Multi-Temporal Detection of Rice Phenological Stages Using Canopy Spectrum

Lin, Wang; Zhang, Fucun; Jing, Yuanshu; Jiang, Xiaodong; Yang, Shuangyan; Xiao, Han

doi:10.1016/s1672-6308(13)60170-5

Cited by 28 publications

(14 citation statements)

References 31 publications

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“…The NDVI values and their differences gradually decreased afterwards and became very weak after 160 DAS, presumably due to the differences in LAI generated by the planting density. This was consistent with the conclusion reached by Suryanarayana et al on the wheat NDVI value study 26 . In addition, the NDVI value increased with the increase of LOV from plant types of planophile to erectophile.…”

Section: Changes In Ndvi Lai and K Of Various Plant Types At Differesupporting

confidence: 94%

“…NDVI is one of the most important vegetation indices in vegetation remote sensing 24,26 . It has a close relationship with plant parameters such as crop LAI 28 .…”

Section: Discussionmentioning

confidence: 99%

“…The NDVI is well correlated with LAI and is more sensitive to changes in the crop canopy when the LAI is low (during the early stage), with the signal becoming saturated when the crop canopy closes 24,25 . Through NDVI from spectroradiometer and its slope curves to monitor rice development, the results demonstrate that they could be used as cultivar-independent phenological indicators 26 . However, using NDVI to conduct research on light distribution in crop populations is still barely reported.…”

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confidence: 97%

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Quantitative monitoring of leaf area index in wheat of different plant types by integrating NDVI and Beer-Lambert law

Tan

Zhang

Zhou

et al. 2020

Sci Rep

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Normalized difference vegetation index (NDVI) is one of the most important vegetation indices in crop remote sensing. It features a simple, fast, and non-destructive method and has been widely used in remote monitoring of crop growing status. Beer-Lambert law is widely used in calculating crop leaf area index (LAI), however, it is time-consuming detection and low in output. Our objective was to improve the accuracy of monitoring LAI through remote sensing by integrating NDVI and Beer-Lambert law. In this study, the Beer-Lambert law was firstly modified to construct a monitoring model with NDVI as the independent variable. Secondly, experimental data of wheat from different years and various plant types (erectophile, planophile and middle types) was used to validate the modified model. The results showed that at 130 DAS (days after sowing), the differences in NDVI, leaf area index (LAI) and extinction coefficient (k) of the three plant types with significantly different leaf orientation values (LOVs) reached the maximum. The NDVI of the planophile-type wheat reached saturation earlier than that of the middle and erectophile types. The undetermined parameters of the model (LAI = −ln (a 1 × nDVi + b 1)/(a 2 × nDVi + b 2)) were related to the plant type of wheat. For the erectophiletype cultivars (LOV ≥ 60°), the parameters for the modified model were, a 1 = 0.306, a 2 = −0.534, b 1 = −0.065, and b 2 = 0.541. For the middle-type cultivars (30° < LoV < 60°), the parameters were, a 1 = 0.392, a 2 = −0.88 1 , b 1 = 0.028, and b 2 = 0.845. And for the planophile-type cultivars (LOV ≤ 30°), those parameters were, a 1 = 0.596, a 2 = −1.306, b 1 = 0.014, and b 2 = 1.130. Verification proved that the modified model based on integrating NDVI and Beer-Lambert law was better than Beer-Lambert law model only or NDVI-LAI direct model only. It was feasible to quantitatively monitor the LAI of different plant-type wheat by integrating NDVI and Beer-Lambert law, especially for erectophile-type wheat (R 2 = 0.905, RMSE = 0.36, RE = 0.10). The monitoring model proposed in this study can accurately reflect the dynamic changes of plant canopy structure parameters, and provides a novel method for determining plant LAI. The leaf area index (LAI), the leaf orientation value (LOV), and the extinction coefficient (k) are important structural parameters of crop populations. By affecting light distribution, they directly affect crop photosynthetic efficiency, and ultimately show an impact on crop biological yield and its distribution in various plant organs 1. Remote sensing technology could provide a practical method for crop LAI estimation, rather than a slow, expensive and complicated chemical method. The advantage of the remote-sensing method is that it can obtain plant canopy information on a large scale without disrupting the normal growth of plants 2,3. Studies using remote sensing to monitor agronomic parameters have been extended from crop soils 4-6 , to fresh leaves 7-9 and entire

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Section: Changes In Ndvi Lai and K Of Various Plant Types At Differesupporting

confidence: 94%

“…NDVI is one of the most important vegetation indices in vegetation remote sensing 24,26 . It has a close relationship with plant parameters such as crop LAI 28 .…”

Section: Discussionmentioning

confidence: 99%

mentioning

confidence: 97%

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Quantitative monitoring of leaf area index in wheat of different plant types by integrating NDVI and Beer-Lambert law

Tan

Zhang

Zhou

et al. 2020

Sci Rep

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“…An essential approach to rice plantation detection came with phenological behavior analysis, which requires a dense time series to distinguish it from other crops or between different rice-growing systems. In the optical image time series, various sensors have been evaluated in mapping rice planting: Landsat Series (Terrestrial Remote Sensing Satellite) [16][17][18], NOAA AVHRR [19][20][21], SPOT [22][23][24], and MODIS [25][26][27][28][29][30]. However, optical data analysis requires sensors with a high temporal resolution to acquire enough cloudless images to create reliable time series.…”

Section: Introductionmentioning

confidence: 99%

Rice Crop Detection Using LSTM, Bi-LSTM, and Machine Learning Models from Sentinel-1 Time Series

et al. 2020

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The Synthetic Aperture Radar (SAR) time series allows describing the rice phenological cycle by the backscattering time signature. Therefore, the advent of the Copernicus Sentinel-1 program expands studies of radar data (C-band) for rice monitoring at regional scales, due to the high temporal resolution and free data distribution. Recurrent Neural Network (RNN) model has reached state-of-the-art in the pattern recognition of time-sequenced data, obtaining a significant advantage at crop classification on the remote sensing images. One of the most used approaches in the RNN model is the Long Short-Term Memory (LSTM) model and its improvements, such as Bidirectional LSTM (Bi-LSTM). Bi-LSTM models are more effective as their output depends on the previous and the next segment, in contrast to the unidirectional LSTM models. The present research aims to map rice crops from Sentinel-1 time series (band C) using LSTM and Bi-LSTM models in West Rio Grande do Sul (Brazil). We compared the results with traditional Machine Learning techniques: Support Vector Machines (SVM), Random Forest (RF), k-Nearest Neighbors (k-NN), and Normal Bayes (NB). The developed methodology can be subdivided into the following steps: (a) acquisition of the Sentinel time series over two years; (b) data pre-processing and minimizing noise from 3D spatial-temporal filters and smoothing with Savitzky-Golay filter; (c) time series classification procedures; (d) accuracy analysis and comparison among the methods. The results show high overall accuracy and Kappa (>97% for all methods and metrics). Bi-LSTM was the best model, presenting statistical differences in the McNemar test with a significance of 0.05. However, LSTM and Traditional Machine Learning models also achieved high accuracy values. The study establishes an adequate methodology for mapping the rice crops in West Rio Grande do Sul.

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“…Canopy hyperspectral (HS) reflectance measurements are increasingly being used as a reliable method for the timely assessment of crop growth and nutritive status [8][9][10]. The timing of the measurements is crucial for the in-season assessment of grain yield because growth stages strongly influence the sensitivity to different wavelengths and the prediction performance [11][12][13]. The spectral signature responds to changes in aboveground biomass (BM), more precisely, leaf area index (LAI) and chlorophyll contents [14].…”

Section: Introductionmentioning

confidence: 99%

Canopy Hyperspectral Sensing of Paddy Fields at the Booting Stage and PLS Regression can Assess Grain Yield

et al. 2018

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Canopy hyperspectral (HS) sensing is a promising tool for estimating rice (Oryza sativa L.) yield. However, the timing of HS measurements is crucial for assessing grain yield prior to harvest because rice growth stages strongly influence the sensitivity to different wavelengths and the evaluation performance. To clarify the optimum growth stage for HS sensing-based yield assessments, the grain yield of paddy fields during the reproductive phase to the ripening phase was evaluated from field HS data in conjunction with iterative stepwise elimination partial least squares (ISE-PLS) regression. The field experiments involved three different transplanting dates (12 July, 26 July, and 9 August) in 2017 for six cultivars with three replicates (n = 3 × 6 × 3 = 54). Field HS measurements were performed on 2 October 2017, during the panicle initiation, booting, and ripening growth stages. The predictive accuracy of ISE-PLS was compared with that of the standard full-spectrum PLS (FS-PLS) via coefficient of determination (R2) values and root mean squared errors of cross-validation (RMSECV), and the robustness was evaluated by the residual predictive deviation (RPD). Compared with the FS-PLS models, the ISE-PLS models exhibited higher R2 values and lower RMSECV values for all data sets. Overall, the highest R2 values and the lowest RMSECV values were obtained from the ISE-PLS model at the booting stage (R2 = 0.873, RMSECV = 22.903); the RPD was >2.4. Selected HS wavebands in the ISE-PLS model were identified in the red-edge (710–740 nm) and near-infrared (830 nm) regions. Overall, these results suggest that the booting stage might be the best time for in-season rice grain assessment and that rice yield could be evaluated accurately from the HS sensing data via the ISE-PLS model.

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Multi-Temporal Detection of Rice Phenological Stages Using Canopy Spectrum

Cited by 28 publications

References 31 publications

Quantitative monitoring of leaf area index in wheat of different plant types by integrating NDVI and Beer-Lambert law

Quantitative monitoring of leaf area index in wheat of different plant types by integrating NDVI and Beer-Lambert law

Rice Crop Detection Using LSTM, Bi-LSTM, and Machine Learning Models from Sentinel-1 Time Series

Canopy Hyperspectral Sensing of Paddy Fields at the Booting Stage and PLS Regression can Assess Grain Yield

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