Assessment of surface soil moisture from ALOS PALSAR-2 in small-scale maize fields using polarimetric decomposition technique

Soil moisture is very important in several disciplines such as agriculture, hydrology and meteorology. It can be mapped using active and passive microwave remote sensing techniques. From literature it is observed that quad polarized data acquired at L-band is sensitive to soil moisture and can map surface soil moisture at high spatial resolution. The main objective of this study is to analyze the potential use of L-band radar data for the retrieval of surface soil moisture over small scale agricultural areas under vegetation cover conditions. Study area selected for this study was Malavalli, village in Karnataka state India which falls in Tropical semi-arid region. Two radar images were acquired using the Phased Array Synthetic Aperture Radar/Advanced Land Observing Satellite (PALSAR/ALOS)-2 sensor over the study area between 23/07/2018 and 17/09/2018 which has spatial resolution of 5m. Ground Soil moisture over 30 sample sites were collected in synchronization with satellite pass over the study area. Acquired ALOS PALSAR-2 images were processed using PolSARpro (Polarimetric SAR data Processing and Education Toolbox). ALOS PALSAR-2 has been processed and lee speckle filter is applied with window size of 3*3. Surface soil moisture distribution over small scale tomato fields are mapped by adding incidence angle using Oh Model. Incidence angle map which is not available with PolSARpro (Polarimetric SAR data Processing and Education Toolbox) software was derived using the polynomial given in the leader file which was required for oh model inversion. Study site clearly shown increasing trend of soil moisture from July to September. It is interesting to note that vegetation and urban areas are clearly discriminated in the PauliRGB images. The retrieval of soil moisture using Oh model is validated using Ground truth samples. The accuracy of Oh model over small scale tomato fields with RMSE of 1.83 m3/m-3.

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Exploring the use of machine-learning techniques to integrate ground- and remote sensing-based observations for efficient near-surface soil moisture mapping

10.5194/egusphere-egu2020-9092 ◽

2020 ◽

Author(s):

Sarah Schönbrodt-Stitt ◽

Paolo Nasta ◽

Nima Ahmadian ◽

Markus Kurtenbach ◽

Christopher Conrad ◽

...

Keyword(s):

Machine Learning ◽

Soil Moisture ◽

Spatial Resolution ◽

Surface Soil ◽

Bare Soil ◽

Small Scale ◽

Surface Soil Moisture ◽

Near Surface ◽

Campania Region

Mapping near-surface soil moisture (&#952;) is of tremendous relevance for a broad range of environment-related disciplines and meteorological, ecological, hydrological and agricultural applications. Globally available products offer the opportunity to address &#952; in large-scale modelling with coarse spatial resolution such as at the landscape level. However, &#952; estimation at higher spatial resolution is of vital importance for many small-scale applications. Therefore, we focus our study on a small-scale catchment (MFC2) belonging to the &#8220;Alento&#8221; hydrological observatory, located in southern Italy (Campania Region). The goal of this study is to develop new machine-learning approaches to estimate high grid-resolution (about 17 m cell size) &#952; maps from mainly backscatter measurements retrieved from C-band Synthetic Aperture Radar (SAR) based on Sentinel-1 (S1) images and from gridded terrain attributes. Thus, a workflow comprising a total of 48 SAR-based &#952; patterns estimated for 24 satellite overpass dates (revisit time of 6 days) each with ascendant and descendent orbits will be presented. To enable for the mapping, SAR-based &#952; data was calibrated with in-situ measurements carried out with a portable device during eight measurement campaigns at time of satellite overpasses (four overpass days in total with each ascendant and descendent satellite overpasses per day in November 2018). After the calibration procedure, data validation was executed from November 10, 2018 till March 28, 2019 by using two stationary sensors monitoring &#952; at high-temporal (1-min recording time). The specific sensor locations reflected two contrasting field conditions, one bare soil plot (frequently kept clear, without disturbance of vegetation cover) and one non-bare soil plot (real-world condition). Point-scale ground observations of &#952; were compared to pixel-scale (17 m &#215; 17 m), SAR-based &#952; estimated for those pixels corresponding to the specific positions of the stationary sensors. Mapping performance was estimated through the root mean squared error (RMSE). For a short-term time series of &#952; (Nov 2018) integrating 136 in situ, sensor-based &#952; (&#952;insitu) and 74 gravimetric-based &#952; (&#952;gravimetric) measurements during a total of eight S1 overpasses, mapping performance already proved to be satisfactory with RMSE=0.039 m&#179;m-&#179; and R&#178;=0.92, respectively with RMSE=0.041 m&#179;m-&#179; and R&#178;=0.91. First results further reveal that estimated satellite-based &#952; patterns respond to the evolution of rainfall. With our workflow developed and results, we intend to contribute to improved environmental risk assessment by assimilating the results into hydrological models (e.g., HydroGeoSphere), and to support future studies on combined ground-based and SAR-based &#952; retrieval for forested land (future missions operating at larger wavelengths e.g. NISARL-band, Biomass P-band sensors).

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An offline reanalysis of land surface variables with LDAS-Monde forced by a kilometric scale NWP system

10.5194/egusphere-egu2020-17527 ◽

2020 ◽

Author(s):

Bertrand Bonan ◽

Clément Albergel ◽

Adrien Napoly ◽

Yongjun Zheng ◽

Jean-Christophe Calvet

Keyword(s):

Soil Moisture ◽

Spatial Resolution ◽

Land Surface ◽

Surface Soil ◽

Weather Prediction ◽

Land Surface Model ◽

Research Centre ◽

Small Scale ◽

Surface Model ◽

Surface Soil Moisture

LDAS-Monde is the offline land data assimilation system (LDAS) developed by M&#233;t&#233;o-France&#8217;s research centre (CNRM) aiming to monitor the evolution of land surface variables (LSVs) at various scales, from regional to global. It combines numerical simulations from the multilayer and interactive vegetation ISBA land surface model and satellite-derived observations of surface soil moisture and leaf area index (LAI). LDAS-Monde has been successfully validated over the globe.In this work, we study the possibility to set up LDAS-Monde to the context of the kilometric spatial resolution. In this context, we assimilate satellite observations of LAI from the Copernicus Global Land Service (CGLS) into the ISBA land surface model forced with M&#233;t&#233;o-France&#8217;s small scale numerical weather prediction system AROME. We produce a reanalysis of LSVs at 2.5-km spatial resolution over the AROME domain centred on France starting from 2017. The quality of this reanalysis is assessed by comparing the obtained reanalysis with satellite products of LAI and surface soil moisture from e.g. CGLS and in-situ measurements of soil moisture from various networks (SMOSMANIA, &#8230;). We also show the ability of our system to monitor the evolution of LSVs in the context of the severe drought that France suffered during the summer 2018. LDAS-Monde at 2.5-km spatial resolution displays a great potential for agricultural monitoring at high resolution. We also plan to adapt our framework to 1.0-km spatial resolution.

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High-resolution near-surface soil moisture through the combination of Sentinel-1 and Cosmic-Ray Neutron Probe in a Mediterranean agroforestry

10.5194/egusphere-egu21-7582 ◽

2021 ◽

Author(s):

Aida Taghavi Bayat ◽

Sarah Schönbrodt-Stitt ◽

Paolo Nasta ◽

Nima Ahmadian ◽

Christopher Conrad ◽

...

Keyword(s):

Soil Moisture ◽

Surface Soil ◽

Cosmic Ray ◽

Small Scale ◽

Surface Soil Moisture ◽

Field Scale ◽

Point Scale ◽

Near Surface ◽

Neutron Probe ◽

Topographic Attributes

The precise estimation and mapping of the near-surface soil moisture (~5cm, SM5cm) is key to supporting sustainable water management plans in Mediterranean agroforestry environments. In the past few years, time series of Synthetic Aperture Radar (SAR) data retrieved from Sentinel-1 (S1) enable the estimation of SM5cm at relatively high spatial and temporal resolutions. The present study focuses on developing a reliable and flexible framework to map SM5cm in a small-scale agroforestry experimental site (~30 ha) in southern Italy over the period from November 2018 to March 2019. Initially, different SAR-based polarimetric parameters from S1 (in total 62 parameters) and hydrologically meaningful topographic attributes from a 5-m Digital Elevation Model (DEM) were derived. These SAR and DEM-based parameters, and two supporting point-scale estimates of SM5cm were used to parametrize a Random Forest (RF) model. The inverse modeling module of the Hydrus-1D model enabled to simulate two &#160;supporting estimates of SM5cm by using i) sparse soil moisture data at the soil depths of 15 cm and 30 cm acquired over 20 locations comprised in a SoilNet wireless sensor network (SoilNet-based approach), and ii) field-scale soil moisture monitored by a Cosmic-Ray Neutron Probe (CRNP-based approach). In the CRNP-based approach, the field-scale SM5cm was further downscaled to obtain point-scale supporting SM5cm data over the same 20 positions by using the physical-empirical Equilibrium Moisture from Topography (EMT) model. Our results show that the CRNP-based approach can provide reasonable SM5cm retrievals with RMSE values ranging from 0.034 to 0.050 cm&#179; cm-3 similar to the ones based on the SoilNet approach ranging from 0.029 to 0.054 cm&#179; cm-3. This study highlights the effectiveness of integrating S1 SAR-based measurements, topographic attributes, and CRNP data for mapping SM5cm at the small agroforestry scale with the advantage of being non-invasive and easy to maintain.&#160;

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