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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.

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.

In different ways, Citizen Science and Remote Sensing (RS) have been recently developing as innovative and inclusive ways to improve data gathering and the comprehension of many environmental biophysical processes. In this framework, the GROW Observatory has been promoting the individual farmer awareness in agriculture as a counterpart to the ever-developing frequency and accuracy of RS products.In this analysis, 456 on-ground sensors from the GROW Observatory have been deployed in the Capitanata Irrigation Consortium (Apulia, Italy), with the aim of measuring the components of the water cycle with a dense, high-resolution pattern. The possibility of channelling these data into a high-resolution, plant-oriented Irrigation Water Need (IWN) parameter has been investigated, as a counterpart of coarser-resolution, spatially distributed monitoring powered by remote sensing and hydrological modelling.The instruments have the possibility of measuring three main variables: Surface Soil Moisture (at a maximum depth of 5 cm), Air temperature and Solar Illuminance (measured a few centimetres above ground). The monitoring period is July-October 2019, contemplating a wide range of different cultivation regimes.Irrigation water needs estimates has been obtained both in a point-wise (plant-oriented) and field-wise (spatial) format, in order to derive an irrigation water management tool. IWN and Surface Soil Moisture data are also employed in inferring back actual irrigation information from on-ground and RS data. These estimates have then be compared with observed data.Intermediate measure of Surface Soil Moisture, Air Temperature and radiation (by the Solar Illuminance proxy) have also been compared both with local measurements (those of and eddy-covariance station in place) and RS products from Sentinel and Landsat. Furthermore, Solar Illuminance data have been processed to extract a Leaf Area Index (LAI) product, also comparable with satellite estimates. These comparisons have been conducted through spatial and temporal correlations between the ground-gathered and remotely-sensed data.The potentiality and also the limitations of these low-cost instruments are presented and discussed.

Remote Sensing (RS) information has progressively found, in recent years, more and more applications in hydrological modelling as a valuable tool for easy and frequent collection of geophysical data. However, this kind of data should be handled carefully, minding its characteristics, spatial resolution and the heterogeneity of the target area.In this work, a scale analysis on evapotranspiration estimates over heterogeneous crops is performed combining a distributed energy-water balance model (FEST-EWB) and high-resolution remotely-sensed Land Surface Temperature (LST) and vegetation data.The FEST-EWB model is calibrated on measured LST, based on a procedure where every single pixel is modified independently one from the other; hence in each pixel of the analysed domain the minimum of the pixel difference between modelled RET and satellite observed LST is searched over the period of calibration.The case study is a Sicilian vineyard, with test dates in the summer of 2008. Meteorological and energy fluxes data are available from an eddy-covariance station, while LST and vegetation data are obtained from low-altitude flights at the high resolution of 1.7 metres.After a preliminary calibration on LST data and validation on energy fluxes, the scale analysis is performed in two ways: model input aggregation and model output aggregation. Four coarser scales are selected in reference to some common satellite products resolution: 10.2 m (in reference to Sentinel’s 10 m), 30.6 m (Landsat, 30 m), 244.8 m (MODIS visible, 250 m) and 734.4 m (MODIS, 1000 m). First, modelled surface temperature and evapotranspiration are aggregated to each scale by progressive averaging. Then, model inputs are upscaled to the same spatial resolutions and the model is calibrated anew, obtaining independent results directly at the target scale.The results of the two procedures are found to be quite similar, testifying to the capacity of the model to provide accurate products for a heterogeneous area even at low resolutions. The robustness of the analysis is strengthened by a further comparison with two well-established energy-balance algorithms: the one source Surface Energy Balance Algorithm for Land (SEBAL) and the Two-Source Energy Balance (TSEB) model.

Numerous surface soil moisture (SSM) products are available from remote sensing, ranging different spatial and temporal resolutions. Varying techniques are employed to retrieve SSM and different spatial scales highlight different distributions. Notwithstanding this variety between the available data, all of them should be coherent with the recorded rainfall and irrigation.In this work we have crossed recorded precipitations with a number of SSM products deriving from remote sensing: Soil Moisture Ocean Salinity (SMOS) mission, Soil Moisture Active Passive (SMAP) mission, European Space Agency Climate Change Initiative (ESA-CCI) products, Copernicus Global Land Operations product, a Neural Network SSM retrieval algorithm and AMSR-2 data.All the dataset products have been compared with recorded precipitation from on-ground stations over two agricultural sites in Italy: one in the north, near Lake Garda (Chiese Irrigation Consortium) and the other in the south-east in the Apulia region (Capitanata Irrigation Consortium).In both cases, a first SSM-rain comparison through well-established indexes (Pearson and Spearman correlations) has not yielded encouraging results.Then, a methodology has been developed to determine whether the variation of SSM is consistent with the presence/absence of precipitation. An Agreement Index (AI) has been derived as a way to measure the coherency between SSM and precipitation. Any time a measure of SSM is available, a positive or negative value for the AI is recorded, according to the rainfall registered since the previous measurement. During the irrigation season (March through September), the presence of this artificial input of water into the system is also taken into account. For every year, the proportion between “coherent” SSM-rainfall pairings (positive AIs) and “non-coherent” pairings (negative AIs) has been computed.This method is applied to all SSM products in the dataset, and results are compared. When aggregating the results for all the pixels within the irrigation consortia, all seem to align to a similar proportion between “coherent” and “non-coherent” SSM-rainfall pairings, notwithstanding the wide variety of data types, spatial resolutions and retrieval methods. However, even if the overall performances of the products are similar, each shows different spatial distributions, as each product is influenced differently by the physical features of the different areas.

The increase of irrigation efficiency and crop productivity in agriculture is nowadays a general requirement at national and international level to mitigate planet food security problems due to: the freshwater supply variability and scarcity enhanced by climate change (FAO, 2018), the increasing water demand due to population growth and also due to using ancient and sometimes empirical agricultural techniques.To test the effectiveness of these irrigation strategies, on-field surveys alone are not enough. In the laboratory environment, a higher degree of insight is accessible, with a number of measurements that would be difficult on site.In this work, we have tested the water dynamics related to the particular irrigation strategy developed in the SIM project (Smart Irrigation from soil moisture forecast using satellite and hydro-meteorological Modelling). The basic principle behind the strategy is that the soil moisture in the root-zone should be kept between the plant stress threshold and reducing deep percolation at all times. In that way, the irrigation amount is always enough so that the crop does not suffer water stress, and any water loss is avoided. As a comparison, two more common irrigation strategies have been tested in the same conditions: potential and deficit irrigation.The laboratory set-up involves a wide range of instruments and devices: a lysimeter, a high-resolution (2g tolerance) scale, a thermal camera, a spectrometer, an infrared and an ultraviolet lamp, a radiometer and a leaf porometer. To increase the accuracy of the measurements, instead of working directly on the lysimeter, the crops have been cultivated in separate boxes, placed directly above the lysimeter. Three boxes have been managed according to each irrigation strategy. As a reference, one box has been kept with bare soil throughout the whole testing period, one has been filled with water and, finally, dense grass has been cultivated in another, totalling 12 boxes. The subdivision in boxes allows weighing each separately, guaranteeing higher accuracy.The laboratory routine consisted of daily measurements of the weight of the boxes, together with measurements of temperature and leaf irradiance spectra. The evapotranspiration and percolation from each box are derived from the weight difference, and the water mass balance is closed for every box. At the end of the experimental set-up, the productivity for each irrigation strategy has been computed by measuring the final crop yield of each box.Positive results, in terms of crop health and water savings, have been obtained with the SIM strategy.