Machine learning models to predict daily actual evapotranspiration of citrus orchards under regulated deficit irrigation

Precise estimations of actual evapotranspiration (ETa) are essential for various environmental issues, including those related to agricultural ecosystem sustainability and water management. Indeed, the increasing demands of agricultural production, coupled with increasingly frequent drought events in many parts of the world, necessitate a more careful evaluation of crop water requirements. Artificial Intelligence-based models represent a promising alternative to the most common measurement techniques, e.g. using expensive Eddy Covariance (EC) towers. In this context, the main challenges are choosing the best possible model and selecting the most representative features. The objective of this research is to evaluate two different machine learning algorithms, namely Multi-Layer Perceptron (MLP) and Random Forest (RF), to predict daily actual evapotranspiration (ETa) in a citrus orchard typical of the Mediterranean ecosystem using different feature combinations. With many features available coming from various infield sensors, a thorough analysis was performed to measure feature importance, scatter matrix observations, and Pearson's correlation coefficient calculation, which resulted in the selection of 12 promising feature combinations. The models were calibrated under regulated deficit irrigation (RDI) conditions to estimate ETa and save irrigation water. On average up to 38.5% water savings were obtained, compared to full irrigation. Moreover, among the different input variables adopted, the soil water content (SWC) feature appears to have a prominent role in the prediction of ETa. Indeed, the presented results show that by choosing the appropriate input features, the accuracy of the proposed machine learning models remains acceptable even when the number of features is reduced to only 4. The best performance was achieved by the Random Forest method, with seven input features, obtaining a root mean square error (RMSE) and a coefficient of determination (R2) of 0.39 mm/day and 0.84, respectively. Finally, the results show that the joint use of SWC, weather and satellite data significantly improves the performance of evapotranspiration forecasts compared to models using only meteorological variables

Modeling Actual Evapotranspiration with MSI-Sentinel Images and Machine Learning Algorithms

The modernization of computational resources and application of artificial intelligence algorithms have led to advancements in studies regarding the evapotranspiration of crops by remote sensing. Therefore, this research proposed the application of machine learning algorithms to estimate the ETrF (Evapotranspiration Fraction) of sugar can crop using the METRIC (Mapping Evapotranspiration at High Resolution with Internalized Calibration) model with data from the Sentinel-2 satellites constellation. In order to achieve this goal, images from the MSI sensor (MultiSpectral Instrument) from the Sentinel-2 and the OLI (Operational Land Imager) and TIRS (Thermal Infrared Sensor) sensors from the Landsat-8 were acquired nearly at the same time between the years 2018 and 2020 for sugar cane crops. Images from OLI and TIR sensors were intended to calculate ETrF through METRIC (target variable), while for the MSI sensor images, the explanatory variables were extracted in two approaches, using 10 m (approach 1) and 20 m (approach 2) spatial resolution. The results showed that the algorithms were able to identify patterns in the MSI sensor data to predict the ETrF of the METRIC model. For approach 1, the best predictions were XgbLinear (R2 = 0.80; RMSE = 0.15) and XgbTree (R2 = 0.80; RMSE = 0.15). For approach 2, the algorithm that demonstrated superiority was the XgbLinear (R2 = 0.91; RMSE = 0.10), respectively. Thus, it became evident that machine learning algorithms, when applied to the MSI sensor, were able to estimate the ETrF in a simpler way than the one that involves energy balance with the thermal band used in the METRIC model

Drainage models: an evaluation of their applicability for the design of drainage systems in arid regions

Only 5%–10% of irrigated lands in least developed countries (LDCs) are currently drained. Although drainage simulation models (DSMs) are used to evaluate alternative designs, it is unclear which drainage model is suitable for LDCs' arid and semi-arid regions. This study evaluates selected DSMs (ADAPT, RZWQM2, DRAINMOD, EPIC, HYDRUS-1D, WaSim and SWAP) and critically assesses their applicability to arid and semi-arid areas. Also, establish and apply selection criteria based on the availability of data in LDCs with Libya as a case study, and identify the most suitable model for application in Libya. DRAINMOD had the highest overall score, and alternative methods to predict missing input parameters for DRAINMOD are discussed. Evaluating the feasibility of using predicted input parameters for DSMs to design drainage systems in LDCs would help farmers, planners and decision-makers to reduce the overall cost of drainage system and, also, make DRAINMOD a more accessible tool to evaluate different drainage designs

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

Atti del XXV Convegno Nazionale di Agrometeorologia. L’Agrometeorologia per la gestione delle risorse e delle limitazioni ambientali in agricoltura

La razionale gestione delle risorse ambientali e naturali ha nella modellistica agrometeorologia il suo supporto di base. Del resto, le note e tristi vicende che negli ultimi giorni hanno duramente interessato i territori dell’Emilia-Romagna e delle Marche, impongono alla comunità scientifica e ai gestori del territorio un’attenta valutazione degli effetti che il cambiamento climatico ha sul territorio. Pertanto, i modelli agrometeorologici sono uno strumento essenziale per il processo gestionale e decisionale sia nell’ambito dei sistemi colturali che in quello zootecnico. Numerosi sono gli studi sui meccanismi e sulle relazioni che regolano le dinamiche ambientali e produttive del territorio stesso per descrivere le sue reali potenzialità produttive e quindi, pianificare e razionalizzare l’uso delle risorse utilizzate nel processo produttivo. La caratterizzazione meteorologica è uno dei primi passi da intraprendere per la conoscenza di un territorio, valutando non solo l’andamento dei valori medi dei principali parametri misurati al suolo, ma soprattutto la loro variabilità spaziotemporale. AIAM 2023 è l’appuntamento annuale tra i ricercatori e i tecnici dei servizi agrometeorologici regionali per presentare i risultati degli studi e dei progetti di ricerca per la gestione degli stress abiotici, dei mezzi di previsione e gestione delle avversità che interessano il mondo agricolo, con riferimento alle politiche di sviluppo agricolo del PSN 2023-27

Water Security and Governance in Catchments

This book addresses several issues on water security and governance, helping readers to understand how the desire for water-secure basins can be accomplished through an interplay of water security, water resources management and water policies. The book contains a collection of 12 papers addressing specific as well as interlinked topics within the Special Issue scope. The editors are grateful to all contributors who made the book a reality

Big Earth Data and Machine Learning for Sustainable and Resilient Agriculture

Big streams of Earth images from satellites or other platforms (e.g., drones and mobile phones) are becoming increasingly available at low or no cost and with enhanced spatial and temporal resolution. This thesis recognizes the unprecedented opportunities offered by the high quality and open access Earth observation data of our times and introduces novel machine learning and big data methods to properly exploit them towards developing applications for sustainable and resilient agriculture. The thesis addresses three distinct thematic areas, i.e., the monitoring of the Common Agricultural Policy (CAP), the monitoring of food security and applications for smart and resilient agriculture. The methodological innovations of the developments related to the three thematic areas address the following issues: i) the processing of big Earth Observation (EO) data, ii) the scarcity of annotated data for machine learning model training and iii) the gap between machine learning outputs and actionable advice. This thesis demonstrated how big data technologies such as data cubes, distributed learning, linked open data and semantic enrichment can be used to exploit the data deluge and extract knowledge to address real user needs. Furthermore, this thesis argues for the importance of semi-supervised and unsupervised machine learning models that circumvent the ever-present challenge of scarce annotations and thus allow for model generalization in space and time. Specifically, it is shown how merely few ground truth data are needed to generate high quality crop type maps and crop phenology estimations. Finally, this thesis argues there is considerable distance in value between model inferences and decision making in real-world scenarios and thereby showcases the power of causal and interpretable machine learning in bridging this gap.Comment: Phd thesi

Remote Sensing in Agriculture: State-of-the-Art

The Special Issue on “Remote Sensing in Agriculture: State-of-the-Art” gives an exhaustive overview of the ongoing remote sensing technology transfer into the agricultural sector. It consists of 10 high-quality papers focusing on a wide range of remote sensing models and techniques to forecast crop production and yield, to map agricultural landscape and to evaluate plant and soil biophysical features. Satellite, RPAS, and SAR data were involved. This preface describes shortly each contribution published in such Special Issue

Sustainable Agriculture for Climate Change Adaptation

© 2020 by the authors. This is an open access work distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.As we lie firmly entrenched within what many have termed the Anthropocene, the time of humans, human influence on the functioning of the planet has never been greater or in greater need of mitigation. Climate change, the accelerated warming of the planet’s surface attributed to human activities, is now at the forefront of global politics. The 21st United Nations Climate Change Conference of the Parties (COP21) Paris Agreement saw a landmark agreement reached between countries belonging to the United Nations Framework Convention on Climate Change (UNFCCC). The agreement seeks to arrest climate change and maintain the global temperature rise below a 2 ◦C increase compared to pre-industrial levels, and to devise means and ways to adapt to its effects. The agriculture sector not only contributes to climate change but, as a land-based industry, is also greatly affected by climate change. This publication is a collection of carefully selected papers addressing multiple climate related issues from across the five continents, providing a truly global perspective