Is It Possible to Quantify Irrigation Water‐Use by Assimilating a High‐Resolution Satellite Soil Moisture Product?

Jalilvand, Ehsan; Abolafia‐Rosenzweig, Ronnie; Tajrishy, Masoud; Kumar, Sujay V.; Mohammadi, Mohammadreza; Das, Narendra N.

doi:10.1029/2022wr033342

Cited by 15 publications

(5 citation statements)

References 107 publications

(191 reference statements)

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“…The failure of S1-B halved the number of available observations (one every ∼4 days in Europe, 12 days elsewhere), making the buddy check approach (used within a S1 DA system) unsuitable outside of Europe until the launch of the next satellite (S1-C, expected in the near future). Other synthetic studies assimilating S1-related SSM products (Abolafia-Rosenzweig et al, 2019;Jalilvand et al, 2023;Ouaadi et al, 2021;Zappa et al, 2022) also highlighted the importance of frequent observations. Zappa et al (2022) reported large irrigation underestimations when observations are too sparse in time.…”

Section: Novel Approach To Estimate Irrigation In a Da Systemmentioning

confidence: 97%

“…Abolafia-Rosenzweig et al ( 2019) performed SMAP SSM DA into the variable infiltration capacity (VIC) LSM, using a particle batch smoother. With the intent of going to a finer resolution, Jalilvand et al (2023) used a similar approach using the S1-SMAP SSM product (Das et al, 2019). Ouaadi et al (2021) assimilated S1-derived SSM data into the FAO-56 (Allen et al, 1998) model with a particle filter.…”

Section: Introductionmentioning

confidence: 99%

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Irrigation Quantification Through Backscatter Data Assimilation With a Buddy Check Approach

Busschaert,

Bechtold,

Modanesi

et al. 2024

J Adv Model Earth Syst

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Irrigation is an important component of the terrestrial water cycle, but it is often poorly accounted for in models. Recent studies have attempted to integrate satellite data and land surface models via data assimilation (DA) to (a) detect and quantify irrigation, and (b) better estimate the related land surface variables such as soil moisture, vegetation, and evapotranspiration. In this study, different synthetic DA experiments are tested to advance satellite DA for the estimation of irrigation. We assimilate synthetic Sentinel‐1 backscatter observations into the Noah‐MP model coupled with an irrigation scheme. When updating soil moisture, we found that the DA sets better initial conditions to trigger irrigation in the model. However, DA updates to wetter conditions can inhibit irrigation simulation. Building on this limitation, we propose an improved DA algorithm using a buddy check approach. The method still updates the land surface, but now the irrigation trigger is not primarily based on the evolution of soil moisture, but on an adaptive innovation (observation minus forecast) outlier detection. The new method was found to be optimal for more temperate climates where irrigation events are less frequent and characterized by higher application rates. It was found that the DA outperforms the model‐only 14‐day irrigation estimates by about 20% in terms of root‐mean‐squared differences, when frequent (daily or every other day) observations are available. With fewer observations or high levels of noise, the system strongly underestimates the irrigation amounts. The method is flexible and can be expanded to other DA systems, also real‐world cases.

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Section: Novel Approach To Estimate Irrigation In a Da Systemmentioning

confidence: 97%

Section: Introductionmentioning

confidence: 99%

Irrigation Quantification Through Backscatter Data Assimilation With a Buddy Check Approach

Busschaert,

Bechtold,

Modanesi

et al. 2024

J Adv Model Earth Syst

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“…The third group of approaches estimates irrigation amounts directly by assimilating SM observations into Land Surface Models and simulating irrigation events with this information [39][40][41]. Even if quite robust and accurate, LSMs often require many inputs to completely simulate the plant and soil at each step, and extensive calibration, reducing the possibility of large-scale operationalization of the approach.…”

Section: Introductionmentioning

confidence: 99%

PrISM at Operational Scale: Monitoring Irrigation District Water Use during Droughts

Paolini,

Escorihuela,

Bellvert

et al. 2024

Remote Sensing

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Efficient water management strategies are of utmost importance in drought-prone regions, given the fundamental role irrigation plays in avoiding yield losses and food shortages. Traditional methodologies for estimating irrigation amounts face limitations in terms of overall precision and operational scalability. This study proposes to estimate irrigation amounts from soil moisture (SM) data by adapting the PrISM (Precipitation Inferred from Soil Moisture) methodology. The PrISM assimilates SM into a simple Antecedent Precipitation Index (API) model using a particle filter approach, which allows the creation and estimation of irrigation events. The methodology is applied in a semi-arid region in the Ebro basin, located in the north-east of Spain (Catalonia), from 2016 to 2023. Multi-year drought, which started in 2020, particularly affected the region starting from the spring of 2023, which led to significant reductions in irrigation district water allocations in some of the areas of the region. This study demonstrates that the PrISM approach can correctly identify areas where water restrictions were adopted in 2023, and monitor the water usage with good performances and reliable results. When compared with in situ data for 8 consecutive years, PrISM showed a significant person’s correlation between 0.58 and 0.76 and a cumulative weekly root mean squared error (rmse) between 7 and 11 mm. Additionally, PrISM was applied to three irrigation districts with different levels of modernization, due to the different predominant irrigation systems: flood, sprinkler, and drip. This analysis underlined the strengths and limitations of PrISM depending on the irrigation techniques monitored. PrISM has good performances in areas irrigated by sprinkler and flood systems, while difficulties are present over drip irrigated areas, where the very localized and limited irrigation amounts could not be detected from SM observations.

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“…They also highlighted the importance of tackling the issue of persistent biases between simulated and observed SSM. Jalilvand et al (2023) applied the same approach but using the 1 km resolution SMAP-S1 SSM data (Das et al, 2019) over an irrigation district in Iran. They confirmed the importance of an a priori knowledge of irrigation frequencies in their approach and obtained an underestimation of seasonal cumulative irrigation at the irrigated pixels by an average of 19%, explained by the loss of irrigation signal due to the mismatch between the spatial resolution (1 km) of SSM data and the scale of irrigation practices.…”

Section: Introductionmentioning

confidence: 99%

“…However, this approach has the disadvantage of potentially deleting signals when the observations contain some information that is not modeled, such as irrigation (Kumar et al, 2015;Nair and Indu, 2019). Another way to minimize such a bias is to calibrate model parameters on rainfed pixels (e.g., Dari et al, 2020Dari et al, , 2022Jalilvand et al, 2023), although this implies the strong (and unverified) assumption that optimal parameters for rainfed pixels are also optimal for irrigated pixels. Another approach is to remove a systematic bias (calculated on rainfed pixels) from the simulated irrigations (Jalilvand et al, 2019), although part of the signal may be lost due to the difference in climatology or soil properties between both pixel types (Brombacher et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Retrieving the irrigation actually applied at district scale: assimilating high-resolution Sentinel-1-derived soil moisture data into a FAO-56-based model

Laluet¹,

Olivera-Guerra²,

Altés³

et al. 2023

Preprint

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Irrigation is the most water consuming activity in the world. Knowing the timing andamount of irrigation that is actually applied is therefore fundamental for watermanagers. However, this information is rarely available at all scales and is subject tolarge uncertainties due to the great diversity of existing agricultural practices andassociated irrigation regimes (full irrigation, deficit irrigation, or over irrigation). To fillthis gap, we propose a two-step approach based on 15 m resolution Sentinel-1 (S1)surface soil moisture (SSM) data to retrieve the actual irrigation at the weekly scaleover an entire irrigation district. As a first step, the S1-derived SSM is assimilated into aFAO-56-based crop water balance model (SAMIR) to retrieve for each crop type boththe irrigation amount (Idose) and the soil moisture threshold (SMthreshold) at whichirrigation is triggered. For this, a particle filter method is implemented, with particlesreset each month to provide time varying SMthreshold and Idose. As a second step,the retrieved SMthreshold and Idose values are used as input to SAMIR for estimatingthe weekly irrigation and its uncertainty. The assimilation approach (SSM-ASSIM) istested over the 8000 hectare Algerri-Balaguer irrigation district located in north-easternSpain, where in situ irrigation data integrating the whole district are available at theweekly scale during 2019. For assessment, the performance of SSM-ASSIM iscompared against that of the default FAO-56 irrigation module (called FAO56-DEF),which sets SMthreshold to the critical soil moisture value and systematically fills thesoil reservoir for each irrigation event. During 2019, with a yearly observed irrigation of687 mm, SSM-ASSIM (FAO56-DEF) shows a Root Mean Square Difference betweenretrieved and in situ irrigation of 6.7 (17) mm week-1, and a Pearson correlationcoefficient of 0.88 (0.47). The SSM-ASSIM approach shows great potential forretrieving the weekly water use over extended areas for any irrigation regime, includingover irrigation.

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Is It Possible to Quantify Irrigation Water‐Use by Assimilating a High‐Resolution Satellite Soil Moisture Product?

Cited by 15 publications

References 107 publications

Irrigation Quantification Through Backscatter Data Assimilation With a Buddy Check Approach

Irrigation Quantification Through Backscatter Data Assimilation With a Buddy Check Approach

PrISM at Operational Scale: Monitoring Irrigation District Water Use during Droughts

Retrieving the irrigation actually applied at district scale: assimilating high-resolution Sentinel-1-derived soil moisture data into a FAO-56-based model

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