Assessing Crop Coefficients for Natural Vegetated Areas Using Satellite Data and Eddy Covariance Stations

Modeling windthrow effects on water runoff and hillslope stability in a mountain catchment affected by the VAIA storm

Windthrows seriously affect forest landscapes, causing several issues in hydrological and geomorphological terms. In this regard, Airborne Laser Scanning (ALS) topographic data recently increased the opportunity to investigate in detail physical processes at the catchment scale. Moreover, topographically based hydrological and geomorphological models allow quantifying runoff alteration due to windthrows-driven land cover changes and detect the occurrence of land degradative processes at the sub-catchment scale. In this connection, accurate investigations about windthrows role in varying local runoff regimes over time are still obscure, as well as the possibility of predicting terrain instabilities due to windstorm occurrence. This research aims to investigate the interaction between windthrows, runoff alterations and hillslope failures affecting a landslide-prone mountain catchment (northern Italy). Hydrological HEC-HMS and geomorphological RESS models were applied. Windthrows' role in altering runoff regimes and hillslope stability was investigated starting from the elaboration of ALS-derived points clouds acquired before and after the occurrence of the Vaia storm. Digital Terrain Models (DTMs) were elaborated for the two scenarios to compare daily runoff variations and predict the activation of terrain instabilities by looking at land cover changes driven by the blowdown event at the sub-catchment detail. Results attested the key role of windstorms in altering local runoff, with a maximum relative runoff increment equal to 2.56 % and a maximum runoff difference equal to 3.12 mmh-1, as well as in encouraging the activation of the observed shallow landslide. The correlation between windthrows occurrence and runoff alterations was validated by performing regression analysis (R2 = 0.76), while the accuracy of instabilities predictions was tested through the Distance to Perfect Classification (D2PC) index and True Skill Statistic (TSS) score, respectively resulted equal to 0.076 and 0.898. This research represents a valid tool for investigating similar issues at a wider scale, also providing suggestions for promoting interventions in wind-disturbed forest areas.

Estimating Processing Tomato Water Consumption, Leaf Area Index, and Height Using Sentinel-2 and VENµS Imagery

Crop monitoring throughout the growing season is key for optimized agricultural production. Satellite remote sensing is a useful tool for estimating crop variables, yet continuous high spatial resolution earth observations are often interrupted by clouds. This paper demonstrates overcoming this limitation by combining observations from two public-domain spaceborne optical sensors. Ground measurements were conducted in the Hula Valley, Israel, over four growing seasons to monitor the development of processing tomato. These measurements included continuous water consumption measurements using an eddy-covariance tower from which the crop coefficient (Kc) was calculated and measurements of Leaf Area Index (LAI) and crop height. Satellite imagery acquired by Sentinel-2 and VENµS was used to derive vegetation indices and model Kc, LAI, and crop height. The conjoint use of Sentinel-2 and VENµS imagery facilitated accurate estimation of Kc (R2 = 0.82, RMSE = 0.09), LAI (R2 = 0.79, RMSE = 1.2), and crop height (R2 = 0.81, RMSE = 7 cm). Additionally, our empirical models for LAI estimation were found to perform better than the SNAP biophysical processor (R2 = 0.53, RMSE = 2.3). Accordingly, Sentinel-2 and VENµS imagery was demonstrated to be a viable tool for agricultural monitoring.

Reliable Evapotranspiration Predictions with a Probabilistic Machine Learning Framework

Evapotranspiration is often expressed in terms of reference crop evapotranspiration (ETo), actual evapotranspiration (ETa), or surface water evaporation (Esw), and their reliable predictions are critical for groundwater, irrigation, and aquatic ecosystem management in semi-arid regions. We demonstrated that a newly developed probabilistic machine learning (ML) model, using a hybridized “boosting” framework, can simultaneously predict the daily ETo, Esw, & ETa from local hydroclimate data with high accuracy. The probabilistic approach exhibited great potential to overcome data uncertainties, in which 100% of the ETo, 89.9% of the Esw, and 93% of the ETa test data at three watersheds were within the models’ 95% prediction intervals. The modeling results revealed that the hybrid boosting framework can be used as a reliable computational tool to predict ETo while bypassing net solar radiation calculations, estimate Esw while overcoming uncertainties associated with pan evaporation & pan coefficients, and predict ETa while offsetting high capital & operational costs of EC towers. In addition, using the Shapley analysis built on a coalition game theory, we identified the order of importance and interactions between the hydroclimatic variables to enhance the models’ transparency and trustworthiness.