Carmelo Cammalleri

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.

A thermal inertia model for soil water content retrieval using thermal and multispectral images

Soil moisture is difficult to quantify because of its high spatial variability. Consequently, great efforts have been undertaken by the research community to develop practical remote sensing approaches to estimate the spatial distribution of surface soil moisture over large areas and with high spatial detail. Many methodologies have been developed using remote sensing data acquiring information in different parts of the electromagnetic spectrum. Conventional field measurement techniques (including gravimetric and time-domain reflectometry) are point-based, involve on-site operators, are time expensive and, in any case, do not provide exhaustive information on the spatial distribution of soil moisture because it strongly depends on pedology, soil roughness and vegetation cover. The technological development of imaging sensors acquiring in the visible (VIS), near infrared (NIR) and thermal infrared (TIR), renewed the research interest in setting up remote sensed based techniques aimed to retrieve soil water content variability in the soil-plant-atmosphere system (SPA). In this context different approaches have been widely applied at regional scale throughout synthetic indexes based on VIS, NIR and TIR spectral bands. A laboratory experiment has been carried out to verify a physically based model based on the remote estimation of the soil thermal inertia, P, to indirectly retrieve the soil surface water content, θ. The paper shows laboratory retrievals using simultaneously a FLIR A320G thermal camera, a six bands customized TETRACAM MCA II (Multiple Camera Array) multispectral camera working in the VIS/NIR part of the spectrum. Using these two type of sensors a set of VIS/NIR and TIR images were acquired as the main input dataset to retrieve the spatial variability of the thermal inertia values. Moreover, given that the accuracy of the proposed approach strongly depends on the accurate estimation of the soil thermal conductivity, a Decagon Device KD2 PRO thermal analyzer was used to verify the remotely estimate of thermal conductivity. Remotely estimated water contents were validated using the gravimetric method. The considered thermal inertia approach allowed prediction of the spatial distribution of the soil water with a satisfactory level of accuracy.

Monitoring water and carbon fluxes at fine spatial scales using HyspIRI-like measurements

Remotely sensed observations in the visible to the shortwave infrared (VSWIR) and thermal infrared (TIR) regions of the electromagnetic spectrum can be used synergistically to provide valuable products of land surface properties for reliable assessments of carbon and water fluxes. The high spatial, spectral and temporal resolution VSWIR and TIR observations provided by the proposed Hyperspectral - InfraRed (HyspIRI) mission will enable a new era of global agricultural monitoring, critical for addressing growing issues of food insecurity. To enable predictions at fine spatial resolution (<;100m), modeling efforts must rely on a combination of high-frequency temporal and highresolution spatial information. In this study, spatialtemporal sampling frequency is improved by employing a multi-scale and multi-sensor data fusion approach, integrating spatial detail from Landsat (30m/16 day) with the high temporal frequency of MODIS (1km/daily).

Critical analysis of empirical ground heat flux equations on a cereal field using micrometeorological data

The rate at which the net radiation is transferred to the soil as ground heat flux varies with surface characteristics. Surface energy balance algorithms use empirical relationships taking into account the effects of the canopy cover to insulate the soil through vegetation indexes, the soil capacity to absorb incoming net radiation via the albedo, and the surface temperature promoting the energy transfer. However empirical relationships are often dependent on local conditions, such as the soil humidity and vegetation type. Ground heat flux assumes a minimum value in case of full canopy cover and a maximum value for dry bare soil. Aim of the present research is the critical analysis of some ground heat flux equations on a homogeneous field of cereal using measured data acquired between February and May 2008. The study period covers almost a full phenological cycle, including phases characterised by a significant change in both reflected radiation and vegetation cover. The dataset begins with the emergence phase, in November, within which shoots emerge from the ground and finishes with the flowering phase, in May, when tiny white stems begin to come-out; moreover the dataset includes a bare soil period (from September up to November). The daily evapotranspiration is calculated in energy balance models under the hypotheses of negligible daily ground heat flux and constant daily evaporative fraction. Actually micrometeorological data show that daily average ground heat flux is not null but characterised by an increasing or decreasing transient. As a consequence, it is particular important to assess the effects of neglecting the daily ground heat flux on daily evapotranspiration estimation.

Surface soil humidity retrieval using remote sensing techniques: a triangle method validation

Soil humidity plays a key-role in hydrological and agricultural processes. In the rainfall-runoff processes the knowledge of its spatial distribution is fundamental to accurately model these phenomena. Furthermore in agronomy and agricultural sciences, assessing the water content of the root zone is required in order to optimize the plant productivity and to improve the irrigation systems management. Despite the importance of this variable the in situ measurements techniques based on Time Domain Reflectometry (TDR) or on the standard thermo-gravimetric methods, are neither cost-effective nor representative of its spatial and temporal variability. Indirect estimations via Earth Observation (EO) images include the triangle method, which shows that Land Surface Temperature (LST) is prevalently controlled by surface and root zone humidity in bare and vegetated soils respectively. The effects of pre-processing techniques correcting for altimetry and seasonality are analyzed by means of shortwave and longwave airborne images acquired on a vineyard during a whole phenological period. The paper also discusses the advantages induced by replacing the absolute temperatures with relative values, that were obtained subtracting the temperatures measured by micrometeorological station or the surface temperature of high thermal inertia surfaces (as small irrigation reservoir) chosen as reference values. The validation with in situ data also highlights that a higher spatial resolution not necessarily imply a higher accuracy.

On the Role of Land Surface Temperature as Proxy of Soil Moisture Status for Drought Monitoring in Europe

Remotely sensed Land Surface Temperature (LST) represents a valuable source of data for a simple modelling of the dynamic of soil moisture (SM) over large areas. In this paper we evaluated the capability of LST monthly anomalies, derived from the MOD11C3 standard product, to capture the SM dynamic as modelled over Europe by means of an ensemble of three land surface models. The direct use of LST as proxy of SM outperformed other LST-derived quantities, such as surface-to-air temperature gradient and day-night temperature variations, returning significant correlation values over the whole domain. LST performed better over Southern Europe compared to the Northern part of the domain, with the best results over areas characterized by water-limited conditions and moderate stress. Additionally, the analysis of the contingency matrix shows that the LST model is skillful in capturing extreme dry SM events, and it also has a good overall capability to correctly detect the dry events in 66% of the cases, with an average probability of false alarm of about 30%. Overall, the use of LST anomalies seems a promising starting point for a reliable modelling of the SM dynamic with a minimum amount of information. Even if the adopted approach is simple, the results are encouraging for a practical use of LST in an operational drought monitoring system over the study area.

Comparing actual evapotranspiration and plant water potential on a vineyard

Agricultural water requirement in arid and semi-arid environments represents an important fraction of the total water consumption, suggesting the need of appropriate water management practices to sparingly use the resource. Furthermore the quality and quantity of some crops products, such as grape, is improved under a controlled amount of water stress. The latter is related, on a side to actual evapotranspiration (ET) through water demand, on the other side to plant water content through leaf water potential. Residual energy balance approaches based on remote sensing allow to estimate the spatial distribution of daily actual ET at plant scale, representing an useful tool to detect its spatial variability across different cultivars and even within each parcel. Moreover, the connection between actual ET and leaf water potential is still not well assessed, especially under water stress conditions, even if farmers use leaf water potential to plan irrigation. However residual energy balance methods are based on the hypothesis that storage terms are negligible, at least during the remote sensor overpass. Indeed, energy balance approaches estimate daily actual ET from the instantaneous value at the overpass time using a daily integration method. The paper first verifies this latter assumption using field data acquired by a flux tower on a whole phenological period. Then, the actual ET values measured by eddy covariance tower were analyzed together with water potential measured using a Scholander chamber; the analysis highlights that, under water stress conditions, daily actual ET is inversely linearly related with water potential. These results suggest the possibility to use remote sensing-based ET as support for irrigation management at plot scale.

Development of an operational low-flow index for hydrological drought monitoring over Europe

ABSTRACT Near real-time monitoring of hydrological drought requires the implementation of an index capable of capturing the dynamic nature of the phenomenon. Starting from a dataset of modelled daily streamflow data, a low-flow index was developed based on the total water deficit of the discharge values below a certain threshold. In order to account for a range of hydrological regimes, a daily 95th percentile threshold was adopted, which was computed by means of a 31-day moving window. The observed historical total water deficits were statistically fitted by means of the exponential distribution and the corresponding probability values were used as a measure of hydrological drought severity. This approach has the advantage that it directly exploits daily streamflow values, as well as allowing a near real-time update of the index at regular time steps (i.e. 10 days, or dekad). The proposed approach was implemented on discharge data simulated by the LISFLOOD model over Europe during the period 1995–2015; its reliability was tested on four case studies found within the European drought reference database, as well as against the most recent summer drought observed in Central Europe in 2015. These validations, even if only qualitative, highlighted the ability of the index to capture the timing (starting date and duration) of the main historical hydrological drought events, and its good performance in comparison with the commonly used standardized runoff index (SRI). Additionally, the spatial evolution of the most recent event was captured well in a simulated near real-time test case, suggesting the suitability of the index for operational implementation within the European Drought Observatory.

Regional-scale modeling of reference evapotranspiration: Intercomparison of two simplified temperature- and radiation-based approaches

AbstractTwo regionalized models for the distributed estimation of daily reference evapotranspiration, the temperature-based Hargreaves (HE) and the radiation-based Makkink (MK) equations, are applied in Southern Italy during the years 2007 and 2008. Spatially distributed meteorological inputs, such as air temperature and incoming solar radiation, were derived from geostatistical interpolation of ground data and from the Land Surface Analysis Satellite Application Facility (LSA-SAF) surface radiation product, respectively. Comparison of the latter with 83,394 daily measurements provided by 128 weather stations shows a not negligible seasonal error in daily solar radiation that is corrected by means of a periodic equation. A preliminary local calibration of the MK coefficient highlights its strong dependency on the interactions between moist winds from the Atlantic Ocean and orographic obstructions, leading to two western and eastern subzones. Hence, a regionalization of the MK-adjusted coefficient was perf...

Spatial sharpening of land surface temperature for daily energy balance applications

Daily high spatial resolution assessment of actual evapotranspiration is essential for water management and crop water requirement estimation under stress conditions. The application of energy balance models usually requires satellite observations of radiometric surface temperature with high geometrical and temporal resolutions. By now, however, high spatial resolution (~ 100 m) is available with low time frequency (approximately every two weeks); at the opposite daily acquisition are characterised by poor spatial resolution. The analysis of vegetation index (VI) and land surface temperature (LST) spatial relationship, shows in substance a scale invariant behaviour; this consideration allows the application of spatial sharpening algorithms of thermal data, by means of a combination of high spatial resolution data in VIS/NIR range with high temporal acquisition on TIR. In this paper, a sharpening algorithm was applied using the thermal bands of MODIS (MOderate resolution Imaging Spectroradiometer) and vegetation indices derived by ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) sensor; the choice of this sensors is justified by the simultaneous acquisition time. The results of this sharpening process was firstly compared against LST estimation (at the same spatial resolution) by means of the ASTER simultaneous data; then the derived high spatial resolution LST distribution was used in order to investigate the effect of the disaggregation on the outputs of surface energy balance models. The above described application was performed on a Sicilian study area.