Capability of Sentinel-2 data for estimating maximum evapotranspiration and irrigation requirements for tomato crop in Central Italy

Abstract The occurrence of water shortages ascribed to projected climate change, especially in the Mediterranean region, fosters the interest in remote sensing (RS) applications to optimize water use in agriculture. Remote sensing evapotranspiration and water demand estimation over large cultivated areas were used to manage irrigation to minimize losses during the crop growing cycle. The research aimed to explore the potential of the MultiSpectral Instrument (MSI) sensor on board Sentinel-2A to estimate crop parameters, mainly surface albedo (α) and Leaf Area Index (LAI) that influence the dynamics of potential evapotranspiration (ETp) and Irrigation Water Requirements (IWR) of processing tomato crop (Solanum lycopersicum L.). Maximum tomato ETp was calculated according to the FAO Penman-Monteith equation (FAO-56 PM) using appropriate values of canopy parameters derived by processing Sentinel-2A data in combination with daily weather information. For comparison, we used the actual crop evapotranspiration (ETa) derived from the soil water balance (SWB) module in the Environmental Policy Integrated Climate (EPIC) model and calibrated with in-situ Root Zone Soil Moisture (RZSM). The experiment was set up in a privately-owned farm located in the Tarquinia irrigation district (Central Italy) during two growing seasons, within the framework of the EU Project FATIMA (FArming Tools for external nutrient Inputs and water Management). The results showed that canopy growth, maximum evapotranspiration (ETp) and IWR were accurately inferred from satellite observations following seasonal rainfall and air temperature patterns. The net estimated IWR from satellite observations for the two-growing seasons was about 272 and 338 mm in 2016 and 2017, respectively. Such estimated requirement was lower compared with the actual amount supplied by the farmer with sprinkler and drip micro-irrigation system in both growing seasons resulting in 364 (276 mm drip micro-irrigation, and 88 mm sprinkler) and 662 (574 mm drip micro-irrigation, and 88 mm sprinkler) mm, respectively. Our findings indicated the suitability of Sentinel-2A to predict tomato water demand at field level, providing useful information for optimizing the irrigation over extended farmland.

[1]  R. Lal,et al.  Soil-Specific Farming : Precision Agriculture , 2015 .

[2]  P. Nagler,et al.  Vegetation index‐based crop coefficients to estimate evapotranspiration by remote sensing in agricultural and natural ecosystems , 2011 .

[3]  F. Baret,et al.  PROSPECT: A model of leaf optical properties spectra , 1990 .

[4]  C. Atzberger,et al.  Spatially constrained inversion of radiative transfer models for improved LAI mapping from future Sentinel-2 imagery , 2012 .

[5]  S. Dondeyne,et al.  World Reference Base for Soil Resources , 2013 .

[6]  Pete Falloon,et al.  Climate impacts on European agriculture and water management in the context of adaptation and mitigation--the importance of an integrated approach. , 2010, The Science of the total environment.

[7]  Guido D'Urso,et al.  Operative Approaches To Determine Crop Water Requirements From Earth Observation Data: Methodologies And Applications , 2006 .

[8]  S. K. Balasundram,et al.  A Review: The Role of Remote Sensing in Precision Agriculture , 2010 .

[9]  Biagio Bianchi,et al.  Satellite-based irrigation advisory services: A common tool for different experiences from Europe to Australia , 2015 .

[10]  S. Arafat,et al.  Estimation of Evapotranspiration ETc and Crop Coefficient Kc of Wheat, in south Nile Delta of Egypt Using integrated FAO-56 approach and remote sensing data , 2012 .

[11]  K. Nagel,et al.  Screening for drought tolerance of maize hybrids by multi-scale analysis of root and shoot traits at the seedling stage. , 2016, Journal of experimental botany.

[12]  Richard G. Allen,et al.  Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)—Model , 2007 .

[13]  D. Lobell,et al.  Adaptation potential of European agriculture in response to climate change , 2014 .

[14]  M. Mccabe,et al.  Estimating Land Surface Evaporation: A Review of Methods Using Remotely Sensed Surface Temperature Data , 2008 .

[15]  A. Huete A soil-adjusted vegetation index (SAVI) , 1988 .

[16]  Clement Atzberger,et al.  Data Service Platform for Sentinel-2 Surface Reflectance and Value-Added Products: System Use and Examples , 2016, Remote. Sens..

[17]  P. Zarco-Tejada,et al.  DIRECTORATE-GENERAL FOR INTERNAL POLICIES POLICY DEPARTMENT B: STRUCTURAL AND COHESION POLICIES AGRICULTURE AND RURAL DEVELOPMENT PRECISION AGRICULTURE: AN OPPORTUNITY FOR EU FARMERS-POTENTIAL SUPPORT WITH THE CAP 2014-2020 , 2014 .

[18]  V. Singh,et al.  The EPIC model. , 1995 .

[19]  M. S. Moran,et al.  Opportunities and limitations for image-based remote sensing in precision crop management , 1997 .

[20]  S. T. Gower,et al.  Rapid Estimation of Leaf Area Index in Conifer and Broad-Leaf Plantations , 1991 .

[21]  Compton J. Tucker,et al.  Monitoring corn and soybean crop development with hand-held radiometer spectral data , 1979 .

[22]  W. Verhoef Light scattering by leaf layers with application to canopy reflectance modelling: The SAIL model , 1984 .

[23]  Guido D'Urso,et al.  Current Status and Perspectives for the Estimation of Crop Water Requirements from Earth Observation , 2010 .

[24]  J. Ritchie,et al.  Field Measurement of Evaporation from Soil Shrinkage Cracks1 , 1974 .

[25]  Martha C. Anderson,et al.  Use of Landsat thermal imagery in monitoring evapotranspiration and managing water resources , 2012 .

[26]  A. Holtslag,et al.  A remote sensing surface energy balance algorithm for land (SEBAL)-1. Formulation , 1998 .

[27]  G. Thuillier,et al.  The Solar Spectral Irradiance from 200 to 2400 nm as Measured by the SOLSPEC Spectrometer from the Atlas and Eureca Missions , 2003 .

[28]  Juan Manuel Sánchez,et al.  Estimating high resolution evapotranspiration from disaggregated thermal images , 2016 .

[29]  I. Herrmann,et al.  LAI assessment of wheat and potato crops by VENμS and Sentinel-2 bands , 2011 .

[30]  Wolfram Mauser,et al.  Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping , 2012, Remote. Sens..

[31]  J. L. Amprako The United Nations World Water Development Report 2015: Water for a Sustainable World , 2016 .

[32]  A thermal-based remote sensing modelling system for estimating crop water use and stress from field to regional scales , 2016 .

[33]  Simona Consoli,et al.  Remote sensing to estimate ET-fluxes and the performance of an irrigation district in southern Italy , 2006 .

[34]  R. Francaviglia,et al.  Soil carbon dynamics and crop productivity as influenced by climate change in a rainfed cereal system under contrasting tillage using EPIC , 2011 .

[35]  D. Mulla Twenty five years of remote sensing in precision agriculture: Key advances and remaining knowledge gaps , 2013 .

[36]  Dale F. Heermann,et al.  Development of reflectance-based crop coefficients for corn , 1990 .

[37]  N. Breda Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. , 2003, Journal of experimental botany.

[38]  F. Vuolo,et al.  The irrigation advisory plan of Campania region : from research to operational support for the water directive in agriculture , 2013 .

[39]  N. Meinshausen,et al.  Warming caused by cumulative carbon emissions towards the trillionth tonne , 2009, Nature.

[40]  Alfonso Calera,et al.  Remote Sensing for Crop Water Management: From ET Modelling to Services for the End Users , 2017, Sensors.

[41]  G. D. Urso Simulation and management of on-demand irrigation systems: a combined agrohydrological and remote sensing approach , 2001 .

[42]  Luis S. Pereira,et al.  Water, Agriculture and Food: Challenges and Issues , 2017, Water Resources Management.

[43]  A. Hoekstra,et al.  Four billion people facing severe water scarcity , 2016, Science Advances.

[44]  B. Mckersie Planning for food security in a changing climate. , 2015, Journal of experimental botany.

[45]  Matthias Drusch,et al.  Sentinel-2: ESA's Optical High-Resolution Mission for GMES Operational Services , 2012 .

[46]  W. Verhoef,et al.  Bayesian object-based estimation of LAI and chlorophyll from a simulated Sentinel-2 top-of-atmosphere radiance image , 2014 .

[47]  Guido D'Urso,et al.  Estimation of Evapotranspiration and Crop Coefficients of Tendone Vineyards Using Multi-Sensor Remote Sensing Data in a Mediterranean Environment , 2015, Remote. Sens..

[48]  L. S. Pereira,et al.  Evapotranspiration information reporting: I. Factors governing measurement accuracy , 2011 .

[49]  Jan G. P. W. Clevers,et al.  Experimental Sentinel-2 LAI estimation using parametric, non-parametric and physical retrieval methods - A comparison , 2015 .