Guido D’Urso

Experimental assessment of the Sentinel-2 band setting for RTM-based LAI retrieval of sugar beet and maize

The present work aimed at testing the potential of the upcoming Earth Observation satellite Sentinel-2 (European Global Monitoring for Environment and Security (GMES) programme) for the operational estimation of the leaf area index (LAI) of two contrasting agricultural crops (sugar beet and maize). Mapping of LAI was achieved using a look-up table (LUT) based inversion of a physically based radiative transfer model (SAILH + PROSPECT). In addition to the Sentinel-2 spectral sampling, another band set described as “ideal” for vegetation studies has been evaluated in a comparative way. Analyses were mainly carried out using hyperspectral data acquired by the optical airborne instrument Compact Airborne Spectrographic Imager (CASI) during the European Space Agency (ESA) AgriSAR 2006 campaign. Additionally, data from two other experiments were tested to extend the validation database. Alternative inversion methods, i.e., an iterative optimization technique (SQP) and a neural network (NN), have been evaluated for comparison purposes. The GMES defined precision of 10% for LAI estimation, evaluated with in situ LAI measurements, was met for sugar beet (8%–9%) but not for maize (16%–22%). The inversion approach and band setting had only a minor influence on the retrieval accuracy, with the only exception being the iterative optimization technique, which failed to give reliable results. The results demonstrate the importance of using an appropriate radiative transfer model for each crop. For row crops with strong leaf clumping and not completely covering the soil surface, such as maize in the early stage, the standard SAILH + PROSPECT model does not appear to be suitable.

Estimation of Evapotranspiration and Crop Coefficients of Tendone Vineyards Using Multi-Sensor Remote Sensing Data in a Mediterranean Environment

The sustainable management of water resources plays a key role in Mediterranean viticulture, characterized by scarcity and competition of available water. This study focuses on estimating the evapotranspiration and crop coefficients of table grapes vineyards trained on overhead “tendone” systems in the Apulia region (Italy). Maximum vineyard transpiration was estimated by adopting the “direct” methodology for ETp proposed by the Food and Agriculture Organization in Irrigation and Drainage Paper No. 56, with crop parameters estimated from Landsat 8 and RapidEye satellite data in combination with ground-based meteorological data. The modeling results of two growing seasons (2013 and 2014) indicated that canopy growth, seasonal and 10-day sums evapotranspiration values were strictly related to thermal requirements and rainfall events. The estimated values of mean seasonal daily evapotranspiration ranged between 4.2 and 4.1 mm·d−1, while midseason estimated values of crop coefficients ranged from 0.88 to 0.93 in 2013, and 1.02 to 1.04 in 2014, respectively. The experimental evapotranspiration values calculated represent the maximum value in absence of stress, so the resulting crop coefficients should be used with some caution. It is concluded that the retrieval of crop parameters and evapotranspiration derived from remotely-sensed data could be helpful for downscaling to the field the local weather conditions and agronomic practices and thus may be the basis for supporting grape growers and irrigation managers.

Remote sensing for crop water management : From ET modelling to services for the end users

The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e., professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies (Geographic Information Systems based on web technologies). This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools, which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.

Evaluation of the PROSAIL Model Capabilities for Future Hyperspectral Model Environments: A Review Study

Upcoming satellite hyperspectral sensors require powerful and robust methodologies for making optimum use of the rich spectral data. This paper reviews the widely applied coupled PROSPECT and SAIL radiative transfer models (PROSAIL), regarding their suitability for the retrieval of biophysical and biochemical variables in the context of agricultural crop monitoring. Evaluation was carried out using a systematic literature review of 281 scientific publications with regard to their (i) spectral exploitation, (ii) vegetation type analyzed, (iii) variables retrieved, and (iv) choice of retrieval methods. From the analysis, current trends were derived, and problems identified and discussed. Our analysis clearly shows that the PROSAIL model is well suited for the analysis of imaging spectrometer data from future satellite missions and that the model should be integrated in appropriate software tools that are being developed in this context for agricultural applications. The review supports the decision of potential users to employ PROSAIL for their specific data analysis and provides guidelines for choosing between the diverse retrieval techniques.

The response of ecosystem carbon fluxes to LAI and environmental drivers in a maize crop grown in two contrasting seasons.

The eddy correlation technique was used to investigate the influence of biophysical variables and crop phenological phases on the behaviour of ecosystem carbon fluxes of a maize crop, in two contrasting growing seasons. In 2009, the reduced water supply during the early growing stage limited leaf area expansion, thus negatively affecting canopy photosynthesis. The variability of gross primary production (GPP) and ecosystem respiration (Reco) was mainly explained by seasonal variation of leaf area index (LAI). The seasonal variation of Reco was positively influenced by soil temperatures (Tsoil) in 2008 but not in 2009. In 2008, a contribution of both autotrophic and heterotrophic components to total Reco could be hypothesized, while during 2009, autotrophic respiration is supposed to be the most important component. Crop phenological phases affected the response of ecosystem fluxes to biophysical drivers.

Estimation of Land Surface Parameters Through Modeling Inversion of Earth Observation Optical Data

Earth observation (EO) optical data represent one of the main sources of information in the retrieval of land surface parameters (i.e., leaf area index and surface albedo). These parameters are widely used in research and applications in agriculture for improving water and land resources management, especially in the field of precision farming, to monitor crop status, predict crop yield, detect disease and insect infestations, and support the management of farming tasks. During recent years, the technical capabilities of airborne and satellite remote sensing imagery have been improved to include hyperspectral and multiangular observations. In parallel with the advancement of observation techniques, there has been an important development in the study of the interaction of solar radiation with Earth’s surface. This process can be described by using canopy reflectance models of different complexity, which can also be used in operative applications in the field of agricultural water and land management. As such, enhanced EO data and canopy reflectance models can be combined together to reduce the empiricism of traditional methods based on simplified approaches and to increase the estimation accuracy.

APPLYING EARTH OBSERVATION TO DETECT NON-AUTHORISED IRRIGATION: THE CASE STUDY OF CONSORZIO SANNIO ALIFANO (ITALY)

In addition to reducing global water availability unauthorized irrigation and over-consumption can have social consequences in terms of conflicting water use. In its Water Framework Directive, the European Union (EU) has outlined an agenda for future water policy, emphasizing that, to ensure a sustainable use of water resources, these practices should be strongly opposed. In order to address this problem efficiently, water managers need to map irrigated area, plan the rational use of water resources under limited availability, and prevent unauthorized irrigation. We are currently developing an innovative system to do this based on a series of multi-spectral satellite acquisitions from two sensors having different spatial and temporal resolutions (DEIMOS, Rapid Eye). In this system, the irrigated area is identified based on temporal pattern recognition, exploiting the differing developmental rates between irrigated and not irrigated crops. This method was applied in the district of Consorzio Sannio Alifano, located in Southern Italy, where irrigation is required for most crops including corn, alfalfa, fruit trees and vegetables. An accuracy assessment of the methodology has been performed and has demonstrated positive results of this approach. Future system upgrades will exploit information derived from shortwave infrared data obtained using of the newly developed Sentinel-2 sensor. The approach described herein is the technological basis of a recently-funded EU H2020 project, named Detection and Integrated Assessment of Non-authorised water Abstractions using Earth Observation or DIANA.