Multi-scale remote sensing to support insurance policies in agriculture: from mid-term to instantaneous deductions

ABSTRACT

Climate change is today one of the biggest issues for farmers. The increasing number of natural disasters and change of seasonal trends is making insurance companies more interested in new technologies that can somehow support them in quantifying and mapping risks. Remotely sensed data, with special focus on free ones, can certainly provide the most of information they need, making possible to better calibrate insurance fees in space and time. In this work, a prototype of service based on free remotely sensed data is proposed with the aim of supporting insurance companies’ strategies. The service is thought to calibrate annual insurance rates, longing for their reduction at such level that new customers could be attracted. The study moves from the entire Piemonte region (NW Italy), to specifically focus onto the Cuneo province (Southern Piemonte), which is mainly devoted to agriculture. MODIS MOD13Q1-v6 and Sentinel-2 L2A image time series were jointly used. NDVI maps from MODIS data were useful to describe the midterm phenological trends of main crops at regional level in the period 2000–2018; differently, Sentinel-2 data permitted to map local crop differences at field level in 2016 and 2017 years. With reference to MODIS data, the average phenological behavior of main crop classes in the area, obtained from the CORINE Land Cover map Level 3, was considered using a time series decomposition approach. Trend analyses showed that the most of the crop classes alternated three phases (about 7 years) suggesting that, presently, this is probably the time horizon to be considered to tune mid-term algorithms for risk estimates in the agricultural context. Crop classes trends were consequently split into three phases and each of them modeled by a first-order polynomial function used to update correspondent insurance risk rate. Sentinel-2 data were used to map phenological anomalies at field level for the 2016 and 2017 growing seasons; shifts from class average behavior were considered to locally and temporarily tune insurance premium around its average trend as described at the previous step. Synthesizing, one can say that this approach, integrating MODIS and Sentnel-2 data, makes possible to locally and temporarily calibrate premiums of indexed insurance policies by describing the average trends of crop performance (NDVI) at regional level by MODIS data and refining it at field and specific crop level by Sentinel-2 data.     

Foraminiferal associations of the Mid-Tertiary Edegem Sands at Terhagen, Belgium

A factor-analytic approach (varimax rotated factors) was used in studying the benthonic foraminiferal associations of the Mid-Tertiary Edegem Sands at Terhagen (near Antwerpen) Belgium. The fauna can be described by two groups. The lower and upper part of the stratigraphic section are characterized by one group and the middle transitional part by a second group. The results can be correlated with the number of the planktonic forms and with the fossil richness of the sediment.     

Gas-liquid phase transitions and critical point in hot white dwarf matter

We consider the gas-liquid first-order phase transitions and prove the existence of the critical point in white dwarf matter. The latent heat released in the liquefaction processes can be used in the interpretation of the spreading of the white dwarf sequence in H-R diagram. Some thermodynamic quantities, e.g., the saturation pressure, the latent heat, etc., are calculated along the gas-liquid coexistence curve, and their behaviour near critical point are studied.     

The Linear Coregionalization Model and the Product–Sum Space–Time Variogram

The product covariance model, the product–sum covariance model, and the integrated product and integrated product–sum models have the advantage of being easily fitted by the use of marginal variograms. These models and the use of the marginals are described in a series of papers by De Iaco, Myers, and Posa. Such models allow not only estimating values at nondata locations but also prediction in future times, hence, they are useful for analyzing air pollution data, meteorological data, or ground water data. These three kinds of data are nearly always multivariate and because the processes determining the deposition or dynamics will affect all variates, a multivariate approach is desirable. It is shown that the use of marginal variograms for space–time modeling can be extended to the multivariate case and in particular to the use of the Linear Coregionalization Model (LCM) for cokriging in space–time. An application to an environmental data set is given.     

Near-Surface Flow Fields Deduced Using Correlation Tracking and Time-Distance Analyses

Near-photospheric flow fields on the Sun are deduced using two independent methods applied to the same time series of velocity images observed by SOI-MDI on SOHO. Differences in travel times between f modes entering and leaving each pixel measured using time-distance helioseismology are used to determine sites of supergranular outflows. Alternatively, correlation tracking analysis of mesogranular scales of motion applied to the same time series is used to deduce the near-surface flow field. These two approaches provide the means to assess the patterns and evolution of horizontal flows on supergranular scales even near disk center, which is not feasible with direct line-of-sight Doppler measurements. We find that the locations of the supergranular outflows seen in flow fields generated from correlation tracking coincide well with the locations of the outflows determined from the time-distance analysis, with a mean correlation coefficient after smoothing of _s=0.890. Near-surface velocity field measurements can be used to study the evolution of the supergranular network, as merging and splitting events are observed to occur in these images. The data consist of one 2048-min time series of high-resolution (0.6 pixels) line-of-sight velocity images taken by MDI on 1997 January 16–18 at a cadence of one minute.     

Remotely sensed estimation of forest canopy density: A comparison of the performance of four methods

In recent years, a number of alternative methods have been proposed to predict forest canopy density from remotely sensed data. To date, however, it remains difficult to decide which method to use, since their relative performance has never been evaluated. In this study the performance of: (1) an artificial neural network, (2) a multiple linear regression, (3) the forest canopy density mapper and (4) a maximum likelihood classification method was compared for prediction of forest canopy density using a Landsat ETM+ image. Comparison of confusion matrices revealed that the regression model performed significantly worse than the three other methods. These results were based on a z-test for comparison of weighted kappa statistics, which is an appropriate statistic for analysis of ranked categories. About 89% of the variance of the observed canopy density was explained by the artificial neural networks, which outperformed the other three methods in this respect. Moreover, the artificial neural networks gave an unbiased prediction, while other methods systematically under or over predicted forest canopy density. The choice of biased method could have a high impact on canopy density inventories.     

Impact of storm runoff from tropical watersheds on coastal water quality and productivity

Storm runoff in the steep watersheds in Hawaii leads to sediment and freshwater pulses to coastal waters that quickly affect nearshore water quality. This is particularly true in semi-enclosed embayments, such as Kaneohe Bay, Oahu, where water has a relatively long residence time compared to more open coastal areas of the islands. In this paper the authors discuss water quality and productivity in Kaneohe Bay after back-to-back rain events in late November and early December 2003, following a particularly dry summer. The short-term biogeochemical response of coastal waters and the ecosystem to runoff and physical forcing was evaluated through a combination of continuous in situ measurements and adaptive synoptic sampling carried out on a variety of temporal and spatial scales.     

Analysis and modeling of the seasonal South China Sea temperature cycle using remote sensing

The present paper describes the analysis and modeling of the South China Sea (SCS) temperature cycle on a seasonal scale. It investigates the possibility to model this cycle in a consistent way while not taking into account tidal forcing and associated tidal mixing and exchange. This is motivated by the possibility to significantly increase the model’s computational efficiency when neglecting tides. The goal is to develop a flexible and efficient tool for seasonal scenario analysis and to generate transport boundary forcing for local models. Given the significant spatial extent of the SCS basin and the focus on seasonal time scales, synoptic remote sensing is an ideal tool in this analysis. Remote sensing is used to assess the seasonal temperature cycle to identify the relevant driving forces and is a valuable source of input data for modeling. Model simulations are performed using a three-dimensional baroclinic-reduced depth model, driven by monthly mean sea surface anomaly boundary forcing, monthly mean lateral temperature, and salinity forcing obtained from the World Ocean Atlas 2001 climatology, six hourly meteorological forcing from the European Center for Medium range Weather Forecasting ERA-40 dataset, and remotely sensed sea surface temperature (SST) data. A sensitivity analysis of model forcing and coefficients is performed. The model results are quantitatively assessed against climatological temperature profiles using a goodness-of-fit norm. In the deep regions, the model results are in good agreement with this validation data. In the shallow regions, discrepancies are found. To improve the agreement there, we apply a SST nudging method at the free water surface. This considerably improves the model’s vertical temperature representation in the shallow regions. Based on the model validation against climatological in situ and SST data, we conclude that the seasonal temperature cycle for the deep SCS basin can be represented to a good degree. For shallow regions, the absence of tidal mixing and exchange has a clear impact on the model’s temperature representation. This effect on the large-scale temperature cycle can be compensated to a good degree by SST nudging for diagnostic applications.     

The energy balance experiment EBEX-2000. Part II: Intercomparison of eddy-covariance sensors and post-field data processing methods

The eddy-covariance method is the primary way of measuring turbulent fluxes directly. Many investigators have found that these flux measurements often do not satisfy a fundamental criterion—closure of the surface energy balance. This study investigates to what extent the eddy-covariance measurement technology can be made responsible for this deficiency, in particular the effects of instrumentation or of the post-field data processing. Therefore, current eddy-covariance sensors and several post-field data processing methods were compared. The differences in methodology resulted in deviations of 10% for the sensible heat flux and of 15% for the latent heat flux for an averaging time of 30 min. These disparities were mostly due to different sensor separation corrections and a linear detrending of the data. The impact of different instrumentation on the resulting heat flux estimates was significantly higher. Large deviations from the reference system of up to 50% were found for some sensor combinations. However, very good measurement quality was found for a CSAT3 sonic together with a KH20 krypton hygrometer and also for a UW sonic together with a KH20. If these systems are well calibrated and maintained, an accuracy of better than 5% can be achieved for 30-min values of sensible and latent heat flux measurements. The results from the sonic anemometers Gill Solent-HS, ATI-K, Metek USA-1, and R.M. Young 81000 showed more or less larger deviations from the reference system. The LI-COR LI-7500 open-path H₂O/CO₂ gas analyser in the test was one of the first serial numbers of this sensor type and had technical problems regarding direct solar radiation sensitivity and signal delay. These problems are known by the manufacturer and improvements of the sensor have since been made. The National Center for Atmospheric Research is supported by the National Science Foundation.     

Analysis of the precipitation and cloudiness associated with COLs occurrence in the Iberian Peninsula

Summary The Iberian Peninsula is one of the regions in the world with higher occurrence of cut-off low systems (COL). The aim of this paper is to analyse the weather events (rainfall and cloudiness layer) associated to COLs in the Iberian Peninsula with tools not previously used: (a) the use of the new multidecadal COLs database developed by Nieto et al (2005) that permit us to study a 41 years period (1958–1998), (b) the checking of the expected weather events (rainfall and cloudiness layer) associated with COLs in a conceptual model (Winkler et al, 2005) and (c) the extensive use of radiosoundings to analyse convective instability in areas inside and close to the COL. Two points of view are used to make the analysis: (1) a source oriented method, when a particular COL is followed and its associated precipitation and cloudiness is analysed over four quadrants in which Iberia was divided and (2) a receptor oriented method, when the precipitation associated to COLs is analysed in given areas, defined by patterns of precipitation. Results reveal that the precipitation and cloudiness patterns associated to COLs in the conceptual model reproduce quite well the main characteristics found over the Iberian Peninsula. The generalized idea that most of the COLs produce intense convective rainfall is show to be misleading. Convective phenomena are important usually when the centre of the COL is located on the Mediterranean region. Most of the rainfall associated with COLs comes from the baroclinic shield; specially in cases located over the west half of the Iberian Peninsula. It is shown that nearly 30% of COLs do not induce any rainfall; most of them located in the southern half of the Peninsula, and mainly during autumn. Only 30% of COLs produce generalized rainfall over the whole analysed territory, being most of them (about 90%) located over the western half of the Iberian Peninsula.