Temporal changes in soil moisture (SM) and green vegetation affecting the spectral reflectance can heavily reduce the accuracy of topsoil properties estimation from satellite imaging. To minimize these effects on the soil organic carbon (SOC), sand, silt and clay estimations, an external parameter orthogonalization (EPO) model developed using laboratory based measured spectra was tested on PRISMA hyperspectral satellite data. The estimation of soil properties was performed using different machine learning algorithms. The results show that as compared to the uncorrected spectra, removing the effects of both green vegetation and SM (EPOSM+GV) from the reflectance spectra leads to 18%, 13%, 10%, and 24% improvement in the R2for clay, silt, sand and SOC retrieval, respectively. The Gaussian Process Regression (GPR) algorithm provides the best results for all of the soil properties with an RMSE of 9.5%, 14.2%, 6.9% and 0.68% for clay, silt, sand and SOC retrievals, respectively.

The project "EO AFRICA EXPLORERS—PRISMA 4 AFRICA", funded by the ESA, aims at combining products derived from hyperspectral (e.g., PRISMA, EnMAP, DESIS) and thermal data (e.g., ECOSTRESS, Landsat) to detect vegetation anomalies and identify whether they may be related to biotic or abiotic stress factors. In this context, water stress as a main abiotic stress was considered. At this aim we investigated the possibility to exploit PRISMA combined with the ECOSTRESS data to derive evapotranspiration (ET). Even though LST products are available and appear of good quality, the lack of ancillary data prevents ET products to be generated timely. 30-m ET map was produced by combining of ECOSTRESS and PRISMA data and used for water stress estimation. The resulting products proved to be sufficiently accurate to describe the crop water stress at the field scale.

The project "Topsoil properties Estimation from Hyperspectral Remote sensing for Agriculture" (TEHRA), funded by the Italian Space Agency (ASI), aims at developing methods and algorithms for the estimation of soil properties of agronomic and environmental interest from PRISMA satellite hyperspectral data, that could support: 1) the adoption of more sustainable and climate-smart farming practices, e.g. through the implementation of precision agriculture applications; 2) monitoring in support of agricultural and environmental policies, e.g. related to climate change and for the encouragement of the adoption of practices preserving soil health.In this paper, some results of the first year of the project are illustrated. They concern: 1) a scenario definition study; 2) studies on the confounding effect of soil moisture and crop residues; 3) exploitation of multi-temporal PRISMA data and 4) data fusion with proximal soil sensing.

The unique spectral content provided by PRISMA's hyperspectral sensor gives the possibility to study the Earth's surface and environment from space in unprecedented detail. In this respect, our work presents the results of an Italian Space Agency-funded project aiming to develop eight prototypes for providing Value Added products based on such data. Prototypes focus on vegetation, urban areas, water quality, material detection, and natural hazards, combining multiple state-of-the-art techniques based on Machine Learning, physical models, and index-based algorithms. This is particularly relevant given the increasing demand for accurate information to address sustainable policies and support decision-making processes. Through a series of case studies, we highlight the versatility and utility of PRISMA's hyperspectral data for various scientific and operational applications.

The PRISMA (PRecursore IperSpettrale della Missione Applicativa) hyperspectral satellite, launched by the Italian Space Agency (ASI) is presently operational on a global scale. The mission includes the hyperspectral imager PRISMA working in the 400–2500 nm spectral range with 234 bands and a panchromatic (PAN) camera (400–750 nm). In the context of this work, we intend to determine the two noise components (photon and thermal noise) and assess SNR with an image based approach. Results show that the SNR evaluation assessed through the collected images is coherent with the mission requirements and that the PRISMA noise components, derived on the fragmented Pignola test site, in Southern Italy, are comparable to the ones derived on the Rail Road Valley calibration site.

In this research the potential of the PRISMA hyperspectral sensor in comparison with multispectral data (Sentinel-2 MSI and Landsat 8 OLI) was assessed for predicting soil moisture. To this aim, PRISMA, Sentinel-2 and Landsat 8 spectra, resampled according to the spectral bands of each sensor, were simulated from a laboratory soil spectral library. The soil samples used to create the spectral library were collected from different agricultural areas in Central and Southern Italy. Partial Least Square Regression (PLSR), the Normalized Soil Moisture Index (NSMI) and the Soil Moisture Gaussian Model (SMGM) were employed to calibrate soil moisture (SM) estimation models from the resampled spectra. The prediction accuracy of SM estimation was assessed from statistical metrics. The best accuracies in retrieving SM were obtained by PLSR using data resampled at PRISMA spectral resolution. A preliminary test of the application of the calibrated models was carried out using real PRISMA and Sentinel-2 data.

On the 22 March 2019, the Italian Space Agency (ASI) launched the PRISMA satellite, having onboard a hyperspectral imager covering the 400–2500 nm range with 234 spectral bands and about 10 nm of bandwidth. The ground spatial resolution is 30 m, plus a panchromatic camera with 5 m spatial resolution. One of the potential application areas of this scientific mission is for precision agriculture applications, among which the mapping of field-scale variability of topsoil properties is of particular interest. PRISMA clear-sky hyperspectral images were acquired in autumn and spring 2019 over two agricultural areas, Maccarese (Central Italy), and Pignola (Southern Italy). An intensive soil sampling campaign was performed, using a ground sampling scheme adapted to PRISMA spatial 30 and 5 m (PAN) resolutions, in the fields where bare soil was exposed at the satellite acquisition dates. Soil texture (clay, silt, sand) and soil organic carbon (SOC) for the collected soil samples were then determined in the laboratory. The dataset was then used to test calibration and validation of PLS (Partial Least Squares) and Random Forest (RF) regressions, developed using PRISMA surface reflectance data. To this aim, several pre-treatment tests were performed. The results show that good results could be obtained especially for clay estimation.

The Italian Space Agency (ASI) is supporting the calibration/validation (CAL/VAL) activities of the PRISMA hyperspectral mission with the PRISMA Calibration Validation project (PRISCAV). PRISCAV provides, a network of reference test sites to support the PRISMA validation of the L1 and L2 processing chain performance. Among the PRISCAV test sites, representing the different Italian territory, the Pignola test site depicts an agricultural scenario pertaining to the Southern Apennines in the Basilicata Region (Italy). On this site, contemporary to PRISMA acquisitions, a set of ground measurements have been collected from October 2019 to December 2020 to characterize the atmosphere and the ground optical properties and validate the PRISMA radiometry and the L2 reflectance products. Measures are still ongoing on the base of the PRISMA acquisition plan. The comparison of the Modtran simulated radiance with the PRISMA L1 radiance data show the same magnitude and shape. RMSE for the full range of wavelengths vary from 0.000153 to 0.000995 [W/m−2sr−1nm−1]. Further analyses will include the new PRISMA acquisitions and the possible matchups with Sentinel-2, to assure the full exploitation of the PRISMA data for the agricultural monitoring in the Southern Apennines.

The sensitivity of the estimation accuracy of topsoil properties to spatial resolution was explored in the context of the assessment of the forthcoming ESA Copernicus hyperspectral mission CHIME. A methodology was developed to allow a fair comparison across spatial resolution scales of the retrieval accuracy, considering the spatial support size and calibration/validation set size. Under these conditions, the prediction accuracy was found not degraded when decreasing the spatial resolution from 3.6 m (AVIRIS NG) to 20 m (Sentinel 2) or 30 m (CHIME). The best results were obtained for clay estimation, whereas for sand, silt, and soil organic carbon (SOC) the retrieval accuracy was slightly worse, although comparable or even better to what previously reported in the literature. When the spatial correlation between the estimated clay map and the spatialized (block kriging) ground truth map was examined, using the co-dispersion coefficient, the best results were obtained for the 30 m spatial resolution.

Monitoring and mapping weeds within agricultural crops is required for the implementation of precision agriculture approaches such as patch spraying. A precise and targeted weed control would bring about positive consequences from both environmental and economic perspectives. Given the small spectral differences between crop species, VNIR hyperspectral data can be a powerful tool to perform an effective weed monitoring and identification when high spatial and spectral resolution data is available (i.e. UAV platforms). This work explores the spectral differences between crops and weeds to evaluate the ability of UAV hyperspectral data to separate maize crop from weeds and to discriminate different types of weeds. To this aim, UAV and field hyperspectral data were acquired in some maize fields in Italy during the 2016 growing season. Results showed that by exploiting leaf chlorophyll and carotenoid contents, retrieved using spectral indices or by inverting PROSAIL, is it possible to discriminate between maize crop and weeds and, moreover, among weed types. The procedure allowed the quantification of crop/weeds relative ground cover, which showed a good relationship with the corresponding measured relative LAI values.

The assimilation of biophysical crop canopy variables retrieved from remotely sensed data into two crop models of differing degree of complexity is assessed in this study, in the context of the development of tools suitable for the estimation of yield losses due to drought. The more complex AQUACROP model, developed by FAO and the simpler SAFY model were employed to estimate wheat grain yield for an area in the Shaanxi Province in China through the assimilation of biophysical variables retrieved from Landsat and HJ1A and HJ1B satellites for three growing seasons (2013 to 2015). Results were validated with ground yield data.

The paper describes the preliminary results of the January-August 2015 multi-frequency EO data acquisition campaign conducted over the Maccarese (Central Italy) farm. From January to May radar Cosmo SkyMed Ping-Pong (HH-VV), RapidEye and ZY-3 multispectral VHR optical images, as well as in situ data, have been acquired to retrieve biophysical and/or bio-chemical characteristics of soil and crops. LAI trend has been analyzed and compared by using both polarimetric and optical retrieval algorithms while soil moisture measurements have been compared with the radar backscattering.

The aim of this work is the development of methods for the assimilation of biophysical variables, estimated from multi-source remote sensing data, into crop growth models, in order to estimate the yield losses due to drought both at the farm and at the regional scale. A methodology to obtain maps of leaf area index (LAI), and fractional canopy cover (CC), from HJ1A and HJ1B Chinese satellite optical data was established, using an algorithm based on the training of artificial neural networks (ANN) on PROSAIL model simulations. Retrieved values of biophysical variables, such as LAI or CC, will be assimilated into crop growth models in order to estimate wheat yield. The present work focused on testing two different approaches using a common dataset gathered in Xiaotangshan (China) with two crop models of different complexity, in order to compare the procedures and analyse the responses of the models, before the subsequent application at a regional scale in Yangling, Shaanxi, Central China.

The SAP4PRISMA project research activities aimed at supporting the Italian hyperspectral PRISMA mission by developing preliminary processing chains suitable for PRISMA to obtain high level hyperspectral data products for agriculture, land degradation, natural and human hazards.

The main objectives of the PRISMA (Hyperspectral Precursor of the Application Mission) mission are: the implementation of an Earth Observation pre-operative payload, the in-orbit demonstration and qualification of an Italian state-of-the-art hyperspectral/panchromatic technology and the validation of end-to-end data processing system able to support the development of new applications based on high spectral resolution images. The aim of the paper is to provide an overview of the PRISMA mission by describing the current status of the program and giving a brief outline of the work done till now in the framework of the SAP4PRISMA project scientific studies in supporting the exploitation of the future PRISMA hyperspectral images for environmental applications.

The SAP4PRISMA is a four year research project which aims at developing algorithms and products for the future PRISMA mission. The project started on May 2010 and is now entering his full activities as the ”PRISMA like” data set has been defined and the test areas were selected. The paper describes the main project objectives and the activities realized in the first 9 months of the project.