Academic literature on the topic 'Irrigation, Land Surface Model, Remote Sensing, Data Assimilation'

Remote sensing and land surface models promote the understanding of soil moisture dynamics by means of multiple products. These products differ in data sources, algorithms, model structures and forcing datasets, complicating the selection of optimal products, especially in regions with complex land covers. This study compared different products, algorithms and flagging strategies based on in situ observations in Anhui province, China, an intensive agricultural region with diverse landscapes. In general, models outperform remote sensing in terms of valid data coverage, metrics against observations or based on triple collocation analysis, and responsiveness to precipitation. Remote sensing performs poorly in hilly and densely vegetated areas and areas with developed water systems, where the low data volume and poor performance of satellite products (e.g., Soil Moisture Active Passive, SMAP) might constrain the accuracy of data assimilation (e.g., SMAP L4) and downstream products (e.g., Cyclone Global Navigation Satellite System, CYGNSS). Remote sensing has the potential to detect irrigation signals depending on algorithms and products. The single-channel algorithm (SCA) shows a better ability to detect irrigation signals than the Land Parameter Retrieval Model (LPRM). SMAP SCA-H and SCA-V products are the most sensitive to irrigation, whereas the LPRM-based Advanced Microwave Scanning Radiometer 2 (AMSR2) and European Space Agency (ESA) Climate Change Initiative (CCI) passive products cannot reflect irrigation signals. The results offer insight into optimal product selection and algorithm improvement.

Abstract. The recent development of the non-invasive cosmic-ray soil moisture sensing technique fills the gap between point-scale soil moisture measurements and regional-scale soil moisture measurements by remote sensing. A cosmic-ray probe measures soil moisture for a footprint with a diameter of ~ 600 m (at sea level) and with an effective measurement depth between 12 and 76 cm, depending on the soil humidity. In this study, it was tested whether neutron counts also allow correcting for a systematic error in the model forcings. A lack of water management data often causes systematic input errors to land surface models. Here, the assimilation procedure was tested for an irrigated corn field (Heihe Watershed Allied Telemetry Experimental Research – HiWATER, 2012) where no irrigation data were available as model input although for the area a significant amount of water was irrigated. In the study, the measured cosmic-ray neutron counts and Moderate-Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products were jointly assimilated into the Community Land Model (CLM) with the local ensemble transform Kalman filter. Different data assimilation scenarios were evaluated, with assimilation of LST and/or cosmic-ray neutron counts, and possibly parameter estimation of leaf area index (LAI). The results show that the direct assimilation of cosmic-ray neutron counts can improve the soil moisture and evapotranspiration (ET) estimation significantly, correcting for lack of information on irrigation amounts. The joint assimilation of neutron counts and LST could improve further the ET estimation, but the information content of neutron counts exceeded the one of LST. Additional improvement was achieved by calibrating LAI, which after calibration was also closer to independent field measurements. It was concluded that assimilation of neutron counts was useful for ET and soil moisture estimation even if the model has a systematic bias like neglecting irrigation. However, also the assimilation of LST helped to correct the systematic model bias introduced by neglecting irrigation and LST could be used to update soil moisture with state augmentation.

Abstract. The recent development of the non-invasive cosmic-ray soil moisture sensing technique fills the gap between point scale soil moisture measurements and regional scale soil moisture measurements by remote sensing. A cosmic-ray probe measures soil moisture for a footprint with a diameter of ~600 m (at sea level) and with an effective measurement depth between 12 and 76 cm, depending on the soil humidity. In this study, it was tested whether neutron counts also allow to correct for a systematic error in the model forcings. Lack of water management data often cause systematic input errors to land surface models. Here, the assimilation procedure was tested for an irrigated corn field (Heihe Watershed Allied Telemetry Experimental Research – HiWATER, 2012) where no irrigation data were available as model input although the area a significant amount of water was irrigated. Measured cosmic-ray neutron counts and Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature (LST) products were jointly assimilated into the Community Land Model (CLM) with the Local Ensemble Transform Kalman Filter. Different data assimilation scenarios were evaluated, with assimilation of LST and/or cosmic-ray neutron counts, and possibly parameter estimation of leaf area index (LAI). The results show that the direct assimilation of cosmic-ray neutron counts can improve the soil moisture and evapotranspiration (ET) estimation significantly, correcting for lack of information on irrigation amounts. The joint assimilation of neutron counts and LST could improve further the ET estimation, but the information content of neutron counts exceeded the one of LST. Additional improvement was achieved by calibrating LAI, which after calibration was also closer to independent field measurements. It was concluded that assimilation of neutron counts was useful for ET and soil moisture estimation even if the model has a systematic bias like neglecting irrigation. However, also the assimilation of LST helped to correct the systematic model bias introduced by neglecting irrigation and LST could be used to update soil moisture with state augmentation.

Abstract. Worldwide, the amount of water used for agricultural purposes is rising, and the quantification of irrigation is becoming a crucial topic. Because of the limited availability of in situ observations, an increasing number of studies is focusing on the synergistic use of models and satellite data to detect and quantify irrigation. The parameterization of irrigation in large-scale land surface models (LSMs) is improving, but it is still hampered by the lack of information about dynamic crop rotations, or the extent of irrigated areas, and the mostly unknown timing and amount of irrigation. On the other hand, remote sensing observations offer an opportunity to fill this gap as they are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining LSMs and satellite information through data assimilation can offer the optimal way to quantify the water used for irrigation. This work represents the first and necessary step towards building a reliable LSM data assimilation system which, in future analysis, will investigate the potential of high-resolution radar backscatter observations from Sentinel-1 to improve irrigation quantification. Specifically, the aim of this study is to couple the Noah-MP LSM running within the NASA Land Information System (LIS), with a backscatter observation operator for simulating unbiased backscatter predictions over irrigated lands. In this context, we first tested how well modelled surface soil moisture (SSM) and vegetation estimates, with or without irrigation simulation, are able to capture the signal of aggregated 1 km Sentinel-1 backscatter observations over the Po Valley, an important agricultural area in northern Italy. Next, Sentinel-1 backscatter observations, together with simulated SSM and leaf area index (LAI), were used to optimize a Water Cloud Model (WCM), which will represent the observation operator in future data assimilation experiments. The WCM was calibrated with and without an irrigation scheme in Noah-MP and considering two different cost functions. Results demonstrate that using an irrigation scheme provides a better calibration of the WCM, even if the simulated irrigation estimates are inaccurate. The Bayesian optimization is shown to result in the best unbiased calibrated system, with minimal chances of having error cross-correlations between the model and observations. Our time series analysis further confirms that Sentinel-1 is able to track the impact of human activities on the water cycle, highlighting its potential to improve irrigation, soil moisture, and vegetation estimates via future data assimilation.

Abstract. Earth's land surface is characterized by tremendous natural heterogeneity and human-engineered modifications, both of which are challenging to represent in land surface models. Satellite remote sensing is often the most practical and effective method to observe the land surface over large geographical areas. Agricultural irrigation is an important human-induced modification to natural land surface processes, as it is pervasive across the world and because of its significant influence on the regional and global water budgets. In this article, irrigation is used as an example of a human-engineered, often unmodeled land surface process, and the utility of satellite soil moisture retrievals over irrigated areas in the continental US is examined. Such retrievals are based on passive or active microwave observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Soil Moisture Ocean Salinity (SMOS) mission, WindSat and the Advanced Scatterometer (ASCAT). The analysis suggests that the skill of these retrievals for representing irrigation effects is mixed, with ASCAT-based products somewhat more skillful than SMOS and AMSR2 products. The article then examines the suitability of typical bias correction strategies in current land data assimilation systems when unmodeled processes dominate the bias between the model and the observations. Using a suite of synthetic experiments that includes bias correction strategies such as quantile mapping and trained forward modeling, it is demonstrated that the bias correction practices lead to the exclusion of the signals from unmodeled processes, if these processes are the major source of the biases. It is further shown that new methods are needed to preserve the observational information about unmodeled processes during data assimilation.

Abstract. The Earth's land surface is characterized by tremendous natural heterogeneity and human engineered modifications, both of which are challenging to represent in land surface models. Satellite remote sensing is often the most practical and effective method to observe the land surface over large geographical areas. Agricultural irrigation is an important human induced modifications to natural land surface processes, as it is pervasive across the world and because of its significant influence on the regional and global water budgets. In this article, irrigation is used as an example of a human engineered, unmodeled land surface process, and the utility of satellite soil moisture retrievals over irrigated areas in the continental US is examined. Such retrievals are based on passive or active microwave observations from the Advanced Microwave Scanning Radiometer for the Earth Observing System (AMSR-E), the Advanced Microwave Scanning Radiometer 2 (AMSR2), the Soil Moisture Ocean Salinity (SMOS) mission, WindSat and the Advanced Scatterometer (ASCAT). The analysis suggests that the skill of these retrievals for representing irrigation artifacts is mixed, with ASCAT based products somewhat more skillful than SMOS and AMSR2 products. The article then examines the suitability of typical bias correction strategies in current land data assimilation systems when unmodeled processes dominate the bias between the model and the observations. Using a suite of synthetic experiments that includes bias correction strategies such as quantile mapping and trained forward modeling, it is demonstrated that the bias correction practices lead to the exclusion of the signals from unmodeled processes, if these processes are the major source of the biases. It is further shown that new methods are needed to preserve the observational information about unmodeled processes during data assimilation.

Agricultural water use represents more than 70% of the world’s freshwater through irrigation water inputs that are poorly known at the field scale. Irrigation monitoring is thus an important issue for optimizing water use in particular with regards to the water scarcity that the semi-arid regions are already facing. In this context, the aim of this study is to develop and evaluate a new approach to predict seasonal to daily irrigation timing and amounts at the field scale. The method is based on surface soil moisture (SSM) data assimilated into a simple land surface (FAO-56) model through a particle filter technique based on an ensemble of irrigation scenarios. The approach is implemented in three steps. First, synthetic experiments are designed to assess the impact of the frequency of observation, the errors on SSM and the a priori constraints on the irrigation scenarios for different irrigation techniques (flooding and drip). In a second step, the method is evaluated using in situ SSM measurements with different revisit times (3, 6 and 12 days) to mimic the available SSM product derived from remote sensing observation. Finally, SSM estimates from Sentinel-1 are used. Data are collected on different wheat fields grown in Morocco, for both flood and drip irrigation techniques in addition to rainfed fields used for an indirect evaluation of the method performance. Using in situ data, accurate results are obtained. With an observation every 6 days to mimic the Sentinel-1 revisit time, the seasonal amounts are retrieved with R > 0.98, RMSE < 32 mm and bias < 2.5 mm. Likewise, a good agreement is observed at the daily scale for flood irrigation as more than 70% of the detected irrigation events have a time difference from actual irrigation events shorter than 4 days. Over the drip irrigated fields, the statistical metrics are R = 0.74, RMSE = 24.8 mm and bias = 2.3 mm for irrigation amounts cumulated over 15 days. When using SSM products derived from Sentinel-1 data, the statistical metrics on 15-day cumulated amounts slightly dropped to R = 0.64, RMSE = 28.7 mm and bias = 1.9 mm. The metrics on the seasonal amount retrievals are close to assimilating in situ observations with R = 0.99, RMSE = 33.5 mm and bias = −18.8 mm. Finally, among four rainfed seasons, only one false event was detected. This study opens perspectives for the regional retrieval of irrigation amounts and timing at the field scale and for mapping irrigated/non irrigated areas.

Early forecasting of crop yield from field to region is important for stabilizing markets and safeguarding food security. Producing a precise forecasting result with fewer inputs is an ongoing goal for the large-area yield evaluation. We present one approach of yield prediction for maize that was explored by incorporating remote-sensing-derived land surface temperature (LST) and field in-season data into a series of logistic models with only a few parameters. Continuous observation data of maize were utilized to calibrate and validate the corresponding logistic models for regional biomass estimating based on field temperatures (including crop canopy temperature (Tc)) and relative dry/fresh biomass accumulation. The LST maps from MOD11A1 products, which are considered to be matched as Tc in large irrigation districts, were assimilated into the validated models to estimate the biomass accumulation. It was found that the temporal-scale difference between the instantaneous LST and the daily average value of field-measured Tc was eliminated by data normalization method, indicating that the normalized LST could be input directly into the model as an approximation of the normalized Tc. Making one observed biomass in-season as the driving force, the maximum of dry/fresh biomass accumulation (DBA/FBA) at harvest could be estimated. Then, grain yield forecasting could be achieved according to the local harvest index of maize. Silage and grain yields were evaluated reasonably well compared with field observations based on the regional map of LST values obtained in 2017 in Changchun, Jilin Province, China. Here, satisfactory grain and silage yield forecasting was provided by assimilating once measured value of DBA/FBA at the middle growth period (early August) into the model in advance of harvest. Meanwhile, good results were obtained in the application of this approach using field data in 2016 to predict grain yield ahead of harvest in the Jiefangzha sub-irrigation district, Inner Mongolia, China. This study demonstrated that maize yield can be forecasted accurately prior to harvest by assimilating remote-sensing-derived LST and field data into the logistic models at a regional scale considering the spatio-temporal scale extension of ground information and crop dynamic growth in real time.

Regions with high productivity of agriculture, such as the Beqaa Plain, Lebanon, often rely on groundwater supplies for irrigation demand. Recent reports have indicated that groundwater consumption in this region has been unsustainable, and quantifying rates of groundwater depletion has remained a challenge. Here, we utilize 15 years of data (June 2002–April 2017) from the Gravity Recovery and Climate Experiment (GRACE) satellite mission to show Total Water Storage (TWS) changes in Lebanon’s Beqaa Plain. We then obtain complimentary information on various hydrologic cycle variables, such as soil moisture storage, snow water equivalent, and canopy water storage from the Global Land Data Assimilation System (GLDAS) model, and surface water data from the largest body of water in this region, the Qaraaoun Reservoir, to disentangle the TWS signal and calculate groundwater storage changes. After combining the information from the remaining hydrologic cycle variables, we determine that the majority of the losses in TWS are due to groundwater depletion in the Beqaa Plain. Results show that the rate of groundwater storage change in the West Beqaa is nearly +0.08 cm/year, in the Rashaya District is −0.01 cm/year, and in the Zahle District the level of depletion is roughly −1.10 cm/year. Results are confirmed using Sentinel-1 interferometric synthetic aperture radar (InSAR) data, which provide high-precision measurements of land subsidence changes caused by intense groundwater usage. Furthermore, data from local monitoring wells are utilized to further showcase the significant drop in groundwater level that is occurring through much of the region. For monitoring groundwater storage changes, our recommendation is to combine various data sources, and in areas where groundwater measurements are lacking, we especially recommend the use of data from remote sensing.

The spatial and temporal distribution of precipitation is of great importance for the rain-fed agricultural production and the socioeconomics of Mato Grosso (MT), Brazil. MT has a sparse network of ground rain gauges that limits the effective use of precipitation information for sustainable agricultural production and water resources in the region. Several gridded precipitation products from remote sensing and reanalysis of land surface models are currently available that can enhance the use of such information. However, these products are available at different spatial and temporal resolutions which add some challenges to stakeholders (users) to identify their appropriateness for specific applications (e.g., irrigation requirements, length of growing season, and drought monitoring). Thus, it is necessary to provide an assessment of the reliability of these precipitation estimates. The objective of this work was to compare regional precipitation estimates over MT as provided by the Global Land Data Assimilation (GLDAS), Modern-Era Retrospective Analysis for Research and Applications (MERRA), Tropical Rainfall Measurement Mission (TRMM), Global Precipitation Measurement (GPM), and the Global Precipitation Climatology Project (GPCP) with ground-based measurements. The comparison was conducted for the 2000–2018 period at eleven ground-based weather stations that covered different climate zones in MT using daily, monthly, and annual temporal resolutions. The comparison used the Pearson correlation index–r, Willmott index–d, root mean square error—RMSE, and the Wilks methods. The results showed GPM and GLDAS estimates did not differ significantly with the measured daily, monthly, and annual precipitation. TRMM estimates slightly overestimated daily precipitation by about 4.7% but did not show significant difference on the monthly and annual scales when compared with local measurements. The GPCP underestimated annual precipitation by about 7.1%. MERRA underestimated daily, monthly, and annual precipitation by about 22.9% on average. In general, all products satisfactorily estimated monthly precipitation, and most of them satisfactorily estimated annual precipitation; however, they showed low accuracy when estimating daily precipitation. The TRMM, GPM, GPCP, and GLDAS estimates had the highest performance, from high to low, while MERRA showed the lowest performance. The findings of this study can be used to support the decision-making process in the region in application related to water resources management, sustainability of agriculture production, and drought management.

In recent years, human water needs have been steadily increasing and they are currently dominated by agricultural activities for food production worldwide. Furthermore, the increase in population and climatic change are expected to raise the current demand of water highlighting the necessity for more efficient irrigation systems. In this context, the combined effect of human pressure (i.e. irrigation) and the increase of extreme natural phenomena, such as drought events, has a strong impact on the global water budget with a local depletion of water resources, especially groundwater. However, our understanding of the impact of human activities on the water cycle is challenged by the lack of data (such as irrigation benchmark data) and by the difficulty of land surface models (LSM) to represent human processes like irrigation. These knowledge gaps affect water and food security, because they undermine both the ability to accurately monitor and forecast drought events, and the capacity to safely manage water resources. This thesis aims at shedding light on the utility of new earth observation (EO) data to characterize agricultural drought conditions and to detect water cycle modifications induced by anthropogenic activities like irrigation in order to help models to improve their ability to more realistically represent the terrestrial water cycle. Based on that, this research work tries to address two main important research questions: What is the added value of new EO data (i.e., satellite-based soil moisture) in drought monitoring and to which extent are these data able to provide potential information on crop production with respect to LSMs? Can the new generation of high resolution EO data help LSMs to better represent the impact of human activities like irrigation on the terrestrial water cycle? To answer question (1) the activity focuses on the use of an innovative long-term record of satellite-based soil moisture for the development of a standardized agricultural drought index for a regional scale analysis. The novelty of this research is to establish the relation between drought indices and crop yields, through a comparison with a benchmark crop dataset, as well as to analyze the additional information contained in satellite observations about agricultural productivity and water uses as compared to ground-based rainfall and modeled soil moisture. The main findings highlight the crucial role of soil moisture in limiting the crop productivity during drought periods and consequently its key contribution for agricultural drought analysis. Another important aspect highlighted in the results is that satellite estimates of soil moisture contain added information about both water scarcity conditions and anthropogenic impacts on water resources (i.e., irrigation), compared to soil moisture estimates based on model simulations (which do not account for human-related processes). Those outcomes strongly link the agricultural drought analysis with the second part of this research. To answer question (2), the activity explicitly focuses on the quantification of the water used for agricultural purposes. Hydrological studies are converging on the synergistic use of models and satellite data to detect and quantify irrigation. The parameterization of irrigation in large-scale LSMs is improving but not sufficient by themselves to provide correct irrigation estimates, because they are still hampered by the lack of information about dynamic crop rotations, and by out-of-date maps of irrigated areas as well as unknown timing and amount of irrigation. On the other hand, satellite observations are directly affected by, and hence potentially able to detect, irrigation. Therefore, combining LSMs and satellite information through data assimilation (DA) can offer the optimal solution to quantify water used for irrigation at the desired spatio-temporal scale. This research aims at building an innovative and reliable DA system able to investigate the potential of high-resolution microwave EO data from the Sentinel-1 mission to improve irrigation quantification. The main assumption is that the joint update of soil moisture and vegetation model states, through the ingestion of 1-km radar backscatter, can improve irrigation estimation. This is a topic which was not investigated in previous literature. In this context, the optimization of a coupled system comprising a LSM and a backscatter observation operator, is firstly investigated. Results highlight the importance of equipping the LSM with an irrigation scheme to avoid strong biases between satellite observations and backscatter model predictions over intensively irrigated areas, where anthropogenic activities cannot be neglected. Secondly, backscatter observations are assimilated in the calibrated DA system, taking into account the effects of different backscatter polarizations. DA introduces both improvements and degradations in soil moisture, vegetation and irrigation estimates. The spatial and temporal scale of the results have a large impact on the analysis and more contradicting results are found for an analysis at the plot scale, which highlights the need for very high-spatial resolution EO data and model parameterizations. Above all, this study sheds light on the limitations resulting from poorly-parameterized irrigation schemes included in LSMs which prevents large improvements in the irrigation simulation due to DA and points out on future implementations and input developments needed to improve LSM estimates of hydrological variables. The research activities focus on regions characterized by a significant human pressure on the terrestrial water cycle. The first part includes the Karnataka and Maharashtra states, located in central India, whereas the second part includes irrigated areas with a different climate in Europe, i.e. Po Valley, in northern Italy, and Niedersachsen in Germany. Il fabbisogno idrico è in costante aumento e attualmente dominato dalle attività agricole finalizzate alla produzione di cibo a scala globale. Si prevede inoltre che l'aumento della popolazione globale in condizioni di cambiamento climatico aumenterà l'attuale prelievo di acqua mettendo in evidenza la necessità di sistemi di irrigazione più efficienti. Il forte impatto dell'irrigazione sul bilancio idrico globale è evidenziato anche da un generale esaurimento delle risorse idriche a larga scala, soprattutto per quel che riguarda le acque di falda. La riduzione delle risorse idriche è il risultato dell'effetto combinato dell'attività antropica e del cambiamento climatico. Tuttavia, la capacità di quantificare l'impatto umano, ed in particolare l'impatto delle pratiche agricole, sul ciclo idrologico è limitata dalla mancanza di dati osservati (principalmente dati di irrigazione) e dalla difficoltà che i modelli di superficie terrestre (LSM) hanno nel rappresentare pratiche antropiche. Queste limitazioni hanno un impatto significativo sulla sicurezza idrica e alimentare, minando la capacità di monitorare e prevedere eventi di siccità, così come la capacità di gestire in sicurezza la risorsa idrica. Questa tesi mira a far luce sull'utilità di innovativi dati satellitari di rilevamento da remoto per caratterizzare la siccità agricola e per quantificare cambiamenti del ciclo idrologico indotti da attività antropiche, in particolare l'irrigazione, al fine di aiutare i modelli a migliorare la loro capacità di rappresentare più realisticamente il ciclo idrologico terrestre. Sulla base di ciò, l'attività di ricerca affronta due importanti tematiche: Qual è il valore aggiunto di dati satellitari di ultima generazione (nello specifico l'umidità del suolo da satellite) nel monitoraggio della siccità e in che misura questi dati sono in grado di fornire informazioni potenziali sulla produttività delle colture rispetto ai dati ottenuti da LSM? Dati satellitari ad alta risoluzione possono aiutare a rappresentare l'impatto delle attività umane sul ciclo idrologico terrestre nei LSM? Per rispondere alla domanda (1) un innovativo dataset di umidità del suolo da satellite a lungo termine è stato selezionato per lo sviluppo di un indice standardizzato di siccità agricola da utilizzare in un'analisi a scala regionale. L'aspetto innovativo di questa ricerca è quello di stabilire la relazione tra indici di siccità agricola e dati di raccolto, nonché di analizzare le informazioni aggiuntive contenute nelle osservazioni satellitari riguardo la produttività agricola e l'uso antropico di acqua, rispetto a dati in situ di precipitazione e all'umidità del suolo modellata. I risultati principali evidenziano il ruolo cruciale dell'umidità del suolo nel limitare la produttività delle colture durante i periodi siccitosi e di conseguenza il contributo chiave di questa variabile climatica per l'analisi della siccità agricola. Un altro aspetto importante che si evidenzia nei risultati è il grado di informazione contenuto nelle stime di umidità del suolo ottenute da dati satellitari, sia sulle condizioni di scarsità d'acqua che sull'impatto delle attività umane sul ciclo idrologico, rispetto alle stime di umidità del suolo modellato. Tali risultati collegano fortemente l'analisi della siccità agricola alla seconda parte di questa ricerca. Per rispondere alla domanda (2) l'attività si è concentrata esplicitamente sulla quantificazione della risorsa idrica utilizzata a scopo agricolo. Negli ultimi anni, gli studi idrologici stanno convergendo sull'uso sinergico di modelli e dati satellitari per rilevare e quantificare l'irrigazione. La parametrizzazione dell'irrigazione nei LSM su larga scala è migliorata nell'ultimo decennio, ma non è ancora sufficiente a fornire stime corrette di irrigazione, in quanto ostacolata dalla mancanza di informazioni aggiornate sulla rotazione annuale delle colture, aree irrigue e dal fatto che tempi e quantità di irrigazione sono essenzialmente sconosciuti. D'altra parte, le osservazioni satellitari sono direttamente influenzate dall'irrigazione e quindi potenzialmente in grado di rilevarla. Pertanto, l'assimilazione di tali dati (DA) all'interno di modelli può offrire la soluzione ottimale per quantificare l'acqua utilizzata a scopo irriguo alla scala spazio-temporale desiderata. Questa ricerca mira dunque a costruire un sistema di DA innovativo in grado di studiare il potenziale delle osservazioni satellitari alle microonde ad alta risoluzione della missione Sentinel-1 per migliorare la quantificazione dell'irrigazione. L'ipotesi principale è che aggiornando congiuntamente gli stati di umidità del suolo e vegetazione di un LSM, attraverso assimilazione di dati radar di retrodiffusione (backscatter) alla risoluzione di 1 km, possa migliorare l'irrigazione. Tale aspetto non è stato precedentemente analizzato in letteratura. Nell'ambito di questa attività è stato quindi sviluppato un sistema che accoppia un LSM ed un operatore di osservazione (observation operator) per l'assimilazione di dati di backscatter. I risultati evidenziano l'importanza di dotare il LSM anche di uno schema di irrigazione per ridurre significativamente il bias tra osservazioni satellitari e previsioni del modello su aree intensamente irrigate, dove le attività antropiche hanno un ruolo non trascurabile. Una volta calibrato il sistema, le osservazioni di backscatter sono state assimilate, tenendo conto dell'effetto delle diverse polarizzazioni del segnale osservato. L'assimilazione sembra introdurre sia miglioramenti che riduzione delle performance nelle stime di umidità del suolo, vegetazione ed irrigazione. È inoltre possibile osservare come la scala spaziale e temporale dell'analisi abbia un forte impatto sui risultati. In tal senso, l'attività fa luce su vantaggi e limitazioni derivanti dall'uso di schemi di irrigazione caratterizzati da una parametrizzazione semplice, mettendo in evidenza gli step futuri necessari per migliorare la stima di irrigazione e delle variabili idrologiche. L' attività di ricerca è stata svolta su due regioni caratterizzate da una significativa pressione umana sul ciclo idrologico. Nella prima parte si fa riferimento agli stati di Karnataka e Maharashtra, situati nell'India centrale, mentre la seconda parte della ricerca è stata realizzata su aree irrigate in Europa, caratterizzate da condizioni climatiche differenti, ovvero, la Valle del Po, nell'Italia settentrionale, ed il sito di Niedersachsen in Germania.