Understanding the vertical distribution of soil moisture is crucial for making informed decisions in various applications, ranging from precision agriculture to hydrological modeling. Four machine learning algorithms, including random forest, extreme gradient boosting, deep learning, and support vector regression were employed to estimate the soil moisture profile from collected tower-based L-band and P-band brightness temperature observations in Victoria, Australia. The results showed that random forest outperformed the other algorithms, with root mean square error (RMSE) values of 0.03, 0.04, and 0.06 m3/m3for depths of 0-5 cm, 0-30 cm, and 0-60 cm, respectively

Soil moisture is a key state variable in environmental monitoring and in the water, energy and carbon cycles [1] - [3] . Farm management decisions, including the timing of planting, application of fertilizers, pesticides, herbicides, and irrigation scheduling, are influenced by soil moisture status [4] , [5] . Moreover, it is highly variable both in space and time and the estimation of this variable is challenging because the amount of moisture in the surficial soil layer is influenced by soil texture [6] , [7] . Passive microwave remote sensing is a well-accepted technique for estimating soil moisture due to the large contrast between the dielectric properties of liquid water (~80) and that of dry soil matter (~3.5) [8] , and reduced sensitivity to surface roughness and vegetation as compared to active microwave [9] . Current missions, including the Soil Moisture and Ocean Salinity (SMOS; [10] ) and Soil Moisture Active Passive (SMAP; [11] ) operating at L-band radiometer (~21 cm) are only able to detect shallow soil moisture (up to 5 cm in depth; [12] ). Compared with L-band, P-band (~ 40 cm) is even less sensitive to vegetation water content [13] and surface roughness [14] , and is able to penetrate deeper into the soil providing information about moisture over deeper depths (~10 cm; [12] ).

Root zone soil moisture and its distribution throughout the profile play an important role in agricultural productivity and drought monitoring. An inversion scheme including the stratified coherent models of Njoku and Wilheit were employed to retrieve the daily soil moisture profile at 6 AM from simulated L-band and P-band radiometry observations for April 2019 in Cora Lynn, Victoria, Australia. Different levels of noise up to 4 K were imposed in this synthetic study. The average RMSE of retrieved soil moisture at the surface, middle, and bottom (60 cm) of the profile for the Njoku (Wilheit) model were 0.01 (0.04), 0.04 (0.06), and 0.05 (0.07) (all in m3/m3) when a second-order polynomial function was considered as the representative of the soil moisture profile.

Currently, near-surface soil moisture at a global scale is being provided using National Aeronautics and Space Administration's (NASA's) Soil Moisture Active Passive (SMAP) and European Space Agency's (ESA's) Soil Moisture and Ocean Salinity (SMOS) satellites, both of which utilize L-band (1.4 GHz; 21 cm wavelength ) passive microwave remote sensing techniques. However, a fundamental limitation of this technology is that the water content can only be measured for approximately the top 5-cm layer of soil moisture, and only over low-to-moderate vegetation covered areas in order to meet the 0.04 m3/m3target accuracy, limiting its applicability. Consequently, a longer wavelength radiometer is being explored as a potential solution for measuring soil moisture in a deeper surface layer of soil and under denser vegetation. It is expected that P-band ( wavelength of 40 cm and frequency of 750 MHz) could potentially provide soil moisture information for the top ~10-cm layer of soil, being one-tenth to one-quarter of the wavelength. In addition, P-band is expected to have higher soil moisture retrieval accuracy due to its reduced sensitivity to vegetation water content and surface roughness. To demonstrate the potential of P-band passive microwave soil moisture remote sensing, a short-term airborne field experiment was conducted over a center pivot irrigated farm at Cressy in Tasmania, Australia, in January 2017. First results showing a comparison of airborne P-band brightness temperature observations against airborne L-band brightness temperature observations and ground soil moisture measurements are presented. The P-band brightness temperature was found to have a similar but stronger response to soil moisture compared to L-band.

The moisture retrieval depth is commonly held to be the approximately top 5 cm at L-band (~21-cm wavelength/1.41 GHz), which is seen as a limitation for hydrological applications. A widely held view is that this moisture retrieval depth increases with wavelength, ranging approximately from one-tenth to one-fourth of the wavelength. Accordingly, P-band (~40-cm wavelength/0.75 GHz) is under investigation for soil moisture observation over a deeper layer of soil. However, there is no accepted method for predicting the moisture retrieval depth, and there has been no study to confirm that the actual retrieval depth at P-band is indeed deeper than that achieved at L-band. Consequently, this research has estimated the moisture retrieval depth from theory and compared with empirical evidence from tower-based observations. Model predictions and experimental observations agreed that P-band has the potential to retrieve soil moisture over a deeper layer (~7 cm) than L-band (~5 cm) while maintaining the same correlation. However, an alternate interpretation of experimental results is that P-band has a larger correlation with soil moisture (accuracy of retrieval) than L-band but for the same 5-cm moisture retrieval depth. The results also demonstrated the increasing trend of the moisture retrieval depth for increasing wavelength, with the potential to achieving a moisture retrieval depth greater than 10 cm for P-band below 0.5 GHz. Importantly, model predictions showed that moisture retrieval depth was not only dependent on soil moisture content and observation frequency, but also the moisture gradient of the profile.

Soil Moisture is an important geophysical variable that needs reliable quantification for applications in hydrology, meteorology and agriculture. L-band radiometry has proved to be one of the best methods in soil moisture estimation using microwave signals. However, they provide measurements that correspond to a shallow depth of 5 cm and are also affected by the presence of overlaying vegetation and roughness. In contrast, P-band radiometry is expected to provide moisture information on a deeper layer of soil. Moreover, these lower frequency measurements are expected to be less affected by soil roughness and vegetation contributions. Consequently, this pilot study uses the Polarimetric P-band Multibeam Radiometer (PPMR) at 740 MHz to evaluate the response of the P-band radiometer over a realistic range of surface conditions at the field scale. A preliminary framework of P-band Microwave Emission of the Biosphere (P-MEB) has been developed as a forward model that simulates brightness temperature from soil moisture and other ancillary data collected from the field. This paper presents the model for the bare soil condition observed during June 2018 to August 2018. The results show that H-polarised PPMR data has better correlation to the soil moisture over a depth of 10 cm than the V-polarized PPMR data. A model is under improvement by incorporating a more suitable effective temperature formulation.

Soil moisture measurement using L-band radiometry is now widely accepted as the state-of-art remote sensing approach, and has been adopted by both the SMOS and SMAP soil moisture dedicated satellite missions. However, it suffers from the shallow depth of its soil moisture measurement, and the confounding effects of vegetation and soil roughness on soil moisture retrieval. P-band, which is a longer wavelength measurement, provides the potential to retrieve deeper soil moisture information, and to do so more accurately due to reduced soil roughness and vegetation effects. This paper presents some pioneering work on the use of P-band for soil moisture retrieval. The Polarimetric P-band Multibeam Radiometer (PPMR) used in this research operates at 740 MHz / wavelength of 40 cm. It is used together with the Polarimetric L-band Multibeam Radiometer (PLMR) which operates at 1.4 GHz / wavelength of 21 cm. The PPMR and PLMR are mounted onto a 10m high tower in an agricultural farm located at Cora Lynn, Victoria. This paper outlines the initial set up for the study and the experimental plan for understanding PPMR's performance, along with some initial data.

A fundamental limitation of current soil moisture remote sensing technology is that can only provide moisture information on the top 5 cm layer of soil at most, being one-tenth to one-quarter of the wavelength (21 cm at L-band; 1.4 GHz) using the current SMAP and SMOS soil moisture dedicated missions of NASA and ESA. Consequently, we have developed an airborne passive microwave sensing capability at P-band to develop a new state-of-the-art satellite concept that will provide soil moisture data for the top 10 cm layer of soil using radiometer observations at P-band (40 cm; 750 MHz). Not only would P-band provide soil moisture information on a soil layer thickness that more closely relates to that affecting crop and pasture growth, but it is expected to produce greater spatial coverage with improved accuracy to that from L-band. This is because P-band should be less affected by surface roughness conditions and have a reduced attenuation by the overlaying vegetation. This paper describes a series of small airborne field experiments at P-band, and presents some early results of P-band passive microwave observations in comparison with L-band and K-band passive microwave from initial trial flights.