Quantification winter wheat LAI with HJ-1CCD image features over multiple growing seasons

Li, Xinchuan; Zhang, Youjing; Luo, Jiancheng; Jin, Xiuliang; Xu, Ying; Yang, W.

doi:10.1016/j.jag.2015.08.004

Cited by 39 publications

(25 citation statements)

References 50 publications

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“…The results showed that OSVIs were correlated with LAI and biomass ( Table 6). The effects of LAI and biomass on crop canopy spectral reflectance in the NIR and visible spectrum are known [10][11][12][13][14][15][16][17]. Therefore, these OSVIs based on different combinations of visible and NIR reflectance was significantly related with LAI and biomass.…”

Section: Discussionmentioning

confidence: 99%

“…The estimation accuracy of LAI and biomass was higher with the PLSR than the MSR method. Previous studies have shown that PLSR outperforms MSR in estimating biophysical and biochemical parameters [11,59]. This may be because the MSR method can be used to concentrate on some spectral band features with known links to the variables of interest [59].…”

Section: Discussionmentioning

confidence: 99%

“…Crop canopy structure mainly affects the spectral reflectance of crop canopy in the near-infrared (NIR) and visible spectrums. Numerous studies have shown a strong correlation between vegetation indices (VIs) and LAI and biomass using different integrations of visible and NIR reflectance [10][11][12][13][14][15][16][17]. A previous study has shown that normalized difference vegetation index (NDVI) was very sensitive to low LAI values (i.e., LAI < 3) and saturation exists at medium to high LAI values (i.e., LAI > 3) [16].…”

Section: Introductionmentioning

confidence: 99%

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Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

et al. 2015

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Leaf area index (LAI) and biomass are frequently used target variables for agricultural and ecological remote sensing applications. Ground measurements of winter wheat LAI and biomass were made from March to May 2014 in the Yangling district, Shaanxi, Northwest China. The corresponding remotely sensed data were obtained from the earth-observation satellites Huanjing (HJ) and RADARSAT-2. The objectives of this study were (1) to investigate the relationships of LAI and biomass with several optical spectral vegetation indices (OSVIs) and radar polarimetric parameters (RPPs), (2) , p < 0.01) were highly correlated with biomass. The estimation accuracy of LAI and biomass was better using the COSVI-RPPs than using the OSVIs and RPPs alone. The results revealed that the PLSR regression equation better estimated LAI and biomass than the MSR regression equation based on all the COSVI-RPPs, OSVIs, and RPPs. Our results indicated that the COSVI-RPPs can be used to robustly estimate LAI and biomass. This study may provide a guideline for improving the estimations of LAI and biomass of winter wheat using multisource remote sensing data.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

et al. 2015

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“…Vegetation indices combined with reflectance bands showed a high correlation with LAI and AGB (Table 3). VIs have the advantage of obtaining relevant information rapidly and easily, and perform better than a single spectral band in crop monitoring [24,49,50]. In situ measured LAI reached a maximum, while in situ measured dry AGB continued to increase, and the AGB includes more photosynthetically inactive components (e.g., stem, ear) than the leaf area, which is likely to affect the relationship between VIs and the photosynthetically active components [34].…”

Section: Discussionmentioning

confidence: 99%

Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series Data

et al. 2016

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Abstract:The high temporal resolution (4-day) charge-coupled device (CCD) cameras onboard small environment and disaster monitoring and forecasting satellites (HJ-1A/B) with 30 m spatial resolution and large swath (700 km) have substantially increased the availability of regional clear sky optical remote sensing data. For the application of dynamic mapping of rice growth parameters, leaf area index (LAI) and aboveground biomass (AGB) were considered as plant growth indicators. The HJ-1 CCD-derived vegetation indices (VIs) showed robust relationships with rice growth parameters. Cumulative VIs showed strong performance for the estimation of total dry AGB. The cross-validation coefficient of determination (R 2 CV ) was increased by using two machine learning methods, i.e., a back propagation neural network (BPNN) and a support vector machine (SVM) compared with traditional regression equations of LAI retrieval. The LAI inversion accuracy was further improved by dividing the rice growth period into before and after heading stages. This study demonstrated that continuous rice growth monitoring over time and space at field level can be implemented effectively with HJ-1 CCD 10-day composite data using a combination of proper VIs and regression models.

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“…First, this vegetation index is sensitive to LAI. The most commonly used vegetation indices for LAI estimation combine the information in visible and near infrared reflectance [21,30,33,36]. Since the visible domain reduces the perturbation of background soil, and the canopy reflectance in the near-infrared region is larger.…”

Section: Methodsmentioning

confidence: 99%

A Prior Knowledge-Based Method to Derivate High-Resolution Leaf Area Index Maps with Limited Field Measurements

Shi

Wang

et al. 2016

Remote Sensing

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High-resolution leaf area index (LAI) maps from remote sensing data largely depend on empirical models, which link field LAI measurements to the vegetation index. The existing empirical methods often require the field measurements to be sufficient for constructing a reliable model. However, in many regions of the world, there are limited field measurements available. This paper presents a prior knowledge-based (PKB) method to derivate LAI with limited field measurements, in an effort to improve the accuracy of empirical model. Based on the assumption that the experimental sites with the same vegetation type can be represented by similar models, a priori knowledge for crops was extracted from the published models in various cropland sites. The knowledge, composed of an initial guess of each model parameter with the associated uncertainty, was then combined with the local field measurements to determine a semi-empirical model using the Bayesian inversion method. The proposed method was evaluated at a cropland site in the Huailai region of Hebei Province, China. Compared with the regression method, the proposed PKB method can effectively improve the accuracy of empirical model and LAI estimation, when the field measurements were limited. The results demonstrate that a priori knowledge extracted from the universal sites can provide important auxiliary information to improve the representativeness of the empirical model in a given study area.

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Quantification winter wheat LAI with HJ-1CCD image features over multiple growing seasons

Cited by 39 publications

References 50 publications

Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

Combined Multi-Temporal Optical and Radar Parameters for Estimating LAI and Biomass in Winter Wheat Using HJ and RADARSAR-2 Data

Dynamic Mapping of Rice Growth Parameters Using HJ-1 CCD Time Series Data

A Prior Knowledge-Based Method to Derivate High-Resolution Leaf Area Index Maps with Limited Field Measurements

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