scholarly journals ESTIMATION OF SOIL MOISTURE AND EARTH’S SURFACE TEMPERATURE USING LANDSAT-8 SATELLITE DATA

2019 ◽  
Vol XLII-4/W18 ◽  
pp. 327-330
Author(s):  
M. Entezari ◽  
A. Esmaeily ◽  
S. Niazmardi
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Moisture Content ◽  
Surface Temperature ◽  
Soil Moisture Content ◽  
Surface Soil ◽  
Landsat 8 ◽  
Surface Soil Moisture ◽  
Surface Moisture ◽  
Moisture Estimation

Abstract. Soil moisture estimation is essential for optimal water and soil resources management. Surface soil moisture is an important variable in the natural water cycle, which plays an important role in the global equilibrium of water and energy due to its impact on hydrological, ecological and meteorological processes. Soil moisture changes due to the variability of soil characteristics, topography and vegetation in time and place. Soil moisture measurements are performed directly using in situ methods and indirect, by means of transfer functions or remote sensing. Since in-site measurements are usually costly and time-consuming in large areas, we can use methods such as remote sensing to estimate soil moisture at very large scales. The purpose of this study is to estimate soil moisture using surface temperature and vegetation indices for large areas. In this paper, ground temperature was calculated using Landsat-8 thermal band for Mashhad city and was used to estimate the soil moisture content of the study area. The results showed that urban areas had the highest temperature and less humidity at the time of imaging. For this purpose, using the LANDSAT 8 images, the indices were extracted and validated with soil moisture data. In this research, the study area was described and then, using the extracted indices, the estimated model was obtained. The results showed that there is a good correlation between surface soil moisture content with LST and NDVI indices (95%). The results of the verification of the soil moisture estimation model also showed that this model with a mean error of less than 0.001 can predict the surface moisture content, this small amount of error indicates the precision of the proposed model for estimating surface moisture.

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;

scholarly journals Evapotranspiration and evaporation/transpiration partitioning with dual source energy balance models in agricultural lands

2018 ◽  
Vol 380 ◽  
pp. 17-22 ◽  
Author(s):  
Gilles Boulet ◽  
Emilie Delogu ◽  
Sameh Saadi ◽  
Wafa Chebbi ◽  
Albert Olioso ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Water Stress ◽  
Energy Balance ◽  
Surface Temperature ◽  
Surface Soil ◽  
Surface Soil Moisture ◽  
Stress Levels ◽  
Dual Source ◽  
Energy Balance Models

Abstract. EvapoTranspiration (ET) is an important component of the water cycle, especially in semi-arid lands. Its quantification is crucial for a sustainable management of scarce water resources. A way to quantify ET is to exploit the available surface temperature data from remote sensing as a signature of the surface energy balance, including the latent heat flux. Remotely sensed energy balance models enable to estimate stress levels and, in turn, the water status of most continental surfaces. The evaporation and transpiration components of ET are also just as important in agricultural water management and ecosystem health monitoring. Single temperatures can be used with dual source energy balance models but rely on specific assumptions on raw levels of plant water stress to get both components out of a single source of information. Additional information from remote sensing data are thus required, either something specifically related to evaporation (such as surface water content) or transpiration (such as PRI or fluorescence). This works evaluates the SPARSE dual source energy balance model ability to compute not only total ET, but also water stress and transpiration/evaporation components. First, the theoretical limits of the ET component retrieval are assessed through a simulation experiment using both retrieval and prescribed modes of SPARSE with the sole surface temperature. A similar work is performed with an additional constraint, the topsoil surface soil moisture level, showing the significant improvement on the retrieval. Then, a flux dataset acquired over rainfed wheat is used to check the robustness of both stress levels and ET retrievals. In particular, retrieval of the evaporation and transpiration components is assessed in both conditions (forcing by the sole temperature or the combination of temperature and soil moisture). In our example, there is no significant difference in the performance of the total ET retrieval, since the evaporation rate retrieved from the sole surface temperature is already fairly close to the one we can reconstruct from observed surface soil moisture time series, but current work is underway to test it over other plots.

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.

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 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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