ABSTRACT Accurate knowledge of soil moisture is critical for hydrological and agricultural applications such as agricultural irrigation management, drought characterization, and flood detection. Researchers have attempted to provide soil moisture using various methods and techniques. Traditionally, the amount of soil moisture was based on field measurements. On the other hand, remote sensing satellites have been widely used to provide continuous soil moisture measurements worldwide, encountering problems such as the lack of simultaneous spatial and temporal sampling rates and dependence on weather conditions. However, in recent decades, GNSS signals reflected from the Earth’s surface (GNSS-R technique) have been increasingly used for soil moisture monitoring, due to the numerous advantages it offers. This paper aims to provide a comprehensive review of soil moisture retrieved by two space-based GNSS-R missions (TDS-1 and CYGNSS) to show the general past trends, gaps, and opportunities for soil moisture monitoring through GNSS-R observations.

Soil moisture retrieval using space-borne GNSS reflectometry: a comprehensive review

CYGNSS-derived soil moisture: Status, challenges and future

Single-pass soil moisture retrieval has been a key objective of Global Navigation Satellite System-Reflectometry (GNSS-R) for the last decade. Achieving this goal will allow small satellites with GNSS-R payloads to perform such retrievals at high temporal resolutions. Properly modeling the soil surface roughness is key to providing high-quality soil moisture estimations. In the present work, the Physical Optics and Geometric Optics models of the Kirchhoff Approximation are implemented to the coherent and incoherent components of the reflectometry measurements collected by the SMAP radar receiver (SMAP-Reflectometry or SMAP-R). Two surface roughness products are retrieved and compared for a single-polarization approach, critical for single-polarization GNSS-R instruments that target soil moisture retrievals. Then, a polarization decoupling model is implemented for a dual-polarization retrieval approach, where the ratio between two orthogonal polarizations is evaluated to estimate soil moisture. Differences between linear and circular polarization ratios are evaluated using this decoupling parameter, and the theoretical soil moisture error with varying decoupling parameters is analyzed. Our results show a 1-sigma soil moisture error of 0.08 cm3/cm3 for the dual-polarization case for a fixed polarization decoupling value used for the whole Earth, and a 2-sigma error of 0.08 cm3/cm3 when the measured reflectivity and the VOD are used to estimate the polarization decoupling parameter.

/pdf/effective-surface-roughness-impact-in-polarimetric-gnss-r-2woqumsr.pdf

Effective Surface Roughness Impact in Polarimetric GNSS-R Soil Moisture Retrievals

This paper demonstrates machine learning based approach to retrieve soil moisture (SM) and its validation over India using CYGNSS data. CYGNSS mission is mainly designed and dedicated for monitoring the tropical cyclones over ocean.However, recent developments has highlighted the potential of GNSS-Reflectometry for land applications, specially for SM with high spatio-temporal frequency over traditional satellite data sets. It can be directly utilized to retrieve SM as complementary data to fill the spatial and temporal gaps in satellite microwave radiometer derived SM, like from SMAP and SMOS mission to meet the requirements of high spatial and temporal frequency data sets for agricultural applications. In this work, we developed an Artificial Neural Network (ANN) framework to derive SM and validated at selected agricultural sites over India. SMAP derived vegetation and roughness parameters were also used as inputs for training of ANN model to add the effect of vegetation and roughness. Detailed spatial and temporal correlation analyses of CYGNSS SM were performed to test the proposed ANN model using SMAP SM and in-situ observations from hydra probe station data from 2018 to 2019. It was observed from temporal correlation analysis that CYGNSS and SMAP SM follow a good trend with high correlation using in-situ data. Spatial correlation also shows high correlation with Pearson correlation coefficient of 0.69 and RMSD of 0.057 m3/m3during pre-monsoon and 0.65 and 0.053 m3/m3in post monsoon periods, respectively.

Machine Learning Based Soil Moisture Retrieval Algorithm and Validation at Selected Agricultural Sites Over India Using Cygnss Data

Food is the most important and basic requirement for the life of human beings on the planet earth. The quality and the quantity of food from agricultural land is one of the significant parts of our socio-economy venture. It also shares the major part of GDP for a developing country. As the humankind population is increasing, the requirement of more crop yield is also rising. To increases productivity, it is required to use our agricultural lands more efficiently. This can be done by utilizing the natural resources that are soil and water efficiently. Remote sensing and Geographical information system (GIS) are the techniques that can be used for this purpose. These technologies can provide the database for making the crucial decision for the beneficiary of the agricultural sector. These data provide the decision-making capabilities for agricultural and soil management and can be embedded with GIS to maximize its reach at the sector's grassroots. This paper discusses all the major potential applications for agricultural and soil management using remote sensing and GIS. Techniques based on remote sensing that can be utilized for the application that can maximize the net yield production of the crop are discussed in detail. 

Role of Remote Sensing and GIS Strategies to Increase Crop Yield

The exploitation of GNSS signals for soil moisture as one of the land applications is the current interest of researchers due to its multiple advantages over existing soil moisture retrieval techniques based on traditional radiometer and other datasets. Multipath phase and amplitude of GNSS C/N o data have been mostly utilized to determine the sensitivity of soil moisture. However, in this work, we have analyzed the multipath peak frequency to determine its sensitivity for field soil moisture. The multipath frequency is used to consider constant when studies are carried out with multipath phase or amplitude. Here, we have demonstrated that the multipath peak frequency is a function of field soil moisture which can be evaluated efficiently with Lomb Scargle Periodogram (LSP). Navigation with Indian Constellation (NavIC) data has been used to determine the correlation between multipath peak frequency and surface soil moisture. The obtained sensitivity results are very optimistic (correlation coefficient = 0.69), which can be further utilized for developing soil moisture estimation model using NavIC data to cater different land applications.

Sensitivity of Multipath Peak Frequency of Navigation with Indian Constellation (NavIC) towards Surface Soil Moisture over Bare Land

This paper demonstrates the relationship of surface radar reflectivity derived from Cyclone Global Navigation Satellite System (CYGNSS) measurements with the Soil Moisture (SM) derived from Soil Moisture Active Passive (SMAP) over India. CYGNSS is an operational eight-satellite constellation, each with a Delay Doppler Mapping Instrument (DDMI) for deriving wind speed in tropical cyclones over oceans. However, these reflections from the land surface, acquired by the DDMI instrument, can also be used to retrieve Soil Moisture (SM). Surface radar reflectivity was derived from Delay Doppler Map (DDM) at specular points generated by CYGNSS measurements as coherent specular reflections over land using radar range equations. From the analysis carried out for selected data sets, a strong linear relationship between CYGNSS derived radar reflectivity and SMAP derived SM has been demonstrated.

Sensitivity Analysis of CYGNSS derived Radar Reflectivity for Soil Moisture Retrieval over India: Initial Results

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Papers

Sensitivity of Multipath Peak Frequency of Navigation with Indian Constellation (NavIC) towards Surface Soil Moisture over Bare Land

Machine Learning Based Soil Moisture Retrieval Algorithm and Validation at Selected Agricultural Sites Over India Using Cygnss Data

Sensitivity Analysis of CYGNSS derived Radar Reflectivity for Soil Moisture Retrieval over India: Initial Results