Agriculture is the largest consumer of water worldwide and at the same time irrigation is one of the sectors where there is one of the hugest differences between modern technology and the largely diffused ancient traditional practices. Improving water use efficiency in agriculture is an immediate requirement of human society for sustaining the global food security, to preserve quality and quantity of water resources and to reduce causes of poverties, migrations and conflicts among states, which depend on trans-boundary river basins. Climate changes and increasing human pressure together with traditional wasteful irrigation practices are enhancing the conflictual problems in water use also in countries traditionally rich in water. Saving irrigation water improving irrigation efficiency on large areas with modern technics is one of the first urgent action to do. It is well known in fact that agriculture uses large volumes of water with low irrigation efficiency, accounting in Europe for around 24% of the total water use, with peak of 80% in the Southern Mediterranean part and may reach the same percentage in Mediterranean non-EU countries (EEA, 2009; Zucaro 2014). North Africa region has the lowest per-capita freshwater resource availability among all Regions of the world (FAO, 2018).
Several recent researches are done on the optimization of irrigation water management to achieve precision farming using remote sensing information and ground data combined with water balance modelling.
In this session, we will focus on: the use of remote sensing data to estimate irrigation volumes and timing; management of irrigation using hydrological modeling combined with satellite data; improving irrigation water use efficiency based on remote sensing vegetation indices, hydrological modeling, satellite soil moisture or land surface temperature data; precision farming with high resolution satellite data or drones; farm and irrigation district irrigation management; improving the performance of irrigation schemes; irrigation water needs estimates from ground and satellite data; ICT tools for real-time irrigation management with remote sensing and ground data coupled with hydrological modelling.

Convener: Chiara Corbari | Co-conveners: kamal Labbassi, Kaniska Mallick, Francesco Vuolo
| Attendance Thu, 07 May, 14:00–15:45 (CEST)

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Chat time: Thursday, 7 May 2020, 14:00–15:45

D318 |
Jacopo Dari, Pere Quintana-Seguí, María José Escorihuela, Luca Brocca, Renato Morbidelli, and Vivien Stefan

Irrigation practices introduce imbalances in the natural hydrological cycle at different spatial scales and put pressure on water resources, especially under climate changing and population increasing scenarios. Despite the implications of irrigation on food production and on the rational management of the available freshwater, detailed information about the areas where irrigation actually occurs is still lacking. For this reason, the comprehensive knowledge of the dynamics of the hydrological cycle over agricultural areas is often tricky.

The first aim of this study is to evaluate the capability of five remote sensing soil moisture data sets to detect the irrigation signal over an intensely irrigated area located within the Ebro river basin, in the North of Spain, during the biennium 2016-2017. As a second objective, a methodology to map the irrigated areas through the K-means clustering algorithm is proposed. The remotely sensed soil moisture products used in this study are: SMOS (Soil Moisture and Ocean Salinity) at 1 km, SMAP (Soil Moisture Active Passive) at 1 km and 9 km, Sentinel-1 at 1 km and ASCAT (Advanced SCATterometer) at 12.5 km. The 1 km versions of SMOS and SMAP are DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled versions of the corresponding coarser resolution products. An additional data set of soil moisture simulated by the SURFEX-ISBA (Surface Externalisée - Interaction Sol Biosphère Atmosphère) land surface model is used as a support for the performed analyses.

The capability of soil moisture products to detect irrigation has been investigated by exploiting indices representing the spatial and temporal dynamics of soil moisture. The L-band passive microwave downscaled products, especially SMAP at 1 km, result the best performing ones in detecting the irrigation signal over the pilot area; on the basis of these data sets, the K-means algorithm has been employed to classify three kinds of surfaces within the study area: the dryland, the forest or natural areas, and the actually irrigated areas. The resulting maps have been validated by exploiting maps of crops in Catalonia as ground truth data set. The percentage of irrigated areas well classified by the proposed method reaches the value of 78%; this result is obtained for the period May - September 2017. In addition, the method performs well in distinguishing the irrigated areas from rainfed agricultural areas, which are dry during summer, thus representing a useful tool to obtain explicit spatial information about where irrigation practices actually occur over agricultural areas equipped for this purpose.

How to cite: Dari, J., Quintana-Seguí, P., Escorihuela, M. J., Brocca, L., Morbidelli, R., and Stefan, V.: The detection of irrigation through remote sensing soil moisture and a land surface model: a case study in Spain, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3654, https://doi.org/10.5194/egusphere-egu2020-3654, 2020.

D319 |
| Highlight
Marco Mancini, Chiara Corbari, Alessandro Ceppi, Gabriele Lombardi, Josè Sobrino, Drazen Skokovic, Massimo Menenti, Jia Li, Romu Romero, Arnau Amengual, Raffaele Salerno, Stefania Meucci, and Giacomo Branca

The conflicting use of water is becoming more and more evident, also in regions that are traditionally rich in water. With the world’s population projected to increase to 8.5 billion by 2030, the simultaneous growth in income will imply a substantial increase in demand for both water and food (expected to increase by 70% by 2050). Climate change impacts will further stress the water availability enhancing also its conflictual use. The agricultural sector, the biggest and least efficient water user, accounts for around 24% of total water use in Europe, peaking at 80% in the southern regions, is likely to face important challenges in order to sustain food production and parsimonious use of water.

The paper shows the development of a system for operative irrigation water management able to monitor and forecast the crop water need reducing the irrigation losses and increasing the water use efficiency. The system couples together satellite and ground data, with pixel wise hydrological soil water balance model using recent scientifically outcomes on soil moisture retrieval from satellite data and hydrological modelling. Discussion on the methodological approach based on the satellite land surface temperature LST, ground evapotranspiration measures, and pixel wise hydrological modelling is provided proving the reliability of the forecasting system and its benefits.

The activity is part of the European Chinese collaborative project (SIM, Smart Irrigation Modelling, www.sim.polimi.it) which has as main objective the parsimonious use of agricultural water through an operational web tool to reduce the use of water, fertilizer and energy keeping a constant crop yield. The system provides in real-time the present and forecasted irrigation water requirements at high spatial and temporal resolutions with forecast horizons from few up to thirty days, according to different agronomic practices supporting different level of water users from irrigation consortia to single farmers.

The system is applied in different experimental sites which are located in Italy, the Netherlands, China and Spain, which are characterized by different climatic conditions, water availability, crop types and irrigation schemes. This also thanks to the collaboration of several stakeholders as the Italian ANBI, Capitanata and Chiese irrigation consortia and Dutch Aa and Maas water authority

The results are shown for two case studies in Italy and in China The Italian ones is the Sud Fortore District of the Capitanata Irrigation consortium which covers an area of about 50’000 hectares with flat topography, hot summer and warm winter, mainly irrigated with pressurized aqueduct. The district is an intensive cultivation area, mainly devoted to wheat, tomatoes and fresh vegetables cultivation The Chinese one is in the Hehie Daman district covering an area of 20000 ha with fixed time flooding irrigation.

How to cite: Mancini, M., Corbari, C., Ceppi, A., Lombardi, G., Sobrino, J., Skokovic, D., Menenti, M., Li, J., Romero, R., Amengual, A., Salerno, R., Meucci, S., and Branca, G.: SIM: smart irrigation from soil moisture forecast using satellite and hydro –meteorological modelling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-6935, https://doi.org/10.5194/egusphere-egu2020-6935, 2020.

D320 |
Julian Koch, Simon Stisen, Xin He, and Grith Martinsen

Knowledge of irrigation water use is crucial for ensuring food and water security in water scarce regions. Even though irrigation is one of the most important direct human interferences with the terrestrial water cycle, there exists limited knowledge on the extent of irrigated areas and in particular the amount of water applied for irrigation. In this study, we develop a novel approach that estimates net water loss due to irrigation and apply it over the North China Plain domain, which is a global hotspot for severe groundwater depletion caused by extensive irrigation practices. Our goal is to retrieve spatio-temporal patterns of net irrigation amounts, constituted as evaporative loss of irrigated water, at monthly timescale at 1km2 spatial resolution. The analysis is based on a direct comparison of two alternative evapotranspiration (ET) models: (1) A remote sensing based model (PT-JPL-thermal) using various MODIS products as input and (2) a one-dimensional, free drainage hydrological model (mHM). The hydrological model is purely driven by rainfall and will therefore naturally show a strong disagreement with the remote sensing based ET during periods of extensive irrigation. We use this systematic residual term that reflects a non-precipitation-based water source, as quantification of net irrigation. The hydrological model is calibrated against the remote sensing based ET at grids that are not affected by irrigation and discharge records representing natural flow. Total water storage anomalies retrieved by GRACE are utilized to evaluate the derived net irrigation amounts over the North China Plain. We find, that irrigation peaks in May, which corresponds to the peak of the growing season of winter wheat. Moreover total irrigation amounts to 116 mm per year (14km3), which is in good agreement with previous studies. The net irrigation estimates are at an unprecedented spatial and temporal resolution and are extremely valuable input for water resources management as well as for subsequent groundwater modelling where net irrigation can be utilized as pumping boundary condition.

How to cite: Koch, J., Stisen, S., He, X., and Martinsen, G.: Quantifying net irrigation across the North China Plain through dual modelling of evapotranspiration, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8633, https://doi.org/10.5194/egusphere-egu2020-8633, 2020.

D321 |
Hassan Bazzi, Nicolas Baghdadi, Dino Ienco, Mehrez Zribi, and Hatem Belhouchette

Potential of Sentinel-1 and Sentinel-2 data for Mapping Irrigated areas at plot scale

Hassan Bazzi 1, Nicolas Baghdadi 1, Dino Ienco 1, Mehrez Zribi 2, Hatem Belhouchette 3

Irrigation plays a significant role in agricultural production in order to meet the global food requirement under changing climatic conditions. To fulfill the high demand for food with an ever-increasing global population, better planning of irrigation is required. Therefore, more focus is being set on the assessment of irrigation performance for improving water management in order to achieve higher water productivity and increase agricultural water sustainability.

In the context of mapping irrigated areas, we propose an innovative approach to map irrigated areas using Sentinel-1 (S1) SAR (Synthetic Aperture Radar) and Sentinel-2 (S2) optical time series. Our proposed approach is based on the use of S1 and/or S2 time series combined with statistical and mathematical functions such as principal component analysis (PCA) and wavelet transformation (WT). The proposed approach was tested over the Catalonia region, Spain with a dataset containing 126 000 irrigated and 67 000 non-irrigated plots. The novelty of our study resides in eliminating the ambiguity between irrigation and rainfall by comparing between the SAR backscattering signal of each plot and that of the corresponding grid (10 km × 10 km). The potential of S2 images to classify irrigated areas by means of NDVI time series was also investigated in this study. Random forest (RF) and convolutional neural network (CNN) approaches were used to build up classification models using the PCA or WT parameters in three different scenarios: The first using only S1 data, the second using only S2 data, and the third using both S1 and S2 data.

The RF classifiers built using the PCA or WT on the S1 time series perform well in mapping irrigated areas with an accuracy of 90.7% and 89.1% respectively. However, the CNN classification on the S1 temporal series produces a significant overall accuracy of 94.1%. The overall accuracy obtained using the NDVI time series in RF classifier reached 89.5% while that in the CNN reached 91.6%. Finally, the combined use of the SAR and optical data enhanced the accuracy of the RF classification but did not significantly change the overall accuracy of the CNN model.

How to cite: Bazzi, H., Baghdadi, N., Ienco, D., Zribi, M., and Belhouchette, H.: Potential of Sentinel-1 and Sentinel-2 Data for Mapping Irrigated Areas at Plot Scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-10367, https://doi.org/10.5194/egusphere-egu2020-10367, 2020.

D322 |
| Highlight
Peejush Pani, Li Jia, Massimo Menenti, Guangcheng Hu, Chaolei Zheng, Qiting Chen, and Yelong Zeng

This paper proposes a new approach to estimate and map separately gross and net water requirements and actual crop water use by applying multi–spectral satellite data. Remote sensing information is witnessing a boom in the availability of high-resolution multi-spectral data with frequent revisit time, paving the path for improved assessment of precision agriculture and minimizing the wastage of irrigation water. In this study, we have tried to integrate multi-source remote sensing information with farmer’s irrigation practices to evaluate the water use and losses at farm-scale for center pivot irrigation systems (CPIS) in Inner Mongolia autonomous region of China. The region is practicing modernized irrigation methods to efficiently use groundwater. Crop gross water requirements are estimated by evaluating separately the net crop water requirements (CWR) and the water losses inherently from a CPIS, i.e. droplet evaporation to the air directly before they fell on the crop canopy during irrigation application (EA) and canopy interception loss (Ic). The crop water requirement is estimated according to the FAO-56 method based on the Penman-Monteith equation. Actual crop water use is evaluated by estimating separately soil evaporation (ES) and plant transpiration (ET) by applying the ETMonitor model. High-resolution multi–spectral data acquired by Sentinel-2 MSI and Landsat-8 OLI together with meteorological forcing data and soil moisture retrievals were used to construct daily estimates of crop water requirements and actual use. Finally, the performance of irrigation scenarios was assessed by applying a performance indicator (IP), as the ratio between gross water requirement and the volume of irrigation applied, where values closer to unity referring to optimum utilization and minimum loss. Measurements of actual evapotranspiration by eddy covariance system were applied to evaluate the actual evapotranspiration estimates by the ETMonitor. Field experiments were also carried out to validate the estimated irrigation losses, i.e. EA and IC. The estimates were in good agreement with the ground observations, i.e. an R2 of 0.64 – 0.80 for actual water use and 0.66 – 0.97 for water losses. The RMSE was 0.6 – 1.2 mm/day for actual daily water use and 0.64 – 1.55 mm water losses for each irrigation, respectively. The IP was estimated as 1.6 for the performance of CPIS as per the above definition. Overall, the study shows that CPIS has under-performed in minimizing water losses in the study area with losses of 25.4% per season of the total volume of water applied for wheat, and 23.7% per season for potato. This implies that the amount of water applied was largely insufficient to meet the gross water requirements, i.e. including losses.

How to cite: Pani, P., Jia, L., Menenti, M., Hu, G., Zheng, C., Chen, Q., and Zeng, Y.: Evaluating crop water requirements and actual crop water use with center pivot irrigation system in Inner Mongolia of China, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-12539, https://doi.org/10.5194/egusphere-egu2020-12539, 2020.

D323 |
| Highlight
Francesco Morari, Ahmed Harb Rabia, Stefano Lo Presti, Stefano Gobbo, and George Vellidis

Irrigation scheduling is one of the main factors that affect the crop ability to resist stress symptoms in addition to affecting directly the final yield. In the last decade, many remote sensing methods have been developed to help in scheduling irrigation with higher precision. Some of these methods estimate irrigation needs indirectly such as those using normalized difference vegetation index (NDVI) or crop coefficient curve, and other methods that directly calculate Evapotranspiration (ET) through satellite images. Cotton SmartIrrigation App (Cotton App) is one of the recent applications that have been developed to help farmers in scheduling irrigation during the growing season. The App is based on an interactive ET-based soil water balance model. In this study, remote sensing of Evapotranspiration has been used to detect and map crop water requirements in order to enhance the Cotton App predictions for irrigation schedule during the growing season. Two remote sensing ET models based on thermal infrared (TIR), The surface energy balance algorithm for land (SEBAL) and Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC), were used to derive ET over cotton. Results showed higher values of actual Evapotranspiration calculated by both SEBAL and METRIC models during the first 45 days of the growing season compared to the calculated values of ETa from crop coefficient. This is expected to be due to the higher evaporation fraction from bare soil since the plant cover is still very low and accordingly the plant transpiration too. However, later in the second growing stage, the models showed that the crop coefficient calculated ETa (ETa- Calculated) has overestimated the plant Evapotranspiration giving higher values compared to the values from the models. These results indicate that, the use of remote sensing techniques along with the ET-models will increase the app efficiency in giving more precise irrigation scheduling.

How to cite: Morari, F., Harb Rabia, A., Lo Presti, S., Gobbo, S., and Vellidis, G.: Remote Sensing of Evapotranspiration Using SEBAL and Metric Energy Balance Models for Enhanced Precision Agriculture Cotton Irrigation Scheduling, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22272, https://doi.org/10.5194/egusphere-egu2020-22272, 2020.

D324 |
maria calera picazo, Carmen Plaza, andres cuesta, vicente bodas, ramon molina, anna osann, and alfonso calera

In Mediterranean areas, where water scarcity is the main limiting factor, applying good practices in the use of water for irrigation is crucial in order to maximize benefits for farmers and protect the resource. Furthermore, energy costs derived from water pumping from groundwater is one of the most important expenses for farmers in our study area, the South-East of Spain. Variable Rate Irrigation is a promising technique to apply the required water, but VRI faces the challenge to know accurately the crop water requirement distribution in space and time.

The objective of this work is twofold: Firstly, to demonstrate through a practical case the optimization of the irrigation water in an operativity level managing the variability of the plot using time series of free satellite images currently in orbit. Secondly, to put into practice the technology (SicoP system) developed by ACOEMAN that allows the pivot to apply variable rate at medium cost for farmers.

The case study was carried out in a commercial wheat plot of 60ha, irrigated by a central pivot endowed with the SicoP technology, during the campaign of 2018-2019.  The SicoP pivot technology allows to implement a variable angular speed for each sector. The pivot circle was divided into 36 sectors of 10 degrees each. Every Thursday during the growing cycle the crop water requirements were estimated per sector by means of remote sensing and meteorological data by the decision support system developed by AgriSat Iberia as consultant company. Thus, the system applied the irrigation water requirement per sector, calculated through a simplified soil water balance.

The estimation of the actual crop water requirements spatially distributed at 30x30 meter (3x3 pixel) resolution has been based on NDVI-Kc forecasting methodology. The high temporal and spatial resolution provided by free images from satellites Sentinel 2A and Sentinel 2B combined with Landsat 8 images allows the implementation of a remote sensing-based operational approach for this variable rate decision support system.

This paper includes a comparative analysis of the differences between the water volume applied by homogeneous rate, 1 per plot and week, and the variable rate irrigation, 36 rates per plot and week, using the same EO-based methodology. A yield map was obtained by using a yield-monitoring device implemented into the combine harvester.

First promising results regarding the optimization of the use of water have been demonstrated going from 1 irrigation decision in 60ha per week, to 36 irrigation decision per week, one per 1.6ha sector. Modest savings in water volumes at the end of the growing cycle have been observed. This map shows no additional increase of yield spatial variability due to the use of VIR.  Some problems were encountered when the climate conditions were not appropriate for irrigation, mainly high wind speed. The system has reached a high operativity level ready for adoption by farmers. 

How to cite: calera picazo, M., Plaza, C., cuesta, A., bodas, V., molina, R., osann, A., and calera, A.: Estimatimation of crop water requirements by remote sensing for variable rate applications. operational case in a central pivot of wheat, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22463, https://doi.org/10.5194/egusphere-egu2020-22463, 2020.

D325 |
Sara Modanesi, Gabriëlle J. M. De Lannoy, Alexander Gruber, Christian Massari, Luca Brocca, Hans Lievens, and Renato Morbidelli

Given the projected decrease in water availability due to climate change and anthropogenic processes, the quantification of water usage for agricultural purposes is of critical importance. However, an accurate quantification of irrigation and groundwater extraction remains a major challenge for the current generation of scientists. For instance, the parameterization of irrigation in large scale Land Surface Models (LSM) is improving, but still suffers from simplified assumptions, such as the mostly unknown timing and quantity of irrigation, often for lack of enough ground-based data. Remote sensing observations offer an opportunity to fill this gap in our knowledge, as they will detect irrigation activities. Earlier studies have used satellite soil moisture products obtained from microwave sensors to detect irrigated areas, but only some studies have dealt with the quantification of irrigation using satellite soil moisture data.

The aim of this study is to investigate the ability of high-resolution Sentinel-1 observations to detect changes in soil moisture and vegetation caused by irrigation fluxes. The focus area is the Po river Valley, one of the most important agricultural areas in Northern Italy, where in situ data are available for evaluation at four pilot sites. A comparison of Level-2 Sentinel-1 soil moisture retrievals, in situ data and Noah-MP land surface model (LSM) estimates confirms the presence of irrigation at the pilot sites. However, we hypothesize that even more information on both the irrigated soil moisture and vegetation can be extracted from the Level-1 Sentinel-1 signal via backscatter data assimilation. To prepare for such an assimilation system, Level-1 Sentinel-1 backscatter observations, pre-processed to the 1 km EASE-v2 grid, are further compared to the total backscatter simulated by a Water Cloud Model, using the simulated soil moisture obtained by the Noah-MP LSM as part of the NASA Land Information System (LIS). Noah-MP is here selected for its ability to simulate dynamic vegetation. Our results will show that irrigation can indeed also be detected from the mismatch between simulated and observed backscatter values.

How to cite: Modanesi, S., De Lannoy, G. J. M., Gruber, A., Massari, C., Brocca, L., Lievens, H., and Morbidelli, R.: Irrigation detection with Sentinel-1 radar backscatter observations over an agricultural area in the Po River Valley (Italy), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-3884, https://doi.org/10.5194/egusphere-egu2020-3884, 2020.

D326 |
Junming Yang, Yunjun Yao, Ke Shang, Xiaozheng Guo, Xiangyi Bei, Xiaowei Chen, and Haiying Jiang

The study of law of crop water consumption in small scale such as irrigation area requires remote sensing image data with high spatial and temporal resolution, however, remote sensing images that possess both high temporal and spatial resolution cannot be obtained for technical reasons. To solve the problem, this paper present a multisource remote sensing data spatial and temporal reflectance fusion method based on fuzzy C clustering model (FCMSTRFM) and multisource Vegetation index (VI) data spatial and temporal fusion model (VISTFM), the Landsat8 OLI and MOD09GA data are combined to generate high spatial and temporal resolution reflectance data and the landsat8 OLI, MOD09GA and MOD13Q1 data are combined to generate high spatial and temporal resolution normalized vegetation index (NDVI) and enhanced vegetation index (EVI) data.

The rice area is mapped by spectral correlation similarity (SCS) between standard series EVI curve that based the EVI generated by VISTFM and average value of each EVI class that generated by classing Multiphase EVI into several class, the extraction results are verified by two methods: ground sample and Google Earth image. high spatial and temporal resolution Leaf area index (LAI) that covered the mainly rice growth and development stages is generated by higher precision method between artificial neural network and equation fitting that establish the relationship between NDVI, EVI and LAI. The yield of rice in the spatial scale is generated by establishing the relationship between yield and LAI of the mainly growth and development stages that has the maximum correlation with yield. Daily high spatial resolution evapotranspiration is generated by using multisource remote sensing data spatial and temporal reflectance fusion method to fusion the MODIS-like scale and Landsat-like scale evapotranspiration that generated by The Surface Energy Balance Algorithm for Land (SEBAL). Based on the data, the evapotranspiration, LAI and yield of rice, obtained by remote sensing methods, rice water growth function is established by Jensen, Blank, Stewart and Singh model.

How to cite: Yang, J., Yao, Y., Shang, K., Guo, X., Bei, X., Chen, X., and Jiang, H.: Response of Rice Ecological Indicators to Water Consumption Based on Multi-source Data in Irrigation District Scale, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-4363, https://doi.org/10.5194/egusphere-egu2020-4363, 2020.

D327 |
Dazhi Li, Xujun Han, Dhanya C.t., Stefan Siebert, Harry Vereecken, and Harrie-Jan Hendricks Franssen

Irrigation is very important for maintaining the agricultural production and sustaining the increasing population of India. The irrigation requirement can be estimated with land surface models by modeling water storage changes but the estimates are affected by various uncertainties such as regarding the spatiotemporal distribution of areas where and when irrigation is potentially applied. In the present work, this uncertainty is analyzed for the whole Indian domain. The irrigation requirements and hydrological fluxes over India were reconstructed by multiple simulation experiments with the Community Land Model (CLM) version 4.5 for the year of 2010.

These multiple simulation scenarios showed that the modeled irrigation requirement and the land surface fluxes differed between the scenarios, representing the spatiotemporal uncertainty of the irrigation maps. Using a season-specific irrigation map resulted in a higher transpiration-evapotranspiration ratio (T/ET) in the pre-monsoon season compared to the application of a static irrigation map, which implies a higher irrigation efficiency. The remote sensing based evapotranspiration products GLEAM and MODIS ET were used for comparison, showing a similar increasing ET-trend in the pre-monsoon season as the irrigation induced land surface modeling. The correspondence is better if the seasonal irrigation map is used as basis for simulations with CLM. We conclude that more accurate temporal information on irrigation results in modeled evapotranspiration closer to the spatiotemporal pattern of evapotranspiration deduced from remote sensing. Another conclusion is that irrigation modeling should consider the sub-grid heterogeneity to improve the estimation of soil water deficit and irrigation requirement.

How to cite: Li, D., Han, X., C.t., D., Siebert, S., Vereecken, H., and Hendricks Franssen, H.-J.: Assessment of the uncertainty related to irrigation modeling by a land surface model across India, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5222, https://doi.org/10.5194/egusphere-egu2020-5222, 2020.

D328 |
Massimo Tolomio and Raffaele Casa

Irrigation management decision support systems based on remote sensing and hydrological models need to find a balance between simplicity and accuracy in the definition of crop water stress thresholds when irrigation should be triggered. Among the most widely used crop models, which synthesize current mechanistic knowledge of crop water stress processes, there is a wide range of complexity that is worth exploring in order to improve the formalisms of current hydrological models.

In the present work, some of the most widely used crop models (chosen among those freely available and well documented) were examined in their description of crop water stress processes and irrigation thresholds definition. They are: APSIM, AQUACROP, CROPSYST, CROPWAT, DAISY, DSSAT, EPIC, STICS and WOFOST. Model manuals and scientific papers were reviewed to identify differences and similarities in the water stress functions related to crop growth.

A strict categorization of the model features is inappropriate, since the functions utilized are always at least slightly different and the models may focus on different features of the agroecosystem. Nevertheless, major similarities and differences among the models were found:

The models were then calibrated for the maize and tomato crops using field and remote sensing data on crop yield, soil moisture, evapotranspiration (ET) and leaf area index (LAI), for two locations, respectively in Northern and Southern Italy (Calcinato and Capitanata). Simulations were then carried out and compared in terms of the optimal irrigation amounts calculated by the different models and predicted yields.

How to cite: Tolomio, M. and Casa, R.: Defining irrigation thresholds in remote sensing-based decision support systems: a review of crop models mechanistic descriptions of crop water stress, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-5792, https://doi.org/10.5194/egusphere-egu2020-5792, 2020.

D329 |
Improving scheduling, benchmarking and forecasting to boost irrigation productivity
Andrew Western, Danlu Guo, Arash Parehkar, Zitian Gao, Dongryeol Ryu, and Quan Wang
D330 |
Jing Tian and Yongqiang Zhang

As one of the largest arid and semiarid areas in the world, Central Asia (CA) has been facing severe water crisis. Agricultural irrigation consumes most water resources there. However, it is not clear how the irrigation water requirement (IWR) varies spatially and temporally in CA, especially under CO2 fertilization and land use change. This study, for the first time, quantifies changes of IWR for two predominant crops (cotton and winter wheat) over CA under two climate change scenarios (RCP2.6 and RCP4.5, both of which consider CO2 fertilization effects) and land use projections. Our results show that without considering atmospheric CO2 concentration for estimating IWR would result in large errors and even different signs of the changes. In the future, IWR for cotton and winter wheat tends to increase in 2020s and 2040s but decrease in 2060s and 2080s under RCP2.6 and CO2 fertilization. The change magnitude is less than 5%. Under RCP4.5 and CO2 fertilization, most areas in CA exhibit an increase of less than 5%. The maximum increases of 5%-15% for cotton occur in  Tajikistan. The maximum increase of more than 50% for winter wheat occurs in Tajikistan under both climate scenarios. The IWR in Turkmenistan is most sensitive to land use change, with 33% increase compared with IWR in 2015. The other four countries have small differences (less than 10%) between 2015 and 2030. Severe water security pressure is predicted in Turkmenistan and Uzbekistan and the smallest in Tajikistan. This study provides a comprehensive evaluation of IWR for the Central Asian countries in the future and helps the decision maker for sensible water management.

How to cite: Tian, J. and Zhang, Y.: Detecting changes in irrigation water requirement in Central Asia under CO2 fertilization and land use changes, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-8047, https://doi.org/10.5194/egusphere-egu2020-8047, 2020.

D331 |
Matteo Rolle, Stefania Tamea, and Pierluigi Claps

Estimation of crop water needs is essential to understand the role of agriculture in the water balance modeling at various scales. In turn, this is relevant for water management purposes and for the fulfilling of water-related environmental regulations. In this study, a comprehensive assessment of crop water requirement at large scale is presented, both in terms of rainfall (green water) and irrigation (blue water).

A water-balance model is built to provide estimates of actual evapotranspiration and accompanying soil moisture by using high space-time resolution data. The new ERA5 reanalysis dataset, published by the ECMWF within the Copernicus monitoring system and obtained from satellite data and ground measurements, provides the precipitation and temperature input variables to the model. Data available at the hourly time scale are all aggregated on a daily scale and used in the water balance model over  a grid of cultivated areas from the MIRCA2000 dataset. Cultivated areas are available for 26 crops for year 2000 at a spatial resolution of 5 arcmin (about 9 km at the Equator). Data from MIRCA2000 are separated between rainfed areas and areas equipped for irrigation and are characterized by specific monthly calendars of the crop growing seasons.

The model performs the daily soil water balance throughout the whole year, considering all crops at their growth stage and assuming as initial condition at each crop sowing date a monthly average soil moisture. Results quantify the volumes of green and blue water necessary for crop growth and describe the spatial variability of the water requirements of each individual crop. The high spatial and temporal resolution of Copernicus ERA5 data enables a great improvement in the characterization of hydro-climatic forcings with respect to previous assessments and a greater accuracy in the crop water requirement estimates.

Finally, the knowledge of water requirements is an important step to quantify the irrigation volumes used in agriculture, on which there is a high uncertainty and little spatially distributed information. The model proposed enables the investigation of spatio-temporal variability associated to varying meteorological forcings and of the effects of different irrigation techniques, enabling an improved management of water resources.

How to cite: Rolle, M., Tamea, S., and Claps, P.: Improved large-scale crop water requirement estimation through new high-resolution reanalysis dataset, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19289, https://doi.org/10.5194/egusphere-egu2020-19289, 2020.

D332 |
Laurent Tits, Jeroen Degerickx, Karin Viergever, Sven Gilliams, and Livia Peiser

Monitoring irrigated areas has received considerable attention given the importance for e.g. food security, and for the management of water resources. However, the current extent of irrigated areas at continental to global scale is still uncertain. Existing maps, especially covering large areas, are mainly derived from country-level statistics.

Remote sensing has proven to be a valuable tool to map agricultural production areas, yet mapping irrigated areas has proven to be challenging. On the one hand, agricultural areas are difficult to map in general, while on the other hand the step from land cover (cropland) to land use (irrigation practice) requires additional data sources other than the optical satellite data.

Recently, a generic irrigation mapping method was developed in the framework of the FAO WaPOR data portal (https://wapor.apps.fao.org), to provide irrigation maps at a continental scale for Africa and the Near- East on a seasonal and annual basis. The method combines information on (i) the land cover, (ii) the phenology as obtained from time series analysis, (iii) actual Evapotranspiration (ETa), and (iv) precipitation. In short, the Water Deficit Index (WDI) is computed as the ratio of the seasonal precipitation over the seasonal ETa, with values below one when the water consumption is larger than the water availability on a seasonal basis. Although very good results were obtained with a WDI threshold of 0.9 on a continental scale, some issues remain, especially in areas where the water availability and the consumption are very similar, or where only supplementary irrigation is applied.

In addition to this physically-based approach, supervised classification methods have proven to be a suitable irrigation mapping method as well, yet suffer from the drawback that they require a large amount of reference information. To evaluate which method is best suited to distinguish between irrigated and rainfed agriculture, a comparison between both is made for three different irrigation schemes: (i) the Nile delta in Egypt, which receives full irrigation, (ii) the Bekaa valley in Lebanon where both irrigated and rainfed croplands are present, and (iii), the Koga region in Ethiopia, which is rainfed during the rainy season, but irrigated in the dry season.

From the analysis, it is clear that for regions with very little precipitation (Nile delta), the physical-based method is very well suited to map the irrigated areas, without the need for ground reference information. However, in more complex systems, such as the Bekaa valley, the confusion between irrigated and rainfed areas is quite substantial for the physical-based method, and the supervised classification method obtained more promising results.

This may suggest that for continental or global irrigation mapping, a combination of both methods is desirable. Irrigated areas in regions with low precipitation could best be mapped based on the WDI approach, while in more complex areas, supervised classification methods may be required. This would strongly reduce the need for detailed reference data over large areas, while maintaining a high mapping accuracy.

How to cite: Tits, L., Degerickx, J., Viergever, K., Gilliams, S., and Peiser, L.: Irrigation mapping in Africa and the Near East: physical-based versus supervised classification, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-19918, https://doi.org/10.5194/egusphere-egu2020-19918, 2020.

D333 |
David Bretreger, In-Young Yeo, Greg Hancock, and Garry Willgoose

Irrigated agriculture has been identified as using approximately 72% of water globally. Australia, like many places in the world, is subject to water sharing plans that cross government boarders and are subject to a mixture of management policies. There is a pressing need to develop a method to monitor irrigation water use to aid in water resource assessments and monitoring. This paper aims to test a previously developed method which monitors irrigation water use using remotely sensed observations over the catchment scale, without the need for in-situ observations, ground data or in‑depth knowledge of crops and their planting dates. Using conservative assumptions about agricultural land management practice, irrigation is calculated as Irr=AET-P. The method tests three vegetation indices derived from Landsat 5/7/8 images to calculate crop coefficients (Kc) based on multiple published relationships. These are combined through the FAO56 methodology using gridded rainfall and two reference evapotranspiration (ET0) products to find actual evapotranspiration as AET=ET0xKc, providing six ET0-Kc combinations. Validation data is sourced from Irrigation Infrastructure Operators (IIO) from across the Murray-Darling Basin, Australia which are required to record irrigation water deliveries for billing purposes. The majority of these regions are in arid or semi-arid regions. Data periods used in this study range from 2003/04 to 2016/17. Results indicate this method can effectively assess irrigation water use over a range of catchment sizes from ~6,000 to ~600,000 ha. The best results returned a monthly irrigation RMSE ranging from 1.13 to 2.42 mm/month. Issues arise when regions have a designated low water allocation volume for that season (<40%). The allocation percentage is a function of water storage levels, demand and forecasts. Comparisons with the Standardised Precipitation Index (SPI) and Evaporative Stress Index (ESI) show that the proposed method is robust to the rapid onset and short-term droughts. However, its performance was poor during the long term droughts with low water allocation years. The study results during these years has been predominately attributed to water stress in certain crops being undetected, agricultural managers skipping annual crop commodities as well as stock and domestic water use making up larger portions of total water use. This is a limitation of this approach, although when only comparing results in years with greater than 40% allocations, the results improved significantly showing it can monitor water use effectively. When adequate water is available, this approach is able to accurately predict irrigation water use for the sites examined.

How to cite: Bretreger, D., Yeo, I.-Y., Hancock, G., and Willgoose, G.: Quantifying Irrigation Water Use over Regional Scales with Landsat and Climate Data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21255, https://doi.org/10.5194/egusphere-egu2020-21255, 2020.

D334 |
Sujong Lee, Halim Lee, Hyun-Woo Jo, Youngjin Ko, Chul-Hee Lim, and Woo-Kyun Lee

In 2019, The Food and Agriculture Organization(FAO) announced that North Korea was a food shortage country and which is closely related to the agricultural drought frequency. These agricultural drought frequencies derived from global climate change are increasing and in terms of climate change, agricultural drought is not just a national problem, but a global scale issue. To respond to agricultural drought-related with food shortage, various studies and projects are conducted based on the remote sensing data and modeling such as hydrological model, crop model, but access to public data in North Korea is limited, and also objectivity is difficult to be guaranteed. In this study, the estimation of rice yield and irrigation water demand based on the RCP (Representative Concentration Pathway) climate change scenario was conducted using Environmental Policy Integrated Climate(EPIC) model which calculates various variables related to agriculture by using climatic data, Soil data and topographic data. For validating the parameter of the model, the study area was set to the Korean Peninsula and the parameter was set stepwise compared results of the model with South Korea national statistics. The results of rice yield and irrigation water demand in the Korean Peninsula was validated by using statistics of international organizations. The assessment of Rice Yield and Irrigation Water Demand Change based on the EPIC model is considered a method for complementing the field test and statistical limitations in North Korea. This study can be used as basic data for agricultural drought in North Korea and Based on the model results, it is necessary to concern food security.

How to cite: Lee, S., Lee, H., Jo, H.-W., Ko, Y., Lim, C.-H., and Lee, W.-K.: Assessment of Rice Yield and Irrigation Water Demand Change in the Korean Peninsula based on RCP Climate Change Scenario, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21524, https://doi.org/10.5194/egusphere-egu2020-21524, 2020.

D335 |
Ioannis Varvaris, Zampela Pittaki-Chrysodonta, Christen D. Børgesen, and Bo V. Iversen

Fertilizers and pesticides are intensively applied in agriculture. However, their use has been documented as a source of contamination of groundwater and surface water. Therefore, it is important to be able to assess the fate and transport of pesticides and nitrates in agricultural soils. Before simulating solute transport and considering chemical species, accurate water flow models for understanding the internal flow pathways are essential. The HYDRUS-2D software package was used to develop a hydrogeological model for simulating the drainage dynamics in a tile-drained agricultural field. A two-dimensional single porosity model was used to simulate the water flow dynamics. The initial parameterization of the hydraulic properties in model relied on in situ and laboratory measurements and satellite data. For estimating the soil water release characteristics, the Campbell function was used. Specifically, the field average Campbell soil-water retention curve (SWRC) was predicted using an existing satellite image model. The estimated SWRC was validated using laboratory data. The suggested approach gave a satisfactory fit to the hydrograph features presenting the potential of remote to be used as an alternative for initial parameterization.

How to cite: Varvaris, I., Pittaki-Chrysodonta, Z., D. Børgesen, C., and V. Iversen, B.: Modelling water flow dynamics using image spectral data, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21682, https://doi.org/10.5194/egusphere-egu2020-21682, 2020.

D336 |
Francesco Novelli, Heide Spiegel, Taru Sandén, and Francesco Vuolo

The work is based on a previously published study with the aim to further analyse the results obtained. Remote sensing data, crop growth models, and optimization routines constitute a toolset that can be used together to map crop yield over large areas when access to field data is limited. In this study, Leaf Area Index (LAI) data from the Copernicus Sentinel-2 satellite were combined with the Environmental Policy Integrated Climate (EPIC) model to estimate crop yield. The experiment was implemented for a winter wheat crop during two growing seasons (2016 and 2017) under four different fertilization management strategies. A number of field measurements were conducted spanning from LAI to biomass and crop yields.
LAI showed a good correlation between the Sentinel-2 estimates and the ground measurements using non-destructive method. Better RMSE and RRMSE were obtained in 2017 compared to 2016 (RMSE = 0.44 vs. 0.46) (RRMSE = 17% vs. 19%). In 2016 year, a slightly lower R2 value was found compared to 2017 (R2 = 0.72 vs. 0.89). A correlating fit between satellite LAI curves and EPIC modelled LAI curves was also observed. The work shows that the assimilation of remote sensing data into the crop growth model can help to overtake some structural problems of the model.  The assimilation framework has to be tested under different environmental conditions before being applied on a larger scale with limited field data.

How to cite: Novelli, F., Spiegel, H., Sandén, T., and Vuolo, F.: Assimilation of Sentinel-2 Leaf Area Index Data into a Physically-Based Crop Growth Model for Yield Estimation , EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-21951, https://doi.org/10.5194/egusphere-egu2020-21951, 2020.

D337 |
| Highlight
Maria Mar Alsina, Kyle Knipper, Martha Anderson, WIlliam Kustas, Nicolas Bambach, Lynn McKee, Joe Alfieri, James O'DOnnel, Jessica Parsons, Brodie McCarthy, Lawrence Hipps, Andrew McElrone, Feng Gao, Alfonso Torres, Mac McKee, Nurit Agam, Luis Sanchez, Nick Dokoozlian, and John Prueger

Grapevines are one of the major drivers of agriculture in California, representing a production equivalent to $6.25 billion in 2018. Water is scarce, and increasingly intense and prolonged drought periods, like one that recently occurred in the 2012-2016 period, may happen with greater frequency. Consequently, there is a need to develop irrigation management decision tools to help growers maximize water use while maintaining productivity. Furthermore, grapevines are deficit irrigated, and a correct management of the vine water status during the season is key to achieve the target yield and quality. Traditionally, viticulturists use visual clues and/or leaf level indicators of vine water status to regulate the water deficit along the season. However, these methods are time-consuming and only provide discrete data that do not represent the often-high spatial variability of vineyards.  Remote sensing techniques may represent a fast real-time decision-making tool for irrigation management, able to extensively cover multiple vineyards with low human or economic investments. 
While growers currently calculate the vine water demands using the reference evapotranspiration from a weather station located in the region and a crop coefficient, usually from literature, they don't have any means to measure or estimate the actual water used by the vines. Knowing the actual evapotranspiration (ET) in real-time and at a sub-field scale would provide essential information to monitor vine water status and adjust the irrigation amounts to the real water needs. The aim of the GRAPEX (Grape Remote sensing Atmospheric Profile and Evapotranspiration eXperiment) project, has been to provide growers with an irrigation toolkit that integrates the spatial distribution of vine water use and water status. The project focuses on grapevines, but it will be easily extrapolated to orchards and other crop types.
We present the results of a pilot experiment where we applied the scientific developments of the GRAPEX project into a practical tool that growers can use for irrigation management. We run this pilot experiment over 6 commercial grapevine blocks, located in Cloverdale, Sonoma, CA. During the 2019 growing season, we provided the viticulturists with weekly maps of actual ET calculated using the DisALEXI model, Sentinel-2 Normalized Difference Vegetation and Normalized Vegetation Water Indices as well as local weather data, forecasted ET and soil moisture. The data were delivered weekly in a dashboard, including spatial and tabular views, as well as an irrigation recommendation derived from the past week's vine water use and water status data. Along with the remote sensing data, we took periodic measurements of leaf area index, leaf water potential, and gas exchange to evaluate the irrigation practices. We compared the irrigation prescription based on the provided data with the grower's practices. The total season irrigation ranged between 70 and 120 mm depending on the block, and our irrigation recommendations deviated between 10 and 30 mm from the growers' practices, also depending on the block. This analyzes the performance of the ET toolkit in assisting irrigation scheduling for improving water use efficiency of the vineyard blocks.

How to cite: Alsina, M. M., Knipper, K., Anderson, M., Kustas, W., Bambach, N., McKee, L., Alfieri, J., O'DOnnel, J., Parsons, J., McCarthy, B., Hipps, L., McElrone, A., Gao, F., Torres, A., McKee, M., Agam, N., Sanchez, L., Dokoozlian, N., and Prueger, J.: Using a Remote Sensing data based toolkit to monitor vine water use and water status for real time irrigation scheduling in California vineyards, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22248, https://doi.org/10.5194/egusphere-egu2020-22248, 2020.

D338 |
Fatima Ezzahra El Ghandour, Adnane Habib, Youssef Houali, Yassine Labbassi, Lorenzo Iannini, Silvia Maria Alfieri, Kamal Labbassi, and Massimo Menenti

Investigations carried out under the MOSESH2020 project in the Doukkala irrigation scheme (western Morocco) allowed the generation of several data products during the agricultural seasons 2016-2017 and 2017-2018: Seasonal probabilistic weather forecast, Early-season and In-season crop mapping, Monitoring of crop water demand and Short-term forecasts of irrigation water requirements.

This study was focused on the assessment of the adequacy of the water applied to meet the crop water demand in the two irrigation seasons 2016-2017 and 2017-2018.

Monitoring of Crop Water Demand (CWD) was based on the estimation of the maximum crop evapotranspiration, obtained from remote sensing data of the monitored area. Such output is updated frequently (e.g. every week) during the irrigation season and compared to the weekly surface irrigation water volumes allocated. Although the assessment of adequacy of allocations against the crop water demand (CWD) showed that the latter was much larger with 10-15% than allocated surface water for the entire area, with this difference being small at the beginning of the growing season.

The use of MOSES products during the irrigation management operations would help the water management authority to save water, especially during the winter season, leaving additional water available to meet requirements in spring and summer.

How to cite: El Ghandour, F. E., Habib, A., Houali, Y., Labbassi, Y., Iannini, L., Alfieri, S. M., Labbassi, K., and Menenti, M.: Water Saving in the Semi-Arid Doukkala Irrigation Scheme (Western Morocco), EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22275, https://doi.org/10.5194/egusphere-egu2020-22275, 2020.

D339 |
Andres Cuesta, Carmen Plaza, María Calera, Vicente Bodas, Anna Osann, Alfonso Calera, Raúl Moreno, Javier Sánchez, and David Cifuentes

The rigorous management of water in agriculture must be seen from the point of view of all its actors, covering the information and knowledge needs of each one of them: from supporting the farmer in making irrigation decisions at the foot of the plot, until the collection and management of objective information at the basin level, through planning and control at the level of user communities. Today it is not conceived to address this enormous task without resorting to the available technological arsenal, but to speak of complex technologies is to speak of a high degree of specialization that escapes individual capacities. In this context, successful solutions arise from cooperation between entities of different nature. An example of this is the collaboration between the Remote Sensing Section and GIS of the University of Castilla La Mancha and the company AgriSat Iberia SL, which have created a dynamic of continuous innovation work to, firstly, transfer complex knowledge in format to the farmer of simple services of direct application, later, with the information generated at the intraparcel level, to scale to the level required by the entities or authorities involved in water governance, and finally, to redirect efforts and resources in research, development and innovation from of a better knowledge of their perception, degree of adoption and suggestions for improvement in this regard.

The last result of this fruitful collaboration has been the development of an application that integrates information on the state of the crops, from satellite images, to predict reliably and at an intraparcel scale (with a resolution level of 100 m2) your needs water a week seen. This allows quantifying, at any moment of the crop cycle, its accumulated demand for water, and adding it spatially to the exploitation level, of the irrigation community or of the river basin. From the estimation of the relative photosynthetic activity obtained from the images, it is possible to know the evolution of the crops throughout their growth and development cycle, as well as their spatial variability, in a simple and intuitive way.

There are three technologies that jointly facilitate this important leap in water management: remote sensing, geographic information systems (GIS), and information and communication technologies (ICT).

Its online character makes it a service accessible from anywhere with data connection, and in turn makes it a “live” system not only for its capacity for functional expansion but for the possibility of increasing the quantity and quality of the sources of information, allowing access to each new improvement immediately.

How to cite: Cuesta, A., Plaza, C., Calera, M., Bodas, V., Osann, A., Calera, A., Moreno, R., Sánchez, J., and Cifuentes, D.: from the plot to the river basin - agrisatwebgis®: a tool for efficient water management, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-22644, https://doi.org/10.5194/egusphere-egu2020-22644, 2020.