A novel approach for next generation water-use mapping using Landsat and Sentinel-2 satellite data

Singh, R. K.; Khand, Kul; Kagone, Stefanie; Schauer, Matthew; Senay, Gabriel B.; Wu, Zhuoting

doi:10.1080/02626667.2020.1817461

Cited by 18 publications

(16 citation statements)

References 49 publications

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“…The change detection method estimates soil moisture from σ 0 vv directly, while the Dubois et al [27] and Fung et al [23] methods compute the ε dielectric constant from σ 0 vv , so that a further step is needed to estimate θ from ε. To relate θ with ε through the Γ operator, we used the common Topp et al [76] equation [36,41,77]:…”

Section: Methods For Soil Moisture Retrieval From Sentinel 1 Datamentioning

confidence: 99%

“…Several methods have been developed to estimate soil moisture over bare soil surfaces from SAR observations, varying from physical models (e.g., the Integral Equation Model (IEM; Fung et al [23]), the Advanced Integral Equation Model (AIEM; [24]), and the Integral Equation Model for Multiple Scattering [25], to empirical and semi-empirical models [24,[26][27][28]. Recent efforts have estimated soil moisture in bare soil plots from Sentinel 1 data using both physical models [29][30][31][32] and empirical approaches [33][34][35][36]. The unprecedented high frequency of the Sentinel 1 satellite passes has also encouraged the use of simple change detection techniques [5,37], where soil moisture is estimated linearly scaling observed radar backscatter.…”

Section: Introductionmentioning

confidence: 99%

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Soil Moisture Estimates in a Grass Field Using Sentinel-1 Radar Data and an Assimilation Approach

2021

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The new constellation of synthetic aperture radar (SAR) satellite, Sentinel-1, provides images at a high spatial resolution (up to 10 m) typical of radar sensors, but also at high time resolutions (6–12 revisit days), representing a major advance for the development of operational soil moisture mapping at a plot scale. Our objective was to develop and test an operational approach to assimilate Sentinel 1 observations in a land surface model, and to demonstrate the potential of the use of the new satellite sensors in soil moisture predictions in a grass field. However, for soil moisture retrievals from Sentinel 1 observations in grasslands, there is still the need to identify robust and parsimonious solutions, accounting for the effects of vegetation attenuation and their seasonal variability. In a grass experimental site in Sardinia, where field measurements of soil moisture were available for the 2016–2018 period, three common retrieval methods have been compared to estimate soil moisture from Sentinel 1 data, with increasing complexity and physical interpretation of the processes: the empirical change detection method, the semi-empirical Dubois model, and the physically-based Fung model. In operational approaches for soil moisture mapping from remote sensing, the parameterization simplification of soil moisture retrieval techniques is encouraged, looking for parameter estimates without a priori information. We have proposed a simplified approach for estimating a key parameter of retrieval methods, the surface roughness, from the normalized difference vegetation index (NDVI) derived by simultaneous Sentinel 2 optical observations. Soil moisture was estimated better using the proposed approach and the Dubois model than by using the other methods, which accounted vegetation effects through the common water cloud model. Furthermore, we successfully merged radar-based soil moisture observations and a land surface model, through a data assimilation approach based on the Ensemble Kalman filter, providing robust predictions of soil moisture.

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Section: Methods For Soil Moisture Retrieval From Sentinel 1 Datamentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Soil Moisture Estimates in a Grass Field Using Sentinel-1 Radar Data and an Assimilation Approach

2021

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“…Optical imagery from one satellite system could supplement the imagery from another system to address this problem. Previous studies have analyzed the performance of such conjunction of imagery from different platforms, for example, Landsat-7 and Landsat-8 [16], MODIS and Landsat-8 [17], as well as Landsat-8 and Sentinel-2 [18][19][20][21], and finally, Landsat-7, Landsat-8, and Sentinel-2 combined [22]. Similarly, the present study exploits the possibility of conjoint use of imagery acquired by the Sentinel-2 and the new Vegetation and Environment monitoring on a New MicroSatellite (VENµS) satellite, which has similar spectral bands in the visual, near infrared spectral region, and a 5-10 m spatial resolution (depending on the Collection) as Sentinel-2 in addition to a very high temporal resolution of two days [23].…”

Section: Introductionmentioning

confidence: 99%

Estimating Processing Tomato Water Consumption, Leaf Area Index, and Height Using Sentinel-2 and VENµS Imagery

et al. 2021

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Crop monitoring throughout the growing season is key for optimized agricultural production. Satellite remote sensing is a useful tool for estimating crop variables, yet continuous high spatial resolution earth observations are often interrupted by clouds. This paper demonstrates overcoming this limitation by combining observations from two public-domain spaceborne optical sensors. Ground measurements were conducted in the Hula Valley, Israel, over four growing seasons to monitor the development of processing tomato. These measurements included continuous water consumption measurements using an eddy-covariance tower from which the crop coefficient (Kc) was calculated and measurements of Leaf Area Index (LAI) and crop height. Satellite imagery acquired by Sentinel-2 and VENµS was used to derive vegetation indices and model Kc, LAI, and crop height. The conjoint use of Sentinel-2 and VENµS imagery facilitated accurate estimation of Kc (R2 = 0.82, RMSE = 0.09), LAI (R2 = 0.79, RMSE = 1.2), and crop height (R2 = 0.81, RMSE = 7 cm). Additionally, our empirical models for LAI estimation were found to perform better than the SNAP biophysical processor (R2 = 0.53, RMSE = 2.3). Accordingly, Sentinel-2 and VENµS imagery was demonstrated to be a viable tool for agricultural monitoring.

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“…The European Space Agency (ESA) under the Copernicus Program provides Sentinel-2 with temporal and spatial (10-20 m) resolution of 5 days and 10-20 m, respectively, which has opened new vistas for many applications for having higher resolution than both MODIS and Landsat [78][79][80][81][82][83][84][85]. Sentinel-2 is a multispectral operational imaging mission for worldwide land observation.…”

Section: Most Common Satellitesmentioning

confidence: 99%

An Overview of Groundwater Monitoring through Point-to Satellite-Based Techniques

Masood¹,

Tariq

Hashmi³

et al. 2022

Water

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Groundwater supplies approximately half of the total global domestic water demand. It also complements the seasonal and annual variabilities of surface water. Monitoring of groundwater fluctuations is mandatory to envisage the composition of terrestrial water storage. This research provides an overview of traditional techniques and detailed discussion on the modern tools and methods to monitor groundwater fluctuations along with advanced applications. The groundwater monitoring can broadly be classified into three groups. The first one is characterized by the point measurement to measure the groundwater levels using classical instruments and electronic and physical investigation techniques. The second category involves the extensive use of satellite data to ensure robust and cost-effective real-time monitoring to assess the groundwater storage variations. Many satellite data are in use to find groundwater indirectly. However, GRACE satellite data supported with other satellite products, computational tools, GIS techniques, and hydro-climate models have proven the most effective for groundwater resources management. The third category is groundwater numerical modeling, which is a very useful tool to evaluate and project groundwater resources in future. Groundwater numerical modeling also depends upon the point-based groundwater monitoring, so more research to improve point-based detection methods using latest technologies is required, as these still play the baseline role. GRACE and numerical groundwater modeling are suggested to be used conjunctively to assess the groundwater resources more efficiently.

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A novel approach for next generation water-use mapping using Landsat and Sentinel-2 satellite data

Cited by 18 publications

References 49 publications

Soil Moisture Estimates in a Grass Field Using Sentinel-1 Radar Data and an Assimilation Approach

Soil Moisture Estimates in a Grass Field Using Sentinel-1 Radar Data and an Assimilation Approach

Estimating Processing Tomato Water Consumption, Leaf Area Index, and Height Using Sentinel-2 and VENµS Imagery

An Overview of Groundwater Monitoring through Point-to Satellite-Based Techniques

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