Assimilation of remotely sensed canopy variables into crop models for an assessment of drought-related yield losses: A comparison of models of different complexity

Casa, Raffaele; Silvestro, Paolo Cosmo; Yang, Hao; Pignatti, Stefano; Pascucci, Simone; Yang, Guijun

doi:10.1109/igarss.2016.7730548

Cited by 5 publications

(5 citation statements)

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“…The latter is more complex than SAFY; the number of influential parameters is higher and the influence of scenario characteristics is lower than for SAFY. The strength of Aquacrop is its capability of taking into account crop physiological processes related to water stress, which are ignored by SAFY, but of great interest for the estimation of crop water requirements and yield response to drought from remote sensing [7,31]. However, despite being less complex than other crop models such as e.g., EPIC [22] or CERES [26], the larger number of parameters of Aquacrop makes it harder to calibrate and slower to run than SAFY, therefore making it less suitable for an assimilation application.…”

Section: Discussionmentioning

confidence: 99%

Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models

et al. 2017

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Accurate yield estimation at the field scale is essential for the development of precision agriculture management, whereas at the district level it can provide valuable information for supply chain management. In this paper, Huan Jing (HJ) satellite HJ1A/B and Landsat 8 Operational Land Imager (OLI) images were employed to retrieve leaf area index (LAI) and canopy cover (CC) in the Yangling area (Central China). These variables were then assimilated into two crop models, Aquacrop and simple algorithm for yield (SAFY), in order to compare their performances and practicalities. Due to the models' specificities and computational constraints, different assimilation methods were used. For SAFY, the ensemble Kalman filter (EnKF) was applied using LAI as the observed variable, while for Aquacrop, particle swarm optimization (PSO) was used, using canopy cover (CC). These techniques were applied and validated both at the field and at the district scale. In the field application, the lowest relative root-mean-square error (RRMSE) value of 18% was obtained using EnKF with SAFY. On a district scale, both methods were able to provide production estimates in agreement with data provided by the official statistical offices. From an operational point of view, SAFY with the EnKF method was more suitable than Aquacrop with PSO, in a data assimilation context. Keywords: leaf area index (LAI); canopy cover (CC); Landsat 8; HJ1A/B; artificial neural network (ANN); ensemble Kalman filter (EnKF); particle swarm optimization (PSO)

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Section: Discussionmentioning

confidence: 99%

Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models

et al. 2017

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“…The results obtained in this study, performed using a contrasting range of climatic scenarios of winter wheat growing areas in Maccarese, Italy (Mediterranean climate) and Xiaotangshan, China (continental climate), allowed to obtain essential information on the sensitivity of the AOS model, especially required for their application within regional-scale studies [63][64][65]. It is well known that the results of the sensitivity analysis studies depend on the boundary conditions chosen [66].…”

Section: Elementary Effects Using the Morris Methodsmentioning

confidence: 94%

A Comparison of Moment-Independent and Variance-Based Global Sensitivity Analysis Approaches for Wheat Yield Estimation with the Aquacrop-OS Model

et al. 2020

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The present work reports the global sensitivity analysis of the Aquacrop Open Source (AOS) model, which is the open-source version of the original Aquacrop model developed by the Food and Agriculture Organization (FAO). Analysis for identifying the most influential parameters was based on different strategies of global SA, density-based and variance-based, for the wheat crop in two different geographical locations and climates. The main objectives were to distinguish the model’s influential and non-influential parameters and to examine the yield output sensitivity. We compared two different methods of global sensitivity analysis: the most commonly used variance-based method, EFAST, and the moment independent density-based PAWN method developed in recent years. We have also identified non-influential parameters using Morris screening method, so to provide an idea of the use of non-influential parameters with a dummy parameter approach. For both the study areas (located in Italy and in China) and climates, a similar set of influential parameters was found, although with varying sensitivity. When compared with different probability distribution functions, the probability distribution function of yield was found to be best approximated by a Generalized Extreme Values distribution with Kolmogorov–Smirnov statistic of 0.030 and lowest Anderson–Darling statistic of 0.164, as compared to normal distribution function with Kolmogorov–Smirnov statistic of 0.122 and Anderson–Darling statistic of 4.099. This indicates that yield output is not normally distributed but has a rather skewed distribution function. In this case, a variance-based approach was not the best choice, and the density-based method performed better. The dummy parameter approach avoids to use a threshold as it is a subjective question; it advances the approach to setting up a threshold and gives an optimal way to set up a threshold and use it to distinguish between influential and non-influential parameters. The highly sensitive parameters to crop yield were specifically canopy and phenological development parameters, parameters that govern biomass/yield production and temperature stress parameters rather than root development and water stress parameters.

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“…Other studies combined two or more state variables to improve yield estimates [52,75]. About 21% of studies combined LAI with other state variables such as SM [82,83], sowing date (SD) [45,66], evapotranspiration (ET) [16,63], canopy cover [51,84], CNA [85], AGB [73], leaf nitrogen accumulation (LNA) [86], FAPAR [46], phenology [87], and vegetation indices [32,88] to improve model simulations. Only 4% of studies used the forcing method to incorporate data from RS into PBCMs [60,89] (Tables A1-A3).…”

Section: Data Assimilation Methods and Application Scalementioning

confidence: 99%

“…The studies reviewed have integrated RS data into many PBCMs to improve crop growth and yield (Tables A1-A3). These include WOrld FOod STudies (WOFOST) [45,46], Decision Support System for Agro-technology Transfer (DSSAT) [47,48], a Simple Algorithm For Yield (SAFY) [49,50], AquaCrop [51,52], and Soil Water Atmosphere Plant-WOrld FOod STudies (SWAP-WOFOST) [53,54]. Most of these studies used data assimilation to improve crop growth and yield estimates of staple crops (94%), consisting of maize, rice, soybeans, and wheat [55,56] (Tables A1-A3).…”

Section: Crop Modelsmentioning

confidence: 99%

“…There is a need to compare the performance of different crop models. For example, some studies in this review compared the integration of RS into AquaCrop and SAFY to improve wheat yield estimation [51,84]. There is also a need to combine the advantages of different models to improve the overall applicability and capability of crop models.…”

Section: Future Opportunitiesmentioning

confidence: 99%

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A Global Systematic Review of Improving Crop Model Estimations by Assimilating Remote Sensing Data: Implications for Small-Scale Agricultural Systems

Dlamini,

Crespo,

van Dam

et al. 2023

Remote Sensing

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There is a growing effort to use access to remote sensing data (RS) in conjunction with crop model simulation capability to improve the accuracy of crop growth and yield estimates. This is critical for sustainable agricultural management and food security, especially in farming communities with limited resources and data. Therefore, the objective of this study was to provide a systematic review of research on data assimilation and summarize how its application varies by country, crop, and farming systems. In addition, we highlight the implications of using process-based crop models (PBCMs) and data assimilation in small-scale farming systems. Using a strict search term, we searched the Scopus and Web of Science databases and found 497 potential publications. After screening for relevance using predefined inclusion and exclusion criteria, 123 publications were included in the final review. Our results show increasing global interest in RS data assimilation approaches; however, 81% of the studies were from countries with relatively high levels of agricultural production, technology, and innovation. There is increasing development of crop models, availability of RS data sources, and characterization of crop parameters assimilated into PBCMs. Most studies used recalibration or updating methods to mainly incorporate remotely sensed leaf area index from MODIS or Landsat into the WOrld FOod STudies (WOFOST) model to improve yield estimates for staple crops in large-scale and irrigated farming systems. However, these methods cannot compensate for the uncertainties in RS data and crop models. We concluded that further research on data assimilation using newly available high-resolution RS datasets, such as Sentinel-2, should be conducted to significantly improve simulations of rare crops and small-scale rainfed farming systems. This is critical for informing local crop management decisions to improve policy and food security assessments.

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Assimilation of remotely sensed canopy variables into crop models for an assessment of drought-related yield losses: A comparison of models of different complexity

Cited by 5 publications

References 10 publications

Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models

Estimating Wheat Yield in China at the Field and District Scale from the Assimilation of Satellite Data into the Aquacrop and Simple Algorithm for Yield (SAFY) Models

A Comparison of Moment-Independent and Variance-Based Global Sensitivity Analysis Approaches for Wheat Yield Estimation with the Aquacrop-OS Model

A Global Systematic Review of Improving Crop Model Estimations by Assimilating Remote Sensing Data: Implications for Small-Scale Agricultural Systems

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