with RMSE=0.041 m&amp;#179;m&lt;sup&gt;-&lt;/sup&gt;&amp;#179; and R&amp;#178;=0.91. First results further reveal that estimated satellite-based &lt;em&gt;&amp;#952;&lt;/em&gt; patterns respond to the evolution of rainfall. With our workflow developed and results, we intend to contribute to improved environmental risk assessment by assimilating the results into hydrological models (e.g., HydroGeoSphere), and to support future studies on combined ground-based and SAR-based &lt;em&gt;&amp;#952;&lt;/em&gt; retrieval for forested land (future missions operating at larger wavelengths e.g. NISARL-band, Biomass P-band sensors).&lt;/p&gt;

scholarly journals Soil Moisture Retrival Based on Sentinel-1 Imagery under Sparse Vegetation Coverage

Sensors ◽  
2019 ◽  
Vol 19 (3) ◽  
pp. 589 ◽  
Author(s):  
Shuai Huang ◽  
Jianli Ding ◽  
Jie Zou ◽  
Bohua Liu ◽  
Junyong Zhang ◽  
...  
Keyword(s):  
Spatial Distribution ◽  
Soil Moisture ◽  
Moisture Content ◽  
Vegetation Cover ◽  
Soil Moisture Content ◽  
Surface Soil ◽  
Retrieval Model ◽  
Surface Soil Moisture ◽  
Microwave Scattering ◽  
Sparse Vegetation

Soil moisture is an important aspect of heat transfer process and energy exchange between land-atmosphere systems, and it is a key link to the surface and groundwater circulation and land carbon cycles. In this study, according to the characteristics of the study area, an advanced integral equation model was used for numerical simulation analysis to establish a database of surface microwave scattering characteristics under sparse vegetation cover. Thus, a soil moisture retrieval model suitable for arid area was constructed. The results were as follows: (1) The response of the backscattering coefficient to soil moisture and associated surface roughness is significantly and logarithmically correlated under different incidence angles and polarization modes, and, a database of microwave scattering characteristics of arid soil surface under sparse vegetation cover was established. (2) According to the Sentinel-1 radar system parameters, a model for retrieving spatial distribution information of soil moisture was constructed; the soil moisture content information was extracted, and the results were consistent with the spatial distribution characteristics of soil moisture in the same period in the research area. (3) For the 0–10 cm surface soil moisture, the correlation coefficient between the simulated value and the measured value reached 0.8488, which means that the developed retrieval model has applicability to derive surface soil moisture in the oasis region of arid regions. This study can provide method for real-time and large-scale detection of soil moisture content in arid areas.

Down-scaling MODIS operational vegetation products with machine learning and fused gap-free high resolution reflectance data in Google Earth Engine

2020 ◽  
Author(s):  
Álvaro Moreno Martínez ◽  
Emma Izquierdo Verdiguier ◽  
Gustau Camps Valls ◽  
Marco Maneta ◽  
Jordi Muñoz Marí ◽  
...  
Keyword(s):  
Machine Learning ◽  
Spatial Resolution ◽  
High Spatial Resolution ◽  
Google Earth ◽  
Radiative Transfer Model ◽  
Transfer Model ◽  
Learning Approaches ◽  
Area Index ◽  
Uncertainty Estimates ◽  
Google Earth Engine

&lt;p&gt;Among Essential Climate Variables (ECVs) for global climate observation, the Leaf Area Index (LAI) and the Fraction of Absorbed Photosynthetically Active Radiation (FAPAR) are the most widely used to study land vegetated surfaces. The NASA&amp;#8217;s Moderate&amp;#160; Resolution Imaging Spectro-radiometer (MODIS) is a key instrument aboard the Terra and Aqua platforms and allows to estimate both biophysical variables at coarse resolution (500 m) and global scales. The MODIS operational algorithm to retrieve LAI and FAPAR (MOD15/MYD15/MCD15) uses a physically-based radiative transfer model (RTM) to compute their estimates with corrected surface spectral information content. This algorithm has been heavily validated and compared with field measurements and other sensors but, so far, no equivalent products at high spatial resolution and continental or global scales are routinely produced.&amp;#160;&lt;/p&gt;&lt;p&gt;Here, we introduce and validate a methodology to create a set of high spatial resolution LAI/FAPAR products by learning the MODIS RTM using advanced machine learning approaches and gap filled Landsat surface reflectances. The latter are smoothed and gap-filled by the HIghly Scalable Temporal Adaptive Reflectance Fusion Model (HISTARFM). HISTARTFM has a great potential to improve the original Landsat reflectances by reducing their noise and recovering missing data due to cloud contamination. In addition, HISTARFM runs very fast in cloud computing platforms such as Google Earth Engine (GEE) and provides uncertainty estimates which can be propagated through the models. These estimates allow to compute numerical uncertainties beyond the typical and qualitative control information layers provided in operational products such as the MODIS LAI/FAPAR. The introduced high spatial resolution biophysical products here could be of interest to the users to achieve the needed levels of spatial detail to adequately monitor croplands and heterogeneously vegetated landscapes.&lt;/p&gt;&lt;p&gt;&amp;#160;&lt;/p&gt;

scholarly journals Spatial variability and patterns of surface soil moisture in a field plot of karst area in southwest China

2011 ◽  
Vol 57 (No. 9) ◽  
pp. 409-417 ◽  
Author(s):  
J.G. Zhang ◽  
H.S. Chen ◽  
Y.R. Su ◽  
X.L. Kong ◽  
W. Zhang ◽  
...  
Keyword(s):  
Soil Moisture ◽  
Moisture Content ◽  
Rainy Season ◽  
Soil Moisture Content ◽  
Southwest China ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Field Plot ◽  
Rock Outcrops ◽  
Karst Depression

A field plot (100 m &times; 50 m) was chosen in a karst depression area of Huanjiang County, Guangxi Province of southwest China, with the aim of characterizing the variability and patterns of upper 15 cm soil moisture. Soil moisture content was measured at 5 m intervals by gravimetric method during dry and rainy seasons in 2005. Results indicated that the surface soil moisture presented a strong spatial dependence at the sampling times in the field scale. The variability of soil moisture by CV values and sill decreased with the increasing mean field soil moisture content either in dry or rainy season. In the dry season, mean soil moisture had a little influence on the sill owing to the previous tillage. But, in the rainy season, a heavy rain event could decrease the variability of soil moisture. The anisotropy characteristics were found that the variance was lower in 0&deg; direction than that in 90&deg; direction based on the northeast axis, and the range had opposite trend except for the sampling on March 15, 2005. The mosaic patterns of soil moisture exhibited the variability and its anisotropy visually. The rainfall (mean soil moisture), topography and micro-relief (rock outcrops) had important influence on the variability of soil moisture. To better understand the variability of soil moisture in the karst depression area, more soil samples should be required in the dry season and in a field with more rock outcrops.

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