Assimilation of coupled microwave/thermal infrared soil moisture profiles into a crop model for robust maize yield estimates over Southeast United States

Mishra, Vikalp; Cruise, J. F.; Mecikalski, John R.

doi:10.1016/j.eja.2020.126208

Cited by 22 publications

(11 citation statements)

References 74 publications

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“…In traditional breeding, selection procedures have been developed to identify and subsequently multiply maize verities with improved heat tolerance ( Gong et al, 2015 ; Gedil and Menkir, 2019 ). Breeding heat-tolerant varieties is an effective strategy for improving heat tolerance in the spring maize grain-filling stage ( Mishra et al, 2021 ). Many maize cultivars have been screened for canopy structure, flag leaf stomata, and rate of photosynthesis to obtain maximum yield and heat tolerance ( Sah et al, 2020 ).…”

Section: Approaches For Improving Thermotolerancementioning

confidence: 99%

Heat Stress-Mediated Constraints in Maize (Zea mays) Production: Challenges and Solutions

et al. 2022

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Graphical AbstractThis review summarized heat stress-mediated morphological and physiological changes in maize and elucidated the molecular mechanisms responsible for maize response to heat stress. Furthermore, plausible approaches to dissecting the regulatory network associated with heat stress response and improving maize adaptation to global warming have been discussed. This figure was made using BioRender.

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Section: Approaches For Improving Thermotolerancementioning

confidence: 99%

Heat Stress-Mediated Constraints in Maize (Zea mays) Production: Challenges and Solutions

et al. 2022

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“…The paucity of spatial field observations within the field, or at a regional scale, can be partly overcome by remotely sensed observations [9]. The accuracy of crop yield estimation has improved thanks to the assimilation of remote sensing observation into crop growth models at field [10,11] and regional scales [8,[12][13][14]. Data assimilation exploits the advantages of process-based crop growth models in physiological simulations of crop growth and development in combination with remote sensing data that provide near-continuous estimation of land surface variables during the entire growing season at different spatial and temporal scales [13,[15][16][17].…”

Section: Introductionmentioning

confidence: 99%

Wheat Yield Estimation at High Spatial Resolution through the Assimilation of Sentinel-2 Data into a Crop Growth Model

Bouras,

Olsson,

Thapa

et al. 2023

Remote Sensing

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Monitoring crop growth and estimating crop yield are essential for managing agricultural production, ensuring food security, and maintaining sustainable agricultural development. Combining the mechanistic framework of a crop growth model with remote sensing observations can provide a means of generating realistic and spatially detailed crop growth information that can facilitate accurate crop yield estimates at different scales. The main objective of this study was to develop a robust estimation methodology of within-field winter wheat yield at a high spatial resolution (20 m × 20 m) by combining a light use efficiency-based model and Sentinel-2 data. For this purpose, Sentinel-2 derived leaf area index (LAI) time series were assimilated into the Simple Algorithm for Yield Estimation (SAFY) model using an ensemble Kalman filter (EnKF). The study was conducted on rainfed winter wheat fields in southern Sweden. LAI was estimated using vegetation indices (VIs) derived from Sentinel-2 data with semi-empirical models. The enhanced two-band vegetation index (EVI2) was found to be a useful VI for LAI estimation, with a coefficient of determination (R2) and a root mean square error (RMSE) of 0.80 and 0.65 m2/m2, respectively. Our findings demonstrate that the assimilation of LAI derived from Sentinel-2 into the SAFY model using EnKF enhances the estimation of within-field spatial variability of winter wheat yield by 70% compared to the baseline simulation without the assimilation of remotely sensed data. Additionally, the assimilation of LAI improves the accuracy of winter wheat yield estimation by decreasing the RMSE by 53%. This study demonstrates an approach towards practical applications of freely accessible Sentinel-2 data and a crop growth model through data assimilation for fine-scale mapping of crop yield. Such information is critical for quantifying the yield gap at the field scale, and to aid the optimization of management practices to increase crop production.

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“…Given the population growth, the demand for food supplies is increasing all over the world ( Mishra et al., 2021 ). Furthermore, limited arable land and frequent extreme weather events have resulted in significant stress on food security.…”

Section: Introductionmentioning

confidence: 99%

Evaluating how lodging affects maize yield estimation based on UAV observations

Liu

Nie²,

Zhang³

et al. 2023

Front. Plant Sci.

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Timely and accurate pre-harvest estimates of maize yield are vital for agricultural management. Although many remote sensing approaches have been developed to estimate maize yields, few have been tested under lodging conditions. Thus, the feasibility of existing approaches under lodging conditions and the influence of lodging on maize yield estimates both remain unclear. To address this situation, this study develops a lodging index to quantify the degree of lodging. The index is based on RGB and multispectral images obtained from a low-altitude unmanned aerial vehicle and proves to be an important predictor variable in a random forest regression (RFR) model for accurately estimating maize yield after lodging. The results show that (1) the lodging index accurately describes the degree of lodging of each maize plot, (2) the yield-estimation model that incorporates the lodging index provides slightly more accurate yield estimates than without the lodging index at three important growth stages of maize (tasseling, milking, denting), and (3) the RFR model with lodging index applied at the denting (R5) stage yields the best performance of the three growth stages, with R2 = 0.859, a root mean square error (RMSE) of 1086.412 kg/ha, and a relative RMSE of 13.1%. This study thus provides valuable insight into the precise estimation of crop yield and demonstra\tes that incorporating a lodging stress-related variable into the model leads to accurate and robust estimates of crop grain yield.

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Assimilation of coupled microwave/thermal infrared soil moisture profiles into a crop model for robust maize yield estimates over Southeast United States

Cited by 22 publications

References 74 publications

Heat Stress-Mediated Constraints in Maize (Zea mays) Production: Challenges and Solutions

Heat Stress-Mediated Constraints in Maize (Zea mays) Production: Challenges and Solutions

Wheat Yield Estimation at High Spatial Resolution through the Assimilation of Sentinel-2 Data into a Crop Growth Model

Evaluating how lodging affects maize yield estimation based on UAV observations

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