Water use efficiency in agriculture can be improved by implementing advisory systems that support on-farm irrigation scheduling, with reliable forecasts of the actual crop water requirements, where ...crop evapotranspiration (ET
) is the main component. The development of such advisory systems is highly dependent upon the availability of timely updated crop canopy parameters and weather forecasts several days in advance, at low operational costs. This study presents a methodology for forecasting ET
, based on crop parameters retrieved from multispectral images, data from ground weather sensors, and air temperature forecasts. Crop multispectral images are freely provided by recent satellite missions, with high spatial and temporal resolutions. Meteorological services broadcast air temperature forecasts with lead times of several days, at no subscription costs, and with high accuracy. The performance of the proposed methodology was applied at 18 sites of the Campania region in Italy, by exploiting the data of intensive field campaigns in the years 2014-2015. ET
measurements were forecast with a median bias of 0.2 mm, and a median root mean square error (RMSE) of 0.75 mm at the first day of forecast. At the 5
day of accumulated forecast, the median bias and RMSE become 1 mm and 2.75 mm, respectively. The forecast performances were proved to be as accurate and as precise as those provided with a complete set of forecasted weather variables.
Mapping soil organic carbon (SOC) stock can serve as a resilience indicator for climate change. As part of the carbon dioxide (CO2) sink, soil has recently become an integral part of the global ...carbon agenda to mitigate climate change. We used hyperspectral remote sensing to model the SOC stock in the Sele River plain located in the Campania region in southern Italy. To this end, a soil spectral library (SSL) for the Campania region was combined with an aerial hyperspectral image acquired with the AVIRIS–NG sensor mounted on a Twin Otter aircraft at an altitude of 1433 m. The products of this study were four raster layers with a high spatial resolution (1 m), representing the SOC stocks and three other related soil attributes: SOC content, clay content, and bulk density (BD). We found that the clay minerals’ spectral absorption at 2200 nm has a significant impact on predicting the examined soil attributes. The predictions were performed by using AVIRIS–NG sensor data over a selected plot and generating a quantitative map which was validated with in situ observations showing high accuracies in the ground-truth stage (OC stocks RPIQ = 2.19, R2 = 0.72, RMSE = 0.07; OC content RPIQ = 2.27, R2 = 0.80, RMSE = 1.78; clay content RPIQ = 1.6 R2 = 0.89, RMSE = 25.42; bulk density RPIQ = 1.97, R2 = 0.84, RMSE = 0.08). The results demonstrated the potential of combining SSLs with remote sensing data of high spectral/spatial resolution to estimate soil attributes, including SOC stocks.
The study region is represented by seven irrigation districts distributed under different climate and topography conditions in Italy.
This study explores the reliability and consistency of the global ...ERA5 single levels and ERA5-Land reanalysis datasets in predicting the main agrometeorological estimates commonly used for crop water requirements calculation. In particular, the reanalysis data was compared, variable-by-variable (e.g., solar radiation, Rs; air temperature, Tair; relative humidity, RH; wind speed, u10; reference evapotranspiration, ET0), with in situ agrometeorological observations obtained from 66 automatic weather stations (2008–2020). In addition, the presence of a climate-dependency on their accuracy was assessed at the different irrigation districts.
A general good agreement was obtained between observed and reanalysis agrometeorological variables at both daily and seasonal scales. The best performance was obtained for Tair, followed by RH, Rs, and u10 for both reanalysis datasets, especially under temperate climate conditions. These performances were translated into slightly higher accuracy of ET0 estimates by ERA5-Land product, confirming the potential of using reanalysis datasets as an alternative data source for retrieving the ET0 and overcoming the unavailability of observed agrometeorological data.
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•The air temperature estimates offered the most accurate reanalysis predictions.•Climate reanalysis provided reliable reference evapotranspiration estimates.•Different performances were obtained by ERA5 and ERA5-L for some variables.•The accuracy of the reanalysis products was influenced by the climatic conditions.
Remote sensing can provide important and updated information for agricultural water accounting (AWA). In this study, data from the open-access portal (WaPOR) of the Food and Agricultural Organization ...was used in AWA to assess levels of agricultural water consumption and to provide possible solutions for water deficiency in the North Jordan Valley (NJV). Consolidated procedures have been applied to complement and validate the WaPOR products. These included the use of climatic and ground data, the multispectral remote-sensing data of Sentinel-2 and Landsat 8 to derive land use/cover maps, GIS layers, and calibrated evapotranspiration (ET) estimates using the surface energy balance algorithm for land (SEBAL). The data of water inflows and outflows were analyzed using the water accounting plus (WA+) system. Results showed that the WaPOR data of actual ET and interception (AETI) were highly correlated with SEBAL-ET, with WaPOR data overestimating ET for irrigated areas. Precipitation data from WaPOR, on the other hand, were underestimating inflow from rainfall, although significant correlations were observed between these data and rainfall records. As a result, the quality of WaPOR data affected the outputs from agricultural water accounting. The main impact on water accounting outputs was the underestimation of percolated water that could be utilized as a possible solution to water deficiency in the NJV. In addition, the water accounting performance indicators were relatively affected, although they reflected the nature of the study area where water deficiency predominated as a result of inter-basin transfer. The study compared outputs from water accounting in terms of the possible solutions to water deficiency in the NJV and concluded that considerable amounts of recoverable water could be developed when compared with the option of developing surface water from the side wadis. Also, it emphasized the important role of remote-sensing sources for providing information for AWA needed for improved water management and governance.
The improvement of performance of irrigation systems plays a fundamental role in increasing their efficiency in order to reach a sound use of irrigation water. The COPAM (Combined Optimization and ...Performance Analysis Model) has proven its usefulness in performance evaluation of on-demand irrigation systems; however, in many cases, input data, such as water volumes delivered by hydrants, is not readily available. To support a wider application of the COPAM, we tested the possibility of using irrigation volumes estimated by means of space-borne remote sensing. The Sentinel-2 (S2) constellation provides high spatial resolution images with a frequency between 2 and 5 days, which is compatible with COPAM input requirements. In the present work, an irrigation sector in the Capitanata irrigation network (Foggia Province, no. 6 of District 10) in Italy was chosen to assess its performance by using COPAM with volumes estimated from Sentinel-2 data. As an input of COPAM, the upstream discharge was determined after a proper transformation of the estimated irrigation water requirement volumes and the recorded volumes into flowrates. The estimation of the irrigation water requirement volumes was accomplished through the estimation of crop evapotranspiration, Etcrop, and effective precipitation, Pn, by combining crop parameters (leaf area index - LAI, fractional vegetation cover - fc, and Albedo) derived from S2 images and the meteorological data from the ERA5 single levels reanalysis dataset collected for the whole study period, from June 1st to September 30th, 2019. The study comprised a comparison of the estimated irrigation water volumes and the corresponding recorded volumes. The results showed a good agreement between the estimated and the registered volumes in a large time scale for 10 days and a one-month period, while a large difference was observed in a daily time scale. The performance analysis was carried out for the overall system and at hydrant level. The estimated discharge was lower than the registered discharge, indicating better performance. Last but not least, some recommendations were proposed for improving performance in critical zones.
A new approach is proposed to derive evapotranspiration (E) and irrigation requirements by implementing the combination equation models of Penman–Monteith and Shuttleworth and Wallace with surface ...parameters and resistances derived from Sentinel-2 data. Surface parameters are derived from Sentinel-2 and used as an input in these models; namely: the hemispherical shortwave albedo, leaf area index and water status of the soil and canopy ensemble evaluated by using a shortwave infrared-based index. The proposed approach has been validated with data acquired during the GRAPEX (Grape Remote-sensing Atmospheric Profile and Evapotranspiration eXperiment) in California irrigated vineyards. The E products obtained with the combination equation models are evaluated by using eddy covariance flux tower measurements and are additionally compared with surface energy balance models with Landsat-7 and -8 thermal infrared data. The Shuttleworth and Wallace (S-W S-2) model provides an accuracy comparable to thermal-based methods when using local meteorological data, with daily E errors < 1 mm/day, which increased from 1 to 1.5 mm/day using meteorological forcing data from atmospheric models. The advantage of using the S-W S-2 modeling approach for monitoring ET is the high temporal revisit time of the Sentinel-2 satellites and the finer pixel resolution. These results suggest that, by integrating the thermal-based data fusion approach with the S-W S-2 modeling scheme, there is the potential to increase the frequency and reliability of satellite-based daily evapotranspiration products.
The experiences gathered during the past 30 years support the operational use of irrigation scheduling based on frequent multi-spectral image data. Currently, the operational use of dense time series ...of multispectral imagery at high spatial resolution makes monitoring of crop biophysical parameters feasible, capturing crop water use across the growing season, with suitable temporal and spatial resolutions. These achievements, and the availability of accurate forecasting of meteorological data, allow for precise predictions of crop water requirements with unprecedented spatial resolution. This information is greatly appreciated by the end users, i.e., professional farmers or decision-makers, and can be provided in an easy-to-use manner and in near-real-time by using the improvements achieved in web-GIS methodologies (Geographic Information Systems based on web technologies). This paper reviews the most operational and explored methods based on optical remote sensing for the assessment of crop water requirements, identifying strengths and weaknesses and proposing alternatives to advance towards full operational application of this methodology. In addition, we provide a general overview of the tools, which facilitates co-creation and collaboration with stakeholders, paying special attention to these approaches based on web-GIS tools.
This work evaluates different procedures for the application of a semi-empirical model to derive time-series of Leaf Area Index (LAI) maps in operation frameworks. For demonstration, multi-temporal ...observations of DEIMOS-1 satellite sensor data were used. The datasets were acquired during the 2012 growing season over two agricultural regions in Southern Italy and Eastern Austria (eight and five multi-temporal acquisitions, respectively). Contemporaneous field estimates of LAI (74 and 55 measurements, respectively) were collected using an indirect method (LAI-2000) over a range of LAI values and crop types. The atmospherically corrected reflectance in red and near-infrared spectral bands was used to calculate the Weighted Difference Vegetation Index (WDVI) and to establish a relationship between LAI and WDVI based on the CLAIR model. Bootstrapping approaches were used to validate the models and to calculate the Root Mean Square Error (RMSE) and the coefficient of determination (R2) between measured and predicted LAI, as well as corresponding confidence intervals. The most suitable approach, which at the same time had the minimum requirements for fieldwork, resulted in a RMSE of 0.407 and R2 of 0.88 for Italy and a RMSE of 0.86 and R2 of 0.64 for the Austrian test site. Considering this procedure, we also evaluated the transferability of the local CLAIR model parameters between the two test sites observing no significant decrease in estimation accuracies. Additionally, we investigated two other statistical methods to estimate LAI based on: (a) Support Vector Machine (SVM) and (b) Random Forest (RF) regressions. Though the accuracy was comparable to the CLAIR model for each test site, we observed severe limitations in the transferability of these statistical methods between test sites with an increase in RMSE up to 24.5% for RF and 38.9% for SVM.
Monitoring spatial and temporal variability of vegetation is important to manage land and water resources, with significant impact on the sustainability of modern agriculture. Cloud cover noticeably ...reduces the temporal resolution of retrievals based on optical data. COSMO-SkyMed (the new Italian Synthetic Aperture RADAR-SAR) opened new opportunities to develop agro-hydrological applications. Indeed, it represents a valuable source of data for operational use, due to the high spatial and temporal resolutions. Although X-band is not the most suitable to model agricultural and hydrological processes, an assessment of vegetation development can be achieved combing optical vegetation indices (VIs) and SAR backscattering data. In this paper, a correlation analysis has been performed between the crossed horizontal-vertical (HV) backscattering (s°HV) and optical VIs (VIopt) on several plots. The correlation analysis was based on incidence angle, spatial resolution and polarization mode. Results have shown that temporal changes of s°HV (Δs°HV) acquired with high angles (off nadir angle; θ > 40°) best correlates with variations of VIopt (ΔVI). The correlation between ΔVI and Δs°HV has been shown to be temporally robust. Based on this experimental evidence, a model to infer a VI from s° (VISAR) at the time, ti + 1, once known, the VIopt at a reference time, ti, and Δs°HV between times, ti + 1 and ti, was implemented and verified. This approach has led to the development and validation of an algorithm for coupling a VIopt derived from DEIMOS-1 images and s°HV. The study was carried out over the Sele plain (Campania, Italy), which is mainly characterized by herbaceous crops. In situ measurements included leaf area index (LAI), which were collected weekly between August and September 2011 in 25 sites, simultaneously to COSMO-SkyMed (CSK) and DEIMOS-1 imaging. Results confirm that VISAR obtained using the combined model is able to increase the feasibility of operational satellite-based products for supporting agricultural practices. This study is carried out in the framework of the COSMOLAND project (Use of COSMO-SkyMed SAR data for LAND cover classification and surface parameters retrieval over agricultural sites) funded by the Italian Space Agency (ASI).
Upcoming satellite hyperspectral sensors require powerful and robust methodologies for making optimum use of the rich spectral data. This paper reviews the widely applied coupled PROSPECT and SAIL ...radiative transfer models (PROSAIL), regarding their suitability for the retrieval of biophysical and biochemical variables in the context of agricultural crop monitoring. Evaluation was carried out using a systematic literature review of 281 scientific publications with regard to their (i) spectral exploitation, (ii) vegetation type analyzed, (iii) variables retrieved, and (iv) choice of retrieval methods. From the analysis, current trends were derived, and problems identified and discussed. Our analysis clearly shows that the PROSAIL model is well suited for the analysis of imaging spectrometer data from future satellite missions and that the model should be integrated in appropriate software tools that are being developed in this context for agricultural applications. The review supports the decision of potential users to employ PROSAIL for their specific data analysis and provides guidelines for choosing between the diverse retrieval techniques.
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