A review of data assimilation of remote sensing and crop models

Jin, Xiuliang; Kumar, Lalit; Li, Zhenhai; Feng, Haikuan; Xu, Xingang; Yang, Guijun; Wang, Jihua

doi:10.1016/j.eja.2017.11.002

Cited by 380 publications

(259 citation statements)

References 127 publications

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“…More sophisticated methods integrate LAI with a crop simulation model (CSM) using the DA method to assist crop yield modeling and irrigation management. X. L. Jin et al () provided a recent review of crop models, remote sensing technology, and DA methods. Different DA methods to use LAI in CSMs, of various degrees of complexity and integration, have been proposed (Baret et al, ; Delécolle et al, ; Moulin et al, ).…”

Section: Lai Applicationsmentioning

confidence: 99%

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Fang

Baret

Plummer

et al. 2019

Reviews of Geophysics

505

252

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Leaf area index (LAI) is a critical vegetation structural variable and is essential in the feedback of vegetation to the climate system. The advancement of the global Earth Observation has enabled the development of global LAI products and boosted global Earth system modeling studies. This overview provides a comprehensive analysis of LAI field measurements and remote sensing estimation methods, the product validation methods and product uncertainties, and the application of LAI in global studies. First, the paper clarifies some definitions related to LAI and introduces methods to determine LAI from field measurements and remote sensing observations. After introducing some major global LAI products, progresses made in temporal compositing and prospects for future LAI estimation are analyzed. Subsequently, the overview discusses various LAI product validation schemes, uncertainties in global moderate resolution LAI products, and high resolution reference data. Finally, applications of LAI in global vegetation change, land surface modeling, and agricultural studies are presented. It is recommended that (1) continued efforts are taken to advance LAI estimation algorithms and provide high temporal and spatial resolution products from current and forthcoming missions; (2) further validation studies be conducted to address the inadequacy of current validation studies, especially for underrepresented regions and seasons; and (3) new research frontiers, such as machine learning algorithms, light detection and ranging technology, and unmanned aerial vehicles be pursued to broaden the production and application of LAI.

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Section: Lai Applicationsmentioning

confidence: 99%

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Fang

Baret

Plummer

et al. 2019

Reviews of Geophysics

505

252

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“…Accurate crop growth simulation is important for crop yield prediction, field management, and climate change evaluation [1][2][3]. A crop growth model could quantify crop physiological development growth and yield based on the interactions among environmental characteristics (such as soil, weather, and water) [4][5][6]. The WOFOST model is one of the widely used crop model, which has been successfully applied at the field scale in previous studies [7,8].…”

Section: Introductionmentioning

confidence: 99%

“…However, some parameters (e.g., leaf angle distribution) in the radiation transfer model are constants, which is inconsistent with reality, leading to uncertainties. Biophysical parameters were increasingly applied in the assimilation, since they are significant at the crop growth stage and can be obtained from both the crop model and remote sensing data [6]. Most studies employed satellite-retrieved LAI as the state variable to optimize the crop model for various applications [21][22][23].…”

Section: Introductionmentioning

confidence: 99%

Assimilating Remote Sensing Phenological Information into the WOFOST Model for Rice Growth Simulation

Zhou

Liu

2019

Remote Sensing

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Precise simulation of crop growth is crucial to yield estimation, agricultural field management, and climate change. Although assimilation of crop model and remote sensing data has been applied in crop growth simulation, few studies have considered optimizing the crop model with respect to phenology. In this study, we assimilated phenological information obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) time series data into the World Food Study (WOFOST) model to improve the accuracy of rice growth simulation at the regional scale. The particle swarm optimization (PSO) algorithm was implemented to optimize the initial phenology development stage (IDVS) and transplanting date (TD) in the WOFOST model by minimizing the difference between simulated and observed phenology, including heading and maturity date. Assimilating phenology improved the accuracy of the rice growth simulation, with correlation coefficients (R) equal to 0.793, 0822, and 0.813 at three fieldwork dates. The performance of the proposed strategy is comparable with that of the enhanced vegetation index (EVI) time series assimilation strategy, with less computation time. Additionally, the result confirms that the proposed strategy could be applied with different spatial resolution images and the difference of simulated LAI mean is less than 0.35 in three experimental areas. This study offers a novel assimilation strategy with regard to the phenology development process, which is efficient and scalable for crop growth simulation.

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“…But due to the small size of these devices and the limited battery capabilities, they are mostly used in precision farming applications to provide the within farm variability of factors influencing crop growth. Their operational use of small UAVs in regional scale is questioned [77], thus medium-altitude long-endurance (MALE) UAVs have been developed [78,79]. Several studies have examined the use of UAVs to obtain crop status, health and to provide effective support to crop management in general [80][81][82].…”

Section: Estimation Of Crop Parameters From Remote Sensingmentioning

confidence: 99%

“…During forcing, model variables are replaced or adjusted using remote sensing data, while in recalibration model parameters or initial conditions are adjusted based on remote sensing data. An extensive recent review on data assimilation of remote sensing and crop models showing the potential to improve estimation accuracy of canopy variables, soil properties and yield have been conducted by Jin et al [77].…”

Section: Linking Remote Sensing With Crop Growth Modelsmentioning

confidence: 99%

Contribution of Remote Sensing on Crop Models: A Review

et al. 2018

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Abstract:Crop growth models simulate the relationship between plants and the environment to predict the expected yield for applications such as crop management and agronomic decision making, as well as to study the potential impacts of climate change on food security. A major limitation of crop growth models is the lack of spatial information on the actual conditions of each field or region. Remote sensing can provide the missing spatial information required by crop models for improved yield prediction. This paper reviews the most recent information about remote sensing data and their contribution to crop growth models. It reviews the main types, applications, limitations and advantages of remote sensing data and crop models. It examines the main methods by which remote sensing data and crop growth models can be combined. As the spatial resolution of most remote sensing data varies from sub-meter to 1 km, the issue of selecting the appropriate scale is examined in conjunction with their temporal resolution. The expected future trends are discussed, considering the new and planned remote sensing platforms, emergent applications of crop models and their expected improvement to incorporate automatically the increasingly available remotely sensed products.

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A review of data assimilation of remote sensing and crop models

Cited by 380 publications

References 127 publications

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

An Overview of Global Leaf Area Index (LAI): Methods, Products, Validation, and Applications

Assimilating Remote Sensing Phenological Information into the WOFOST Model for Rice Growth Simulation

Contribution of Remote Sensing on Crop Models: A Review

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