Microwave remote sensing for soil moisture monitoring: synergy of active and passive observations and validation of retrieved products

Dente, Laura

doi:10.3990/1.9789036541121

Cited by 3 publications

(3 citation statements)

References 99 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Recent and comprehensive reviews on the application of optical and thermal remote sensing for SM estimation emphasize the importance of these methods in addressing the limitations of low-resolution large-scale remote sensing products [41]. This approach provides a unique opportunity for the direct observation of soil moisture, making it an essential method for monitoring soil moisture over large areas [42].…”

Section: Introductionmentioning

confidence: 99%

The High-Resolution Calibration of the Topographic Wetness Index Using PAZ Satellite Radar Data to Determine the Optimal Positions for the Placement of Smart Sustainable Drainage Systems (SuDS) in Urban Environments

Allende-Prieto,

Roces-García,

Sañudo-Fontaneda

2024

Sustainability

View full text Add to dashboard Cite

This study addresses the growing interest in utilizing remote sensing tools for locating sustainable drainage systems (SuDS) in urban environments. SuDS, recognized as Nature-based Solutions (NbS), play a crucial role in enhancing urban resilience against climate change. This study focuses on the calibration process required to establish a correlation between the Topographic Wetness Index (TWI), derived from high-precision digital elevation models (DEMs), and soil moisture (SM) data obtained from satellite imaging. This calibration serves as a method to optimize the placement of sustainable urban drainage system vegetated techniques in urban areas. This study leveraged the exceptional resolution of PAZ satellite radar data to effectively detect variations in SM, particularly in grass-type vegetated land. The sensitivity of the X-band radar signal to moisture levels and changes in ground roughness proved valuable in tracking SM dynamics. The core of the study involved deriving the TWI from a high-resolution digital terrain model (DTM). The correlation between the TWI and SM values demonstrates robustness, with an R2 value of 0.77. These findings significantly advance the calibration of TWI values with SM measurements, enhancing their practicality in identifying areas prone to water accumulation. The study’s outcomes provide valuable insights for guiding the strategic placement of SuDS in urban environments, contributing to the effective management of water-related challenges in the face of urbanization and climate change.

show abstract

Section: Introductionmentioning

confidence: 99%

The High-Resolution Calibration of the Topographic Wetness Index Using PAZ Satellite Radar Data to Determine the Optimal Positions for the Placement of Smart Sustainable Drainage Systems (SuDS) in Urban Environments

Allende-Prieto,

Roces-García,

Sañudo-Fontaneda

2024

Sustainability

View full text Add to dashboard Cite

show abstract

“…Soil moisture and evapotranspiration are inversely related to LST. Dente (2016) and Mohanty et al (2017) have shown that soil moisture is sensitive to LST and leaf area index (LAI). Saha et al (2019), using the normalized difference vegetation index (NDVI), obtained equations for calculating the minimum and maximum LSTs for one of the Indian basins and used them to calculate the SMI.…”

Section: Introductionmentioning

confidence: 99%

Estimation of land surface temperature in agricultural lands using Sentinel 2 images: A case study for sunflower fields

Nasseri

Bansouleh

Azari

2023

Irrigation and Drainage

View full text Add to dashboard Cite

Land surface temperature (LST) is usually calculated based on the thermal bands of satellite images. The present study aimed to estimate LST with reasonable accuracy for on‐time agricultural management by integrating Sentinel 2 with Landsat 8 or MODIS images. For this purpose, LST was first calculated based on Landsat 8 and MODIS images in the Satar Plain located in the western of Kermanshah province, Iran. Then, three methods for LST estimation using Sentinel 2 images were presented and evaluated. In the first method, a relationship was established between LST and elevation and the normalized difference vegetation index. In the second method, LST was determined based on the relationship between Sentinel 2 spectral bands and Landsat‐based LST in sunflower fields and agricultural lands. In the third method, the LST product of the MODIS was downscaled using spectral bands of Sentinel 2. Finally, the results of the three methods were compared with Landsat‐based LST. The results showed that the second method (the relationship between LST and spectral bands of Sentinel 2) with coefficient of determination and root mean square error values of 0.9 and 1.48°C in sunflower fields and 0.625 and 3.85°C in agricultural lands, respectively, has better accuracy than the other two methods.

show abstract

“…In SAR technology, the radar signal follows a logarithmic function with the soil surface roughness. The radar signal and roughness parameter have more strong relationship in the L-band (0.5-1.5 GHz) followed by the C-band (4-8 GHz) and lowest in X-band (8-12 GHz) and are more sensitive to soil roughness at high incidence angles (Zribi and Dechambre 2002;Baghdadi et al 2012;Hosseini et al 2015;Dente 2016;Liu 2016).…”

Section: Introductionmentioning

confidence: 99%

Semi-empirical model for retrieval of soil moisture using RISAT-1 C-Band SAR data over a sub-tropical semi-arid area of Rewari district, Haryana (India)

et al. 2018

View full text Add to dashboard Cite

We have estimated soil moisture (SM) by using circular horizontal polarization backscattering coefficient (σ o RH), differences of circular vertical and horizontal σ o (σ o RV −σ o RH) from FRS-1 data of Radar Imaging Satellite (RISAT-1) and surface roughness in terms of RMS height (RMS height). We examined the performance of FRS-1 in retrieving SM under wheat crop at tillering stage. Results revealed that it is possible to develop a good semi-empirical model (SEM) to estimate SM of the upper soil layer using RISAT-1 SAR data rather than using existing empirical model based on only single parameter, i.e., σ o. Near surface SM measurements were related to σ o RH , σ o RV −σ o RH derived using 5.35 GHz (C-band) image of RISAT-1 and RMS height. The roughness component derived in terms of RMS height showed a good positive correlation with σ o RV −σ o RH (R 2 = 0.65). By considering all the major influencing factors (σ o RH , σ o RV −σ o RH , and RMS height), an SEM was developed where SM (volumetric) predicted values depend on σ o RH , σ o RV −σ o RH , and RMS height. This SEM showed R 2 of 0.87 and adjusted R 2 of 0.85, multiple R=0.94 and with standard error of 0.05 at 95% confidence level. Validation of the SM derived from semi-empirical model with observed measurement (SM Observed) showed root mean square error (RMSE) = 0.06, relative-RMSE (R-RMSE) = 0.18, mean absolute error (MAE) = 0.04, normalized RMSE (NRMSE) = 0.17, Nash-Sutcliffe efficiency (NSE) = 0.91 (≈1), index of agreement (d) = 1, coefficient of determination (R 2) = 0.87, mean bias error (MBE) = 0.04, standard error of estimate (SEE) = 0.10, volume error (VE) = 0.15, variance of the distribution of differences (S 2 d) = 0.004. The developed SEM showed better performance in estimating SM than Topp empirical model which is based only on σ o. By using the developed SEM, top soil SM can be estimated with low mean absolute percent error (MAPE) = 1.39 and can be used for operational applications.

show abstract

Microwave remote sensing for soil moisture monitoring: synergy of active and passive observations and validation of retrieved products

Cited by 3 publications

References 99 publications

The High-Resolution Calibration of the Topographic Wetness Index Using PAZ Satellite Radar Data to Determine the Optimal Positions for the Placement of Smart Sustainable Drainage Systems (SuDS) in Urban Environments

The High-Resolution Calibration of the Topographic Wetness Index Using PAZ Satellite Radar Data to Determine the Optimal Positions for the Placement of Smart Sustainable Drainage Systems (SuDS) in Urban Environments

Estimation of land surface temperature in agricultural lands using Sentinel 2 images: A case study for sunflower fields

Semi-empirical model for retrieval of soil moisture using RISAT-1 C-Band SAR data over a sub-tropical semi-arid area of Rewari district, Haryana (India)

Contact Info

Product

Resources

About