Temporal Evolution of Corn Mass Production Based on Agro-Meteorological Modelling Controlled by Satellite Optical and SAR Images

Baup, Frédéric; Ameline, Maël; Fieuzal, Rémy; Frappart, Frédéric; Corgne, Samuel; Berthoumieu, Jean-François

doi:10.3390/rs11171978

Cited by 13 publications

(9 citation statements)

References 52 publications

(82 reference statements)

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“…Consequently, the possibility to drive these two dependent variables by means of independent time series (SAR-derived DM and optically-derived GAI) should provide the right conditions for optimizing rapeseed monitoring and yields modeling. Benefits of combined assimilation of optically-derived GAI and SAR-derived DM in agrometeorological models have been already proved for maize [25], [26], soybean [50], [51] or sunflower [52] but remained to be demonstrated for rapeseed. Besides, the analysis of residuals distribution by phenological stages carried out in section III.D offered the opportunity to develop an assimilation strategy by periods through a weighing scheme according to the confidence in SAR and/or optical -based relationship for each phenological stage.…”

Section: Complementarity and Potential Of Combined Sar/opticalderived Bp For Monitoring And Modeling Fields Of Rapeseedmentioning

confidence: 99%

Evaluation of Multiorbital SAR and Multisensor Optical Data for Empirical Estimation of Rapeseed Biophysical Parameters

Alliès

Roumiguié

Dejoux

et al. 2021

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

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This paper aims to evaluate the potential of multitemporal and multi-orbital remote sensing data acquired both in the microwave and optical domain to derive rapeseed biophysical parameters (crop height, dry mass, fresh mass and plant water content). Dense temporal series of 98 Landsat-8 and Sentinel-2 images were used to derive Normalized Difference Vegetation Index (NDVI), green fraction cover (fCover) and Green Area Index (GAI), while backscattering coefficients and radar vegetation index (RVI) were obtained from 231 mages acquired by Synthetic Aperture Radar (SAR) onboard Sentinel-1 platform. Temporal signatures of these Remote Sensing Indicators (RSI) were physically interpreted, compared each other and to ground measurements of biophysical parameters acquired over 14 winter rapeseed fields throughout the 2017-2018 crop season. We introduced new indicators based on the cumulative sum of each RSI that showed a significant improvement of their predictive power. Results particularly reveal the complementarity of SAR and optical data for rapeseed crop monitoring throughout its phenological cycle. They highlight the potential of the newly introduced indicator based on: the VH polarized backscatter coefficient to estimate height (R 2 = 0.87), plant water content (R 2 = 0.77, from flowering to harvest) and fresh mass (R 2 = 0.73) and RVI to estimate dry mass (R 2 = 0.82). Results also demonstrate that multi-orbital SAR data can be merged without significantly degrading the performance of SAR-based relationships, while strongly increasing the temporal sampling of the monitoring. These results are

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Section: Complementarity and Potential Of Combined Sar/opticalderived Bp For Monitoring And Modeling Fields Of Rapeseedmentioning

confidence: 99%

Evaluation of Multiorbital SAR and Multisensor Optical Data for Empirical Estimation of Rapeseed Biophysical Parameters

Alliès

Roumiguié

Dejoux

et al. 2021

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

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“…[25] and [38] have already demonstrated the benefits of the simultaneous assimilation of optical and SAR data into SAFY for maize monitoring. They showed better results for DM and yield retrieval by the combined assimilation of LAI derived from Landsat-8 and LAI derived from backscatter coefficients of Sentinel-1.…”

Section: A Benefits Of the Concomitant Assimilation Of Sar And Optical Data And Possible Improvement Of The Modelmentioning

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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“…This constraint is partially mitigated because of the ongoing Sentinel missions that will provide high spatial-(10 m) and temporal-resolution (5 days) images all across the globe. Additionally, the assimilation of the C-band synthetic-aperture radar (SAR) from Sentinel-1 should be considered to lift this constraint, as shown in many studies [71][72][73][74]. Finally, thermal-infrared remote sensing, which allows the estimation of vegetation water stress [75], could be considered to better constrain the model.…”

Section: Limitations and Potential Improvementsmentioning

confidence: 99%

Combining High-Resolution Remote Sensing Products with a Crop Model to Estimate Carbon and Water Budget Components: Application to Sunflower

et al. 2020

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The global increase in food demand in the context of climate change requires a clear understanding of cropland function and of its impact on biogeochemical cycles. However, although gas exchange between croplands and the atmosphere is measurable in the field, it is difficult to quantify at the plot scale over relatively large areas because of the heterogeneous character of landscapes and differences in crop management. However, assessing accurate carbon and water budgets over croplands is essential to promote sustainable agronomic practices and reduce the water demand and the climatic impacts of croplands while maintaining sufficient yields. From this perspective, we developed a crop model, SAFYE-CO2, that assimilates high spatial- and temporal-resolution (HSTR) remote sensing products to estimate daily crop biomass, water and CO2 fluxes, annual yields, and carbon budgets at the parcel level over large areas. This modeling approach was evaluated for sunflower against two in situ datasets. First, the model’s output was compared to data acquired during two cropping seasons at the Auradé integrated carbon observation system (ICOS) instrumented site in southwestern France. The model accurately simulated the daily net CO2 flux (root mean square error (RMSE) = 0.97 gC·m−2·d−1 and determination coefficient (R2) = 0.83) and water flux (RMSE = 0.68 mm·d−1 and R2 = 0.79). The model’s performance was then evaluated against biomass and yield data collected from 80 plots located in southwestern France. The model was able to satisfactorily estimate biomass dynamics and yield (RMSE = 66 and 54 g·m−2, respectively). To investigate the potential application of the proposed approach at a large scale, given that soil properties are important factors affecting the model, a sensitivity analysis of two existing soil products (GlobalSoilMap and SoilGrids) was carried out. Our results show that these products are not sufficiently accurate for inclusion as inputs to the model, which requires more accurate information on soil water retention capacity to assess water fluxes. Additionally, we argue that no water stress should be considered in the crop growth computation since this stress is already present because of remote sensing information in the proposed approach. This study should be considered a first step to fulfill the existing gap in quantifying carbon budgets at the plot scale over large areas and to accurately estimate the effects of management practices, such as the use of cover crops or specific crop rotations on cropland C and water budgets.

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Temporal Evolution of Corn Mass Production Based on Agro-Meteorological Modelling Controlled by Satellite Optical and SAR Images

Cited by 13 publications

References 52 publications

Evaluation of Multiorbital SAR and Multisensor Optical Data for Empirical Estimation of Rapeseed Biophysical Parameters

Evaluation of Multiorbital SAR and Multisensor Optical Data for Empirical Estimation of Rapeseed Biophysical Parameters

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

Combining High-Resolution Remote Sensing Products with a Crop Model to Estimate Carbon and Water Budget Components: Application to Sunflower

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