Integration of soil moisture as an auxiliary parameter for the anchor pixel selection process in SEBAL using Landsat 8 and Sentinel - 1A images

As a basic agricultural parameter in the formation, transformation, and consumption of surface water resources, soil moisture has a very important influence on the vegetation growth, agricultural production, and healthy operation of regional ecosystems. The Aksu river basin is a typical semi-arid agricultural area which seasonally suffers from water shortage. Due to the lack of knowledge on soil moisture change, the water management and decision-making processes have been a difficult issue for local government. Therefore, soil moisture monitoring by remote sensing became a reasonable way to schedule crop irrigation and evaluate the irrigation efficiency. Compared to in situ measurements, the use of remote sensing for the monitoring of soil water content is convenient and can be repetitively applied over a large area. To verify the applicability of the typical drought index to the rapid acquisition of soil moisture in arid and semi-arid regions, this study simulated, compared, and validated the effectiveness of soil moisture inversion. GF-1 WFV images, Landsat 8 OLI images, and the measured soil moisture data were used to determine the Perpendicular Drought Index (PDI), the Modified Perpendicular Drought Index (MPDI), and the Vegetation Adjusted Perpendicular Drought Index (VAPDI). First, the determination coefficients of the correlation analyses on the PDI, MPDI, VAPDI, and measured soil moisture in the 0–10, 10–20, and 20–30 cm depth layers based on the GF-1 WFV and Landsat 8 OLI images were good. Notably, in the 0–10 cm depth layers, the average determination coefficient was 0.68; all models met the accuracy requirements of soil moisture inversion. Both indicated that the drought indices based on the Near Infrared (NIR)-Red spectral space derived from the optical remote sensing images are more sensitive to soil moisture near the surface layer; however, the accuracy of retrieving the soil moisture in deep layers was slightly lower in the study area. Second, in areas of vegetation coverage, MPDI and VAPDI had a higher inversion accuracy than PDI. To a certain extent, they overcame the influence of mixed pixels on the soil moisture spectral information. VAPDI modified by Perpendicular Vegetation Index (PVI) was not susceptible to vegetation saturation and, thus, had a higher inversion accuracy, which makes it performs better than MPDI’s in vegetated areas. Third, the spatial heterogeneity of the soil moisture retrieved by the GF-1 WFV and Landsat 8 OLI image were similar. However, the GF-1 WFV images were more sensitive to changes in the soil moisture, which reflected the actual soil moisture level covered by different vegetation. These results provide a practical reference for the dynamic monitoring of surface soil moisture, obtaining agricultural information and agricultural condition parameters in arid and semi-arid regions.

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A new method for estimating of evapotranspiration and surface soil moisture from optical and thermal infrared measurements: the simplified triangle

10.31219/osf.io/65xkt ◽

2020 ◽

Author(s):

Toby N. Carlson ◽

George Petropoulos

Keyword(s):

Soil Moisture ◽

Land Surface ◽

Vegetation Index ◽

Normalized Difference Vegetation Index ◽

Heat Fluxes ◽

Surface Model ◽

Landsat 8 ◽

Simple Method ◽

Practical Applications ◽

Infrared Measurements

Earth Observation (EO) provides a promising approach towards deriving accurate spatiotemporal estimates of key parameters characterizing land surface interactions, such as latent (LE) and sensible (H) heat fluxes as well as soil moisture content. This paper proposes a very simple method to implement, yet reliable to calculate evapotranspiration fraction (EF) and surface moisture availability (Mo) from remotely sensed imagery of Normalized Difference Vegetation Index (NDVI) and surface radiometric temperature (Tir). The method is unique in that it derives all of its information solely from these two images. As such, it does not depend on knowing ancillary surface or atmospheric parameters, nor does it require the use of a land surface model. The procedure for computing spatiotemporal estimates of these important land surface parameters is outlined herein stepwise for practical application by the user. Moreover, as the newly developedscheme is not tied to any particular sensor, it can also beimplemented with technologically advanced EO sensors launched recently or planned to be launched such as Landsat 8 and Sentinel 3. The latter offers a number of key advantages in terms of future implementation of the method and wider use for research and practical applications alike.

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Combined Use of GF-3 and Landsat-8 Satellite Data for Soil Moisture Retrieval over Agricultural Areas Using Artificial Neural Network

Advances in Meteorology ◽

10.1155/2018/9315132 ◽

2018 ◽

Vol 2018 ◽

pp. 1-11 ◽

Cited By ~ 4

Author(s):

Qingyan Meng ◽

Linlin Zhang ◽

Qiuxia Xie ◽

Shun Yao ◽

Xu Chen ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Soil Moisture ◽

Synthetic Aperture Radar ◽

Synthetic Aperture ◽

Landsat 8 ◽

Combined Use ◽

Artificial Neural ◽

Agricultural Areas ◽

Aperture Radar

Soil moisture is the basic condition required for crop growth and development. Gaofen-3 (GF-3) is the first C-band synthetic-aperture radar (SAR) satellite of China, offering broad land and ocean imaging applications, including soil moisture monitoring. This study developed an approach to estimate soil moisture in agricultural areas from GF-3 data. An inversion technique based on an artificial neural network (ANN) is introduced. The neural network was trained and tested on a training sample dataset generated from the Advanced Integral Equation Model. Incidence angle and HH or VV polarization data were used as input variables of the ANN, with soil moisture content (SMC) and surface roughness as the output variables. The backscattering contribution from the vegetation was eliminated using the water cloud model (WCM). The acquired soil backscattering coefficients of GF-3 and in situ measurement data were used to validate the SMC estimation algorithm, which achieved satisfactory results (R2 = 0.736; RMSE = 0.042). These results highlight the contribution of the combined use of the GF-3 synthetic-aperture radar and Landsat-8 images based on an ANN method for improving SMC estimates and supporting hydrological studies.

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Surface soil moisture retrievals over partially vegetated areas from the synergy of Sentinel-1 and Landsat 8 data using a modified water-cloud model

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2018.05.026 ◽

2018 ◽

Vol 72 ◽

pp. 76-85 ◽

Cited By ~ 30

Author(s):

Yansong Bao ◽

Libin Lin ◽

Shanyu Wu ◽

Khidir Abdalla Kwal Deng ◽

George P. Petropoulos

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Cloud Model ◽

Landsat 8 ◽

Surface Soil Moisture ◽

Water Cloud

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Estimating Soil Moisture Using the Optical Trapezoid Model (OPTRAM) in a Semi-Arid Area of SONGNEN Plain, China Based On Landsat-8 Data

IGARSS 2019 - 2019 IEEE International Geoscience and Remote Sensing Symposium ◽

10.1109/igarss.2019.8897798 ◽

2019 ◽

Author(s):

Fang Huang ◽

Ping Wang ◽

Yue Ren ◽

Rong Liu

Keyword(s):

Soil Moisture ◽

Arid Area ◽

Landsat 8 ◽

Songnen Plain ◽

Semi Arid

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Soil Moisture Estimation Using Citizen Observatory Data, Microwave Satellite Imagery, and Environmental Covariates

Water ◽

10.3390/w12082160 ◽

2020 ◽

Vol 12 (8) ◽

pp. 2160

Author(s):

Daniel Kibirige ◽

Endre Dobos

Keyword(s):

Soil Moisture ◽

Expert Knowledge ◽

Coefficient Of Determination ◽

Landsat 8 ◽

Regression Kriging ◽

Environmental Covariates ◽

Observatory Data ◽

Digital Elevation ◽

Elevation Model

Soil moisture (SM) is a key variable in the climate system and a key parameter in earth surface processes. This study aimed to test the citizen observatory (CO) data to develop a method to estimate surface SM distribution using Sentinel-1B C-band Synthetic Aperture Radar (SAR) and Landsat 8 data; acquired between January 2019 and June 2019. An agricultural region of Tard in western Hungary was chosen as the study area. In situ soil moisture measurements in the uppermost 10 cm were carried out in 36 test fields simultaneously with SAR data acquisition. The effects of environmental covariates and the backscattering coefficient on SM were analyzed to perform SM estimation procedures. Three approaches were developed and compared for a continuous four-month period, using multiple regression analysis, regression-kriging and cokriging with the digital elevation model (DEM), and Sentinel-1B C-band and Landsat 8 images. CO data were evaluated over the landscape by expert knowledge and found to be representative of the major SM distribution processes but also presenting some indifferent short-range variability that was difficult to explain at this scale. The proposed models were evaluated using statistical metrics: The coefficient of determination (R2) and root mean square error (RMSE). Multiple linear regression provides more realistic spatial patterns over the landscape, even in a data-poor environment. Regression kriging was found to be a potential tool to refine the results, while ordinary cokriging was found to be less effective. The obtained results showed that CO data complemented with Sentinel-1B SAR, Landsat 8, and terrain data has the potential to estimate and map soil moisture content.

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Two new soil moisture indices based on the NIR-red triangle space of Landsat-8 data

International Journal of Applied Earth Observation and Geoinformation ◽

10.1016/j.jag.2016.03.018 ◽

2016 ◽

Vol 50 ◽

pp. 176-186 ◽

Cited By ~ 30

Author(s):

Meisam Amani ◽

Saeid Parsian ◽

S. Mohammad MirMazloumi ◽

Omid Aieneh

Keyword(s):

Soil Moisture ◽

Landsat 8

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Simulation of streamflow in two Mediterranean catchments using a process-based model and remote sensing products

10.5194/egusphere-egu2020-10378 ◽

2020 ◽

Author(s):

Tiago Ramos ◽

Lucian Simionesei ◽

Marta Basso ◽

Vivien Stefan ◽

Ana Oliveira ◽

...

Keyword(s):

Remote Sensing ◽

Soil Moisture ◽

Catchment Area ◽

Vegetation Index ◽

Crop Growth ◽

Assessment Tools ◽

Landsat 8 ◽

Network Computing ◽

Area Index ◽

Model Simulations

Watershed modelling is one of the most important assessment tools in watershed planning and management. Nonetheless, the classic calibration of watershed models, in which a few discharge gauges near the outlet of a catchment are used to compare measured and simulated streamflow, is often criticized by not assuring that relevant processes such as evapotranspiration, soil moisture, crop growth, and groundwater recharge are well represented in the catchment area. This study aimed to simulate streamflow in two Mediterranean catchments, Orba (778km2) in Italy and Segre (1286km2) in Spain, using the physically-based, fully distributed MOHID-Land model. Model calibration/validation of streamflow was first performed following a classical approach. Different products derived from remote sensing platforms were then used to evaluate the adequacy of model simulations of crop growth and soil moisture in the catchment area.The MOHID-Land model considers four compartments or mediums (atmosphere, porous media, soil surface and river network), computing water dynamics through the different mediums using mass and momentum conservation equations. The model was implemented in the two simulated catchments with a resolution of 1 km. Data inputs included the Digital Elevation Model over Europe (EU-DEM) with a resolution of 30 m; the soil hydraulic properties map from EU-SoilHydroGrids ver1.0 with a resolution of 250 m; the CORINE land cover map from 2012 with a resolution of 100 m; the hourly weather data (precipitation, wind velocity, relative air humidity, solar radiation and surface air temperature) from local weather stations; and the reservoir discharge data from governmental and/or regional agencies. Simulations were run from 2006-2014 for Orba and from 2008-2018 for Segre, and included a model warm-up, a calibration, and a validation period. Comparison between simulated and measured flows were performed in 2 and 10 hydrometric stations located in the Orba and Segre catchments, respectively. Four statistical parameters (R2, RMSE, PBIAS and NSE) were used to evaluate model performance, confirming the good fitting of model simulations to measured data.Model simulations of leaf area index (LAI) were then compared with LAI maps at 30 m resolution derived from ATCOR and Landsat 8 imagery data using the Normalized Difference Vegetation Index (NDVI) and the Soil Adjusted Vegetation Index (SAVI). Furthermore, model simulation of soil moisture were also compared at the surface depth (0-5 cm) with soil moisture maps at 1 km resolution created with the DISaggregation based on a Physical And Theoretical scale CHange (DISPATCH) algorithm for the downscaling of the 40 km SMOS (Soil Moisture and Ocean Salinity) soil moisture data using land surface temperature (LST) and NDVI data. Results showed the fundamental differences between the MOHID-Land and remote sensing outputs, with major differences being analyzed by soil units and land use classes.

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