Retrieval of Soil Moisture Content Based on a Modified Hapke Photometric Model: A Novel Method Applied to Laboratory Hyperspectral and Sentinel-2 MSI Data

Soil moisture is the crucial carrier of the global hydrologic cycle and the dynamic energy balance regulation process. Therefore, it is of great significance to monitor surface soil moisture content (SMC) accurately for the study of the natural ecological environment. The Hapke model is the most widely used photometric model in soil remote sensing research, but the development of this model is limited by the lack of valid multi–angular data. The main innovations of this paper have two aspects: (1) A novel soil moisture retrieval approach based on the Hapke (SMR–Hapke) model is derived by exploring the relationship between single scattering albedo (SSA) and SMC on the optical bands from 400 to 2400 nm. The performance of the proposed model was verified on a dataset consisting of four different soil samples, and the experimental results indicated that the inverted soil moisture from SMR–Hapke model coincided with the measurement values, with the R2 being generally more than 0.9 in the solar domain. (2) The SMR–Hapke model has been reduced to a linear form on the SWIR field and a physically-based normalized difference soil moisture index N D S M I H a p k e has been proposed. Based on the laboratory-based hyperspectral data, we compared the performance of N D S M I H a p k e with other traditional soil moisture indices using linear regression analysis, and the results demonstrate that the proposed N D S M I H a p k e had a great potential for estimating SMC with R2 values of 0.88. Finally, high–resolution SMC map was produced by combining the Sentinel–2 MSI data with N D S M I H a p k e . This study provides a novel extended Hapke model for the estimation of surface soil moisture content.

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Remote Soil Moisture Measurement from Drone-Borne Reflectance Spectroscopy: Applications to Hydroperiod Measurement in Desert Playas

Remote Sensing ◽

10.3390/rs13051035 ◽

2021 ◽

Vol 13 (5) ◽

pp. 1035

Author(s):

Joseph S. Levy ◽

Jessica T. E. Johnson

Keyword(s):

Soil Moisture ◽

Moisture Content ◽

Soil Moisture Content ◽

Surface Soil ◽

Clay Content ◽

Test Site ◽

Surface Soil Moisture ◽

Remotely Sensed Data ◽

Moisture Index ◽

Spatial And Temporal Scales

The extent, timing, and magnitude of soil moisture in wetlands (the hydropattern) is a primary physical control on biogeochemical processes in desert environments. However, determining playa hydropatterns is challenged by the remoteness of desert basin sites and by the difficulty in determining soil moisture from remotely sensed data at fine spatial and temporal scales (hundreds of meters to kilometers, and hours to days). Therefore, we developed a new, reflectance-based soil moisture index (continuum-removed water index, or CRWI) that can be determined via hyperspectral imaging from drone-borne platforms. We compared its efficacy at remotely determining soil moisture content to existing hyperspectral and multispectral soil moisture indices. CRWI varies linearly with in situ soil moisture content (R2 = 0.89, p < 0.001) and is comparatively insensitive to soil clay content (R2 = 0.4, p = 0.01), soil salinity (R2 = 0.82, p < 0.001), and soil grain size distribution (R2 = 0.67, p < 0.001). CRWI is negatively correlated with clay content, indicating it is not sensitive to hydrated mineral absorption features. CRWI has stronger correlation with surface soil moisture than other hyperspectral and multispectral indices (R2 = 0.69, p < 0.001 for WISOIL at this site). Drone-borne reflectance measurements allow monitoring of soil moisture conditions at the Alvord Desert playa test site over hectare-scale soil plots at measurement cadences of minutes to hours. CRWI measurements can be used to determine surface soil moisture at a range of desert sites to inform management decisions and to better reveal ecosystem processes in water-limited environments.

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

ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences ◽

10.5194/isprs-archives-xlii-5-861-2018 ◽

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

Abstract. 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 35km & 3&ndash;40km. 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 (&sim;10m). 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&ndash;16m3m&minus;3 of soil moisture, with coefficient of determination (R2) as 0.45, RMSE of 2.35 and a p-value of 0.005. However, over a higher range of soil moisture (21&ndash;33m3m&minus;3), which occurred after the rainfall, the R2 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.

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

Sensors ◽

10.3390/s19030589 ◽

2019 ◽

Vol 19 (3) ◽

pp. 589 ◽

Cited By ~ 14

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.

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

Remote Sensing ◽

10.3390/rs13112099 ◽

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.

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Simulation of Sentinel-2 data using Hyperspectral Data for Bare Surface Soil Moisture Estimation

10.1109/agro-geoinformatics50104.2021.9530321 ◽

2021 ◽

Author(s):

Jayantrao Mohite ◽

Suryakant Sawant ◽

Ankur Pandit ◽

Srinivasu Pappula

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Hyperspectral Data ◽

Surface Soil Moisture ◽

Moisture Estimation ◽

Bare Surface ◽

Sentinel 2

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MARMIT: A multilayer radiative transfer model of soil reflectance to estimate surface soil moisture content in the solar domain (400–2500 nm)

Remote Sensing of Environment ◽

10.1016/j.rse.2018.07.031 ◽

2018 ◽

Vol 217 ◽

pp. 1-17 ◽

Cited By ~ 10

Author(s):

A. Bablet ◽

P.V.H. Vu ◽

S. Jacquemoud ◽

F. Viallefont-Robinet ◽

S. Fabre ◽

...

Keyword(s):

Soil Moisture ◽

Radiative Transfer ◽

Moisture Content ◽

Soil Moisture Content ◽

Surface Soil ◽

Radiative Transfer Model ◽

Transfer Model ◽

Surface Soil Moisture ◽

Soil Reflectance

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

Plant Soil and Environment ◽

10.17221/374/2010-pse ◽

2011 ◽

Vol 57 (No. 9) ◽

pp. 409-417 ◽

Cited By ~ 19

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 × 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° direction than that in 90° 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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