We present the preliminary results in the multitemporal mapping of vegetal fuel in two large test areas in southern Italy, one stretching over the heterogeneous forests of northern Sicily, and one ...covering Mt. Vesuvius and the surrounding areas in the Campania region, Naples. We report on the preliminary results of project HYPERFUEL, started in 2022 and due to completion late in spring 2024. HYPERFUEL focuses on the exploitation of the hyperspectral payload HYC, onboard the LEO platform PRISMA operated by the Italian Space Agency, in combination with multispectral payloads onboard Sentinel-2A and-2B. It is aimed to providing forest planners and fire risk modelers with frequently updated vegetal fuel maps at the nominal scale 1/50,000.
Measuring irrigation water volumes at national and regional scales is one of the priorities that the Italian legislation has identified among the requirements to support environmental policies and ...land monitoring. Crop evapotranspiration estimates under standard conditions represent a key component for the indirect evaluation of irrigation water volumes at regional scale. In this study, we present a way to assess crop evapotranspiration by combining visible and near-infrared (VIS-NIR) satellite crop imagery and meteorological reanalysis data, in the hypothesis that reanalysis products represent a valid proxy of past weather data when ground-based meteorological observations are missing. The study was conducted in Campania region (Southern Italy) where VIS-NIR high-resolution multispectral satellite crop images have been validated with ground LAI measurements performed in two maize fields during the irrigation seasons of years 2014 and 2015. For the same seasons, full sets of weather data were recorded by 18 automatic weather stations distributed across the region. The results show that the use of reanalysis data as proxy of past weather data for crop evapotranspiration estimates introduces acceptable errors (with overall RMSE of about 0.65 mm day -1 ) in the assessment of the crop evapotranspiration and clearly support the idea that for regions with limited past weather data archives or served by sparse and irregular monitoring networks, reanalysis data can be successfully exploited as a source of gridded weather data in similar agricultural and hydrological applications.
A new methodology is suggested for forecasting crop evapotranspiration under standard conditions (also referred to as potential evapotranspiration ET p ) by combining VIS-NIR satellite images and ...numerical weather forecasts. The methodology is proposed for farms where a reliable weather station is operating for assessing current reference evapotranspiration according to FAO Penman-Monteith equation (ET 0-PM ). Measured weather data are exploited for correcting systematic errors in weather forecasts and for locally calibrating Hargreaves-Samani (HS) equation for estimating the reference ET (ET 0-HS ). VIS-NIR crop images and current weather conditions are employed for updating a crop coefficient (K c ), analytically derived as the ratio of crop potential evapotranspiration computed with the Penman-Monteith equation (ET P-PM ) and ET 0-PM . Crop parameters in ET P-PM are assessed by means of albedo and LAI, both retrieved from VIS-NIR images. \mathrm {E}\mathrm {T}_{\mathrm {P}} forecasts are then computed with the HS formula by the product ET P-HS = K c .ET 0-HS . The methodology was applied to two experimental farms in Southern Italy, by employing COSMO-LEPS numerical weather forecasts, LANDSAT 8 and DEIMOS-1 VIS-NIR images. ET P-HS forecasts resulted to be more accurate and precise than ET P-PM forecasts. Differently from ET P-PM forecasts, ET P-HS relies only on temperature forecasts, which are generally less uncertain than the forecasts of the other atmospheric variables, which are required for computing ET P-PM .
The aim of this work is to analyze different methodologies for the estimation of leaf area index (LAI) and canopy chlorophyll content (CCC), using the Sentine1-2 satellite. LAI and CCC are ...biophysical parameters indicator of crop health state and fundamental in the productivity prediction. The purpose is to define the most optimal LAI and CCC estimation method for operational use in the monitoring of agricultural areas. Moreover, the CCC and LAI automatic products obtained directly through the Sentinel Application Platform Software (SNAP) biophysical processor and Sentine1-2 images by means of an artificial neural network (ANN) are validated. On the other hand, common vegetation indices used to LAI and CCC retrieval are analyzed. Both methods were tested using a dataset composed of LAI and CCC in situ data, obtained in an agricultural area near Caserta (Italy). As a result, Sentine1-2 automatic products present good statistics for LAI (\mathrm{R}^{2}=0.86, RMSE =0.80) and CCC (\mathrm{R}^{2}=0.85, RMSE =0.68\mathrm{g}/\mathrm{m}^{2}), without producing saturation at high LAI values. On the other hand, the best index for LAI retrieval was the normalized SeLI index (\mathrm{R}^{2} = 0.81, RMSE = 0.87) and for CCC, the three-band TCARI index (\mathrm{R}^{2} = 0.81, RMSE = 0.61 \mathrm{g}/\mathrm{m}^{2}). But the SeLI index produces a saturation process with LAI values higher than 3.5. The main conclusion of this study, hence, is that Sentine1-2 Level 2A products, such as the LAI and CCC parameter, have great potential to be used automatically and operationally in agricultural studies, minimizing time and economic costs.
This study explored the possibility to optimize irrigation scheduling through the integrated use of crop data derived from multispectral satellite imagery and an agro-hydrological model. The study ...was conducted with reference to an industrial tomato crop in an irrigated open field. Three methods for estimating irrigation needs were compared: estimates obtained with a calibrated AquaCrop model; estimates obtained by applying the AquaCrop model with sequential assimilation of crop cover retrieved from multispectral images; estimates obtained with the IRRISAT irrigation advisory service, based only crop state parameters retrieved from satellite multispectral images. The results confirm the usefulness of integrating agro-hydrological models and satellite observations to improve the prediction of crop water requirements. The agro-hydrological model offers more reliable estimates of the water irrigation requirements in the early stages of crop development, being able to simulate the effect of evaporative losses from the soil, when the canopy cover is still small. On the other hand, satellite data allows reducing model simulation errors in the most advanced stages of crop development and during senescence.
