A Method to Estimate Surface Soil Moisture and Map the Irrigated Cropland Area Using Sentinel-1 and Sentinel-2 Data

Considering variations in surface soil moisture (SSM) is essential in improving crop yield and irrigation scheduling. Today, most remotely sensed soil moisture products have difficulties in resolving irrigation signals at the plot scale. This study aims to use Sentinel-1 radar backscatter and Sentinel-2 multispectral imagery to estimate SSM at high spatial (10 m) and temporal resolution (at least 5 days) over an agricultural domain. Three supervised machine learning algorithms, multilayer perceptron (MLP), a convolutional neural network (CNN), and linear regression models, were trained to estimate changes in SSM based on the variation in surface reflectance and backscatter over five different crops. Results showed that CNN is the best algorithm as it understands spatial relations and better represents two-dimensional images. Estimated values for SSM were in agreement with in-situ measurements regardless of the crop type, with RMSE=0.0292 (cm3/cm3) and R2=0.92 for the Sentinel-2 derived SSM and RMSE=0.0317 (cm3/cm3) and R2=0.84 for the Sentinel-1 soil moisture data. Moreover, a time series of estimated SSM based on Sentinel-1 (SSM-S1), Sentinel-2 (SSM-S2), and SSM derived from SMAP-Sentinel1 was compared. The developed SSM data showed a significantly higher mean SSM state over irrigated agriculture relative to the rainfed cropland area during the irrigation season. The multiple comparisons (fisher LSD) were tested and found that these two groups are different (pvalue=0.035 in 95% confidence interval). Therefore, by employing the maximum likelihood classification on the SSM data, we managed to map the irrigated agriculture. The overall accuracy of this unsupervised classification is 77%, with a kappa coefficient of 65%.

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Mapping of tank silt application using Sentinel-2 images over the Berambadi catchment (India).

10.5194/egusphere-egu21-8125 ◽

2021 ◽

Author(s):

Cécile Gomez ◽

Dharumarajan Subramanian ◽

Philippe Lagacherie ◽

Jean Riotte ◽

Sylvain Ferrant ◽

...

Keyword(s):

Soil Moisture ◽

Soil Properties ◽

South India ◽

Surface Soil ◽

Soil Surface ◽

Calibration Data ◽

Surface Soil Moisture ◽

Soil Color ◽

Color Changes ◽

Sentinel 2

Mapping soil properties is becoming more and more challenging due to the increase in anthropogenic modification of the landscape, calling for new methods to identify these changes. A striking example of anthropogenic modifications of soil properties is the widespread practice in South India of applying large quantities of silt from dry river dams (or &#8220;tanks&#8221;) to agricultural fields. Whereas several studies have demonstrated the interest of tank silt for soil fertility, no assessment of the actual extent of this age-old traditional practice exists. Over pedological contexts characterized by Vertisol, Ferralsols and Chromic Luvisols in sub-humid and semi-arid Tropical climate, this practice is characterized by an application of black-colored tank silt providing from Vertisol, to red-colored soils such as Ferralsols. The objective of this work was to evaluate the usefulness of Sentinel-2 images for mapping tank silt applications, hypothesizing that observed changes in soil surface color can be a proxy for tank silt application.We used data collected in a cultivated watershed (Berambadi, Karnataka state, South India) including 217 soil surface samples characterized in terms of Munsell color. We used two Sentinel-2 images acquired on February 2017 and April 2017. The surface soil color over each Sentinel-2 image was classified into two-class (&#8220;Black&#8221; and &#8220;Red&#8221; soils). A change of soil color from &#8220;Red&#8221; in February 2017 to &#8220;Black&#8221; in April 2017 was attributed to tank silt application. Soil color changes were analyzed accounting for possible surface soil moisture changes. The proposed methodology was based on a well-balanced Calibration data created from the initial imbalanced Calibration dataset thanks to the Synthetic Minority Over-sampling Technique (SMOTE) methodology, coupled to the Cost-Sensitive Classification And Regression Trees (Cost-Sensitive CART) algorithm. To estimate the uncertainties of i) the two-class classification at each date and ii) the change of soil color from &#8220;Red&#8221; to &#8220;Black&#8221;, a bootstrap procedure was used providing fifty two-class classifications for each Sentinel-2 image.The results showed that 1) the CART method allowed to classify the &#8220;Red&#8221; and &#8220;Black&#8221; soil with overall accuracy around 0.81 and 0.76 from the Sentinel-2 image acquired on February and April 2017, respectively, 2) a tank silt application was identified over 97 fields with high confidence and over 107 fields with medium confidence, based on the bootstrap results and 3) the identified soil color changes are not related to a surface soil moisture change between both dates. With the actual availability of the Sentinel-2 and the past availability of the LANDSAT satellite imageries, this study may open a way toward a simple and accurate method for delivering tank silt application mapping and so to study and possibly quantify retroactively this farmer practice.

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Irrigation and Precipitation Hydrological Consistency with SMOS, SMAP, ESA-CCI, Copernicus SSM1km, and AMSR-2 Remotely Sensed Soil Moisture Products

Remote Sensing ◽

10.3390/rs12223737 ◽

2020 ◽

Vol 12 (22) ◽

pp. 3737

Author(s):

Nicola Paciolla ◽

Chiara Corbari ◽

Ahmad Al Bitar ◽

Yann Kerr ◽

Marco Mancini

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

European Space Agency ◽

Irrigation Season ◽

Surface Soil Moisture ◽

Irrigation Period ◽

Space Agency ◽

Ocean Salinity ◽

Passive Data ◽

Active Data

Numerous Surface Soil Moisture (SSM) products are available from remote sensing, encompassing different spatial, temporal, and radiometric resolutions and retrieval techniques. Notwithstanding this variety, all products should be coherent with water inputs. In this work, we have cross-compared precipitation and irrigation with different SSM products: Soil Moisture Ocean Salinity (SMOS), Soil Moisture Active Passive (SMAP), European Space Agency (ESA) Climate Change Initiative (ESA-CCI) products, Copernicus SSM1km, and Advanced Microwave Scanning Radiometer 2 (AMSR2). The products have been analyzed over two agricultural sites in Italy (Chiese and Capitanata Irrigation Consortia). A Hydrological Consistency Index (HCI) is proposed as a means to measure the coherency between SSM and precipitation/irrigation. Any time SSM is available, a positive or negative consistency is recorded, according to the rainfall registered since the previous measurement and the increase/decrease of SSM. During the irrigation season, some agreements are labeled as “irrigation-driven”. No SSM dataset stands out for a systematic hydrological coherence with the rainfall. Negative consistencies cluster just below 50% in the non-irrigation period and lose 20–30% in the irrigation period. Hybrid datasets perform better (+15–20%) than single-technology measurements, among which active data provide slightly better results (+5–10%) than passive data.

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Synergetic utilization of sentinel-1 SAR and sentinel-2 optical remote sensing data for surface soil moisture estimation for Rupnagar, Punjab, India

Geocarto International ◽

10.1080/10106049.2020.1815865 ◽

2020 ◽

pp. 1-22

Author(s):

Akshar Tripathi ◽

Reet Kamal Tiwari

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Surface Soil ◽

Remote Sensing Data ◽

Optical Remote Sensing ◽

Surface Soil Moisture ◽

Sensing Data ◽

Moisture Estimation ◽

Sentinel 2

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Advances in soil moisture retrieval from multispectral remote sensing using unoccupied aircraft systems and machine learning techniques

Hydrology and Earth System Sciences ◽

10.5194/hess-25-2739-2021 ◽

2021 ◽

Vol 25 (5) ◽

pp. 2739-2758

Author(s):

Samuel N. Araya ◽

Anna Fryjoff-Hung ◽

Andreas Anderson ◽

Joshua H. Viers ◽

Teamrat A. Ghezzehei

Keyword(s):

Machine Learning ◽

Remote Sensing ◽

Soil Moisture ◽

Surface Soil ◽

Potential Evapotranspiration ◽

Learning Algorithms ◽

Variable Importance ◽

Machine Learning Algorithms ◽

Surface Soil Moisture ◽

Multispectral Remote Sensing

Abstract. This study investigates the ability of machine learning models to retrieve the surface soil moisture of a grassland area from multispectral remote sensing carried out using an unoccupied aircraft system (UAS). In addition to multispectral images, we use terrain attributes derived from a digital elevation model and hydrological variables of precipitation and potential evapotranspiration as covariates to predict surface soil moisture. We tested four different machine learning algorithms and interrogated the models to rank the importance of different variables and to understand their relationship with surface soil moisture. All the machine learning algorithms we tested were able to predict soil moisture with good accuracy. The boosted regression tree algorithm was marginally the best, with a mean absolute error of 3.8 % volumetric moisture content. Variable importance analysis revealed that the four most important variables were precipitation, reflectance in the red wavelengths, potential evapotranspiration, and topographic position indices (TPI). Our results demonstrate that the dynamics of soil water status across heterogeneous terrain may be adequately described and predicted by UAS remote sensing and machine learning. Our modeling approach and the variable importance and relationships we have assessed in this study should be useful for management and environmental modeling tasks where spatially explicit soil moisture information is important.

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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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Retrieving Surface Soil Moisture over Wheat-Covered Areas Using Data from Sentinel-1 and Sentinel-2

Water ◽

10.3390/w13141981 ◽

2021 ◽

Vol 13 (14) ◽

pp. 1981

Author(s):

Yan Li ◽

Chengcai Zhang ◽

Weidong Heng

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Cloud Model ◽

Vegetation Indices ◽

Multiple Linear Regression Model ◽

Optical Data ◽

Support Vector ◽

Surface Soil Moisture ◽

Vegetation Fraction ◽

Sentinel 2

Surface soil moisture (SSM) is a major factor that affects crop growth. Combined microwave and optical data have been widely used to improve the accuracy of SSM retrievals. However, the influence of vegetation indices derived from the red-edge spectral bands of multi-spectral optical data on retrieval accuracy has not been sufficiently analyzed. In this study, we retrieved soil moisture from wheat-covered surfaces using Sentinel-1/2 data. First, a modified water cloud model (WCM) was proposed to remove the influence of vegetation from the backscattering coefficient of the radar data. The vegetation fraction (FV) was then introduced in this WCM, and the vegetation water content (VWC) was calculated using a multiple linear regression model. Subsequently, the support vector regression technique was used to retrieve the SSM. This approach was validated using in situ measurements of wheat fields in Hebi, located in northern Henan Province, China. The key findings of this study are: (1) Based on vegetation indices obtained from Sentinel-2 data, the proposed VWC estimation model effectively eliminated the influence of vegetation; (2) Compared with vertical transmit and horizontal receive (VH) polarization, vertical transmit and vertical receive (VV) polarization was better for detecting changes in SSM key phenological phases of wheat; (3) The validated model indicates that the proposed approach successfully retrieved SSM in the study area using Sentinel-1 and Sentinel-2 data.

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Developing pedotransfer functions using Sentinel-2 satellite spectral indices and Machine learning for estimating the surface soil moisture

Journal of Hydrology ◽

10.1016/j.jhydrol.2021.127423 ◽

2022 ◽

pp. 127423

Author(s):

Azadeh Sedaghat ◽

Mahmoud Shabanpour Shahrestani ◽

Ali Akbar Noroozi ◽

Alireza Fallah Nosratabad ◽

Hossein Bayat

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Surface Soil ◽

Pedotransfer Functions ◽

Spectral Indices ◽

Surface Soil Moisture ◽

Sentinel 2

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

Remote Sensing ◽

10.3390/rs12142239 ◽

2020 ◽

Vol 12 (14) ◽

pp. 2239 ◽

Cited By ~ 1

Author(s):

Yuan Zhang ◽

Kun Tan ◽

Xue Wang ◽

Yu Chen

Keyword(s):

Soil Moisture ◽

Moisture Content ◽

Soil Moisture Content ◽

Surface Soil ◽

Linear Regression Analysis ◽

Single Scattering ◽

Hyperspectral Data ◽

Surface Soil Moisture ◽

Moisture Index ◽

Sentinel 2

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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