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Agriculture will progressively require more and more attention as changing climatic conditions and reduced water availability threaten food security worldwide. The optimization of the agricultural production is obtained with constant monitoring of the plant health (in terms of e.g., soil moisture, leaf temperature or evapotranspiration), which can be challenging if crop fields are too extensive.Thermal observations from remote sensing are extensively used in agricultural monitoring to power (mostly-residual) energy balance model that provide evapotranspiration estimates. Two main issues hinder the quality of the results from these models: (a) sub-pixel heterogeneity, in particular related to mixed crops (e.g. row and tree crops), which can be captured only partially by the available LST information and (b) temporal frequency of the information, which for most freely-available products is usually at odds with spatial resolution (e.g., 1 km data from MODIS is available daily, whereas 90 m data from Landsat only once every 7-8 days). Furthermore, tree crops draw water from deep layers of soil, further disconnecting the satellite information from the biophysical processes involved in plant growth.In this work, the use of a continuous, two-source, double-soil-layer coupled energy-water balance model is displayed as a solution of these issues. The link between the two balances allows to compute surface temperature internally, meaning that satellite LST observations are used, only when available, for the calibration process. Furthermore, the use of a double source in the energy exchanges allows to properly address the intra-pixel heterogeneity. Finally, the double soil layer allows to address the soil water and energy vertical gradient in complex systems, properly framing the surface observation from remote sensing within the overall environment.Two pear tree fields in the Po Valley have been chosen as focus to study the effectiveness of this model, via a monitoring of the 2022 irrigation season, employing Sentinel 2 observations for the vegetation data and Landsat 8 LST for the calibration process. ET estimates are evaluated against flux tower observations. The increased accuracy of these estimates is key to enforce a more precise and effective irrigation and optimize the use of the water resource.

Agriculture is the largest consumer of water worldwide, accounting for about 70% of the global freshwater withdrawals. Thus, crop water use efficiency and impacts of water stress on crop water consumption are the key concerns for agricultural water management.Present study investigates the variability of evapotranspiration (ET) and crop water use efficiency by integrating very high spatial resolution (1 – 4 m) thermal infrared (TIR) data from airborne measurements and visible to near infrared data from Planet satellite with a numerical water-energy balance model and a diagnostic surface energy balance model. The analysis is done for an intensive agriculture area in central Italy near the city of Modena, where several fruit trees fields are present along with fresh vegetables. An intensive airborne campaign was organized in the summer of 2022 for three consecutive days in July. A hyperspectral TIR camera (Telops Hyper-Cam LW) has been operated at a spectral resolution of 8 cm-1, resulting in 64 wavebands, and covering a wavelength range of 850 cm-1 to 1350 cm-1 (7,39 µm – 11,8 µm). During the 3 days of flight acquisitions, three overpasses per day are planned: 9:00, 12:30 and 16:00 h, respectively and two areas were intensively surveyed at both 4 and 1 m spatial resolution. Planet data at 3.7 m spatial resolution were used to derive different vegetation indices, such as vegetation fraction coverage, NDVI and leaf area index. During airborne overpasses ground data of spectral reflectance, vegetation variables, LST and soil water content (SM) were collected in different fields. In addition, two different pear trees fields were monitored with an eddy covariance station and soil moisture profile measurements, respectively.To investigate the diurnal and spatial patterns of evapotranspiration, soil moisture variability and crop water use efficiency, we used two numerical models: the surface energy balance model STIC based on Penman-Monteith and Shuttleworth-Wallace (Mallick et al., 2018) and the water-energy balance model FEST-EWB which computes continuously in time and is distributed in space soil moisture and evapotranspiration fluxes solving for a land surface temperature that closes the energy–water balance equations (Corbari et al., 2011).Differences and similarities in ET estimates have been analysed from the two models for different soil moisture conditions and crop types, considering crop water use efficiency and water stress, and have been compared to eddy covariance measurements for accuracy evaluation considering both instantaneous and daily data. The assimilation of instantaneous estimates of ET into the water-energy balance model allowed to directly derived soil moisture maps at high spatial resolution which have been found in agreement with ground SM measurements.

Agriculture will progressively require more and more attention due to changing climatic conditions and increasing population, consequently threatening food security worldwide. Improving irrigation efficiency and its control on large agricultural areas has become a must for the present and next future. Satellite data coupled with pixel-wise energy and water balance plays a relevant role in the soil moisture assessment and relative irrigation water needs for different crop and soil types. In this framework, data from remote sensing is a potential source of information and in particular land surface temperature is nowadays extensively used in agricultural monitoring as input of energy balance models (residuals) that provide evapotranspiration estimates and so irrigation water needs. Two main issues hinder the quality of the results from these models: (a) sub-pixel heterogeneity, in particular related to mixed crops (e.g. row and tree crops), which can be captured only partially by the available LST spatial resolution and (b) temporal frequency of the information, which for most freely-available products is usually in contrast with spatial resolution (e.g., 1 km data from MODIS is available daily, whereas 90 m data from Landsat only once every 7-8 days). This work discusses the use of land surface temperature for calibration and validation of a pixel-wise soil-energy-water balance model and its impact on irrigation volumes for different case studies. The discussion is carried out on several case studies characterized by different land use heterogeneity due to arboreal or crop cover and comparing satellite and ground data. LST data at different grid resolutions (10^0 to 10^2 m) are available and have been used with a corresponding spatial scheme to model the pixel soil, energy and water balances.

Drought prediction is crucial especially where the rainfall regime is irregular and agriculture is mainly based on irrigated crops, such as in Mediterranean countries. In this work, the main objective is to develop an EO-based agricultural drought monitoring system (ADMOS) for operative irrigation networks management at different spatial and temporal scales. Different levels of drought are identified based on an integrated indicator combining anomalies of rainfall, soil moisture, land surface temperature and vegetation indices, allowing to consider the different droughts types and their timing looking on the end-user’s perspective. Multiple remote sensing data, which differ on sensing techniques, spatial and temporal resolutions and electromagnetic frequencies, are used for each anomaly computation. The analyses have been performed over two Irrigation Consortia in Italy (the Chiese and Capitanata ones), which differ for climate, irrigation volumes and techniques, and crop types. The obtained results show a negative correlation between cumulated ADMOS and the irrigation volumes in the Capitanata area, while in the Chiese Consortium a zero correlation is obtained with an almost constant amount of irrigation volumes provided to the crops every year independently from the drought condition. In both areas, crop yields seem to be almost uncorrelated to the drought index, as production is highly sustained by irrigation. Moreover, discrepancies on the anomalies sign is observed, especially when soil moisture is considered. The results also clearly show that asynchronies may exist especially between soil moisture anomalies and vegetation or land surface temperature anomalies.

One of the main issues with obtaining accurate Evapotranspiration (ET) measurements for heterogeneous crops is managing the partition between the contribution of bare soil / cover crop and that of the main crop. Mostly, ET estimates are obtained as an aggregate of the two components, as direct measurements of distinct Evaporation (E) and Transpiration (T) are possible only with high-accuracy and time-costly field lysimeters. Hydrological modelling can provide these kinds of estimates, with the dichotomy between single-source (one energy balance equation for the whole pixel) and two-source (one balance equation each for the vegetated and the non-vegetated pixel fraction) models approaching the problem from different perspectives. In this work, a laboratory lysimeter was employed to obtain disaggregated fluxes from a global ET value and use them to validate the partitioned estimates from a two-source version (FEST-2-EWB) of the single-source FEST-EWB distributed hydrological model, which was also included in the validation as a reference. The lysimeter was sown with grass distributed in three rows, alternated with similar rows of bare soil, with irrigation being provided to the former and not to the latter. Thermal imagery from proximal sensing observations was used to calibrate the models. Two boxes were placed on the lysimeter, one completely vegetated and the other left bare. These boxes were periodically weighted separately from the lysimeter, obtaining accurate measurements of their ET, that were then scaled back to the correspondent areas in the main lysimeter. The model runs, provided similar calibration performances, showed similar global ET values, close to those measured over the lysimeter, but diverged when looking ad transpiration alone. The two-source model offered estimates much closer to those derived from the lysimeter than the single-source model.