Advantages using combined VNIR-SWIR and LWIR hyperspectral remote sensing for estimation of soil properties in the Amyntaio agricultural region, Northern Greece

A deeper understanding of the agricultural sector is needed to provide the informed and transparent framework required to meet increasing resource demands and pressures, without compromising sustainability. In this regard, an integrated management of the ecosystems is critical to address the priorities laid out by global policies and, achieve land degradation neutrality and resource efficient regions. Soils are an essential component of the ecosystem, they function as an important carbon storage, and provide the basis of agricultural activity. For the sustainable management of soil resources, and to prevent land degradation the regular assessments of spatially referenced soil conditions is essential. Critical soil properties, such as texture and organic and inorganic carbon content, provides farmers with the information to detect soil vulnerable to soil erosion and land degradation in its early stages in order to locally intervene and to assess soil fertility. Hyperspectral remote sensing been proven to be an effective method for the quantitative prediction of topsoil properties. However, remote sensing observations of the traditionally used visible-near infrared (VNIR) and shortwave infrared (SWIR) wavelength regions (0.4-2.5 µm) can be limited for the estimation of coarse texture soils due to the lack of distinct spectral characteristics of these properties in the VNIR-SWIR (e.g., sand content, quartz and feldspar mineralogy). Spectral information from the longwave infrared region (LWIR, 8-12 μm) has the potential to improve the determination of these properties, due to the presence of fundamental vibration modes of silicate and carbonate minerals, as well carbon-hydrogen bonds in this spectral range.

The main objective of this study is to evaluate the increased analytical potential of combined VNIR-SWIR and LWIR hyperspectral remote sensing for the estimation of soil properties with the focus on soil organic matter, texture and mineralogical composition. In the frame of EnMAP GFZ/FU airborne campaign in Northern Greece in September 2019, an airborne survey with the HySpex VNIR-SWIR and Hyper-Cam LWIR cameras mounted on a Cessna airplane. A simultaneous ground sampling campaign took place at the agricultural landscape of the Amyntaio region including fields spectroscopy for calibration and validation porpoise, as well as soil sampling of bare soil fields. Fields in the study area have highly variable topsoil composition ranging from silicate to carbonate rich mineralogy, loamy to clay texture and to organic carbon rich fields around a lignite mine in the south-east of the area. Different statistical and machine learning methods such as Partial Least Squares (PLS) and Random Forest (RF) regression are applied to derive soil properties and the variable importance of the spectral dataset is discussed. A further goal of this study is the simulation and validation of the soil products with recent relevant satellite sensors (e.g., EnMAP, PRISMA, ECOSTRESS), as well as upcoming next generation of hyperspectral optical and thermal multispectral satellite missions (ESA CHIME and LSTM, NASA/JPL SBG) to evaluate their potential for quantitative soil properties mapping.