Guido D'Urso

Mapping daily evapotranspiration at field to continental scales using geostationary and polar orbiting satellite imagery

Thermal infrared (TIR) remote sensing of landsurface temperature (LST) provides valuable information about the sub-surface moisture status required for estimating evapotranspiration (ET) and detecting the onset and severity of drought. While empirical indices measuring anomalies in LST and vegetation amount (e.g., as quantified by the Normalized Difference Vegetation Index; NDVI) have demonstrated utility in monitoring ET and drought conditions over large areas, they may provide ambiguous results when other factors (e.g., air temperature, advection) are affecting plant functioning. A more physically based interpretation of LST and NDVI and their relationship to subsurface moisture conditions can be obtained with a surface energy balance model driven by TIR remote sensing. The Atmosphere-Land Exchange Inverse (ALEXI) model is a multi-sensor TIR approach to ET mapping, coupling a two-source (soil + canopy) land-surface model with an atmospheric boundary layer model in time-differencing mode to routinely and robustly map daily fluxes at continental scales and 5 to 10-km resolution using thermal band imagery and insolation estimates from geostationary satellites. A related algorithm (DisALEXI) spatially disaggregates ALEXI fluxes down to finer spatial scales using moderate resolution TIR imagery from polar orbiting satellites. An overview of this modeling approach is presented, along with strategies Correspondence to: M. C. Anderson (martha.anderson@ars.usda.gov) for fusing information from multiple satellite platforms and wavebands to map daily ET down to resolutions on the order of 10 m. The ALEXI/DisALEXI model has potential for global applications by integrating data from multiple geostationary meteorological satellite systems, such as the US Geostationary Operational Environmental Satellites, the European Meteosat satellites, the Chinese Fen-yung 2B series, and the Japanese Geostationary Meteorological Satellites. Work is underway to further evaluate multi-scale ALEXI implementations over the US, Europe, Africa and other continents with geostationary satellite coverage.

Evaluation of Sentinel-2 Spectral Sampling for Radiative Transfer Model Based LAI Estimation of Wheat, Sugar Beet, and Maize

The present study aimed at testing the potential of the future E.O. mission Sentinel-2 (European GMES programme) for the operational estimation of the Leaf Area Index (LAI) of three contrasting agricultural crops (wheat, sugar beet, and maize). Retrieval of LAI was achieved by using a look-up table (LUT) based inversion of a radiative transfer model (SAILH+PROSPECT). Analyses were mainly carried out using hyperspectral data acquired by the optical airborne instrument CASI, simulating the future Sentinel-2 band setting. Estimated LAI was evaluated using measurements of effective Plant Area Index (PAIeff) collected during the ESA AgriSAR 2006 campaign. Additionally, measurements from two other experiments were tested to enrich the validation database. The GMES targeted precision of 10% for green LAI estimation was met for sugar beet (8%), at the limit for wheat (11%) but not for maize (19%). For the three crops the RMSE was in the range 0.4-0.6. The results demonstrate the importance of using crop specific radiative transfer models. For row crops with incomplete coverage and strong leaf clumping, such as maize at early stage, the standard SAILH+PROSPECT model does not appear suitable. However, results must be taken cautiously in view of possible uncertainties of the PAIeff measurements. Within a future Sentinel-2 product validation framework, a standard protocol for reference measurements is required to assure consistency of validation data sets.

Optimal Exploitation of the Sentinel-2 Spectral Capabilities for Crop Leaf Area Index Mapping

The continuously increasing demand of accurate quantitative high quality information on land surface properties will be faced by a new generation of environmental Earth observation (EO) missions. One current example, associated with a high potential to contribute to those demands, is the multi-spectral ESA Sentinel-2 (S2) system. The present study focuses on the evaluation of spectral information content needed for crop leaf area index (LAI) mapping in view of the future sensors. Data from a field campaign were used to determine the optimal spectral sampling from available S2 bands applying inversion of a radiative transfer model (PROSAIL) with look-up table (LUT) and artificial neural network (ANN) approaches. Overall LAI estimation performance of the proposed LUT approach (LUTN50) was comparable in terms of retrieval performances with a tested and approved ANN method. Employing seven- and eight-band combinations, the LUTN50 approach obtained LAI RMSE of 0.53 and normalized LAI RMSE of 0.12, which was comparable to the results of the ANN. However, the LUTN50 method showed a higher robustness and insensitivity to different band settings. Most frequently selected wavebands were located in near infrared and red edge spectral regions. In conclusion, our results emphasize the potential benefits of the Sentinel-2 mission for agricultural applications.

Experimental corrections of simplified methods for predicting water retention curves in clay-loamy soils from particle-size determination

Abstract The Arya and Paris model for predicting soil water retention curves from particle-size distribution data is a commonly accepted method for rigid soils with medium grain size but limitations to its application for fine textured soils occur, due to the dominant role of internal structure in such soils. Moving from the consideration that simplified models for determining soil hydraulic characteristics may be usefully adopted to reduce laboratory investigation costs, in this study an attempt is made to extend the Arya-Paris formulation to clay-loamy soils by means of an experimental calibration function which takes into account the effective soil water retention behaviour. The methodology is based on an inversion procedure of the Arya-Paris model from observed values of water retention and potential during evaporation processes on undisturbed soil columns. It has been found that the resulting calibration function is typical for each soil and it substantially improves the prediction of the soil water retention curve from textural information.

Estimation of Leaf Area Index Using DEIMOS-1 Data: Application and Transferability of a Semi-Empirical Relationship between two Agricultural Areas

This work evaluates different procedures for the application of a semi-empirical model to derive time-series of Leaf Area Index (LAI) maps in operation frameworks. For demonstration, multi-temporal observations of DEIMOS-1 satellite sensor data were used. The datasets were acquired during the 2012 growing season over two agricultural regions in Southern Italy and Eastern Austria (eight and five multi-temporal acquisitions, respectively). Contemporaneous field estimates of LAI (74 and 55 measurements, respectively) were collected using an indirect method (LAI-2000) over a range of LAI values and crop types. The atmospherically corrected reflectance in red and near-infrared spectral bands was used to calculate the Weighted Difference Vegetation Index (WDVI) and to establish a relationship between LAI and WDVI based on the CLAIR model. Bootstrapping approaches were used to validate the models and to calculate the Root Mean Square Error (RMSE) and the coefficient of determination (R 2 ) between measured and predicted LAI, as

Regional application of one-dimensional water flow models for irrigation management

Numerical models for the simulation of soil water processes can be used to evaluate the spatial and temporal variations of crop water requirements; this information can support the irrigation management in a rationale usage of water resources. This latter objective requires the knowledge of spatially distributed input parameters concerning vegetation status, soil hydraulic behaviour and groundwater interaction. This task can be achieved by a conjunctive use of remote sensing techniques, geographical information systems and hydrological simulation models. The present work focuses on the criteria applied for the implementation of a one-dimensional model for water flow in each parcel of an irrigation district in southern Italy having extension of 3000 ha.

Retrieval of Leaf Area Index from CHRIS/PROBA data: an analysis of the directional and spectral information content

The use of Earth Observation (EO) data to retrieve biophysical variables of vegetated surfaces has proved to be useful in many operative tools to gather information repetitively, at spatial and temporal resolution, for agricultural and water management applications. The launch of the European Space Agency (ESA) Compact High‐Resolution Imaging Spectrometer/Project for On‐Board Autonomy (CHRIS/PROBA) mission has provided an opportunity to study a multiangular and hyperspectral dataset of images with high spatial resolution. The objective of the study was to use the CHRIS/PROBA data, in both directional and spectral domains, to estimate the Leaf Area Index (LAI). For this purpose, inversion of a canopy reflectance model was performed against CHRIS data. LAI estimates were validated by using ground truth LAI measurements and compared, in terms of accuracy, to a semi‐empirical approach. It was shown that, for a given spectral configuration, the directional information always improved the LAI estimation. For the best case (corn), this was achieved with an LAI root mean square error (RMSE) of 0.41 by using five angles and 62 spectral bands compared to a value of 1.42 by using one angle and four bands, as in the Landsat Thematic Mapper (TM) configuration.

Mapping crop coefficients in irrigated areas from Landsat TM images

It is well known that reflectance of Earth surface largely depends upon amount of biomass, crop type, development stage, ground coverage. The knowledge of these parameters -- together with groundbased meteorological data -- allows for the estimate of crop water requirements and their spatial distribution. Recent research has shown the possibility of using multispectral satellite images in combination with other information for mapping crop coefficients in irrigated areas. This approach is based on the assumption that crop coefficients (Kc) are greatly influenced by canopy development and vegetation fractional ground cover; since these parameters directly affect the reflectance of cropped areas, it is possible to establish a correlation between multispectral measurements of canopies reflectance and the corresponding Kc values. Within this frame, two different approaches may be applied: (1) definition of spectral classes corresponding to different crop coefficient values and successive supervised classification for the derivation of crop coefficients maps; (2) use of analytical relationships between the surface reflectance and the corresponding values of vegetation parameters, i.e., the leaf area index, the albedo and the surface roughness, needed for the calculation of the potential evapotranspiration according to the combination type equation. The two different techniques are discussed with reference to the results of their application to specific case-studies. The aim of this report is to illustrate the suitability of remote sensing techniques as an operational tool for assessing crop water demand at regional scale.

Investigating the Relationship between X-Band SAR Data from COSMO-SkyMed Satellite and NDVI for LAI Detection

Monitoring spatial and temporal variability of vegetation is important to manage land and water resources, with significant impact on the sustainability of modern agriculture. Cloud cover noticeably reduces the temporal resolution of retrievals based on optical data. COSMO-SkyMed (the new Italian Synthetic Aperture RADAR-SAR) opened new opportunities to develop agro-hydrological applications. Indeed, it represents a valuable source of data for operational use, due to the high spatial and temporal resolutions. Although X-band is not the most suitable to model agricultural and hydrological processes, an assessment of vegetation development can be achieved combing optical vegetation indices (VIs) and SAR backscattering data. In this paper, a correlation analysis has been performed between the crossed horizontal-vertical (HV) backscattering (s°HV) and optical VIs (VIopt) on several plots. The correlation analysis was based on incidence angle, spatial resolution and polarization mode. Results have shown that temporal changes of s°HV (Δs°HV) acquired with high angles (off nadir angle; θ > 40°) best correlates with variations of VIopt (ΔVI). The correlation between ΔVI and Δs°HV has been shown to be temporally robust. Based on this experimental evidence, a model to infer a VI from s° (VISAR) at the time, ti + 1, once known, the VIopt at a reference time, ti, and Δs°HV between times, ti + 1 and ti, was implemented and verified. This approach has led to the development and validation of an algorithm for coupling a VIopt derived from DEIMOS-1 images and s°HV. The study was carried out over the Sele plain (Campania, Italy), which is mainly characterized by herbaceous crops. In situ measurements included leaf area index (LAI), which were collected weekly between August and September 2011 in 25 sites, simultaneously to COSMO-SkyMed (CSK) and DEIMOS-1 imaging. Results confirm that VISAR obtained using the combined model is able to increase the feasibility of operational satellite-based products for supporting agricultural practices. This study is carried out in the framework of the COSMOLAND project (Use of COSMO-SkyMed SAR data for LAND cover classification and surface parameters retrieval over agricultural sites) funded by the Italian Space Agency (ASI).

Performance indicators for the statistical evaluation of digital image classifications

A methodology is proposed for a post-processing assessment of the performance of training set selections and classification procedures. This paper focuses on the necessity and usefulness of a critical validation of classification results. A statistical procedure is proposed to evaluate the algorithms for the numerical classification of images. The approach is based on the derivation of performance indicators from measurements of signature separability and thresholding analysis. Although these measurements are not new in image processing techniques, they are used in this study in an original way for the comparison of outputs resulting from different classification criteria. The theoretical description of the suggested method is followed by their practical application to a case-study for mapping crop coefficients in an irrigation district.