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Topographic and angular corrections on Sentinel-2 imagery are crucial for the generation of consistent surface reflectance. We propose a novel topographic-angular integrated normalization approach based on the combination of the path length correction (PLC) and C-factor approaches. The PLC-C normalization approach is a semiphysical method with limited use of auxiliary data: only a digital elevation model and a fixed set of kernel coefficients, ensuring its transferability for operational implementation. For the validation, we used two Sentinel-2A images over a mountainous area observed in backward (BS) and forward scattering (FS) directions from laterally adjacent orbit swaths. PLC-C significantly reduced both the topographic and directional anisotropy effects: the overlapping ratio between BS and FS observations was increased from 84.1% to 92.8% for the near-infrared band, and from 81.0% to 93.1% for the red band; the coefficient of variation of the reflectances across different aspects, which was used as a criterion of topographic effects, was reduced from 9.8% / 12.2% to 3.6%/5.7% in BS/FS direction for the near-infrared band, and from 8.1%/9.7% to 4.5%/4.2% for the red band. PLC-C will contribute to the generation of analysis ready data from Sentinel-2 top of canopy reflectance.
Gaofei Yin; Jing Li; Baodong Xu; Yelu Zeng; Shengbiao Wu; Kai Yan; Aleixandre Verger; Guoxiang Liu. PLC-C: An Integrated Method for Sentinel-2 Topographic and Angular Normalization. IEEE Geoscience and Remote Sensing Letters 2020, 18, 1446 -1450.
AMA StyleGaofei Yin, Jing Li, Baodong Xu, Yelu Zeng, Shengbiao Wu, Kai Yan, Aleixandre Verger, Guoxiang Liu. PLC-C: An Integrated Method for Sentinel-2 Topographic and Angular Normalization. IEEE Geoscience and Remote Sensing Letters. 2020; 18 (8):1446-1450.
Chicago/Turabian StyleGaofei Yin; Jing Li; Baodong Xu; Yelu Zeng; Shengbiao Wu; Kai Yan; Aleixandre Verger; Guoxiang Liu. 2020. "PLC-C: An Integrated Method for Sentinel-2 Topographic and Angular Normalization." IEEE Geoscience and Remote Sensing Letters 18, no. 8: 1446-1450.
Topographic correction is a prerequisite for generating radiometrically consistent Landsat 8 OLI vegetation reflectances in support of temporally continuous and spatially mosaicked applications. Path length correction (PLC) is a physically solid topographic correction method that avoids the involvement of any empirical parameter and is therefore suitable for reproducing the inherent reflectance of vegetation. This article compared two different implementation pathways of PLC, i.e., the explicit method (EM) and the implicit method (IM), which are based on the numerical inverse and analytical approximation of the PLC model, respectively. The results show that both EM and IM can obviously reduce the topographic effects on Landsat 8 OLI vegetation reflectances. EM performed slightly better than IM in eliminating the correlation between the topographic characteristics and the vegetation reflectances: the coefficient of determination between the green/red/ near-infrared (Nir) band reflectance and the local illumination was reduced from 0.257/0.148/0.467 for the uncorrected (UNCORR) case to 0.016/0.004/0.012 and 0.027/0.014/0.094 for the EM and IM corrected results, respectively. The coefficient of variation of the three band reflectances across different aspects was reduced from 16.5%/18.5%/18.7% for the UNCORR case to 3.2%/1.8%/0.9% and 5.3%/7.1%/7.3% for the EM and IM corrected results, respectively. In addition, the intraclass reflectance variability was also reduced after both the EM and IM corrections. Nevertheless, due to the ill-posed nature of the numerical inverse process, EM cannot fully reproduce the inherent vegetation reflectances, and the reflectances after topographic correction overestimated the inherent vegetation values. In contrast, the IM can achieve an appropriate tradeoff between topographic effect elimination and vegetation inherent reflectance preservation. In addition, IM is computationally very efficient compared to EM: using an ordinary laptop, IM can finish the topographic correction for a Landsat OLI image within several seconds, while this would take more than 20 h for EM. This article highlights the potential of using IM for generating radiometrically consistent Landsat 8 OLI vegetation reflectances.
Gaofei Yin; Lei Ma; Wei Zhao; Yelu Zeng; Baodong Xu; Shengbiao Wu. Topographic Correction for Landsat 8 OLI Vegetation Reflectances Through Path Length Correction: A Comparison Between Explicit and Implicit Methods. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 8477 -8489.
AMA StyleGaofei Yin, Lei Ma, Wei Zhao, Yelu Zeng, Baodong Xu, Shengbiao Wu. Topographic Correction for Landsat 8 OLI Vegetation Reflectances Through Path Length Correction: A Comparison Between Explicit and Implicit Methods. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (12):8477-8489.
Chicago/Turabian StyleGaofei Yin; Lei Ma; Wei Zhao; Yelu Zeng; Baodong Xu; Shengbiao Wu. 2020. "Topographic Correction for Landsat 8 OLI Vegetation Reflectances Through Path Length Correction: A Comparison Between Explicit and Implicit Methods." IEEE Transactions on Geoscience and Remote Sensing 58, no. 12: 8477-8489.
Global biophysical products at decametric resolution derived from Sentinel-2 imagery have emerged as a promising dataset for fine-scale ecosystem modeling and agricultural monitoring. Evaluating uncertainties of different Sentinel-2 biophysical products over various regions and vegetation types is pivotal in the application of land surface models. In this study, we quantified the performance of Sentinel-2-derived Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Active Radiation (FAPAR), and Fractional Vegetation Cover (FVC) estimates using global ground observations with consistent measurement criteria. Our results show that the accuracy of vegetation and non-vegetated classification based on Sentinel-2 surface reflectance products is greater than 95%, which indicates the vegetation identification is favorable for the practical application of biophysical estimates, as several LAI, FAPAR, and FVC retrievals were derived for non-vegetated pixels. The rate of best retrievals is similar between LAI and FAPAR estimates, both accounting for 87% of all vegetation pixels, while it is almost 100% for FVC estimates. Additionally, the Sentinel-2 FAPAR and FVC estimates agree well with ground-measurements-derived (GMD) reference maps, whereas a large discrepancy is observed for Sentinel-2 LAI estimates by comparing with both GMD effective LAI (LAIe) and actual LAI (LAI) reference maps. Furthermore, the uncertainties of Sentinel-2 LAI, FAPAR and FVC estimates are 1.09 m2/m2, 1.14 m2/m2, 0.13 and 0.17 through comparisons to ground LAIe, LAI, FAPAR, and FVC measurements, respectively. Given the temporal difference between Sentinel-2 observations and ground measurements, Sentinel-2 LAI estimates are more consistent with LAIe than LAI values. The robustness of evaluation results can be further improved as long as more multi-temporal ground measurements across different regions are obtained. Overall, this study provides fundamental information about the performance of Sentinel-2 LAI, FAPAR, and FVC estimates, which imbues our confidence in the broad applications of these decametric products.
Qiong Hu; Jingya Yang; Baodong Xu; Jianxi Huang; Muhammad Sohail Memon; Gaofei Yin; Yelu Zeng; Jing Zhao; Ke Liu. Evaluation of Global Decametric-Resolution LAI, FAPAR and FVC Estimates Derived from Sentinel-2 Imagery. Remote Sensing 2020, 12, 912 .
AMA StyleQiong Hu, Jingya Yang, Baodong Xu, Jianxi Huang, Muhammad Sohail Memon, Gaofei Yin, Yelu Zeng, Jing Zhao, Ke Liu. Evaluation of Global Decametric-Resolution LAI, FAPAR and FVC Estimates Derived from Sentinel-2 Imagery. Remote Sensing. 2020; 12 (6):912.
Chicago/Turabian StyleQiong Hu; Jingya Yang; Baodong Xu; Jianxi Huang; Muhammad Sohail Memon; Gaofei Yin; Yelu Zeng; Jing Zhao; Ke Liu. 2020. "Evaluation of Global Decametric-Resolution LAI, FAPAR and FVC Estimates Derived from Sentinel-2 Imagery." Remote Sensing 12, no. 6: 912.
Solar Induced chlorophyll Fluorescence (SIF) shows promise as an approach for estimating gross primary production (GPP) remotely. However, sun-target-sensor geometry and within-canopy absorption of SIF can alter the relationship between measured SIF and GPP, because sensors can only retrieve some unknown fraction of the total emitted SIF. Radiative transfer models that allow for variation in canopy structure and sensor angles are therefore needed to properly interpret SIF measurements. Spectral invariants allow decoupling of the wavelength-independent canopy structure and the wavelength-dependent leaf and soil spectrum in the radiative transfer process. Here we develop a simple analytical Fluorescence Radiative Transfer model based on Escape and Recollision probability (FluorRTER) to investigate the impact of canopy structure and sun-target-sensor geometry on SIF emissions. SIF simulations using the FluorRTER model agreed well the one-dimensional Soil-Canopy Observation of Photochemistry and Energy balance (SCOPE) model and the three-dimensional Fluorescence model with Weighted Photon Spread (FluorWPS) model. The fractional vegetation cover (FVC) and clumping effect have a large influence the SIF emission of 3D discontinuous canopies. For a moderate solar zenith angle (30°) and a clumped canopy (FVC = 0.6), the difference between the directional observed SIF of a 3D discontinuous canopy and a 1D homogeneous canopy was as large as 43.2% and 38.4% for Photosystem I + II fluorescence at 685 nm and at 740 nm, respectively. By bridging the gap between observed SIF and total emitted SIF over 3D heterogeneous vegetation canopies, the FluorRTER model can assist with the angular normalization of SIF measurements and enable the more robust interpretation of how variations in SIF from directional and hemispherical in-situ, airborne and satellite observations relate to leaf and whole-canopy physiological processes.
Yelu Zeng; Grayson Badgley; Min Chen; Jing Li; Leander D.L. Anderegg; Ari Kornfeld; Qinhuo Liu; Baodong Xu; Bin Yang; Kai Yan; Joseph A. Berry. A radiative transfer model for solar induced fluorescence using spectral invariants theory. Remote Sensing of Environment 2020, 240, 111678 .
AMA StyleYelu Zeng, Grayson Badgley, Min Chen, Jing Li, Leander D.L. Anderegg, Ari Kornfeld, Qinhuo Liu, Baodong Xu, Bin Yang, Kai Yan, Joseph A. Berry. A radiative transfer model for solar induced fluorescence using spectral invariants theory. Remote Sensing of Environment. 2020; 240 ():111678.
Chicago/Turabian StyleYelu Zeng; Grayson Badgley; Min Chen; Jing Li; Leander D.L. Anderegg; Ari Kornfeld; Qinhuo Liu; Baodong Xu; Bin Yang; Kai Yan; Joseph A. Berry. 2020. "A radiative transfer model for solar induced fluorescence using spectral invariants theory." Remote Sensing of Environment 240, no. : 111678.
Land cover mixture at moderate- to coarse-resolution is an important cause for the uncertainty of global leaf area index (LAI) products. The accuracy of LAI retrievals over land-water mixed pixels is adversely impacted because water absorbs considerable solar radiation and thus can greatly lower pixel-level reflectance especially in the near-infrared wavelength. Here we proposed an approach named Reduced Water Effect (RWE) to improve the accuracy of LAI retrievals by accounting for water-induced negative bias in reflectances. The RWE consists of three parts: water area fraction (WAF) calculation, subpixel water reflectance computation in land-water mixed pixels and LAI retrieval using the operational MODIS LAI algorithm. The performance of RWE was carefully evaluated using the aggregated Landsat ETM+ reflectance of water pixels over different regions and observation dates and the aggregated 30-m LAI reference maps over three sites in the moderate-resolution pixel grid (500-m). Our results suggest that the mean absolute errors of water endmember reflectance in red and NIR bands were both <0.016, which only introduced mean absolute (relative) errors of <0.15 (15%) for the pixel-level LAI retrievals. The validation results reveal that the accuracy of RWE LAI was higher than that of MODIS LAI over land-water mixed pixels especially for pixels with larger WAFs. Additionally, the mean relative difference between RWE LAI and aggregated 30-m LAI did not vary with WAF, indicating that water effects were significantly reduced by the RWE method. A comparison between RWE and MODIS LAI shows that the maximum absolute and relative differences caused by water effects were 0.9 and 100%, respectively. Furthermore, the impact of water mixed in pixels can induce the LAI underestimation and change the day selected for compositing the 8-day LAI product. These results indicate that RWE can effectively reduce water effects on the LAI retrieval of land-water mixed pixels, which is promising for the improvement of global LAI products.
Baodong Xu; Jing Li; Taejin Park; Qinhuo Liu; Yelu Zeng; Gaofei Yin; Kai Yan; Chi Chen; Jing Zhao; Weiliang Fan; Yuri Knyazikhin; Ranga B. Myneni. Improving leaf area index retrieval over heterogeneous surface mixed with water. Remote Sensing of Environment 2020, 240, 111700 .
AMA StyleBaodong Xu, Jing Li, Taejin Park, Qinhuo Liu, Yelu Zeng, Gaofei Yin, Kai Yan, Chi Chen, Jing Zhao, Weiliang Fan, Yuri Knyazikhin, Ranga B. Myneni. Improving leaf area index retrieval over heterogeneous surface mixed with water. Remote Sensing of Environment. 2020; 240 ():111700.
Chicago/Turabian StyleBaodong Xu; Jing Li; Taejin Park; Qinhuo Liu; Yelu Zeng; Gaofei Yin; Kai Yan; Chi Chen; Jing Zhao; Weiliang Fan; Yuri Knyazikhin; Ranga B. Myneni. 2020. "Improving leaf area index retrieval over heterogeneous surface mixed with water." Remote Sensing of Environment 240, no. : 111700.
The in situ measurement of the leaf area index (LAI) from gap fraction is often affected by terrain slope. Path length correction (PLC) is commonly used to mitigate the topographic effect on the LAI measurements. However, the terrain-induced uncertainty and the accuracy improvement of the PLC for LAI measurements have not been systematically analyzed, hindering the establishment of an appropriate protocol for LAI measurements over mountainous regions. In this article, the above knowledge gap was filled using a computer simulation framework, which enables the estimated LAI before and after PLC to be benchmarked against the known and precise model truth. The simulation was achieved by using CANOPIX software and a dedicatedly designed ray-tracing method for continuous and discrete canopies, respectively. Simulations show that the slope distorts the angular pattern of the gap fraction, i.e., increasing the gap fraction in the down-slope direction and reducing it in the up-slope direction. The horizontally equivalent hemispheric gap fraction from the PLC can reconstruct the azimuthally symmetric angular pattern of the real horizontal surface. The azimuthally averaged gap fraction for sloping terrain can both be underestimated or overestimated depending on the LAI and can be successfully corrected through PLC. The topography-induced uncertainty in LAI measurements is found to be ~14.3% and >20% for continuous and discrete canopies, respectively. This uncertainty can be, respectively, reduced to ~1.8% and <7.3% after PLC, meeting the up-to-date uncertainty threshold of 15% established by the Global Climate Observing System (GCOS). Closer analysis shows that the topographic effect is influenced by fractional crown cover, and the largest uncertainty which corresponds to extensively clumping canopy can reach nearly up to 50%. The accuracy of the estimated LAI after PLC safely meets the GCOS uncertainty threshold even for this extreme case. This study demonstrates the necessity of a topographic correction for LAI measurements and the applicability of PLC for reconstructing the horizontally equivalent gap fraction and improving the LAI measurements over sloping terrains. The results of this article throw light on the design of a protocol for LAI measurements over mountainous regions.
Gaofei Yin; Biao Cao; Jing Li; Weiliang Fan; Yelu Zeng; Baodong Xu; Wei Zhao. Path Length Correction for Improving Leaf Area Index Measurements Over Sloping Terrains: A Deep Analysis Through Computer Simulation. IEEE Transactions on Geoscience and Remote Sensing 2020, 58, 4573 -4589.
AMA StyleGaofei Yin, Biao Cao, Jing Li, Weiliang Fan, Yelu Zeng, Baodong Xu, Wei Zhao. Path Length Correction for Improving Leaf Area Index Measurements Over Sloping Terrains: A Deep Analysis Through Computer Simulation. IEEE Transactions on Geoscience and Remote Sensing. 2020; 58 (7):4573-4589.
Chicago/Turabian StyleGaofei Yin; Biao Cao; Jing Li; Weiliang Fan; Yelu Zeng; Baodong Xu; Wei Zhao. 2020. "Path Length Correction for Improving Leaf Area Index Measurements Over Sloping Terrains: A Deep Analysis Through Computer Simulation." IEEE Transactions on Geoscience and Remote Sensing 58, no. 7: 4573-4589.
The availability of global high-resolution land cover maps provides promising a priori knowledge for characterizing subpixel heterogeneity and improving predictions of directional reflectance of coarse-resolution pixels. Due to mutual shadowing and sheltering effects between the adjacent forest and cropland patches, the spectral nonlinear mixing of patchy ecotones is significant, especially when the sun illuminates the ecotone from the forest side with high solar zenith angle. The spectral linear mixture (SLM) approach leads to overestimation of the bidirectional reflectance factor (BRF) in the red band in the principal plane (PP), with a maximum absolute error (MAE) of 0.0063 and a maximum relative error (MRE) of 52.5%, and to underestimation in the near-infrared band in PP with an MAE of 0.0940 and an MRE of 14.5%. In a scenario with randomly distributed boundary orientations, the overestimation of SLM increases with the degree of fragmentation and the view zenith angle. We propose a Radiative Transfer model for patchy ECotones (RTEC). which improves R² from 0.61 to 0.94 in the red band of Landsat-8 directional reflectance at the validation site. The RTEC model provides an efficient and analytical approach for directional reflectance predictions over heterogeneous patchy landscapes at coarse resolution and will be used for biophysical parameter retrievals [e.g., the leaf area index (LAI)] in future applications.
Yelu Zeng; Jing Li; Qinhuo Liu; Alfredo R. Huete; Baodong Xu; Gaofei Yin; Weiliang Fan; Yixuan Ouyang; Kai Yan; Dalei Hao; Min Chen. A Radiative Transfer Model for Patchy Landscapes Based on Stochastic Radiative Transfer Theory. IEEE Transactions on Geoscience and Remote Sensing 2019, 58, 2571 -2589.
AMA StyleYelu Zeng, Jing Li, Qinhuo Liu, Alfredo R. Huete, Baodong Xu, Gaofei Yin, Weiliang Fan, Yixuan Ouyang, Kai Yan, Dalei Hao, Min Chen. A Radiative Transfer Model for Patchy Landscapes Based on Stochastic Radiative Transfer Theory. IEEE Transactions on Geoscience and Remote Sensing. 2019; 58 (4):2571-2589.
Chicago/Turabian StyleYelu Zeng; Jing Li; Qinhuo Liu; Alfredo R. Huete; Baodong Xu; Gaofei Yin; Weiliang Fan; Yixuan Ouyang; Kai Yan; Dalei Hao; Min Chen. 2019. "A Radiative Transfer Model for Patchy Landscapes Based on Stochastic Radiative Transfer Theory." IEEE Transactions on Geoscience and Remote Sensing 58, no. 4: 2571-2589.
Crop yield estimation at a regional scale over a long period of time is of great significance to food security. In past decades, the integration of remote sensing observations and crop growth models has been recognized as a promising approach for crop growth monitoring and yield estimation. Optical remote sensing data are susceptible to cloud and rain, while synthetic aperture radar (SAR) can penetrate through clouds and has all-weather capabilities. This allows for more reliable and consistent crop monitoring and yield estimation in terms of radar sensor data. The aim of this study is to improve the accuracy for winter wheat yield estimation by assimilating time series soil moisture images, which are retrieved by a water cloud model using SAR and optical data as input, into the crop model. In this study, SAR images were acquired by C-band SAR sensors boarded on Sentinel-1 satellites and optical images were obtained from a Sentinel-2 multi-spectral instrument (MSI) for Hengshui city of Hebei province in China. Remote sensing data and ground data were all collected during the main growing season of winter wheat. Both the normalized difference vegetation index (NDVI), derived from Sentinel-2, and backscattering coefficients and polarimetric indicators, computed from Sentinel-1, were used in the water cloud model to derive time series soil moisture (SM) images. To improve the prediction of crop yields at the field scale, we incorporated remotely sensed soil moisture into the World Food Studies (WOFOST) model using the Ensemble Kalman Filter (EnKF) algorithm. In general, the trend of soil moisture inversion was consistent with the ground measurements, with the coefficient of determination (R2) equal to 0.45, 0.53, and 0.49, respectively, and RMSE was 9.16%, 7.43%, and 8.53%, respectively, for three observation dates. The winter wheat yield estimation results showed that the assimilation of remotely sensed soil moisture improved the correlation of observed and simulated yields (R2 = 0.35; RMSE =934 kg/ha) compared to the situation without data assimilation (R2 = 0.21; RMSE = 1330 kg/ha). Consequently, the results of this study demonstrated the potential and usefulness of assimilating SM retrieved from both Sentinel-1 C-band SAR and Sentinel-2 MSI optical remote sensing data into WOFOST model for winter wheat yield estimation and could also provide a reference for crop yield estimation with data assimilation for other crop types.
Wen Zhuo; Jianxi Huang; Li Li; Xiaodong Zhang; Hongyuan Ma; Xinran Gao; Hai Huang; Baodong Xu; Xiangming Xiao. Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation. Remote Sensing 2019, 11, 1618 .
AMA StyleWen Zhuo, Jianxi Huang, Li Li, Xiaodong Zhang, Hongyuan Ma, Xinran Gao, Hai Huang, Baodong Xu, Xiangming Xiao. Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation. Remote Sensing. 2019; 11 (13):1618.
Chicago/Turabian StyleWen Zhuo; Jianxi Huang; Li Li; Xiaodong Zhang; Hongyuan Ma; Xinran Gao; Hai Huang; Baodong Xu; Xiangming Xiao. 2019. "Assimilating Soil Moisture Retrieved from Sentinel-1 and Sentinel-2 Data into WOFOST Model to Improve Winter Wheat Yield Estimation." Remote Sensing 11, no. 13: 1618.
Satellite data show increasing leaf area of vegetation due to direct factors (human land-use management) and indirect factors (such as climate change, CO2 fertilization, nitrogen deposition and recovery from natural disturbances). Among these, climate change and CO2 fertilization effects seem to be the dominant drivers. However, recent satellite data (2000–2017) reveal a greening pattern that is strikingly prominent in China and India and overlaps with croplands world-wide. China alone accounts for 25% of the global net increase in leaf area with only 6.6% of global vegetated area. The greening in China is from forests (42%) and croplands (32%), but in India is mostly from croplands (82%) with minor contribution from forests (4.4%). China is engineering ambitious programmes to conserve and expand forests with the goal of mitigating land degradation, air pollution and climate change. Food production in China and India has increased by over 35% since 2000 mostly owing to an increase in harvested area through multiple cropping facilitated by fertilizer use and surface- and/or groundwater irrigation. Our results indicate that the direct factor is a key driver of the ‘Greening Earth’, accounting for over a third, and probably more, of the observed net increase in green leaf area. They highlight the need for a realistic representation of human land-use practices in Earth system models.
Chi Chen; Taejin Park; Xuhui Wang; Shilong Piao; Baodong Xu; Rajiv Kumar Chaturvedi; Richard Fuchs; Victor Brovkin; Philippe Ciais; Rasmus Fensholt; Hans Tømmervik; Govindasamy Bala; Zaichun Zhu; Ramakrishna R. Nemani; Ranga B. Myneni. China and India lead in greening of the world through land-use management. Nature Sustainability 2019, 2, 122 -129.
AMA StyleChi Chen, Taejin Park, Xuhui Wang, Shilong Piao, Baodong Xu, Rajiv Kumar Chaturvedi, Richard Fuchs, Victor Brovkin, Philippe Ciais, Rasmus Fensholt, Hans Tømmervik, Govindasamy Bala, Zaichun Zhu, Ramakrishna R. Nemani, Ranga B. Myneni. China and India lead in greening of the world through land-use management. Nature Sustainability. 2019; 2 (2):122-129.
Chicago/Turabian StyleChi Chen; Taejin Park; Xuhui Wang; Shilong Piao; Baodong Xu; Rajiv Kumar Chaturvedi; Richard Fuchs; Victor Brovkin; Philippe Ciais; Rasmus Fensholt; Hans Tømmervik; Govindasamy Bala; Zaichun Zhu; Ramakrishna R. Nemani; Ranga B. Myneni. 2019. "China and India lead in greening of the world through land-use management." Nature Sustainability 2, no. 2: 122-129.
Many applications, including crop growth and yield monitoring, require accurate long-term time series of leaf area index (LAI) at high spatiotemporal resolution with a quantification of the associated uncertainties. We propose an LAI retrieval approach based on a combination of the LAINet observation system, the Consistent Adjustment of the Climatology to Actual Observations (CACAO) method, and Gaussian process regression (GPR). First, the LAINet wireless sensor network provides temporally continuous field measurements of LAI. Then, the CACAO approach generates synchronous reflectance data at high spatiotemporal resolution (30-m and 8-day) from the fusion of multitemporal MODIS and high spatial resolution Landsat satellite imagery. Finally, the GPR machine learning regression algorithm retrieves the LAI maps and their associated uncertainties. A case study in a cropland site in China showed that the accuracy of LAI retrievals is 0.36 (12.7%) in terms of root mean square error and R2 = 0.88 correlation with ground measurements as evaluated over the entire growing season. This paper demonstrates the potential of the joint use of newly developed software and hardware technologies in deriving concomitant LAI and uncertainty maps with high spatiotemporal resolution. It will contribute to precision agriculture, as well as to the retrieval and validation of LAI products.
Gaofei Yin; Aleixandre Verger; Yonghua Qu; Wei Zhao; Baodong Xu; Yelu Zeng; Ke Liu; Jing Li; Qinhuo Liu. Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion. Remote Sensing 2019, 11, 244 .
AMA StyleGaofei Yin, Aleixandre Verger, Yonghua Qu, Wei Zhao, Baodong Xu, Yelu Zeng, Ke Liu, Jing Li, Qinhuo Liu. Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion. Remote Sensing. 2019; 11 (3):244.
Chicago/Turabian StyleGaofei Yin; Aleixandre Verger; Yonghua Qu; Wei Zhao; Baodong Xu; Yelu Zeng; Ke Liu; Jing Li; Qinhuo Liu. 2019. "Retrieval of High Spatiotemporal Resolution Leaf Area Index with Gaussian Processes, Wireless Sensor Network, and Satellite Data Fusion." Remote Sensing 11, no. 3: 244.
Earth’s reflectivity is among the key parameters of climate research. National Aeronautics and Space Administration (NASA)’s Earth Polychromatic Imaging Camera (EPIC) onboard National Oceanic and Atmospheric Administration (NOAA)’s Deep Space Climate Observatory (DSCOVR) spacecraft provides spectral reflectance of the entire sunlit Earth in the near backscattering direction every 65 to 110 min. Unlike EPIC, sensors onboard the Earth Orbiting Satellites (EOS) sample reflectance over swaths at a specific local solar time (LST) or over a fixed area. Such intrinsic sampling limits result in an apparent Earth’s reflectivity. We generated spectral reflectance over sampling areas using EPIC data. The difference between the EPIC and EOS estimates is an uncertainty in Earth’s reflectivity. We developed an Earth Reflector Type Index (ERTI) to discriminate between major Earth atmosphere components: clouds, cloud-free ocean, bare and vegetated land. Temporal variations in Earth’s reflectivity are mostly determined by clouds. The sampling area of EOS sensors may not be sufficient to represent cloud variability, resulting in biased estimates. Taking EPIC reflectivity as a reference, low-earth-orbiting-measurements at the sensor-specific LST tend to overestimate EPIC values by 0.8%to 8%. Biases in geostationary orbiting approximations due to a limited sampling area are between -0.7% and 12%. Analyses of ERTI-based Earth component reflectivity indicate that the disagreement between EPIC and EOS estimates depends on the sampling area, observation time and vary between -10% and 23%.
Wanjuan Song; Yuri Knyazikhin; Guoyong Wen; Alexander Marshak; Matti Mõttus; Kai Yan; Bin Yang; Baodong Xu; Taejin Park; Chi Chen; Yelu Zeng; Guangjian Yan; Xihan Mu; Ranga B. Myneni. Implications of Whole-Disc DSCOVR EPIC Spectral Observations for Estimating Earth’s Spectral Reflectivity Based on Low-Earth-Orbiting and Geostationary Observations. Remote Sensing 2018, 10, 1594 .
AMA StyleWanjuan Song, Yuri Knyazikhin, Guoyong Wen, Alexander Marshak, Matti Mõttus, Kai Yan, Bin Yang, Baodong Xu, Taejin Park, Chi Chen, Yelu Zeng, Guangjian Yan, Xihan Mu, Ranga B. Myneni. Implications of Whole-Disc DSCOVR EPIC Spectral Observations for Estimating Earth’s Spectral Reflectivity Based on Low-Earth-Orbiting and Geostationary Observations. Remote Sensing. 2018; 10 (10):1594.
Chicago/Turabian StyleWanjuan Song; Yuri Knyazikhin; Guoyong Wen; Alexander Marshak; Matti Mõttus; Kai Yan; Bin Yang; Baodong Xu; Taejin Park; Chi Chen; Yelu Zeng; Guangjian Yan; Xihan Mu; Ranga B. Myneni. 2018. "Implications of Whole-Disc DSCOVR EPIC Spectral Observations for Estimating Earth’s Spectral Reflectivity Based on Low-Earth-Orbiting and Geostationary Observations." Remote Sensing 10, no. 10: 1594.
This paper presents a simple radiative transfer model based on spectral invariant properties (SIP). The canopy structure parameters, including the leaf angle distribution and multi-angular clumping index, are explicitly described in the SIP model. The SIP model has been evaluated on its bidirectional reflectance factor (BRF) in the angular space at the radiation transfer model intercomparison platform, and in the spectrum space by the PROSPECT+SAIL (PROSAIL) model. The simulations of BRF by SIP agreed well with the reference values in both the angular space and spectrum space, with a root-mean-square-error (RMSE) of 0.006. When compared with the widely-used Soil-Canopy Observation of Photochemistry and Energy fluxes (SCOPE) model on fPAR, the RMSE was 0.006 and the R2 was 0.99, which shows a high accuracy. This study also suggests the newly proposed vegetation index, the near-infrared (NIR) reflectance of vegetation (NIRv), was a good linear approximation of the canopy structure parameter, the directional area scattering factor (DASF), with an R2 of 0.99. NIRv was not influenced much by the soil background contribution, but was sensitive to the leaf inclination angle. The sensitivity of NIRv to canopy structure and the robustness of NIRv to the soil background suggest NIRv is a promising index in future biophysical variable estimations with the support of the SIP model, especially for the Deep Space Climate Observatory (DSCOVR) Earth Polychromatic Imaging Camera (EPIC) observations near the hot spot directions.
Yelu Zeng; Baodong Xu; Gaofei Yin; Shengbiao Wu; Guoqing Hu; Kai Yan; Bin Yang; Wanjuan Song; Jing Li. Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations. Remote Sensing 2018, 10, 1508 .
AMA StyleYelu Zeng, Baodong Xu, Gaofei Yin, Shengbiao Wu, Guoqing Hu, Kai Yan, Bin Yang, Wanjuan Song, Jing Li. Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations. Remote Sensing. 2018; 10 (10):1508.
Chicago/Turabian StyleYelu Zeng; Baodong Xu; Gaofei Yin; Shengbiao Wu; Guoqing Hu; Kai Yan; Bin Yang; Wanjuan Song; Jing Li. 2018. "Spectral Invariant Provides a Practical Modeling Approach for Future Biophysical Variable Estimations." Remote Sensing 10, no. 10: 1508.
Rugged terrain distorts optical remote sensing signals, and land-cover classification and biophysical parameter retrieval over mountainous regions must account for topographic effects. Therefore, topographic correction is a prerequisite for many remote sensing applications. In this study, we proposed a semi-physically based and simple topographic correction method for vegetation canopies based on path length correction (PLC). The PLC method was derived from the solution to the classic radiative transfer equation, and the influence of terrain on the radiative transfer process within the canopy is explicitly considered, making PLC physically sound. The radiative transfer equation was simplified to make PLC mathematically simple. Near-nadir observations derived from a Landsat 8 Operational Land Imager (OLI) image covering a mountainous region and wide field-of-view observations derived from simulation using a canopy reflectance model were combined to test the PLC correction method. Multi-criteria were used to provide objective evaluation results. The performances were compared to that of five other methods: CC, SCS + C, and SE, which are empirical parameter-based methods, and SCS and D-S, which are semi-physical methods without empirical parameter. All the six methods could significantly reduce the topographic effects. However, SCS showed obvious overcorrection for near-nadir observations. The correction results from D-S showed an obvious positive bias. For near-nadir observations, the performance of PLC was comparable to the well-validated parameter-based methods. For wide field-of-view observations, PLC obviously outperformed all other methods. Because of the physical soundness and mathematical simplicity, PLC provides an efficient approach to correct the terrain-induced canopy BRDF distortion and will facilitate the exploitation of multi-angular information for biophysical parameter retrieval over mountainous regions.
Gaofei Yin; Ainong Li; Shengbiao Wu; Weiliang Fan; Yelu Zeng; Kai Yan; Baodong Xu; Jing Li; Qinhuo Liu. PLC: A simple and semi-physical topographic correction method for vegetation canopies based on path length correction. Remote Sensing of Environment 2018, 215, 184 -198.
AMA StyleGaofei Yin, Ainong Li, Shengbiao Wu, Weiliang Fan, Yelu Zeng, Kai Yan, Baodong Xu, Jing Li, Qinhuo Liu. PLC: A simple and semi-physical topographic correction method for vegetation canopies based on path length correction. Remote Sensing of Environment. 2018; 215 ():184-198.
Chicago/Turabian StyleGaofei Yin; Ainong Li; Shengbiao Wu; Weiliang Fan; Yelu Zeng; Kai Yan; Baodong Xu; Jing Li; Qinhuo Liu. 2018. "PLC: A simple and semi-physical topographic correction method for vegetation canopies based on path length correction." Remote Sensing of Environment 215, no. : 184-198.
Spatial heterogeneity is present in the land surface at every scale and is one of the key factors that introduces inherent uncertainty into simulations of land surface processes and parameter retrieval based on remotely sensed data. Because of a lack of understanding of the heterogeneous characteristics of global mixed pixels, few studies have focused on modeling and inversion algorithms in heterogeneous areas. This paper presents a parameterization scheme to describe land cover heterogeneity quantitatively by composition and boundary information based on high-resolution land cover products. Global heterogeneity features at the 1-km scale are extracted from the ‘GlobeLand30’ land cover dataset with a spatial resolution of 30 m. The composition analysis of global mixed pixels shows that only 35% of pixels over the land surface of Earth are covered by a single land cover type, namely, pure pixels, and only 25.8% are located in vegetated areas. Pixels mixed with water are more common than pixels mixed with any other non-vegetation type. The fragmentation analysis of typical biomes based on the boundary length shows that the savanna is the most heterogeneous biome, while the evergreen broadleaf forest is the least heterogeneous. Deciduous needleleaf forests are significantly affected by canopy height differences, while crop and grass biomes are less affected. Lastly, the strengths and limitations of the method and the application of the land cover heterogeneity characteristics extracted in this study are discussed.
Wentao Yu; Jing Li; Qinhuo Liu; Yelu Zeng; Jing Zhao; Baodong Xu; Gaofei Yin. Global Land Cover Heterogeneity Characteristics at Moderate Resolution for Mixed Pixel Modeling and Inversion. Remote Sensing 2018, 10, 856 .
AMA StyleWentao Yu, Jing Li, Qinhuo Liu, Yelu Zeng, Jing Zhao, Baodong Xu, Gaofei Yin. Global Land Cover Heterogeneity Characteristics at Moderate Resolution for Mixed Pixel Modeling and Inversion. Remote Sensing. 2018; 10 (6):856.
Chicago/Turabian StyleWentao Yu; Jing Li; Qinhuo Liu; Yelu Zeng; Jing Zhao; Baodong Xu; Gaofei Yin. 2018. "Global Land Cover Heterogeneity Characteristics at Moderate Resolution for Mixed Pixel Modeling and Inversion." Remote Sensing 10, no. 6: 856.
Topographic correction methods rarely consider the canopy parameter effects directly and explicitly for sloping canopies. In order to address this problem, the topographic correction method MFM-GOST2 was developed by implementing the second version of the Geometric-Optical model for Sloping Terrains (the GOST2 model) in the multiple forward mode (MFM) inversion framework. First, a look up table (LUT) was constructed by multiple forward modeling of the GOST2 model; second, the radiance of a remotely sensed image and its corresponding topographic data were used for searching potential canopy parameter combinations from the LUT; and third, the corrected radiance was determined by averaging potential radiances of horizontal canopies from the LUT according to the canopy parameter combinations. The MFM-GOST2 and twelve generally used topographic correction methods were evaluated via a case study by visual analysis, linear relationship analysis, and the rose diagram analysis. The result showed that the MFM-GOST2 method successfully removed most of the topographic effects of a subset image of the Landsat-8 image in a case study. The case study also illustrates that the rose diagram analysis is a good way to evaluate topographic corrections, but the linear relationship analysis cannot be used independently for the evaluations because the decorrelation is not a sufficient condition to determine a successful topographic correction.
Weiliang Fan; Jing Li; Qinhuo Liu; Qian Zhang; Gaifei Yin; Ainong Li; Yelu Zeng; Baodong Xu; Xiaojun Xu; Guomo Zhou; Huaqiang Du. Topographic Correction of Forest Image Data Based on the Canopy Reflectance Model for Sloping Terrains in Multiple Forward Mode. Remote Sensing 2018, 10, 717 .
AMA StyleWeiliang Fan, Jing Li, Qinhuo Liu, Qian Zhang, Gaifei Yin, Ainong Li, Yelu Zeng, Baodong Xu, Xiaojun Xu, Guomo Zhou, Huaqiang Du. Topographic Correction of Forest Image Data Based on the Canopy Reflectance Model for Sloping Terrains in Multiple Forward Mode. Remote Sensing. 2018; 10 (5):717.
Chicago/Turabian StyleWeiliang Fan; Jing Li; Qinhuo Liu; Qian Zhang; Gaifei Yin; Ainong Li; Yelu Zeng; Baodong Xu; Xiaojun Xu; Guomo Zhou; Huaqiang Du. 2018. "Topographic Correction of Forest Image Data Based on the Canopy Reflectance Model for Sloping Terrains in Multiple Forward Mode." Remote Sensing 10, no. 5: 717.
Baodong Xu; Jing Li; Taejin Park; Qinhuo Liu; Yelu Zeng; Gaofei Yin; Jing Zhao; Weiliang Fan; Le Yang; Yuri Knyazikhin; Ranga Myneni. An integrated method for validating long-term leaf area index products using global networks of site-based measurements. Remote Sensing of Environment 2018, 209, 134 -151.
AMA StyleBaodong Xu, Jing Li, Taejin Park, Qinhuo Liu, Yelu Zeng, Gaofei Yin, Jing Zhao, Weiliang Fan, Le Yang, Yuri Knyazikhin, Ranga Myneni. An integrated method for validating long-term leaf area index products using global networks of site-based measurements. Remote Sensing of Environment. 2018; 209 ():134-151.
Chicago/Turabian StyleBaodong Xu; Jing Li; Taejin Park; Qinhuo Liu; Yelu Zeng; Gaofei Yin; Jing Zhao; Weiliang Fan; Le Yang; Yuri Knyazikhin; Ranga Myneni. 2018. "An integrated method for validating long-term leaf area index products using global networks of site-based measurements." Remote Sensing of Environment 209, no. : 134-151.
Soybean cultivation in China has significantly decreased due to the rising import of genetically modified soybeans from other countries. Understanding soybean’s extent and change information is of great value for national agricultural policy implications and global food security. Some previous studies have explored the quantitative relationships between crop area and spectral variables derived from remote sensing data. However, both those linear or non-linear relationships were expressed by global regression models, which ignored the spatial non-stationarity of crop spectral signature and may limit the prediction accuracy. This study presented a geographically weighted regression model (GWR) to estimate fractional soybean at 250 m spatial resolution in Heilongjiang Province, one of the most important food production regions in China, using time-series MODIS data and high-quality calibration information derived from Landsat data. A forward stepwise optimization strategy was embedded with the GWR model to select the optimal subset of independent variables for soybeans. Normalized Difference Vegetation Index (NDVI) of Julian day 233 to 257 when soybeans are filling seed was found to be the most important temporal period for sub-pixel soybean area estimation. Our MODIS-based soybean area compared well with Landsat-based results at pixel-level. Also, there was a good agreement between the MODIS-based result and census data at county level, with the coefficient of determination (R2) of 0.80 and the root mean square error (RMSE) was 340.21 km2. Additionally, F-test results showed GWR model had better model goodness-of-fit and higher prediction accuracy than the traditional ordinary least squares (OLS) model. These promising results suggest crop spectral variations both at temporal and spatial scales should be considered when exploring its relationship with pixel-level crop acreage. The optimized GWR model by combining an automated feature selection strategy has great potential for estimating sub-pixel crop area at regional scale based on remote sensing time-series data.
Qiong Hu; Yaxiong Ma; Baodong Xu; Qian Song; Huajun Tang; Wenbin Wu. Estimating Sub-Pixel Soybean Fraction from Time-Series MODIS Data Using an Optimized Geographically Weighted Regression Model. Remote Sensing 2018, 10, 491 .
AMA StyleQiong Hu, Yaxiong Ma, Baodong Xu, Qian Song, Huajun Tang, Wenbin Wu. Estimating Sub-Pixel Soybean Fraction from Time-Series MODIS Data Using an Optimized Geographically Weighted Regression Model. Remote Sensing. 2018; 10 (4):491.
Chicago/Turabian StyleQiong Hu; Yaxiong Ma; Baodong Xu; Qian Song; Huajun Tang; Wenbin Wu. 2018. "Estimating Sub-Pixel Soybean Fraction from Time-Series MODIS Data Using an Optimized Geographically Weighted Regression Model." Remote Sensing 10, no. 4: 491.
The operational Moderate Resolution Imaging Spectroradiometer (MODIS) Leaf Area Index (LAI) and Fraction of Photosynthetically Active Radiation absorbed by vegetation (FPAR) algorithm has been successfully implemented for Visible Infrared Imager Radiometer Suite (VIIRS) observations by optimizing a small set of configurable parameters in Look-Up-Tables (LUTs). Our preliminary evaluation showed reasonable agreement between VIIRS and MODIS LAI/FPAR retrievals. However, there is a need for a more comprehensive investigation to assure continuity of multi-sensor global LAI/FPAR time series, as the preliminary evaluation was spatiotemporally limited. In this study, we use a multi-year (2012–2016) global LAI/FPAR product generated from VIIRS and MODIS to evaluate for spatiotemporal consistency. We also quantify uncertainty of the product by utilizing available ground measurements. For both consistency and uncertainty evaluation, we account for variations in biome type and temporal resolution. Our results indicate that the LAI/FPAR retrievals from VIIRS and MODIS are consistent at different spatial (i.e., global and site) and temporal (i.e., 8-day, seasonal and annual) scales. The estimate of mean discrepancy (−0.006 ± 0.013 for LAI and −0.002 ± 0.002 for FPAR) meets the stability requirement for long-term LAI/FPAR Earth System Data Records (ESDRs) from multi-sensors as suggested by the Global Climate Observing System (GCOS). It is noteworthy that the rate of retrievals from the radiative transfer-based main algorithm is also comparable between two sensors. However, a relatively larger discrepancy over tropical forests was observed due to reflectance saturation and an unexpected interannual variation of main algorithm success was noticed due to instability in input surface reflectances. The uncertainties/relative uncertainties of VIIRS and MODIS LAI (FPAR) products assessed through comparisons to ground measurements are estimated to be 0.60/42.2% (0.10/24.4%) and 0.55/39.3% (0.11/26%), respectively. Note that the validated LAI were only distributed in low domains (~2.5), resulting in large relative uncertainty. Therefore, more ground measurements are needed to achieve a more comprehensive evaluation result of product uncertainty. The results presented here generally imbue confidence in the consistency between VIIRS and MODIS LAI/FPAR products and the feasibility of generating long-term multi-sensor LAI/FPAR ESDRs time series.
Baodong Xu; Taejin Park; Kai Yan; Chi Chen; Yelu Zeng; Wanjuan Song; Gaofei Yin; Jing Li; Qinhuo Liu; Yuri Knyazikhin; Ranga B. Myneni. Analysis of Global LAI/FPAR Products from VIIRS and MODIS Sensors for Spatio-Temporal Consistency and Uncertainty from 2012–2016. Forests 2018, 9, 73 .
AMA StyleBaodong Xu, Taejin Park, Kai Yan, Chi Chen, Yelu Zeng, Wanjuan Song, Gaofei Yin, Jing Li, Qinhuo Liu, Yuri Knyazikhin, Ranga B. Myneni. Analysis of Global LAI/FPAR Products from VIIRS and MODIS Sensors for Spatio-Temporal Consistency and Uncertainty from 2012–2016. Forests. 2018; 9 (2):73.
Chicago/Turabian StyleBaodong Xu; Taejin Park; Kai Yan; Chi Chen; Yelu Zeng; Wanjuan Song; Gaofei Yin; Jing Li; Qinhuo Liu; Yuri Knyazikhin; Ranga B. Myneni. 2018. "Analysis of Global LAI/FPAR Products from VIIRS and MODIS Sensors for Spatio-Temporal Consistency and Uncertainty from 2012–2016." Forests 9, no. 2: 73.
In recent years, China has developed and launched several satellites with high spatial resolutions, such as the resources satellite No. 3 (ZY-3) with a multi-spectral camera (MUX) and 5.8 m spatial resolution, the satellite GaoFen No. 1 (GF-1) with a wide field of view (WFV) camera and 16 m spatial resolution, and the environment satellite (HJ-1A/B) with a charge-coupled device (CCD) sensor and 30 m spatial resolution. First, to analyze the potential application of ZY-3 MUX, GF-1 WFV, and HJ-1 CCD to extract the leaf area index (LAI) at the regional scale, this study estimated LAI from the relationships between physical model-based spectral vegetation indices (SVIs) and LAI values that were generated from look-up tables (LUTs), simulated from the combination of the PROSPECT-5B leaf model and the scattering by arbitrarily inclined leaves with the hot-spot effect (SAILH) canopy reflectance model. Second, to assess the surface reflectance quality of these sensors after data preprocessing, the well-processed surface reflectance products of the Landsat-8 operational land imager (OLI) sensor with a convincing data quality were used to compare the performances of ZY-3 MUX, GF-1 WFV, and HJ-1 CCD sensors both in theory and reality. Apart from several reflectance fluctuations, the reflectance trends were coincident, and the reflectance values of the red and near-infrared (NIR) bands were comparable among these sensors. Finally, to analyze the accuracy of the LAI estimated from ZY-3 MUX, GF-1 WFV, and HJ-1 CCD, the LAI estimations from these sensors were validated based on LAI field measurements in Huailai, Hebei Province, China. The results showed that the performance of the LAI that was inversed from ZY-3 MUX was better than that from GF-1 WFV, and HJ-1 CCD, both of which tended to be systematically underestimated. In addition, the value ranges and accuracies of the LAI inversions both decreased with decreasing spatial resolution.
Jing Zhao; Jing Li; Qinhuo Liu; Hongyan Wang; Chen Chen; Baodong Xu; Shanlong Wu. Comparative Analysis of Chinese HJ-1 CCD, GF-1 WFV and ZY-3 MUX Sensor Data for Leaf Area Index Estimations for Maize. Remote Sensing 2018, 10, 68 .
AMA StyleJing Zhao, Jing Li, Qinhuo Liu, Hongyan Wang, Chen Chen, Baodong Xu, Shanlong Wu. Comparative Analysis of Chinese HJ-1 CCD, GF-1 WFV and ZY-3 MUX Sensor Data for Leaf Area Index Estimations for Maize. Remote Sensing. 2018; 10 (2):68.
Chicago/Turabian StyleJing Zhao; Jing Li; Qinhuo Liu; Hongyan Wang; Chen Chen; Baodong Xu; Shanlong Wu. 2018. "Comparative Analysis of Chinese HJ-1 CCD, GF-1 WFV and ZY-3 MUX Sensor Data for Leaf Area Index Estimations for Maize." Remote Sensing 10, no. 2: 68.
Gaofei Yin; Ainong Li; Huaan Jin; Wei Zhao; Jinhu Bian; Yonghua Qu; Yelu Zeng; Baodong Xu. Derivation of temporally continuous LAI reference maps through combining the LAINet observation system with CACAO. Agricultural and Forest Meteorology 2017, 233, 209 -221.
AMA StyleGaofei Yin, Ainong Li, Huaan Jin, Wei Zhao, Jinhu Bian, Yonghua Qu, Yelu Zeng, Baodong Xu. Derivation of temporally continuous LAI reference maps through combining the LAINet observation system with CACAO. Agricultural and Forest Meteorology. 2017; 233 ():209-221.
Chicago/Turabian StyleGaofei Yin; Ainong Li; Huaan Jin; Wei Zhao; Jinhu Bian; Yonghua Qu; Yelu Zeng; Baodong Xu. 2017. "Derivation of temporally continuous LAI reference maps through combining the LAINet observation system with CACAO." Agricultural and Forest Meteorology 233, no. : 209-221.