RAPAS - Close range aerial sensing of soils for improved remote sensing products

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Context and objectives

Ultra-wideband ground-penetrating radar (GPR) technology has demonstrated its capability for field- scale digital soil mapping of key soil properties such as moisture and roughness. The method of Lambot et al. relies on full-wave modelling and inversion of the radar data and has been recently generalized to near-field antenna conditions for deeper soil characterization. A lightweight radar system is being setup for close range Remotely Piloted Aircraft Systems (RPAS).
For operational retrieval of surface soil moisture from spaceborne remote sensing data, site-specific in situ calibrations remain essential. The calibration of backscattered synthetic aperture radar (SAR) data by classical in situ point measurements using soil sampling or invasive sensors is an expensive and complicated task given the inherent spatiotemporal variability of soil moisture at the field scale. RPAS-based GPR provides a new way for bridging the gap between soil sampling and remote sensing.

The ability of imaging spectroscopy to cover large surfaces in a single campaign and to study the spatial distribution of soil properties with a high spatial resolution could improve soil monitoring and the spatial prediction of physico-chemical soil properties. However soil moisture and roughness are subject to variations both in time and in space and induce changes in soil reflectance that approach or exceed the spectral response of intrinsic soil properties such as organic matter content, and are known to induce a significant anisotropy on the directional distribution of the solar radiation scattered from bare soils. Combining close range hyperspectral spectroscopy with GPR and Sentinel products appears as a promising solution given the complementary information provided by the sensors as well as the relevance of the characterization scales. Soil moisture and roughness estimates provided by GPR is expected to provide valuable information to constrain soil spectroscopy models and infer physico-chemical properties of interests such as soil organic content with a higher accuracy.

The general objectives of the project is:

  1. Use RPAS-based GPR to calibrate Sentinel-1 SAR products for surface soil moisture and to estimate root-zone soil moisture, and ;
  2. To disentangle soil surface moisture and roughness effects from hyperspectral data using a new close range RPAS- based platform.

The four specific objectives are:

  1. To perform RPAS-based GPR measurements to provide high- resolution maps of both surface soil moisture and root-zone soil moisture, plus possibly roughness amplitude;
  2. To process GPR data processing, namely, inversion of the radar data to provide root-zone and surface soil moisture, and roughness amplitude;
  3. To perform a tentative up-scaling of RPAS-based GPR soil moisture information, using GPR measurements as calibrating and validating ground-truths on Sentinel-1 SAR imagery;
  4. To quantify the spectral behaviour of soil properties under real-life conditions using where soil moisture and roughness are dynamic in space and time. Time lapse multi- and hyperspectral data acquired simultaneously with the SAR acquisitions.

 

Project outcome

Expected scientific results

• Setup of the RPAS time-lapse, low and high-frequency GPR data over the test sites.
• Setup of the RPAS based spectrometer and positioning/orientation accessories.
• GPR root-zone and surface soil moisture maps acquired with the RPAS system.
• Hyperspectral soil carbon content maps acquired with the RPAS system.
• GPR-calibrated Sentinel-1 soil moisture maps.

Expected products and services

RPAS-based mapping of soil properties to support precision agriculture (e.g., irrigation, soil property assessment) and hydrological & soil eco-system modelling through the improvement of Sentinel-1 surface soil moisture products.

If Sentinel-1 tentative up-scaling reveals to be sufficiently efficient, expected products are regional soil moisture mapping and soil moisture monitoring.

Research institutions, farmers, environmental and agricultural engineering companies.