A UAS-based RF testbed for water utilization in agroecosystems

Agroecosystems compose large economic sectors in dominantly agriculture-based societies. Availability and management of water resources have a huge influence on the sustainability of agroecosystems. Low soil moisture is a major constraint on crop growth due to its vital role in providing crops with sufficient nutrition for root uptake. Current methodologies in precision agriculture are insufficient for direct soil moisture sensing since reflected shortwave solar radiation and infrared long-wave emission can only provide information about surface characteristics. While microwave signals are known to be highly sensitive to water within plants and soil, its implementation from small Unmanned Aircraft Systems (UAS) platforms are at relatively low technological readiness level compared to the use of shortwave / longwave optical sensors. In this paper, we summarize our efforts to apply radio frequency (RF) / microwave remote sensing from UAS for water utilization in agroecosystems. Recently, we developed a comprehensive UAS-based RF testbed, including a microwave radiometer, a scatterometer, wideband ground penetrating radar system as well as Signals of Opportunity (SoOp) receivers. These instruments operate from UAS platforms and use the microwave / radio wave portions of the spectrum. The testbed is accompanied with proximal sensing via autonomous unmanned ground vehicles that acquire in- situ soil moisture and vegetation geophysical parameters to provide appropriate datasets for training and testing physics aware, machine learning-based models. In this paper, we introduce the RF sensing framework that can enable non-intrusive high-resolution soil moisture estimates at multiple depths of soil via UAS-based active / passive / SoOp RF instruments.

[1]  James R. Wang,et al.  Multifrequency Measurements of the Effects of Soil Moisture, Soil Texture, And Surface Roughness , 1983, IEEE Transactions on Geoscience and Remote Sensing.

[2]  A. Gitelson,et al.  Application of Spectral Remote Sensing for Agronomic Decisions , 2008 .

[3]  Md. Palash Uddin,et al.  PCA-based Feature Reduction for Hyperspectral Remote Sensing Image Classification , 2020 .

[4]  A. Gürbüz,et al.  GNSS Reflectometry from Smartphones: Testing Performance of In-Built Antennas and GNSS Chips , 2020, IGARSS 2020 - 2020 IEEE International Geoscience and Remote Sensing Symposium.

[5]  Kamal Sarabandi,et al.  Microwave Radar and Radiometric Remote Sensing , 2013 .

[6]  E. Njoku,et al.  The Soil Moisture Active and Passive Mission (SMAP): Science and applications , 2009, 2009 IEEE Radar Conference.

[7]  Thomas J. Jackson,et al.  L-Band Radar Estimation of Forest Attenuation for Active/Passive Soil Moisture Inversion , 2009, IEEE Transactions on Geoscience and Remote Sensing.

[8]  Calvin T. Swift,et al.  Passive microwave remote sensing of the ocean—A review , 1980 .

[9]  G. Lewin,et al.  Development of an Autonomous Agricultural Vehicle to Measure Soil Respiration , 2019, 2019 Systems and Information Engineering Design Symposium (SIEDS).

[10]  Mahta Moghaddam,et al.  P-Band Radar Retrieval of Subsurface Soil Moisture Profile as a Second-Order Polynomial: First AirMOSS Results , 2015, IEEE Transactions on Geoscience and Remote Sensing.

[11]  Claire Marais-Sicre,et al.  Contribution of multispectral (optical and radar) satellite images to the classification of agricultural surfaces , 2020, Int. J. Appl. Earth Obs. Geoinformation.

[12]  Mehmet Kurum,et al.  Response of GNSS-R on Dynamic Vegetated Terrain Conditions , 2019, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[13]  Yann Kerr,et al.  The SMOS Mission: New Tool for Monitoring Key Elements ofthe Global Water Cycle , 2010, Proceedings of the IEEE.

[14]  Mohammad Ali Zare Chahooki,et al.  A Survey on semi-supervised feature selection methods , 2017, Pattern Recognit..

[15]  Ali Cafer Gürbüz,et al.  SCoBi Multilayer: A Signals of Opportunity Reflectometry Model for Multilayer Dielectric Reflections , 2020, Remote. Sens..

[16]  Ali Cafer Gurbuz,et al.  Integration of Smartphones Into Small Unmanned Aircraft Systems to Sense Water in Soil by Using Reflected GPS Signals , 2021, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[17]  Timothy A. Warner,et al.  Implementation of machine-learning classification in remote sensing: an applied review , 2018 .

[18]  Mehmet Kurum,et al.  Quantifying scattering albedo in microwave emission of vegetated terrain , 2013 .

[19]  D. Mulla Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps , 2013 .

[20]  Mehmet Kurum,et al.  The Signals of Opportunity Coherent Bistatic Scattering Simulator: A Free Open Source Framework [Software and Data Sets] , 2020, IEEE Geoscience and Remote Sensing Magazine.

[21]  F. Ulaby Radar measurement of soil moisture content , 1974 .

[22]  Matthew Erik Nelson Implementation and evaluation of a software defined radio based radiometer , 2016 .

[23]  David W. Aha,et al.  A Comparative Evaluation of Sequential Feature Selection Algorithms , 1995, AISTATS.

[24]  Adriano Camps,et al.  Tutorial on Remote Sensing Using GNSS Bistatic Radar of Opportunity , 2014, IEEE Geoscience and Remote Sensing Magazine.

[25]  James L. Garrison,et al.  Cramer–Rao Lower Bound for SoOp-R-Based Root-Zone Soil Moisture Remote Sensing , 2020, IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

[26]  Alicia T. Joseph,et al.  SCoBi-Veg: A Generalized Bistatic Scattering Model of Reflectometry From Vegetation for Signals of Opportunity Applications , 2019, IEEE Transactions on Geoscience and Remote Sensing.

[27]  Vipin Kumar,et al.  Integrating Physics-Based Modeling with Machine Learning: A Survey , 2020, ArXiv.

[28]  Mike Schwank,et al.  Portable L-Band Radiometer (PoLRa): Design and Characterization , 2020, Remote. Sens..

[29]  Bernd Bischl,et al.  Tunability: Importance of Hyperparameters of Machine Learning Algorithms , 2018, J. Mach. Learn. Res..

[30]  Sebastian Raschka,et al.  Model Evaluation, Model Selection, and Algorithm Selection in Machine Learning , 2018, ArXiv.