Mapping barrier island soil moisture using a radiative transfer model of hyperspectral imagery from an unmanned aerial system

Abstract

The advent of remote sensing from unmanned aerial systems (UAS) has opened the door to more affordable and effective methods of imaging and mapping of surface geophysical properties with many important applications in areas such as coastal zone management, ecology, agriculture, and defense. We describe a study to validate and improve soil moisture content retrieval and mapping from hyperspectral imagery collected by a UAS system. Our approach uses a recently developed model known as the multilayer radiative transfer model of soil reflectance (MARMIT). MARMIT partitions contributions due to water and the sediment surface into equivalent but separate layers and describes these layers using an equivalent slab model formalism. The model water layer thickness along with the fraction of wet surface become parameters that must be optimized in a calibration step, with extinction due to water absorption being applied in the model based on equivalent water layer thickness, while transmission and reflection coefficients follow the Fresnel formalism. In this work, we evaluate the model in both field settings, using UAS hyperspectral imagery, and laboratory settings, using hyperspectral spectra obtained with a goniometer. Sediment samples obtained from four different field sites representing disparate environmental settings comprised the laboratory analysis while field validation used hyperspectral UAS imagery and coordinated ground truth obtained on a barrier island shore during field campaigns in 2018 and 2019. Analysis of the most significant wavelengths for retrieval indicate a number of different wavelengths in the short-wave infra-red (SWIR) that provide accurate fits to measured soil moisture content in the laboratory with normalized root mean square error (NRMSE)< 0.145, while independent evaluation from sequestered test data from the hyperspectral UAS imagery obtained during the field campaign obtained an average NRMSE = 0.169 and median NRMSE = 0.152 in a bootstrap analysis.

Comparison of Soil Moisture Content Retrieval Models Utilizing Hyperspectral Goniometer Data and Hyperspectral Imagery From an Unmanned Aerial System

“…In the laboratory, we used four soil samples and associated BCRF measurements that were part of an earlier study (Eon & Bachmann, 2021b) that included sediment samples with varying physical features from four distinct locations. That earlier study focused solely on retrieval using the MARMIT model for these laboratory data and a more limited set of hyperspectral imagery collected over Hog Island from UAS systems (Eon & Bachmann, 2021b). One sample is from Algodones Dunes, California (ALG) that was part of another study (Bachmann, Eon, Ambeau, et al., 2018; McCorkel et al., 2017) there involving the NASA Goddard Light Detection And Ranging, Hyperspectral, and Thermal (G‐LiHT) system (Cook et al., 2013).…”

“…The BRF data used in this segment of our analysis was measured in our laboratory using a hyperspectral goniometer, the Goniometer of the Rochester Institute of Technology‐Two (GRIT‐T) (Harms et al., 2017) covering a range of moisture content from dry to fully saturated (Eon & Bachmann, 2021a, 2021b). The GRIT‐T is a sophisticated goniometric system designed for use in field and laboratory settings and has seen widespread application in previous research (Bachmann, Eon, Ambeau, et al., 2018; G. Badura et al., 2019; G. Badura & Bachmann, 2019; G. P. Badura et al., 2019; Eon et al., 2018, 2020; Eon & Bachmann, 2021b; Roth et al., 2020, 2021; Shiltz & Bachmann, 2023). Users can create custom scan patterns to manage the system's arm, head, and carriage.…”

“…In the laboratory, we used four soil samples and associated BCRF measurements that were part of an earlier study (Eon & Bachmann, 2021b) that included sediment samples with varying physical features from four distinct locations. That earlier study focused solely on retrieval using the MARMIT model for these laboratory data and a more limited set of hyperspectral imagery collected over Hog Island from UAS systems (Eon & Bachmann, 2021b).…”

Aquatic ecosystems science using an imaging spectrometer

“…151,152 An extended pitchback maneuver over a large angular range would provide an unprecedented opportunity to examine the spectral BRDF to achieve these goals over coastal zone waters and margins. Data downloaded can be directly related to surface measurements, for example, using a hyperspectral goniometer system, 10,[153][154][155][156][157][158][159] which can measure the surface BRDF for validation purposes. However, to undertake a detailed analysis to understand these distributions would require the direct downlink of each frame in the pitchback maneuver necessitating significantly higher bandwidth.…”

“…Onshore measurements also require even higher SNR since the surface reflectance and the resulting at-aperture radiance are reduced as the soil wetness increases. 10 We have chosen the Decadal Survey SNR requirements of ≥400 (380-1100 nm) and ≥250 (1100-2500 nm) as a threshold although the reflectance variability places a premium on high SNR.…”