Statistics for Spatial Data.

Modeling spatial patterns of fire occurrence in Mediterranean Europe using Multiple Regression and Random Forest

Surface soil moisture is one of the crucial variables in hydrological processes, which influences the exchange of water and energy fluxes at the land surface/atmosphere interface. Accurate estimate of the spatial and temporal variations of soil moisture is critical for numerous environmental studies. Recent technological advances in satellite remote sensing have shown that soil moisture can be measured by a variety of remote sensing techniques, each with its own strengths and weaknesses. This paper presents a comprehensive review of the progress in remote sensing of soil moisture, with focus on technique approaches for soil moisture estimation from optical, thermal, passive microwave, and active microwave measurements. The physical principles and the status of current retrieval methods are summarized. Limitations existing in current soil moisture estimation algorithms and key issues that have to be addressed in the near future are also discussed.

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Satellite remote sensing applications for surface soil moisture monitoring: A review

Soil moisture retrieval using thermal inertia, determined with visible and thermal spaceborne data, validated for European forests

Soil moisture (SM) is a key hydrologic state variable that is of significant importance for numerous Earth and environmental science applications that directly impact the global environment and human society. Potential applications include, but are not limited to, forecasting of weather and climate variability; prediction and monitoring of drought conditions; management and allocation of water resources; agricultural plant production and alleviation of famine; prevention of natural disasters such as wild fires, landslides, floods, and dust storms; or monitoring of ecosystem response to climate change. Because of the importance and wide‐ranging applicability of highly variable spatial and temporal SM information that links the water, energy, and carbon cycles, significant efforts and resources have been devoted in recent years to advance SMmeasurement andmonitoring capabilities from the point to the global scales. This review encompasses recent advances and the state‐of‐the‐art of ground, proximal, and novel SM remote sensing techniques at various spatial and temporal scales and identifies critical future research needs and directions to further advance and optimize technology, analysis and retrieval methods, and the application of SM information to improve the understanding of critical zone moisture dynamics. Despite the impressive progress over the last decade, there are still many opportunities and needs to, for example, improve SM retrieval from remotely sensed optical, thermal, and microwave data and opportunities for novel applications of SM information for water resources management, sustainable environmental development, and food security.

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Ground, Proximal, and Satellite Remote Sensing of Soil Moisture

. In order to evaluate the reliability of the soil moisture product obtained by means of the LISFLOOD hydrological model (De Roo et al., 2000), we compare it to soil moisture estimates derived from ERS scatterometer data (Wagner et al., 1999b). Once evaluated the effect of scale mismatch, we calculate the root mean square error and the correlation between the two soil moisture time series on a pixel basis and we assess the fraction of variance that can be explained by a set of input parameter fields that vary from elevation and soil depth to rainfall statistics and missing or snow covered ERS images. The two datasets show good agreement over large regions, with 90% of the area having a positive correlation coefficient and 66% having a root mean square error minor than 0.5 pF units. Major inconsistencies are located in mountainous regions such as the Alps or Scandinavia where both the methodologies suffer from insufficiently resolved land surface processes at the given spatial resolution, as well as from limited availability of satellite data on the one hand and the uncertainties in meteorological data retrieval on the other hand.

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On the comparison between the LISFLOOD modelled and the ERS/SCAT derived soil moisture estimates

AVHRR (Advanced Very High Resolution Radiometer on board NOAA satellites) data are considered here to evaluate the possibility of using the surface temperature as an indicator of the soil/canopy water content at the short time-scale. This is obtained by means of an indirect approach based on a simplified soil-atmosphere energy balance. The techniques provide sufficiently detailed coverage of the processes in terms of the time and spatial scale with respect to hydrological applications. Two different approaches have been tried: the first based on thermal inertia measurements (Xue & Cracknell, 1995)1 through ATI (Apparent Thermal Inertia), the second based on surface temperature (LST) and vegetation indices (NDVI), with the TVDI (Temperature Vegetation Dryness Index) suggested by Sandholt et al. (2002)2. Both techniques were used in detecting moist areas in a single image (or day/night images), and in multitemporal multitemporal applications. In particular, a new cloud detection algorithm, based on the bimodal frequency distribution of the infrared brightness temperatures (Ch 5 AVHRR) when clouds affect the image, has been proposed for ATI. As regards TVDI, a modified technique has been proposed for fixing the warm edge of the triangle based on the detection of the extreme dryness conditions on a monthly basis. The modified TVDI has been tested in comparison with an antecedent precipitation index (API) for moisture detection in single images. The substitution of day/night land surface temperature differences instead of noon temperatures in the "triangle method" has been also tested with good results in the multitemporal approach. Application of the proposed techniques can allow one to track the evolution of soil moisture in space and time and to improve the knowledge on the relationship between vegetation (NDVI) and soil moisture dynamics.

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Assessing spatial variability of soil water content through Thermal Inertia and NDVI

Annual and monthly average temperatures are analyzed with statistical methods to characterize the temperature regime in Italy. Data from 738 weather stations, with homogeneous spatial cover throughout Italy, are used to estimate the annual and monthly air temperature normals. Geographic and morphologic parameters, computed around the points of measure, are considered as explicative variables within a georegression model. Morphometric variables are defined using the USGS GTOPO30 digital elevation model, which has a resolution of 1 km. On the basis of a stepwise regression analysis, the significant variables are found to be elevation, latitude, distance from the sea, and a measure of terrain concavity. The relationship between the average annual air temperature and the mentioned variables explains 92% of the variance and produces a standard error of 0.89°C. The temperature regime normalized mean temperature for each of the 12 months is reproduced with a two-harmonic Fourier series, with parameters estimated using stepwise regression. Analyses of the reconstruction errors demonstrate that the results are quite satisfactory for many technical purposes, particularly for large-scale climatic characterization.

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Spatial Distribution of the Average Air Temperatures in Italy: Quantitative Analysis

Capturing critical behaviour in soil moisture spatio-temporal dynamics

This work evaluates the potential for drought detection of the Fraction of Absorbed Photosynthetically Active Radiation (fAPAR) estimations operationally produced by the European Space Agency (ESA) as the Medium Resolution Imaging Spectroradiometer (MERIS) Level 2 Land Product. MERIS fAPAR daily data were used to produce 10-day time composites. The selected study area was the Iberia Peninsula. Seasonal anomalies were calculated for the fAPAR dataset as well as for those of other drought indicators from different sources, such as the Standardized Precipitation Index, a widely used drought index, or the Normalized Difference Vegetation Index (NDVI). The resulting time series of anomaly maps of different drought indicators were then compared to assess the correlation among them and their ability to detect recent drought events in the area. Results show that MERIS fAPAR has a capacity to detect droughts and offers an enhanced performance compared to NDVI.

Giovanni Laguardia

Papers

On the comparison between the LISFLOOD modelled and the ERS/SCAT derived soil moisture estimates

Assessing spatial variability of soil water content through Thermal Inertia and NDVI

Spatial Distribution of the Average Air Temperatures in Italy: Quantitative Analysis

Capturing critical behaviour in soil moisture spatio-temporal dynamics

POTENTIAL OF MERIS fAPAR FOR DROUGHT DETECTION