Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery

Xie, Qiaoyun; Dash, Jadunandan; Huete, Alfredo; Jiang, Aihui; Yin, Gaofei; Ding, Yongjian; Peng, Dailiang; Hall, Christopher; Brown, Luke A.; Shi, Yue; Ye, Huichun

doi:10.1016/j.jag.2019.04.019

Cited by 128 publications

(114 citation statements)

References 40 publications

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“…The potential of satellites, such as Sentinel-2, to map crop biophysical variables has been shown by many studies [44,45]. The Sentinel-2 multispectral instrument includes three bands in the red-edge region centered at 705, 740, and 775 nm, which were found to be of great interest for crop monitoring [46].…”

Section: Discussionmentioning

confidence: 99%

Estimating Peanut Leaf Chlorophyll Content with Dorsiventral Leaf Adjusted Indices: Minimizing the Impact of Spectral Differences between Adaxial and Abaxial Leaf Surfaces

et al. 2019

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Relatively little research has assessed the impact of spectral differences among dorsiventral leaves caused by leaf structure on leaf chlorophyll content (LCC) retrieval. Based on reflectance measured from peanut adaxial and abaxial leaves and LCC measurements, this study proposed a dorsiventral leaf adjusted ratio index (DLARI) to adjust dorsiventral leaf structure and improve LCC retrieval accuracy. Moreover, the modified Datt (MDATT) index, which was insensitive to leaves structure, was optimized for peanut plants. All possible wavelength combinations for the DLARI and MDATT formulae were evaluated. When reflectance from both sides were considered, the optimal combination for the MDATT formula was ( R 723 − R 738 ) / ( R 723 − R 722 ) with a cross-validation R2cv of 0.91 and RMSEcv of 3.53 μg/cm2. The DLARI formula provided the best performing indices, which were ( R 735 − R 753 ) / ( R 715 − R 819 ) for estimating LCC from the adaxial surface (R2cv = 0.96, RMSEcv = 2.37 μg/cm2) and ( R 732 − R 754 ) / ( R 724 − R 773 ) for estimating LCC from reflectance of both sides (R2cv = 0.94, RMSEcv = 2.81 μg/cm2). A comparison with published vegetation indices demonstrated that the published indices yielded reliable estimates of LCC from the adaxial surface but performed worse than DLARIs when both leaf sides were considered. This paper concludes that the DLARI is the most promising approach to estimate peanut LCC.

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Section: Discussionmentioning

confidence: 99%

Estimating Peanut Leaf Chlorophyll Content with Dorsiventral Leaf Adjusted Indices: Minimizing the Impact of Spectral Differences between Adaxial and Abaxial Leaf Surfaces

et al. 2019

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“…Chlorophyll Index red-edge ( B7 B5 ) − 1 [32,151,152] Modified Simple Ratio red-edge [32,153,154] Modified Simple Ratio red-edge narrow [32] While less commonly used than other approaches, SMA has successfully assessed burn severity in a few studies [157][158][159]. Quintano et al [158] used a version of SMA called multiple endmember SMA (MESMA) to classify levels of burn severity in three Mediterranean sites located in Spain.…”

Section: Red-edge Spectral Indices Colum Equation Referencesmentioning

confidence: 99%

A Review of the Applications of Remote Sensing in Fire Ecology

2019

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Wildfire plays an important role in ecosystem dynamics, land management, and global processes. Understanding the dynamics associated with wildfire, such as risks, spatial distribution, and effects is important for developing a clear understanding of its ecological influences. Remote sensing technologies provide a means to study fire ecology at multiple scales using an efficient and quantitative method. This paper provides a broad review of the applications of remote sensing techniques in fire ecology. Remote sensing applications related to fire risk mapping, fuel mapping, active fire detection, burned area estimates, burn severity assessment, and post-fire vegetation recovery monitoring are discussed. Emphasis is given to the roles of multispectral sensors, lidar, and emerging UAS technologies in mapping, analyzing, and monitoring various environmental properties related to fire activity. Examples of current and past research are provided, and future research trends are discussed. In general, remote sensing technologies provide a low-cost, multi-temporal means for conducting local, regional, and global-scale fire ecology research, and current research is rapidly evolving with the introduction of new technologies and techniques which are increasing accuracy and efficiency. Future research is anticipated to continue to build upon emerging technologies, improve current methods, and integrate novel approaches to analysis and classification.

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“…Numerous papers have recently shown the suitability of the European Space Agency's (ESA) Sentinel-2 (S2) mission, with its freely available multispectral imagery, to retrieve crop biophysical variables at high spatial resolution including LAI [29][30][31], and to map sub-field variability of crop growth and status as a base for precision farming applications [32][33][34].…”

Section: Introductionmentioning

confidence: 99%

“…A number of publications report on the validation results of the Simplified Level 2 Product Prototype Processor (i.e., a preceding version of the Biophysical Processor that produces similar results, developed in the frame of different ESA project [35]) and found good performances in LAI estimation (RMSE 0.80-0.98) for a range of crops [36,37]. Recent studies investigated the accuracy of biophysical processor LAI estimations in winter wheat [31,38] and durum wheat [38] and found varying results (winter wheat: RMSE 1.16-1.53, durum wheat: RMSE 0.74). The authors of study [31], however, concluded that the tool is applicable for versatile and rapid winter wheat parameter retrieval in areas where ground data are not available.…”

Section: Introductionmentioning

confidence: 99%

“…Recent studies investigated the accuracy of biophysical processor LAI estimations in winter wheat [31,38] and durum wheat [38] and found varying results (winter wheat: RMSE 1.16-1.53, durum wheat: RMSE 0.74). The authors of study [31], however, concluded that the tool is applicable for versatile and rapid winter wheat parameter retrieval in areas where ground data are not available.…”

Section: Introductionmentioning

confidence: 99%

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How Do Methods Assimilating Sentinel-2-Derived LAI Combined with Two Different Sources of Soil Input Data Affect the Crop Model-Based Estimation of Wheat Biomass at Sub-Field Level?

et al. 2020

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The combination of Sentinel-2 derived information about sub-field heterogeneity of crop canopy leaf area index (LAI) and SoilGrids-derived information about local soil properties might help to improve the prediction accuracy of crop simulation models at sub-field level without prior knowledge of detailed site characteristics. In this study, we ran a crop model using either soil texture derived from samples that were taken spatially distributed across a field and analyzed in the lab (AS) or SoilGrids-derived soil texture (SG) as model input in combination with different levels of LAI assimilation. We relied on the LINTUL5 model implemented in the SIMPLACE modeling framework to simulate winter wheat biomass development in 40 to 60 points in each field with detailed measured soil information available, for 14 fields across France, Germany, and the Netherlands during two growing seasons. Water stress was the only growth-limiting factor considered in the model. The model performance was evaluated against total aboveground biomass measurements at harvest with regard to the average per-field prediction and the simulated spatial variability within the field. Our findings showed that a) per-field average biomass predictions of SG-based modeling approaches were not inferior to those using AS-texture as input, but came with a greater prediction uncertainty, b) relying on the generation of an ensemble without LAI assimilation might produce results as accurate as simulations where LAI is assimilated, and c) sub-field heterogeneity was not reproduced well in any of the fields, predominantly because of an inaccurate simulation of water stress in the model. We conclude that research should be devoted to the testing of different approaches to simulate soil moisture dynamics and to the testing in other sites, potentially using LAI products derived from other remotely sensed imagery.

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Retrieval of crop biophysical parameters from Sentinel-2 remote sensing imagery

Cited by 128 publications

References 40 publications

Estimating Peanut Leaf Chlorophyll Content with Dorsiventral Leaf Adjusted Indices: Minimizing the Impact of Spectral Differences between Adaxial and Abaxial Leaf Surfaces

Estimating Peanut Leaf Chlorophyll Content with Dorsiventral Leaf Adjusted Indices: Minimizing the Impact of Spectral Differences between Adaxial and Abaxial Leaf Surfaces

A Review of the Applications of Remote Sensing in Fire Ecology

How Do Methods Assimilating Sentinel-2-Derived LAI Combined with Two Different Sources of Soil Input Data Affect the Crop Model-Based Estimation of Wheat Biomass at Sub-Field Level?

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