Database approach to develop and validate the Soil Moisture and temperature retrieval algorithm using SMOS radiometric data

2015 ◽  
Author(s):  
Z. Ruzicka ◽  
K.V. Muzalevskiy
Keyword(s):  
Soil Moisture ◽  
Retrieval Algorithm ◽  
Temperature Retrieval ◽  
Radiometric Data

scholarly journals Analysis of a Least-Squares Soil Moisture Retrieval Algorithm from L-band Passive Observations

2010 ◽  
Vol 2 (1) ◽  
pp. 352-374 ◽  
Author(s):  
María Piles ◽  
Mercè Vall-llossera ◽  
Adriano Camps ◽  
Marco Talone ◽  
Alessandra Monerris
Keyword(s):  
Soil Moisture ◽  
Least Squares ◽  
Retrieval Algorithm ◽  
L Band

Global retrieval of soil moisture using neural networks trained with synthetic radiometric data

2017 ◽  
Author(s):  
Nemesio J. Rodriguez-Fernandez ◽  
Philippe Richaume ◽  
Yann H. Kerr ◽  
Filipe Aires ◽  
Catherine Prigent ◽  
...  
Keyword(s):  
Neural Networks ◽  
Soil Moisture ◽  
Radiometric Data

scholarly journals Comparison of measured brightness temperatures from SMOS with modelled ones from ORCHIDEE and H-TESSEL over the Iberian Peninsula

2015 ◽  
Vol 12 (12) ◽  
pp. 13019-13067
Author(s):  
A. Barella-Ortiz ◽  
J. Polcher ◽  
P. de Rosnay ◽  
M. Piles ◽  
E. Gelati
Keyword(s):  
Soil Moisture ◽  
Radiative Transfer ◽  
Iberian Peninsula ◽  
Land Surface ◽  
Function Analysis ◽  
Microwave Emission ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Brightness Temperatures

Abstract. L-Band radiometry is considered to be one of the most suitable techniques to estimate surface soil moisture by means of remote sensing. Brightness temperatures are key in this process, as they are the main input in the retrieval algorithm. The work exposed compares brightness temperatures measured by the Soil Moisture and Ocean Salinity (SMOS) mission to two different sets of modelled ones, over the Iberian Peninsula from 2010 to 2012. The latter were estimated using a radiative transfer model and state variables from two land surface models: (i) ORganising Carbon and Hydrology In Dynamic EcosystEms (ORCHIDEE) and (ii) Hydrology – Tiled ECMWF Scheme for Surface Exchanges over Land (H-TESSEL). The radiative transfer model used is the Community Microwave Emission Model (CMEM). A good agreement in the temporal evolution of measured and modelled brightness temperatures is observed. However, their spatial structures are not consistent between them. An Empirical Orthogonal Function analysis of the brightness temperature's error identifies a dominant structure over the South-West of the Iberian Peninsula which evolves during the year and is maximum in Fall and Winter. Hypotheses concerning forcing induced biases and assumptions made in the radiative transfer model are analysed to explain this inconsistency, but no candidate is found to be responsible for it at the moment. Further hypotheses are proposed at the end of the paper.

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

2021 ◽  
Author(s):  
Shivani Tyagi ◽  
Dharmendra Kumar Pandey ◽  
Deepak Putrevu ◽  
Prashant K. Srivastava ◽  
Arundhati Misra
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Retrieval Algorithm

scholarly journals Effect of Structural Uncertainty in Passive Microwave Soil Moisture Retrieval Algorithm

Sensors ◽  
2020 ◽  
Vol 20 (4) ◽  
pp. 1225
Author(s):  
Lanka Karthikeyan ◽  
Ming Pan ◽  
Dasika Nagesh Kumar ◽  
Eric F. Wood
Keyword(s):  
Soil Moisture ◽  
Global Scale ◽  
Passive Microwave ◽  
Radiative Transfer Model ◽  
Retrieval Algorithm ◽  
Transfer Model ◽  
Structural Uncertainty ◽  
Three Solutions ◽  
Brightness Temperatures ◽  
Retrieval Algorithms

Passive microwave sensors use a radiative transfer model (RTM) to retrieve soil moisture (SM) using brightness temperatures (TB) at low microwave frequencies. Vegetation optical depth (VOD) is a key input to the RTM. Retrieval algorithms can analytically invert the RTM using dual-polarized TB measurements to retrieve the VOD and SM concurrently. Algorithms in this regard typically use the τ-ω types of models, which consist of two third-order polynomial equations and, thus, can have multiple solutions. Through this work, we find that uncertainty occurs due to the structural indeterminacy that is inherent in all τ-ω types of models in passive microwave SM retrieval algorithms. In the process, a new analytical solution for concurrent VOD and SM retrieval is presented, along with two widely used existing analytical solutions. All three solutions are applied to a fixed framework of RTM to retrieve VOD and SM on a global scale, using X-band Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) TB data. Results indicate that, with structural uncertainty, there ensues a noticeable impact on the VOD and SM retrievals. In an era where the sensitivity of retrieval algorithms is still being researched, we believe the structural indeterminacy of RTM identified here would contribute to uncertainty in the soil moisture retrievals.

scholarly journals Comparative Analysis of High-Resolution Soil Moisture Simulations from the Soil, Vegetation, and Snow (SVS) Land Surface Model Using SAR Imagery Over Bare Soil

Water ◽  
2019 ◽  
Vol 11 (3) ◽  
pp. 542 ◽  
Author(s):  
Mohammed Dabboor ◽  
Leqiang Sun ◽  
Marco Carrera ◽  
Matthew Friesen ◽  
Amine Merzouki ◽  
...  
Keyword(s):  
Soil Moisture ◽  
High Resolution ◽  
Soil Texture ◽  
Land Surface ◽  
Goodness Of Fit ◽  
Retrieval Algorithm ◽  
Surface Model ◽  
Land Surface Scheme ◽  
Sar Imagery ◽  
Surface Scheme

Soil moisture is a key variable in Earth systems, controlling the exchange of water andenergy between land and atmosphere. Thus, understanding its spatiotemporal distribution andvariability is important. Environment and Climate Change Canada (ECCC) has developed a newland surface parameterization, named the Soil, Vegetation, and Snow (SVS) scheme. The SVS landsurface scheme features sophisticated parameterizations of hydrological processes, including watertransport through the soil. It has been shown to provide more accurate simulations of the temporaland spatial distribution of soil moisture compared to the current operational land surface scheme.Simulation of high resolution soil moisture at the field scale remains a challenge. In this study, wesimulate soil moisture maps at a spatial resolution of 100 m using the SVS land surface scheme overan experimental site located in Manitoba, Canada. Hourly high resolution soil moisture maps wereproduced between May and November 2015. Simulated soil moisture values were compared withestimated soil moisture values using a hybrid retrieval algorithm developed at Agriculture andAgri-Food Canada (AAFC) for soil moisture estimation using RADARSAT-2 Synthetic ApertureRadar (SAR) imagery. Statistical analysis of the results showed an overall promising performanceof the SVS land surface scheme in simulating soil moisture values at high resolution scale.Investigation of the SVS output was conducted both independently of the soil texture, and as afunction of the soil texture. The SVS model tends to perform slightly better over coarser texturedsoils (sandy loam, fine sand) than finer textured soils (clays). Correlation values of the simulatedSVS soil moisture and the retrieved SAR soil moisture lie between 0.753–0.860 over sand and 0.676-0.865 over clay, with goodness of fit values between 0.567–0.739 and 0.457–0.748, respectively. TheRoot Mean Square Difference (RMSD) values range between 0.058–0.062 over sand and 0.055–0.113over clay, with a maximum absolute bias of 0.049 and 0.094 over sand and clay, respectively. Theunbiased RMSD values lie between 0.038–0.057 over sand and 0.039–0.064 over clay. Furthermore,results show an Index of Agreement (IA) between the simulated and the derived soil moisturealways higher than 0.90.

scholarly journals The Polarimetric Sensitivity of SMAP-Reflectometry Signals to Crop Growth in the U.S. Corn Belt

2020 ◽  
Vol 12 (6) ◽  
pp. 1007
Author(s):  
Nereida Rodriguez-Alvarez ◽  
Sidharth Misra ◽  
Mary Morris
Keyword(s):  
United States ◽  
Soil Moisture ◽  
The United States ◽  
Crop Growth ◽  
Corn Belt ◽  
Retrieval Algorithm ◽  
Agricultural Crop ◽  
Crop Height ◽  
Crop Type ◽  
The Impact

Crop growth is an important parameter to monitor in order to obtain accurate remotely sensed estimates of soil moisture, as well as assessments of crop health, productivity, and quality commonly used in the agricultural industry. The Soil Moisture Active Passive (SMAP) mission has been collecting Global Positioning System (GPS) signals as they reflect off the Earth’s surface since August 2015. The L-band dual-polarization reflection measurements enable studies of the evolution of geophysical parameters during seasonal transitions. In this paper, we examine the sensitivity of SMAP-reflectometry signals to agricultural crop growth related characteristics: crop type, vegetation water content (VWC), crop height, and vegetation opacity (VOP). The study presented here focuses on the United States “Corn Belt,” where an extensive area is planted every year with mostly corn, soybean, and wheat. We explore the potential to generate regularly an alternate source of crop growth information independent of the data currently used in the soil moisture (SM) products developed with the SMAP mission. Our analysis explores the variability of the polarimetric ratio (PR), computed from the peak signals at V- and H-polarization, during the United States Corn Belt crop growing season in 2017. The approach facilitates the understanding of the evolution of the observed surfaces from bare soil to peak growth and the maturation of the crops until harvesting. We investigate the impact of SM on PR for low roughness scenes with low variability and considering each crop type independently. We analyze the sensitivity of PR to the selected crop height, VWC, VOP, and Normalized Differential Vegetation Index (NDVI) reference datasets. Finally, we discuss a possible path towards a retrieval algorithm based on Global Navigation Satellite System-Reflectometry (GNSS-R) measurements that could be used in combination with passive SMAP soil moisture algorithms to correct simultaneously for the VWC and SM effects on the electromagnetic signals.

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