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Micronutrient malnutrition is a global challenge affecting >2 billion people, in particular those with a crop-based diet and limited access to nutrient-rich food sources. Conventional methods for measuring the crop nutrients such as wet chemical analysis of grains are time-consuming and cost-prohibitive and, consequently, unsuitable for the consistent quantification of nutrients across space and time. In this study, we propose a new method that is using PRecursore IperSpettrale della Missione Applicativa (PRISMA) and Sentinel-2 images to estimate the nutrient concentrations of crop grains before harvest. We collected grain samples for corn, rice, soybean, and wheat from a farm situated in Italy and measured their nutrient concentrations in the lab. These measurements together with the PRISMA and Sentinel-2 images acquired at the main phases of crop development (vegetative, reproductive, maturity) were used as input for two-band vegetation indices (TBVIs) and Partial Least Squares Regression (PLSR) to predict Calcium (Ca), Iron (Fe), Potassium (K), Magnesium (Mg), Nitrogen (N), Phosphorus (P), Sulphur (S) and Zinc (Zn). Models' performances were assessed using the coefficient of determination (R2) and Root Mean Square Error (RMSE). For PRISMA images, the best prediction results were obtained for P in soybean (R2 = 0.69), K in soybean (R2 = 0.66), Mg in soybean (R2 = 0.58), Fe in soybean (R2 = 0.57), K in wheat (R2 = 0.57), K in corn (R2 = 0.55), P in wheat (R2 = 0.51), S in rice (R2 = 0.58) using TBVIs. In contrast to PRISMA, PLSR outperformed TBVIs when Sentinel-2 images were used as input. For Sentinel-2, the best predictions were obtained for P in soybean (R2 = 0.73), K in wheat (R2 = 0.67), Mg in soybean (R2 = 0.62), Zn in wheat (R2 = 0.56), Fe in soybean (R2 = 0.52), P in wheat (R2 = 0.52). Our study showed that estimating the nutrient composition of crops using remote sensing images has the potential to change how we approach a cost-effective, timely, and spatially explicit representation of the crops' nutritional quality.

The rice growing district in northwestern Italy, where paddies were traditionally flooded throughout spring, was interested by a general decrease of standing water presence caused by the adoption of dry seeding crop practices, with consequences for water management and for the ecology of breeding waterbirds. This communication analyses changes in flooding dynamics in the last four years, estimating them from MODIS data and comparing results with previous knowledge of the same study area. Results highlighted an intensification of the phenomenon in the north-western regions (-3.3 ± 0.6% per year in the period 2013-2021) and the almost complete loss of flooded surfaces east to the Ticino river (reaching in 2021 5% of the flooded extension estimated in 2000). Such findings highlight the importance of monitoring this phenomenon - considered by other authors as the biggest anthropogenic change in surface water of all Europe since 2000 - in near real time from remotely sensed data to monitor dynamics and support sustainable management of water usage at the district level.

The unprecedented availability of optical satellite data in cloud-based computing platforms, such as Google Earth Engine (GEE), opens new possibilities to develop crop trait retrieval models from the local to the planetary scale. Hybrid retrieval models are of interest to run in these platforms as they combine the advantages of physically-based radiative transfer models (RTM) with the flexibility of machine learning regression algorithms. Previous research with GEE primarily relied on processing bottom-of-atmosphere (BOA) reflectance data, which requires atmospheric correction. In the present study, we implemented hybrid models directly into GEE for processing Sentinel-2 (S2) Level-1C (L1C) top-of-atmosphere (TOA) reflectance data into crop traits. To achieve this, a training dataset was generated using the leaf-canopy RTM PROSAIL in combination with the atmospheric model 6SV. Gaussian process regression (GPR) retrieval models were then established for eight essential crop traits namely leaf chlorophyll content, leaf water content, leaf dry matter content, fractional vegetation cover, leaf area index (LAI), and upscaled leaf variables (i.e., canopy chlorophyll content, canopy water content and canopy dry matter content). An important pre-requisite for implementation into GEE is that the models are sufficiently light in order to facilitate efficient and fast processing. Successful reduction of the training dataset by 78% was achieved using the active learning technique Euclidean distance-based diversity (EBD). With the EBD-GPR models, highly accurate validation results of LAI and upscaled leaf variables were obtained against in situ field data from the validation study site Munich-North-Isar (MNI), with normalized root mean square errors (NRMSE) from 6% to 13%. Using an independent validation dataset of similar crop types (Italian Grosseto test site), the retrieval models showed moderate to good performances for canopy-level variables, with NRMSE ranging from 14% to 50%, but failed for the leaf-level estimates. Obtained maps over the MNI site were further compared against Sentinel-2 Level 2 Prototype Processor (SL2P) vegetation estimates generated from the ESA Sentinels' Application Platform (SNAP) Biophysical Processor, proving high consistency of both retrievals (R2 from 0.80 to 0.94). Finally, thanks to the seamless GEE processing capability, the TOA-based mapping was applied over the entirety of Germany at 20 m spatial resolution including information about prediction uncertainty. The obtained maps provided confidence of the developed EBD-GPR retrieval models for integration in the GEE framework and national scale mapping from S2-L1C imagery. In summary, the proposed retrieval workflow demonstrates the possibility of routine processing of S2 TOA data into crop traits maps at any place on Earth as required for operational agricultural applications.

Satellite image data deliver consistent and frequent information for crop yield estimation over large areas. Hyperspectral narrowbands are more sensitive spectrally to changes in crop growth than multispectral broadbands but few studies quantified the gains in the former over the later. The PRecursore IperSpettrale della Missione Applicativa (PRISMA) mission offers narrow (≤10 nm) band capability across the full optical range. The multispectral broadband Sentinel-2 mission carries four experimental red-edge and near infrared (NIR) hyperspectral narrow (≤20 nm) bands. We compared the performance of PRISMA and Sentinel-2 spectral bands at important phases of crop development (vegetative, reproductive, maturity) in estimating field-level biomass and yield for corn, rice, soybean, and wheat. We selected three data-driven methods: two-band vegetation indices (TBVIs), partial least squares regression (PLSR), and random forest (RF). The PRISMA and Sentinel-2 models on average explained approximately 20% more variability in biomass and yield with RF than TBVIs and PLSR. The mean RMSE of the PRISMA RF models was 0.42 and 0.17 kg m−2, which was lower than the Sentinel-2 RF models (0.48 and 0.18 kg m−2). Multidate image (seasonal) model performance was generally higher than single-date image model performance. PRISMA shortwave infrared narrowbands and Sentinel-2 red-edge and near infrared bands were among the top-performing spectral regions. The results highlight potential complementarity between the PRISMA and Sentinel-2 missions for predicting crop biomass and yield. The results also show the benefits, limitations, and pitfalls of hyperspectral imaging in agricultural monitoring, which is important for upcoming operational hyperspectral missions, such as ESA CHIME and NASA Surface Biology and Geology (formerly HysPIRI).

Crop lodging - the bending of crop stems from their upright position or the failure of root-soil anchorage systems - is a major yield-reducing factor in wheat and causes deterioration of grain quality. The severity of lodging can be measured by a lodging score (LS)- an index calculated from the crop angle of inclination (CAI) and crop lodged area (LA). LS is difficult and time consuming to measure manually meaning that information on lodging occurrence and severity is limited and sparse. Remote sensing-based estimates of LS can provide more timely, synoptic and reliable information on crop lodging across vast areas. This information could improve estimates of crop yield losses, inform insurance loss adjusters and influence management decisions for subsequent seasons. This research - conducted in the 600 ha wheat sown area in the Bonifiche Ferraresi farm, located in Jolanda di Savoia, Ferrara, Italy - evaluated the performance of RADARSAT-2 and Sentinel-1 data to discriminate and classify lodging severity based on field measured LS. We measured temporal crop status characteristics related to lodging (e.g. lodged area, CAI, crop height) and collected relevant meteorological data (wind speed and rainfall) throughout May-June 2018. These field measurements were used to distinguish healthy (He) wheat from lodged wheat with different degrees of lodging severity (moderate, severe and very severe). We acquired multi-incidence angle (FQ8-27° and FQ21-41°) RADARSAT-2 and Sentinel-1 (40°) images and derived multiple metrics from them to discriminate and classify lodging severity. As a part of our data exploration, we performed a correlation analysis between the image-based metrics and LS. Next, a multi-temporal discriminant analysis approach, including a partial least squares (PLS-DA) method, was developed to classify lodging severities. We used the area under the curve-receiver operating characteristics (AUC-ROC) and confusion matrices to evaluate the accuracy of the PLS-DA classification models. Results show that (1) volume scattering components were highly correlated with LS at low incidence angles while double and surface scattering was more prevalent at high incidence angles; (2) lodging severity was best classified using low incidence angle R-FQ8 data (overall accuracy 72%) and (3) the Sentinel-1 data-based classification model was able to correctly identify 60% of the lodging severity cases in the study site. The results from this first study on classifying lodging severity using satellite-based SAR platforms suggests that SAR-based metrics can capture a substantial proportion of the observed variation in lodging severity, which is important in the context of operational crop lodging assessment in particular, and sustainable agriculture in general.

Lodging - the bending of crop stems - reduces the quantity and quality of cereal crop yields. Early quantification of crop lodging is important to prevent further losses and to facilitate harvesting operations. Crop angle of inclination (CAI) is a quantitative measure of the lodging stage and a component of lodging severity/score. CAI is an important structural parameter for lodged crops and very few studies have investigated its estimation using satellite-based remote sensing. In this study, the performance of Sentinel-1 and multi-incidence angle (FQ8-27° and FQ21-41°) RADARSAT-2 data were investigated for estimating CAI. Temporal crop biophysical/structural parameters (CAI and crop height) and meteorological data (rainfall and wind speed) were collected throughout May 1-June 30, 2018 in a very large commercial farm located in Jolanda di Savoia, Ferrara, Italy. Field data were grouped into different crop lodging stages (non-lodged/healthy (H), moderate lodging (ML), severe lodging (SL) and very severe lodging (VSL)) based on CAI. Quantitative relationships were established between field-measured CAI values and the RS-derived metrics for Sentinel-1 and RADARSAT-2 timeseries using support vector regression (SVR) models. The RADARSAT-2 FQ8 model performed most robustly with a R2CV (cross-validated R2) of 0.87 and a RMSECV (cross-validated RMSE) of 8.89° while the performance of the Sentinel-1 and RADARSAT-2 FQ21 models were comparable with an RMSECV of 11.35° and 11.63° respectively. Low incidence angle data were particularly sensitive to high CAI values (VSL) while high incidence angle data were useful for predicting lower CAI (ML and SL). While the RADARSAT-2 FQ-8 model outperformed the other two, the Sentinel-1 model still explained 78% of the CAI variability in the study site, which is important in the context of operational crop lodging stage assessment. This is the first study to demonstrate the utility of SAR remote sensing data for estimating CAI as a measure of the lodging stage and a component of lodging severity.

The goal of this work is to investigate the potential of PRecursore IperSpettrale della Missione Applicativa (PRISMA) hyperspectral data to predict the concentration of four macronutrients (K, P, N, S) and four micronutrients (Ca, Fe, Mg, Zn) in final wheat production. All investigated nutrients are essential to improving human nutrition. The initial findings indicate accurate predictions for Zn, P, Mg, S, K, Ca and Fe (R2 ranging from 0.57 to 0.74). N was less accurately estimated (R2 of 0.49). We conclude that the foliar chemical properties and temporal dynamics as detected by hyperspectral data translate successfully to the target micro- and macronutrients composition of the wheat production.

The Copernicus Hyperspectral Imaging Mission for the Environment (CHIME) is in preparation to carry a unique visible to shortwave infrared spectrometer. CHIME will globally provide routine hyperspectral observations to support new and enhanced services for, among others, sustainable agricultural and biodiversity management. The mission shall provide Level 1B, 1C and 2A products, as well a set of downstream products related to the different environmental applications, such as the quantification of vegetation traits. In this context, this work presents the first hybrid retrieval models for the operational delivery of vegetation properties products. Within ESA’s CHIME end-to-end (E2E) simulator study, 13 leaf and canopy trait models were developed as part of the L2B vegetation (L2BV) module. The E2E framework functions as a simulated reality that enabled to test and improve the algorithms. The models were further tuned and validated against campaign data using active learning methods. As a proof of concept, the prototype retrieval models were applied to both hyperspectral airborne (HyPlant) and spaceborne (PRISMA) imagery that were first resampled to CHIME band settings. Among the provided vegetation products, it led to a first space-based canopy nitrogen content map over a heterogeneous landscape. The obtained CHIME-like L2BV traits maps demonstrate the feasibility to routinely deliver a collection of next-generation vegetation products across the globe.

Crop modeling and remote sensing are key tools to gain deeper understanding on cropping system dynamics and, ultimately, to increase the sustainability of agricultural productions. This study presents a system to estimate rice yields at sub-field scale based on the integration of a biophysical model and remotely sensed products. Leaf area index (LAI) data derived from decametric optical imageries (i.e., Landsat-8, Landsat-7 and Sentinel–2A) were assimilated into the WARM rice model via automatic recalibration of crop parameters at a fine spatial resolution (30 m × 30 m), targeting the lowest error between simulated and remotely sensed LAI. The performance of the system was evaluated by comparing simulated yield using default and recalibrated parameters at sub-field scale with yield maps generated by a GPS-equipped harvester. The training dataset included 40 paddy fields in Northern Italy, which were sampled during three cropping seasons, from 2014 to 2016. The assimilation of remotely sensed LAI into model parameters increased the accuracy of the system: MAE and RRMSE were 0.66 t ha-1 [CI: 0.54 t ha-1–0.78 t ha-1] and 13.8% [CI: 11.7%–15.7%], respectively, whereas they were 0.82 t ha-1 [CI: 0.68 t ha-1–0.96 t ha-1) and 15.7% [CI: 14.1%,–17.4%] without assimilation. Moreover, the system allowed to properly reproduce the within-field yield variability, thus laying the basis for possible applications in precision agriculture advisory services.

Rice mapping products were derived from Sentinel-1A and Landsat-8 OLI multi-temporal imagery over Northern Italy at the early stages of the 2015 growing season. A rule-based algorithm was applied to synthetic statistical metrics (TSDs-Temporal Spectra Descriptors) computed from temporal datasets of optical spectral indices and SAR backscattering coefficient. Temporal series are available up to the tillering/full canopy cover stage which is identified as the optimum timing for delivering in-season information on rice area (i.e. mid July). The approach relies on a-priori knowledge on crop dynamics to adapt time horizons for TSD computation and thresholds to local conditions. Output products consist of maps of rice cultivated areas, rice seeding techniques (dry and flooded rice) and flooding practices. Validation showed rice mapping overall accuracy to be 87.8% with commission and omission errors of 3.5% and 24.7%, respectively. Mapping of rice seeding technique showed good agreement with farmer declarations aggregated at the municipality scale (dry rice r2 = 0.71 and flooded rice r2 = 0.91). Finally, flood maps have an overall accuracy above 70%. Geo-products on rice areas and flooding occurrence are relevant information for water management at regional scale especially during summer in presence of multiple crops and water shortage.

Lodging in wheat is one of the main constraints limiting yield and grain quality. Accurate information about crop lodging susceptibility during the growing season is critical for improving yield estimates and for targeting the expenditure on lodging control. In this context, this study aims to estimate safety factor against root lodging ( $SF_{A}$ ) as a measure of lodging susceptibility by exploiting Sentinel-1 data using Extreme Gradient Boosting Regression. Through extensive field experiments during a crop season, several crop variables were collected from several plots in multiple visits, and the corresponding metrics were extracted from the Sentinel-1 images. Our results show that the field measured $SF_{A}$ correlated well with the field lodging and the cross-validated regression model could estimate $SF_{A}$ with an $R_{CV}^{2}=0.73$ and $RMSE_{CV}=0.59$ . Thus, the $SF_{A}$ measure constitutes a state-of-the-art approach in the remote sensing community for the assessment of root lodging susceptibility.

The Project Plan illustrates the management organization adopted in SARAGRI. In particular, it regulates the interface between the Italian Space Agency (ASI) and the Project Team and identifies the responsibilities of the Project Management Group. Moreover, the document summarizes the Risk and the Progress management requirements.