HS6.7

Detailed information about the timing and the amount of water used for irrigation at a high spatial resolution is critical for monitoring and improving agricultural water use efficiency. However, neither statistical surveys nor current remote sensing-based approaches can accommodate this need. Being a natural source of information on the amount of water entering the ground, soil moisture is directly related to irrigation and precipitation. Hence, we present a novel approach based on the TU Wien Sentinel-1 Surface Soil Moisture product to fill this gap, i.e. detect and quantify irrigation water amounts at sub-kilometric scale. Theoretically, irrigation occurring in a specific field should be reflected by a local increase in soil moisture, while surrounding non-irrigated fields exhibit a different behavior.  We harness the spatio-temporal patterns of soil moisture to identify individual irrigation events, and then to estimate irrigation water heights. To retrieve the latter we include formulations of evapotranspiration and drainage as such vertical fluxes have a significant impact on sub-daily soil moisture variations. The proposed approach is evaluated against field scale irrigation data reported by farmers at three sites in Germany with heterogeneous field sizes, crop patterns, irrigation systems and management. 
Our results show that most irrigation events occurring in a field can be detected using soil moisture information at 500 m and 1-3 days resolution, however, lower accuracy is found during the peak of the growing season. The retrieved irrigation water heights increase with the fraction of pixel under irrigation as higher water amounts are expected over largely irrigated pixels. Finally, irrigation estimates are in agreement with reference data, in terms of temporal dynamics as well as spatial patterns, regardless of field-specific characteristics (e.g. crop type, irrigation system). Unlike most approaches based on microwave soil moisture data, the proposed framework does not rely on additional datasets, which are either locally available or do not even exist at (sub-) kilometric resolution. Hence, the proposed approach has the potential to be applied over large regions with varying cropping systems (e.g. national and even continental scale). 

How to cite: Zappa, L., Schlaffer, S., Nendel, C., and Dorigo, W.: Detection and quantification of irrigation water amounts at sub-kilometric scale using Sentinel-1 surface soil moisture, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-1115, https://doi.org/10.5194/egusphere-egu21-1115, 2021.

Irrigation represents a primary source of anthropogenic water consumption, whose effects impact on the natural distribution of water on the Earth’s surface and on food production. Over anthropized basins, irrigation often represents the missing variable to properly close the hydrological balance. Despite this, detailed information on the amounts of water actually applied for irrigation is lacking worldwide. In this study, a method to estimate irrigation volumes applied over a heavily irrigated area in the North East of Spain through high-resolution (1 km) remote sensing soil moisture is presented. Two DISPATCH (DISaggregation based on Physical And Theoretical scale CHange) downscaled data sets have been used: SMAP (Soil Moisture Active Passive) and SMOS (Soil Moisture and Ocean Salinity). The SMAP experiment covers the period from January 2016 to September 2017, while the SMOS experiment is referred to the time span from January 2011 to September 2017. The irrigation amounts have been retrieved through the SM2RAIN algorithm, in which the guidelines provided in the FAO (Food and Agriculture Organization) paper n.56 about the crop evapotranspiration have been implemented for a proper modeling of the crop evapotranspiration. A more detailed analysis has been performed in the context of the SMAP experiment. In fact, the spatial distribution and the temporal occurrence of the irrigation events have been investigated. Furthermore, the loss of accuracy of the irrigation estimates when using different sources for the evapotranspiration data has been assessed. In order to do this, the SMAP experiment has been repeated by forcing the SM2RAIN algorithm with several evapotranspiration data sets, both calculated and observed. Finally, the merging of the results obtained through the two experiments has produced a data set of almost 7 years of irrigation estimated from remote sensing soil moisture.

How to cite: Dari, J., Quintana-Seguí, P., Escorihuela, M. J., Stefan, V., Morbidelli, R., Saltalippi, C., Flammini, A., and Brocca, L.: Irrigation Estimates from Remote Sensing Soil Moisture: A District-Scale Analysis in Spain, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-2914, https://doi.org/10.5194/egusphere-egu21-2914, 2021.

Irrigation water is an expensive and limited resource. Previous studies show that irrigation scheduling can boost efficiency by 20-60%, while improving water productivity by at least 10%. In practice, scheduling decisions are often needed several days prior to an irrigation event, so a key aspect of irrigation scheduling is the accurate prediction of crop water use and soil water status ahead of time. This prediction relies on several key inputs such as soil water, weather and crop conditions. Since each input can be subject to its own uncertainty, it is important to understand how these uncertainties impact soil water prediction and subsequent irrigation scheduling decisions.

This study aims to evaluate the outcomes of alternative irrigation scheduling decisions under uncertainty, with a focus on the uncertainties arising from short-term weather forecast. To achieve this, we performed a model-based study to simulate crop root-zone soil water content, in which we comprehensively explored different combinations of ensemble short-term rainfall forecast and alternative decisions of irrigation scheduling. This modelling produced an ensemble of soil water contents to enable quantification of risks of over- and under-irrigation; these ensemble estimates were summarized to inform optimal timing of next irrigation event to minimize both the risks of stressing crop and wasting water. With inclusion of other sources of uncertainty (e.g. soil water observation, crop factor), this approach shows good potential to be extended to a comprehensive framework to support practical irrigation decision-making for farmers.

How to cite: Guo, D., Western, A., Wang, Q., Ryu, D., Moller, P., and Aughton, D.: Using short-term ensemble weather forecast to evaluate outcomes of irrigation, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4210, https://doi.org/10.5194/egusphere-egu21-4210, 2021.

Timely classification of crop types is critical for agronomic planning in water use and crop production. However, crop type mapping is typically undertaken only after the cropping season, which precludes its uses in later-season water use planning and yield estimation. This study aims 1) to understand how the accuracy of crop type classification changes within cropping season and 2) to suggest the earliest time that it is possible to achieve reliable crop classification. We focused on three main summer crops (corn/maize, cotton and rice) in the Coleambally Irrigation Area (CIA), a major irrigation district in south-eastern Australia consisting of over 4000 fields, for the period of 2013 to 2019. The summer irrigation season in the CIA is from mid-August to mid-May and most farms use surface irrigation to support the growth of summer crops. We developed models that combine satellite data and farmer-reported information for in-season crop type classification. Monthly-averaged Landsat spectral bands were used as input to Random Forest algorithm. We developed multiple models trained with data initially available at the start of the cropping season, then later using all the antecedent images up to different stages within the season. We evaluated the model performance and uncertainty using a two-fold cross validation by randomly choosing training vs. validation periods. Results show that the classification accuracy increases rapidly during the first three months followed by a marginal improvement afterwards. Crops can be classified with a User’s accuracy above 70% based on the first 2-3 months after the start of the season. Cotton and rice have higher in-season accuracy than corn/maize. The resulting crop maps can be used to support activities such as later-season system scale irrigation decision-making or yield estimation at a regional scale.

Keywords: Landsat 8 OLI, in-season, multi-year, crop type, Random Forest

How to cite: Gao, Z., Guo, D., Ryu, D., and Western, A.: In-season crop classification using optical remote sensing with random forest over irrigated agricultural fields in Australia, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-4987, https://doi.org/10.5194/egusphere-egu21-4987, 2021.

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. Climate change impacts will further stress the water availability enhancing also its conflictual use. The agricultural sector is the biggest and least efficient water user, accounts for around 24% of total water use in Europe, peaking at 80% in the southern regions.

This paper shows the implementation of a system for real-time operative irrigation water management at high spatial and temporal able to monitor the crop water needs reducing the irrigation losses and increasing the water use efficiency, according to different agronomic practices supporting different level of water users from irrigation consortia to single farmers. The system couples together satellite (land surface temperature LST and vegetation information) and ground data, with pixel wise hydrological crop soil water energy balance model. In particular, the SAFY (Simple Algorithm for Yield) crop model has been coupled with the pixel wise energy water balance FEST-EWB model, which assimilate satellite LST for its soil parameters calibration. The essence of this coupled modelling is that the SAFY provides the leaf area index (LAI) evolution in time used by the FEST-EWB for evapotranspiration computation while FEST-EWB model provides soil moisture (SM) to SAFY model for computing crop grow for assigned water content.

The FEST-EWB-SAFY has been firstly calibrated in specific fields of Chiese (maize crop) and Capitanata (tomatoes) where ground measurements of evapotranspiration, soil moisture and crop yields are available, as well as LAI from Sentinel2-Landsat 7 and 8 data. The FEST-EWB-SAFY model has then been validated also on several fields of the RICA farms database in the two Italian consortia, where the economic data are available plus the crop yield. Finally, the modelled maps of LAI have then been validated over the whole Consortium area (Chiese and Capitanata) against satellite data of LAI from Landsat 7 and 8, and Sentinel-2.

Optimized irrigation volumes are assessed based on a soil moisture thresholds criterion, allowing to reduce the passages over the field capacity threshold reducing the percolation flux with a saving of irrigation volume without affecting evapotranspiration and so that the crop production. The implemented strategy has shown a significative irrigation water saving, also in this area where a traditional careful use of water is assessed.

The activity is part of the European project RET-SIF (www.retsif.polimi.it).

How to cite: Mancini, M., Corbari, C., Ben Charfi, I., Al Bitar, A., Skokovic, D., Sobrino, J., and Branca, G.: Irrigation management through a coupled energy-water balance and crop growth model driven by remote sensing data, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13345, https://doi.org/10.5194/egusphere-egu21-13345, 2021.

Drought is a serious natural hazard with far-reaching impacts including modification of biodiversity and other ecosystem functions, economic disruption, and a threat to human livelihoods and health through food systems alteration. Climate models project robust increases in drought and dryness in the Mediterranean region because of changing climate conditions.  Despite the scarcity of water, irrigated agriculture plays a major socio-economic role in groundwater-dependent irrigated regions of Morocco. Strategic sectors such as citrus rely on irrigation to maintain or even increase production and citrus stakeholders put sustainable irrigation management at the top priorities. This study aims to assess seasonal drought severity in the Souss plain, the largest citrus’ growing area in Morocco, using VCI (Vegetation Condition Index), TCI (Temperature Condition Index), and VHI (Vegetation Health Index) based on Sentinel-2 and Landsat 8 data. We explored the benefits of using the Soil Water Atmosphere Plant (SWAP) agro-hydrological model to optimize irrigation water management of a citrus orchard. The SWAP model was applied over three growing seasons from 2016 to 2019 to optimize seasonal water supply based on different criteria (e.g., critical soil pressure head and allowable daily stress), particularly during the drought episodes. The VHI was estimated and classified into five classes: extreme, severe, moderate, mild, and no drought. Key outputs of the SWAP model show that the farmers’ irrigation practices did not compensate for the lack of rainfall in the spring, which led to long-term unavailable water during crop development. The SWAP predictive model determined the optimal amount of water and irrigation scheduling systems to make efficient use of while maintaining appropriate yields. The developed algorithm simulation uses the minimal sufficient seasonal amount of water. The designed approach helps prevent critical stress in citrus orchards together with sustainable water distribution in accordance with best agronomic practices.

Keywords: Citrus, drought, water scarcity, sustainable irrigation management, VHI, VCI, TCI, SWAP, Souss plain

How to cite: Labbaci, A., Brouziyne, Y., Hallam, J., and Bouchaou, L.: Assessing farmers’ irrigation practices under drought conditions in semi-arid area: Combining remote sensing data and agro-hydrological modeling, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-8448, https://doi.org/10.5194/egusphere-egu21-8448, 2021.

Governments and engineers have promoted the construction of large-scale, formalised, irrigation schemes across Africa for nearly 100 years. These developments are designed to increase food production and reduce the vulnerability of agriculture to climate shocks. Yet over the past decades, many irrigation schemes have deteriorated or completely failed; due to a wide range of problems from faulty infrastructure to unexpectedly severe climate shocks. Understanding the drivers of successes and challenges on irrigation schemes is complicated by limited long-term statistics. Meanwhile, for historic Earth-observation based analysis, the Landsat archive remains poor for large areas of Africa, and MODIS imagery is too coarse for meaningull mapping.

Here, we demonstrate a multi-sensor fusion methodology to map the expansion and intensification dynamics of irrigation schemes in the 21st century. Our methodology produces monthly Landsat-like images from the fusion of Landsat 5, Landsat 7 SLC-off, and MODIS imagery, which are classified into cropped area estimates. First, we use the StarFM fusion algorithm to generate monthly Landsat-like images from MODIS composites, based on temporally co-located MODIS and cloud free Landsat 5 or Landsat 7 SLC-on images. Next, we adjust these Landsat-like images against Landsat 7 SLC-off pixels by iteratively reweighting within a spatiotemporal Generalised Additive Model. Finally, we classify the derived monthly, Landsat-like, time-series data using a Random Forest classification model, mapping the number of crops harvested per year for the 2000-2020 period.

We test this methodology against two irrigation schemes in West Africa: the Office du Niger scheme in Mali and the Bagre Irrigation Scheme in Burkina Faso. For both sites, the mapped areas correlate with official statistics on cropped areas. Our data highlight infrastructure improvement and expansion on the Office du Niger, and the resilience of the scheme to rainfall variability. Whilst on the Bagre scheme, we show a vulnerability to large rainfall deficits, and a recent expansion in cropping frequency on newly developed extensions. This methodology is applicable to many areas where the Landsat archive is limited, but intra-annual mapping is required.

How to cite: Higginbottom, T., Adhikari, R., Redicker, S., and Foster, T.: Reconstructing intra-annual cropping dynamics on irrigation schemes in data sparse environments by fusing Landsat and MODIS imagery ., EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-12248, https://doi.org/10.5194/egusphere-egu21-12248, 2021.

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.

How to cite: Corbari, C., Paciolla, N., Ben Charfi, I., and Woods, M.: Remote Sensing and Citizen science supporting irrigation monitoring in the Capitanata Irrigation Consortium (Italy), EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-13318, https://doi.org/10.5194/egusphere-egu21-13318, 2021.

During the last decades, there has been a strong increase around the globe in the use of plastic greenhouses (PGs) which respond to the need to provide better water security, overcome adverse weather events, or elude pests. The central valley of Chile has not been an exception and the surface covered by greenhouses has also experienced an increase over the years. In the Valparaiso region, the surface increased from 1122 ha to 1180 ha throughout the decade 1997-2007. However, on one hand, there has not been a new PGs census since 2007 and, on the other hand, its spatial distribution has not ever been mapped. Considering that agriculture in this region employs more than 60000 people and moves 4% of regional GPD, this information should be available to be included in land planning and to be incorporated into hydrological, economic, and food security models. To overcome this, we propose a new method for monitoring the variations of the surface covered by PGs based on the intersection of normalized difference indexes and areas excludes by masks. For this, free Landsat 8 multi-temporal cloud-free images were used, from which five indexes were obtained (Modified Soil-adjusted Vegetation Index, Temperature Brightness Index, Normalized Difference Vegetation Index - Green, Normalized Difference Built-up Index, and Plastic Surface Index). These indexes were then reclassified in binary form and added up. Finally, urban areas and high slope zones were excluded to obtain the final output. This procedure was run in Google Earth Engine, which allowed easy replication and automation for longer time series or in other study sites. Results proved this methodology was able to successfully discriminate the 86% of PG, which suppose 1410 ha. This surface is consistent with the agricultural census developed in 2007 and with the increase of more than 900 subsidies granted by the government for installing PGs. Its performance also supports our confidence to discriminate PGs in areas with different land covers such as reservoirs, rural areas, open crops, bare soil, and roads. Future studies will allow us to estimate the surface of plastic greenhouses in Chile, mapping its spatial distribution in all the country, and monitor changes over time.

How to cite: Aguirre, I. and Lozano-Parra, J.: Mapping plastic greenhouses with satellite imagery in Valparaiso, Chile: development of a new methodology through data cloud platform, EGU General Assembly 2021, online, 19–30 Apr 2021, EGU21-9105, https://doi.org/10.5194/egusphere-egu21-9105, 2021.