An algorithm for generating soil moisture and snow depth maps from microwave spaceborne radiometers: HydroAlgo

Abstract

Abstract.A systematic and timely monitoring of land surface parameters that affect the hydrological cycle at local and global scales is of primary importance in obtaining a better understanding of geophysical processes and in managing environmental resources as well as natural disasters. Soil moisture and snow water equivalent are two quantities that play a major role in these applications. In this paper an algorithm for hydrological purposes (called hereinafter HydroAlgo), which is able to generate maps of snow depth (SD) and soil moisture content (SMC) from AMSR-E data, has been developed and implemented within the framework of the JAXA ADEOS-II/AMSR-E and GCOM/AMSR-2 programs, as well as of a project of the Italian Space Agency that is devoted to civil protection from floods and landslides. As auxiliary output, the algorithm also generates maps of vegetation biomass (VB). An initial phase of pre-processing includes the improvement of spatial resolution, as well as masking for urban areas, water bodies, and dense vegetation. The algorithm was then split into two branches, the first of which focused on the retrieval of SMC and the second, on SD. Both parameters were retrieved using Artificial Neural Network (ANN) methods. The algorithm was calibrated using a wide set of experimental data collected on three sites: Mongolia and Australia (for SMC), and Siberia (for SD), integrated with model simulations. These results were then validated by comparing the algorithm outputs with experimental data collected on two additional sites: a part of a watershed in Northern Italy, and a large portion of Scandinavia. An additional test of the algorithm was also performed on a large scale, and included sites characterized by differing climatic and meteorological conditions.

Soil Moisture Content Estimation Based on Sentinel-1 and Auxiliary Earth Observation Products. A Hydrological Approach

“…Concerning ANNs, their effectiveness in solving inverse remote sensing problems such as those required by SMC monitoring has diachronically been proved [20]. [21] have implemented the SMOSAR algorithm for retrieving SMC from Sentinel-1 multi-temporal data.…”

Developing an operational algorithm based on ANN for the retrieval of SMC from the incoming metop SCA mission

“…The simulator is able to account for the topography and to calculate the Local Incidence Angle for the 3 different beams and for each simulated pixel. Scenes have been simulated at 25x25 km 2 resolution, from the input SMC and vegetation biomass (PWC) products generated by the "Hydroalgo" algorithm [3], developed at IFAC for estimating both parameters from AMSR-E/AMSR2 acquisitions on a global scale, which also includes a disaggregation procedure for enhancing the spatial resolution [4]. The simulator flowchart is represented in Figure 1.…”

A prototype ann based algorithm for the soil moisture retrieval from l- band in view of the incoming SMAP mission

“…colocated SMAP-AMSR2 overpasses). This index, defined as PI X =2 (Tb VX -Tb HX )/(Tb VX +Tb HX ) was demonstrated to be well correlated to the latter parameter [2], and successfully employed for estimating PWC on a global scale in the HydroAlgo algorithm [3]. The PWC was related to PI X using the following relationship that was obtained comparing PI X from AMSR-E and PWC from optical data on a wide portion of Africa, from the Sahara desert to Equatorial forest, including a very high variability of vegetation types and landscape.…”