Assimilation of LAI Derived from UAV Multispectral Data into the SAFY Model to Estimate Maize Yield

Peng, Xingshuo; Han, Wenting; Ao, Jianyi; Wang, Yi

doi:10.3390/rs13061094

Cited by 43 publications

(39 citation statements)

References 61 publications

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“…Crop models in general, including SAFY, are reported to perform poorly when yield is greatly limited by severe stress [48]. Lower yields can result from limitations that are rarely simulated by this kind of dynamic crop model, such as weeds, pests and diseases [49,50]. In another field the model showed poor performance despite the assimilation, simulating 2.7 t•ha −1 of grain yield with respect to the 5.7 t•ha −1 measured at harvest (Figure 7).…”

Section: Coupling Eo Data With Crop Models For Yield Predictionmentioning

confidence: 99%

Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources

et al. 2021

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Novel approaches and algorithms to estimate crop physiological processes from Earth Observation (EO) data are essential to develop more sustainable management practices in agricultural systems. Within this context, this paper presents the results of different research activities carried out within the ESA-MOST Dragon 4 programme. The paper encompasses two research avenues: (a) the retrieval of biophysical variables of crops and yield prediction; and (b) food security related to different crop management strategies. Concerning the retrieval of variables, results show that LAI, derived by radiative transfer model (RTM) inversion, when assimilated into a crop growth model (i.e., SAFY) provides a way to assess yields with a higher accuracy with respect to open loop model runs: 1.14 t·ha−1 vs 4.42 t·ha−1 RMSE for assimilation and open loop, respectively. Concerning food security, results show that different pathogens could be detected by remote sensing satellite data. A k coefficient higher than 0.84 was achieved for yellow rust, thus assuring a monitoring accuracy, and for the diseased samples k was higher than 0.87. Concerning permanent crops, neural network (NN) algorithms allow classification of the Pseudomonas syringae pathogen on kiwi orchards with an overall accuracy higher than 91%.

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Section: Coupling Eo Data With Crop Models For Yield Predictionmentioning

confidence: 99%

Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources

et al. 2021

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“…with empirical parameters, and hence simplifies the process of crop growth modeling. This model avoids the limitations of earlier models and is more applicable under universal conditions 49 – 51 .…”

Section: Introductionmentioning

confidence: 97%

Wheat growth monitoring and yield estimation based on remote sensing data assimilation into the SAFY crop growth model

Liu²,

Ding

et al. 2022

Sci Rep

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Crop growth monitoring and yield estimate information can be obtained via appropriate metrics such as the leaf area index (LAI) and biomass. Such information is crucial for guiding agricultural production, ensuring food security, and maintaining sustainable agricultural development. Traditional methods of field measurement and monitoring typically have low efficiency and can only give limited untimely information. Alternatively, methods based on remote sensing technologies are fast, objective, and nondestructive. Indeed, remote sensing data assimilation and crop growth modeling represent an important trend in crop growth monitoring and yield estimation. In this study, we assimilate the leaf area index retrieved from Sentinel-2 remote sensing data for crop growth model of the simple algorithm for yield estimation (SAFY) in wheat. The SP-UCI optimization algorithm is used for fine-tuning for several SAFY parameters, namely the emergence date (D0), the effective light energy utilization rate (ELUE), and the senescence temperature threshold (STT) which is indicative of biological aging. These three sensitive parameters are set in order to attain the global minimum of an error function between the SAFY model predicted values and the LAI inversion values. This assimilation of remote sensing data into the crop growth model facilitates the LAI, biomass, and yield estimation. The estimation results were validated using data collected from 48 experimental plots during 2014 and 2015. For the 2014 data, the results showed coefficients of determination (R2) of the LAI, biomass and yield of 0.73, 0.83 and 0.49, respectively, with corresponding root-mean-squared error (RMSE) values of 0.72, 1.13 t/ha and 1.14 t/ha, respectively. For the 2015 data, the estimated R2 values of the LAI, biomass, and yield were 0.700, 0.85, and 0.61, respectively, with respective RMSE values of 0.83, 1.22 t/ha, and 1.39 t/ha, respectively. The estimated values were found to be in good agreement with the measured ones. This shows high applicability of the proposed data assimilation scheme in crop monitoring and yield estimation. As well, this scheme provides a reference for the assimilation of remote sensing data into crop growth models for regional crop monitoring and yield estimation.

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“…Among the wide range of crop models, the Simple Algorithm For Yield estimates (SAFY; [32]) is particularly interesting for its simplicity which gives it a fast computational speed facilitating the development of decision support tools. Moreover it has been successfully employed in contrasted climatic conditions for the estimation of wheat [33]- [36], barley [33], maize [25], [37]- [40], sunflower [19], [39], [41] or soybean [26], [42] DM and yields using recalibration or updating assimilation techniques. However, it has never been applied on rapeseed fields.…”

Section: Introductionmentioning

confidence: 99%

Assimilation of Multisensor Optical and Multiorbital SAR Satellite Data in a Simplified Agrometeorological Model for Rapeseed Crops Monitoring

Alliès¹,

Roumiguié²,

Fieuzal³

et al. 2022

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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This paper investigates the potential of the assimilation of both Synthetic Aperture Radar (SAR) -derived Dry Mass (DM) and optically-derived Green Area Index (GAI) in an agro-meteorological model (i.e. SAFY) for a better rapeseed crops modeling. The GAI was derived from both 566 Sentinel-2 and 149 Landsat-8 images, whereas DM was derived from 884 Sentinel-1 images acquired from six different orbits. The ground data were collected during 3 agricultural years on 43 rapeseed fields located in three study areas in France with contrasted pedoclimatic conditions. Results show that the temporal evolutions of both DM and GAI can be accurately simulated over the 43 monitored rapeseed fields (R 2 = 0.84 and 0.92, respectively and relative Root Mean Square Error, RMSEr = 41 % and 28 %, respectively) for the best satellite configuration. Tested assimilation scenarios reveal that the concomitant assimilation of SAR and optical data allows a significant better control of the model than the assimilation of SAR or optical data alone. Small differences in the simulations of the model are observed when it is controlled by either multisensor optical and/or multiorbital SAR data or monosensor optical and/or mono-orbital SAR data. The discrepancies of performance between fields push towards the strengthening of this study by considering other rapeseed fields with ground observations acquired worldwide for the calibration of SARderived DM. These results are however promising in view of the development of a near-real time assimilation scheme as a decision support tool for farmers and decision makers.

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Assimilation of LAI Derived from UAV Multispectral Data into the SAFY Model to Estimate Maize Yield

Cited by 43 publications

References 61 publications

Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources

Sino–EU Earth Observation Data to Support the Monitoring and Management of Agricultural Resources

Wheat growth monitoring and yield estimation based on remote sensing data assimilation into the SAFY crop growth model

Assimilation of Multisensor Optical and Multiorbital SAR Satellite Data in a Simplified Agrometeorological Model for Rapeseed Crops Monitoring

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