Modeling Winter Wheat Leaf Area Index and Canopy Water Content With Three Different Approaches Using Sentinel-2 Multispectral Instrument Data

Pan, Haizhu; Chen, Zhongxin; Ren, Jianqiang; He, Li; Wu, Shangrong

doi:10.1109/jstars.2018.2855564

Cited by 43 publications

(39 citation statements)

References 38 publications

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“…The best performing VIs were constructed with wavelengths centered at visible (green-565 nm) and red edge (715, 725, 735 nm) when using the FSP_10 vs. Field_LAI dataset; red edge (705, 740 nm) and near infrared (865 nm) when using the FSP_S2 vs. Field_LAI dataset; red (665 nm), red edge (783 nm), and near infrared (865 nm) when using the SP_S2 vs. Field_LAI; and green (560 nm), red (665 nm), red edge (705, 783 nm), and near infrared (842 nm) when using the SP_S2 vs. LAI_S2. These findings are similar to those of previous studies for estimating LAI in various crops, e.g., [74] used red edge bands (690-710 nm and 750-900 nm), [75] combined green (580 nm), red edge (700 nm and 710 nm), and near infrared wavelengths (1003 nm); [59] also combined green (550 nm), red (670 nm), and near infrared (800 nm) wavelengths; [76] considered blue (460-480 nm), green (545-565 nm), or red edge (700-710 nm), and red (660-680 nm) wavelengths; and [77] considered red (665 nm), red edge (705, 740 and 783 nm), and near infrared wavelengths (842 nm). Additionally, and as equally found by [14], the three band VIs outperformed the two band VIs formulation for all the datasets.…”

Section: Discussionmentioning

confidence: 99%

“…For the hyperspectral dataset, a 10 nm spectral resolution aggregation (FSP_10) was considered while for the multispectral dataset, the Sentinel-2 band setting was used for aggregation (FSP_S2). Because the field spectra does not match the whole Sentinel-2 spectra, the aggregation was limited to 8 bands (2 A-8 A) acknowledged as being more convenient in LAI estimations [2,29]. Table 3 presents the spectral vegetation indices (VI) assessed as predictors for LAI statistical modelling.…”

Section: Field Spectral Data Pre-processingmentioning

confidence: 99%

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Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data

2018

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Field spectra acquired from a handheld spectroradiometer and Sentinel-2 images spectra were used to investigate the applicability of hyperspectral and multispectral data in retrieving the maize leaf area index in low-input crop systems, with high spatial and intra-annual variability, and low yield, in southern Mozambique, during three years. Seventeen vegetation indices, comprising two and three band indices, and nine machine learning regression algorithms (MLRA) were tested for the statistical approach while five cost functions were tested in the look-up-table (LUT) inversion approach. The three band vegetation indices were selected, specifically the modified difference index (mDId: 725; 715; 565) for the hyperspectral dataset and the modified simple ratio (mSRc: 740; 705; 865) for the multispectral dataset of field spectra and the three band spectral index (TBSIb: 665; 865; 783) for the Sentinel-2 dataset. The relevant vector machine was the selected MLRA for the two datasets of field spectra (multispectral and hyperspectral) while the support vector machine was selected for the Sentinel-2 data. When using the LUT inversion technique, the minimum contrast estimation and the Bhattacharyya divergence cost functions were the best performing. The vegetation indices outperformed the other two approaches, with the TBSIb as the most accurate index (RMSE = 0.35). At the field scale, spectral data from Sentinel-2 can accurately retrieve the maize leaf area index in the study area.

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Section: Discussionmentioning

confidence: 99%

Section: Field Spectral Data Pre-processingmentioning

confidence: 99%

Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data

2018

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“…Optical remote sensing is one of the most attractive options; in particular, Landsat series data have potential in land characterization applications due to their spatial, spectral, and radiometric qualities [2][3][4][5]. Furthermore, the Sentinel-2 satellites have contributed to create greater opportunities for monitoring plant constituents, such as pigments, leaf water contents, and biochemicals [6,7], and the vegetation indices calculated from Sentinel-2 Multispectral Instrument (MSI) data were useful to identify the specific crop types [8,9]. However, optical data are influenced by atmospheric or weather conditions, and the number of available scenes may be restricted.…”

Section: Introductionmentioning

confidence: 99%

Parcel-Based Crop Classification Using Multi-Temporal TerraSAR-X Dual Polarimetric Data

Sonobe

2019

Remote Sensing

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Cropland maps are useful for the management of agricultural fields and the estimation of harvest yield. Some local governments have documented field properties, including crop type and location, based on site investigations. This process, which is generally done manually, is labor-intensive, and remote-sensing techniques can be used as alternatives. In this study, eight crop types (beans, beetroot, grass, maize, potatoes, squash, winter wheat, and yams) were identified using gamma naught values and polarimetric parameters calculated from TerraSAR-X (or TanDEM-X) dual-polarimetric (HH/VV) data. Three indices (difference (D-type), simple ratio (SR), and normalized difference (ND)) were calculated using gamma naught values and m-chi decomposition parameters and were evaluated in terms of crop classification. We also evaluated the classification accuracy of four widely used machine-learning algorithms (kernel-based extreme learning machine, support vector machine, multilayer feedforward neural network (FNN), and random forest) and two multiple-kernel methods (multiple kernel extreme learning machine (MKELM) and multiple kernel learning (MKL)). MKL performed best, achieving an overall accuracy of 92.1%, and proved useful for the identification of crops with small sample sizes. The difference (raw or normalized) between double-bounce scattering and odd-bounce scattering helped to improve the identification of squash and yams fields.

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“…The recent deployment of (European Space Agency) ESA’s Sentinel satellites has established a new paradigm for agricultural applications. The optical satellite Sentinel-2 (S2) provides well-suited spectral and temporal data for LAI retrievals at a high resolution [33,34]. The Sentinel-1 (S1) satellite with a C band SAR (synthetic aperture radar) sensor also has demonstrated its ability to retrieve SM over vegetation-covered surfaces [35,36].…”

Section: Introductionmentioning

confidence: 99%

Joint Assimilation of Leaf Area Index and Soil Moisture from Sentinel-1 and Sentinel-2 Data into the WOFOST Model for Winter Wheat Yield Estimation

Pan

Chen

Wit

et al. 2019

Sensors

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It is well known that timely crop growth monitoring and accurate crop yield estimation at a fine scale is of vital importance for agricultural monitoring and crop management. Crop growth models have been widely used for crop growth process description and yield prediction. In particular, the accurate simulation of important state variables, such as leaf area index (LAI) and root zone soil moisture (SM), is of great importance for yield estimation. Data assimilation is a useful tool that combines a crop model and external observations (often derived from remote sensing data) to improve the simulated crop state variables and consequently model outputs like crop total biomass, water use and grain yield. In spite of its effectiveness, applying data assimilation for monitoring crop growth at the regional scale in China remains challenging, due to the lack of high spatiotemporal resolution satellite data that can match the small field sizes which are typical for agriculture in China. With the accessibility of freely available images acquired by Sentinel satellites, it becomes possible to acquire data at high spatiotemporal resolution (10–30 m, 5–6 days), which offers attractive opportunities to characterize crop growth. In this study, we assimilated remotely sensed LAI and SM into the Word Food Studies (WOFOST) model to estimate winter wheat yield using an ensemble Kalman filter (EnKF) algorithm. The LAI was calculated from Sentinel-2 using a lookup table method, and the SM was calculated from Sentinel-1 and Sentinel-2 based on a change detection approach. Through validation with field data, the inverse error was 10% and 35% for LAI and SM, respectively. The open-loop wheat yield estimation, independent assimilations of LAI and SM, and a joint assimilation of LAI + SM were tested and validated using field measurement observation in the city of Hengshui, China, during the 2016–2017 winter wheat growing season. The results indicated that the accuracy of wheat yield simulated by WOFOST was significantly improved after joint assimilation at the field scale. Compared to the open-loop estimation, the yield root mean square error (RMSE) with field observations was decreased by 69 kg/ha for the LAI assimilation, 39 kg/ha for the SM assimilation and 167 kg/ha for the joint LAI + SM assimilation. Yield coefficients of determination (R2) of 0.41, 0.65, 0.50, and 0.76 and mean relative errors (MRE) of 4.87%, 4.32%, 4.45% and 3.17% were obtained for open-loop, LAI assimilation alone, SM assimilation alone and joint LAI + SM assimilation, respectively. The results suggest that LAI was the first-choice variable for crop data assimilation over SM, and when both LAI and SM satellite data are available, the joint data assimilation has a better performance because LAI and SM have interacting effects. Hence, joint assimilation of LAI and SM from Sentinel-1 and Sentinel-2 at a 20 m resolution into the WOFOST provides a robust method to improve crop yield estimations. However, there is still bias between the key soil moisture in the root zone and the Sentinel-1 C band retrieved SM, especially when the vegetation cover is high. By active and passive microwave data fusion, it may be possible to offer a higher accuracy SM for crop yield prediction.

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Modeling Winter Wheat Leaf Area Index and Canopy Water Content With Three Different Approaches Using Sentinel-2 Multispectral Instrument Data

Cited by 43 publications

References 38 publications

Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data

Retrieval of Maize Leaf Area Index Using Hyperspectral and Multispectral Data

Parcel-Based Crop Classification Using Multi-Temporal TerraSAR-X Dual Polarimetric Data

Joint Assimilation of Leaf Area Index and Soil Moisture from Sentinel-1 and Sentinel-2 Data into the WOFOST Model for Winter Wheat Yield Estimation

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