Fulvio Capodici

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).

Critical analysis of thermal inertia approaches for surface soil water content retrieval

Abstract The “thermal inertia” method to retrieve surface soil water content maps on bare or sparsely-vegetated soils is analysed. The study area is a small experimental watershed, where optical and thermal images (in day and night time) and in situ data were simultaneously acquired. The sensitivity of thermal inertia to the phase difference between incoming radiation and soil temperature is demonstrated. Thus, to obtain an accurate value of the phase difference, the temporal distance between thermographs using a three-temperature approach is evaluated. We highlight when a cosine correction of the temperature needs to be applied, depending on whether the thermal inertia formulation includes two generic acquisition times, or not. Finally, the deviation in soil water content retrieval is quantifies for given values of each parameter by performing a sensitivity analysis on the basic parameters of the thermal inertia method that are usually affected by calibration errors. Citation Maltese, A., Bates, P.D., Capodici, F., Cannarozzo, M., Ciraolo, G., and La Loggia, G., 2013. Critical analysis of thermal inertia approaches for surface soil water content retrieval. Hydrological Sciences Journal, 58 (5), 1144–1161. Editor D. Koutsoyiannis; Associate editor D. Hughes

On the use of multi-temporal series of COSMO-SkyMed data for LANDcover classification and surface parameter retrieval over agricultural sites

The objective of this paper is to report on the activities carried out during the first year of the Italian project “Use of COSMO-SkyMed data for LANDcover classification and surface parameters retrieval over agricultural sites” (COSMOLAND), funded by the Italian Space Agency. The project intends to contribute to the COSMO-SkyMed mission objectives in the agriculture and hydrology application domains.

Mapping soil water content under sparse vegetation and changeable sky conditions: comparison of two thermal inertia approaches

A critical analysis of a thermal inertia approach to map surface soil water content on bare and sparsely vegetated soils by means of remotely sensed data is reported. The study area is an experimental field located in Barrax, Spain. In situ data were acquired within the Barrax 2011 research project. An advanced hyperspectral scanner airborne imager provides images in the visible/near-infrared and thermal infrared bands. Images were acquired both in day and night times by the Instituto Nacional de Tecnica Aeroespacial between 12th and 13th of June 2011. The scene covers a corn irrigation pivot surrounded by bare soil, where a set of in situ data have been collected both previously and simultaneously to overpasses. To validate remotely sensed estimations, an ad hoc dataset has been produced by measuring spectra, radiometric temperatures, surface soil water content, and soil thermal properties. These data were collected on two transects covering bare and sparsely vegetated soils. This ground dataset was used (1) to verify if a thermal inertia method can be applied to map the water content on soil covered by sparse vegetation and (2) to quantify a correction factor accounting for solar radiation reduction due to sky cloudiness. The experiment intended to test a spatially constant and a spatially distributed approach to estimate the phase difference. Both methods were then applied to the airborne images collected during the following days to obtain the spatial distribution of surface soil water content. Results confirm that the thermal inertia method can be applied to sparsely vegetated soil characterized by low fractional cover if the solar radiation reaching the ground is accurately estimated. A spatially constant value of the phase difference allows a good assessment of thermal inertia, whereas the comparison with the three-temperature approach did not give conclusive responses. Results also show that clear sky, only at the time of the acquisition, does not provide a sufficient condition to obtain accurate estimates of soil water content. A corrective coefficient taking into account actual sky cloudiness throughout the day allows better estimates of thermal inertia and, thus, of soil water content.

Time series of COSMO-SkyMed data for landcover classification and surface parameter retrieval over agricultural sites

This paper reports on the results of an Italian project aimed at investigating the use of X-band COSMO-SkyMed (CSK) SAR data for applications in agriculture and hydrology. Existing classification and retrieval algorithms have been tailored to CSK data and time series of crop, leaf area index and soil moisture maps have been retrieved and assessed through the comparison with in situ data collected over three agricultural sites. In addition, the CSK-derived surface parameters have been integrated into crop growth and hydrologic models and the resulting improvements have been assessed. Results indicate that multi-temporal dual-polarized CSK data are very well-suited for agricultural crop classification and that the integration of maps of SAR-derived surface parameters into crop growth and/or hydrologic models, in general, leads to significant improvements in the model performances.

Critical analysis of the thermal inertia approach to map soil water content under sparse vegetation and changeable sky conditions

The paper reports a critical analysis of the thermal inertia approach to map surface soil water content on bare and sparsely vegetated soils by means of remotely sensed data. The study area is an experimental area located in Barrax (Spain). Field data were acquired within the Barrax 2011 research project. AHS airborne images including VIS/NIR and TIR bands were acquired both day and night time by the INTA (Instituto Nacional de Técnica Aeroespacial) between the 11th and 13rd of June 2011. Images cover a corn pivot surrounded by bare soil, where a set of in situ data have been collected previously and simultaneously to overpasses. To validate remotely sensed estimations, a preliminary proximity sensing set up has been arranged, measuring spectra and surface temperatures on transects by means of ASD hand-held spectroradiometer and an Everest Interscience radiometric thermometer respectively. These data were collected on two transects: the first one on bare soil and the second from bare to sparsely vegetated soil; soil water content in both transects ranged approximately between field and saturation values. Furthermore thermal inertia was measured using a KD2Pro probe, and surface water content of soil was measured using FDR and TDR probes. This ground dataset was used: 1) to verify if the thermal inertia method can be applied to map water content also on soil covered by sparse vegetation, and 2) to quantify a correction factor of the downwelling shortwave radiation taking into account sky cloudiness effects on thermal inertia assessment. The experiment tests both Xue and Cracknell approximation to retrieve the thermal inertia from a dumped value of the phase difference and the three-temperature approach of Sobrino to estimate the phase difference spatial distribution. Both methods were then applied on the remotely sensed airborne images collected during the following days, in order to obtain the spatial distribution of the surface soil moisture on bare soils and sparse vegetation coverage. Results verify that the thermal inertia method can be applied on sparsely vegetated soil characterized by fractional cover up to ~0.25 (maximum value within this experiment); a lumped value of the phase difference allows a good estimate of the thermal inertia, whereas the comparison with the three-temperature approach did not give conclusive responses because ground radiometric temperatures were not acquired in optimal conditions. Results also show that clear sky only at the time of the remote sensing acquisitions is not a sufficient condition to apply the thermal inertia method. A corrective coefficient taking into account the actual sky cloudiness throughout the day allows accurate estimates of the spatial distribution of the thermal inertia (r2 ~ 0.9) and soil water content (r2 ~ 0.7).

The impact of soil erosion on soil fertility and vine vigor. A multidisciplinary approach based on field, laboratory and remote sensing approaches

Soil erosion processes in vineyards, beyond surface runoff and sediment transport, have a strong effect on soil organic carbon (SOC) loss and redistribution along the slope. Variation in SOC across the landscape can determine differences in soil fertility and vine vigor. The goal of this research was to analyze the interactions among vines vigor, sediment delivery and SOC in a sloping vineyard located in Sicily. Six pedons were studied along the slope by digging 6 pits up to 60cm depth. Soil was sampled every 10cm and SOC, water extractable organic carbon (WEOC) and specific ultraviolet absorbance (SUVA) were analyzed. Erosion rates, detachment and deposition areas were measured by the pole height method which allowed mapping of the soil redistribution. The vigor of vegetation, expressed as Normalized Difference Vegetation Index (NDVI), derived from high-resolution satellite multispectral data, was compared with measured pruning weight. Results confirmed that soil erosion, sediment redistribution and SOC across the slope was strongly affected by topographic features, slope and curvature. The erosion rate was 16Mgha-1y-1 since the time of planting (6years). SOC redistribution was strongly correlated with the detachment or deposition areas as highlighted by pole height measurements. The off-farm SOC loss over six years amounted to 1.2MgCha-1. SUVA254 values, which indicate hydrophobic material rich in aromatic constituents of WEOC, decreased significantly along the slope, demonstrating that WEOC in the detachment site is more stable in comparison to deposition sites. The plant vigor was strongly correlated with WEOC constituents. Results demonstrated that high resolution passive remote sensing data combined with soil and plant analyses can survey areas with contrasting SOC, soil fertility, soil erosion and plant vigor. This will allow monitoring of soil erosion and degradation risk areas and support decision-makers in developing measures for friendly environmental management.

Coupling two radar backscattering models to assess soil roughness and surface water content at farm scale

Abstract Remote sensing techniques are useful for agro-hydrological monitoring at the farm scale because the availability of spatially and temporally distributed data improves agricultural models for irrigation and crop yield optimization under water scarcity conditions. This research focuses on the surface water content retrieval using active microwave data. Two semi-empirical models were chosen as these showed the best performances in simulating cross and co-polarized backscatter. Thus, these models were coupled to obtain reliable assessments of both soil water content and soil roughness. The use of the coupled model enables one to avoid using roughness measured in situ. Remote sensing images and in situ data were collected between April and July 2006 within the European Space Agency-funded project AgriSAR 2006. The images data set includes L-band in HH, VV and VH polarizations acquired from the airborne E-SAR sensor, operated by the German Aerospace Centre. Results were validated using in situ soil water content and roughness measurements. The results show that reliable assessment of both soil roughness (r 2 up to ˜0.8) and soil water content (r 2 ˜ 0.9) can be retrieved in fields characterized by low fractional coverage. Editor D. Koutsoyiannis; Associate editor C. Onof Citation Capodici, F., Maltese, A., Ciraolo, G., La Loggia, G., and D’Urso, G., 2013. Coupling two radar backscattering models to assess soil roughness and surface water content at the farm scale. Hydrological Sciences Journal, 58 (8), 1677–1689.

Soil Water Content Assessment: Critical Issues Concerning the Operational Application of the Triangle Method

Knowledge of soil water content plays a key role in water management efforts to improve irrigation efficiency. Among the indirect estimation methods of soil water content via Earth Observation data is the triangle method, used to analyze optical and thermal features because these are primarily controlled by water content within the near-surface evaporation layer and root zone in bare and vegetated soils. Although the soil-vegetation-atmosphere transfer theory describes the ongoing processes, theoretical models reveal limits for operational use. When applying simplified empirical formulations, meteorological forcing could be replaced with alternative variables when the above-canopy temperature is unknown, to mitigate the effects of calibration inaccuracies or to account for the temporal admittance of the soil. However, if applied over a limited area, a characterization of both dry and wet edges could not be properly achieved; thus, a multi-temporal analysis can be exploited to include outer extremes in soil water content. A diachronic empirical approach introduces the need to assume a constancy of other meteorological forcing variables that control thermal features. Airborne images were acquired on a Sicilian vineyard during most of an entire irrigation period (fruit-set to ripening stages, vintage 2008), during which in situ soil water content was measured to set up the triangle method. Within this framework, we tested the triangle method by employing alternative thermal forcing. The results were inaccurate when air temperature at airborne acquisition was employed. Sonic and aerodynamic air temperatures confirmed and partially explained the limits of simultaneous meteorological forcing, and the use of proxy variables improved model accuracy. The analysis indicates that high spatial resolution does not necessarily imply higher accuracies.

Enhancing TIR image resolution via bayesian smoothing for IRRISAT irrigation management project

Accurate estimation of physical quantities depends on the availability of High Resolution (HR) observations of the Earth surface. However, due to the unavoidable tradeoff between spatial and time resolution, the acquisition instants of HR data hardly coincides with those required by the estimation algorithms. A possible solution consists in constructing a synthetic HR observation at a given time k by exploiting Low Resolution (LR) and HR data acquired at different instants. In this work we recast this issue as a smoothing problem, thus focusing on cases in which observations acquired both before and after time k are available. The proposed approach is validated on a region of interest for the IRRISAT irrigation management project in which the surface thermal inertia estimation, requiring multiple HR images at specific instants, constitute a key step.