Geodaysit 2023

Non-photosynthetic vegetation (NPV) detection and quantification represent a key variable in remote sensing of conservative agriculture, and, more recently, in carbon farming due to its important role in water, nutrient and carbon cycling. For this reason, both mapping and characterization of NPV represent a relevant topic in the exploitation of Earth Observation (EO) data for agriculture monitoring.
Studies on NPV mapping by EO data benefit from the availability of hyperspectral data due to the high spectral resolution particularly at wavelengths from 1.6 to 2.3m, where the spectral features of carbon-based constituents of plants are distinctive. The launch of new generation hyperspectral satellites, as PRISMA (PRecursore IperSpettrale della Missione Applicativa) and, more recently, EnMAP (Environmental Mapping and Analysis Program) offers research opportunities in the field, which before was mainly investigated by proximal and aerial sensing.
Early studies already proved the potential of PRISMA in NPV due to the prominence of the cellulose-lignin key absorption feature at 2.1m. More recent studies on PRISMA make use of machine learning regression algorithm (MLRA) trained on the basis of radiative transfer model simulations, or on the basis of Exponential Gaussian Optimization (EGO) of specific absorption features on sensed data.
This second approach, proposed in this study, is aimed at the determination of Crop Residue Cover (CRC) using PRISMA hyperspectral imagery by a two-step approach making use of: i) firstly, an Exponential Gaussian Optimization to model pre-selected absorption features, also reducing the spectral dimension; ii) secondly, a Random Forest paradigm, performing non-linear regression to finally predict and map CRC.
This study exploits for the training phase an extensive and well documented spectral library, namely “Reflectance spectra of agricultural field conditions supporting remote sensing evaluation of non-photosynthetic vegetation cover” made available online by USGS (https://doi.org/10.5066/P9XK3867). It consists of 916 in situ surface reflectance spectra collected using a proximal full range spectroradiometer (350 to 2500 nm). Spectra are annotated with the corresponding fractions of NPV, Soil and (if any) Green Vegetation, as estimated by point sampling digital photograph of the radiometer field-of-view.
This spectral library was resampled to PRISMA spectral resolution, prior to the Gaussian Exponential Optimization (EGO) on 4 spectral intervals of interest, already tested in previous studies, and corresponding to absorption bands of: cellulose-lignin, plant pigments, vegetation water content and clays.
The EGO algorithm optimizes continuum-removed spectra by 4 parameters - absorption band depth, center, width and asymmetry – and since this is performed for each spectral interval, it results in 16 parameters. This is a reduced space as compared to the one of the input spectra (around 230 bands). This parameter space was used to train a Random Forest to model the regression between Crop Residue Cover percentage and EGO parameters, achieving a determination coefficient around 0.8 (RPD ˜2.1; MSE ˜ 0.02) on the test set.
The RF model was firstly validated against an independent spectral library of around 100 spectra, collected during a proximal sensing survey with a portable full range spectroradiometer, conducted in a large farm test site (3800ha) located in Jolanda di Savoia (Italy). Also in this case, spectra are annotated with Crop Residue Cover percentages, and resampled to PRISMA spectral resolution. The model performance on this dataset is in agreement with the test on the USGS spectral library.
Finally, the regression model was applied to a PRISMA image , acquired on the Jolanda di Savoia farm (June 21st 2021), for CRC mapping. The resulting map was validated against field observations: the CRC map show values and patterns in good agreement with ground data confirming encouraging prediction capabilities of the model
In conclusion, the proposed classification approach, trained on a spectral library is predictive, as proved on an independent spectral data set and on the PRISMA image. Further work will encompass testing the robustness of the model by collecting field ground data of Crop Residue Cover at the PRISMA scale; monitoring CRC dynamics on PRISMA time series; and, the use of Radiative Transfer Model simulations to enlarge the training set, accounting also for different factors controlling reflectance (e.g. soil moisture).