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Article ; Online: A dynamic-leaf light use efficiency model for improving gross primary production estimation

Lingxiao Huang / Wenping Yuan / Yi Zheng / Yanlian Zhou / Mingzhu He / Jiaxin Jin / Xiaojuan Huang / Siyuan Chen / Meng Liu / Xiaobin Guan / Shouzheng Jiang / Xiaofeng Lin / Zhao-Liang Li / Ronglin Tang

Environmental Research Letters, Vol 19, Iss 1, p

2024 Volume 014066

Abstract: Accurate quantification of terrestrial gross primary production (GPP) is integral for enhancing our understanding of the global carbon budget and climate change. The light use efficiency (LUE) model is undoubtedly the most extensively applied method for ... ...

Abstract	Accurate quantification of terrestrial gross primary production (GPP) is integral for enhancing our understanding of the global carbon budget and climate change. The light use efficiency (LUE) model is undoubtedly the most extensively applied method for GPP estimation. However, the two-leaf (TL)-LUE model using a ‘potential’ sunlit leaf area index (LAI _su ) can separate a portion of LAI _su even when the canopy does not receive any direct radiation, leading to the underestimation of GPP under cloudy and overcast days. Here, we developed a dynamic-leaf (DL) LUE model by introducing an ‘effective’ LAI _su to improve GPP estimation, which considers the comprehensive contribution of LAI _su when the canopy does and does not receive direct radiation. In particular, the new model decreases LAI _su to zero when direct radiation reaches zero. Our evaluation at eight ChinaFLUX sites showed that (1) the DL-LUE model outperformed the most well-known BL-LUE (namely, the MOD17 GPP algorithm) and TL-LUE models in reproducing the daily in situ GPP, especially at four forest sites [reducing the root mean square error (RMSE) from 1.74 g C m ^−2 d ^−1 and 1.53 g C m ^−2 d ^−1 to 1.36 g C m ^−2 d ^−1 and increasing the coefficient of determination ( R ^2 ) from 0.74 and 0.79–0.82, respectively]. Moreover, the improvements were particularly pronounced at longer temporal scales, as indicated by the RMSE decreasing from 29.32 g C m ^−2 month ^−1 and28.11 g C m ^−2 month ^−1 to 25.81 g C m ^−2 month ^−1 at a monthly scale and from 231.82 g C m ^−2 yr ^−1 and 221.60 g C m ^−2 yr ^−1 –200.00 g C m ^−2 yr ^−1 at a yearly scale; (2) the DL-LUE model mitigated the systematic underestimation of the in situ GPP by both the TL-LUE and BL-LUE models when the clearness index (CI) was below 0.5, as indicated by the Bias reductions of 0.25 g C m ^−2 d ^−1 and 0.46 g C m ^−2 d ^−1 , respectively; and (3) the contributions of the shaded GPP to the total GPP from the DL-LUE model were higher by 0.07–0.16 than those from the TL-LUE model across the ...
Keywords	gross primary production ; light use efficiency (LUE) models ; dynamic-leaf LUE model ; big-leaf and two-leaf LUE models ; sunlit and shaded leaves ; Environmental technology. Sanitary engineering ; TD1-1066 ; Environmental sciences ; GE1-350 ; Science ; Q ; Physics ; QC1-999
Subject code	550
Language	English
Publishing date	2024-01-01T00:00:00Z
Publisher	IOP Publishing
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Uncertainty analysis of SVD-based spaceborne far–red sun-induced chlorophyll fluorescence retrieval using TanSat satellite data

Shilei Li / Maofang Gao / Zhao-Liang Li / Sibo Duan / Pei Leng

International Journal of Applied Earth Observations and Geoinformation, Vol 103, Iss , Pp 102517- (2021)

2021

Abstract: The singular value decomposition (SVD) method, a sun-induced chlorophyll fluorescence (SIF) retrieval approach, has been used widely in far-red SIF spaceborne retrievals on a global scale. However, due to its semi-empirical nature, setting different ... ...

Abstract	The singular value decomposition (SVD) method, a sun-induced chlorophyll fluorescence (SIF) retrieval approach, has been used widely in far-red SIF spaceborne retrievals on a global scale. However, due to its semi-empirical nature, setting different parameter values may affect its retrieval accuracy, and ultimately have a large impact on its application. Hence, in this study, we evaluated the impact of parameter selection on the far-red SIF retrieval of this approach using TanSat satellite data. We first retrieved the far-red SIF within a narrow spectral window of 757.4–759.2 nm using the first four singular vectors (SVs) that were derived from the snow and soil spectra in a globally distributed training set. The retrievals are highly consistent with TanSat mission SIF (R2 = 0.72), indicating the reliability of our retrieval results. Then, the uncertainty was executed based on the above SIF retrievals and evaluated from five metrics: the number of SVs, length of the fitting window, type of training set elements, proportion of training set elements, and spatial distribution of the training set. Results showed that an unwise selection of the number of SVs would result in large retrieval errors (R2 = 0.03). Meanwhile, a fitting window that is too short or does not include a strong Fraunhofer line would cause severe errors and a large number of negative values (R2 = 0.06). Further, failure to include training samples below the equatorial zone in the Southern Hemisphere, and high latitude samples in the Northern Hemisphere, leads to poorer outcomes (R2 = 0.57). In contrast, the other global sampling metrics had little effect on the retrieval results (generally, R2 > 0.97). Accordingly, the relevant suggestions for using this method in the future are listed in this study for reference, with the potential to improve the accuracy of SIF retrievals from ultra-high spectral resolution instruments in the far-red spectrum.
Keywords	Singular value decomposition (SVD) ; Sun-induced chlorophyll fluorescence (SIF) ; TanSat satellite ; Physical geography ; GB3-5030 ; Environmental sciences ; GE1-350
Language	English
Publishing date	2021-12-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Evaluation of Three Parametric Models for Estimating Directional Thermal Radiation from Simulation, Airborne, and Satellite Data

Xiangyang Liu / Bo-Hui Tang / Zhao-Liang Li

Remote Sensing, Vol 10, Iss 3, p

2018 Volume 420

Abstract: An appropriate model to correct thermal radiation anisotropy is important for the wide applications of land surface temperature (LST). This paper evaluated the performance of three published directional thermal radiation models—the Roujean–Lagouarde (RL) ...

Abstract	An appropriate model to correct thermal radiation anisotropy is important for the wide applications of land surface temperature (LST). This paper evaluated the performance of three published directional thermal radiation models—the Roujean–Lagouarde (RL) model, the Bidirectional Reflectance Distribution Function (BRDF) model, and the Vinnikov model—at canopy and pixel scale using simulation, airborne, and satellite data. The results at canopy scale showed that (1) the three models could describe directional anisotropy well and the Vinnikov model performed the best, especially for erectophile canopy or low leaf area index (LAI); (2) the three models reached the highest fitting accuracy when the LAI varied from 1 to 2; and (3) the capabilities of the three models were all restricted by the hotspot effect, plant height, plant spacing, and three-dimensional structure. The analysis at pixel scale indicated a consistent result that the three models presented a stable effect both on verification and validation, but the Vinnikov model had the best ability in the erectophile canopy (savannas and grassland) and low LAI (barren or sparsely vegetated) areas. Therefore, the Vinnikov model was calibrated for different land cover types to instruct the angular correction of LST. Validation with the Surface Radiation Budget Network (SURFRAD)-measured LST demonstrated that the root mean square (RMSE) of the Moderate Resolution Imaging Spectroradiometer (MODIS) LST product could be decreased by 0.89 K after angular correction. In addition, the corrected LST showed better spatial uniformity and higher angular correlation.
Keywords	land surface temperature (LST) ; directional thermal radiation ; parametric model ; MODIS ; AATSR ; Science ; Q
Subject code	550
Language	English
Publishing date	2018-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: A two-stage light-use efficiency model for improving gross primary production estimation in agroecosystems

Lingxiao Huang / Xiaofeng Lin / Shouzheng Jiang / Meng Liu / Yazhen Jiang / Zhao-Liang Li / Ronglin Tang

Environmental Research Letters, Vol 17, Iss 10, p

2022 Volume 104021

Abstract: Accurate quantification of gross primary production (GPP) in agroecosystems not only improves our ability to understand the global carbon budget but also plays a critical role in human welfare and development. Light-use efficiency (LUE) models have been ... ...

Abstract	Accurate quantification of gross primary production (GPP) in agroecosystems not only improves our ability to understand the global carbon budget but also plays a critical role in human welfare and development. Light-use efficiency (LUE) models have been widely applied in estimating regional and global GPP due to their simple structure and clear physical basis. However, maximum LUE ( ${\varepsilon _{{\text{max}}}}$ ), a key photosynthetic parameter in LUE models, has generally been treated as a constant, leading to common overestimation and underestimation of low and high magnitudes of GPP, respectively. Here, we propose a parsimonious and practical two-stage LUE (TS-LUE) model to improve GPP estimates by (a) considering seasonal variations of ${\varepsilon _{{\text{max}}}}$ , and (b) separately re-parameterizing ${\varepsilon _{{\text{max}}}}$ in the green-up and senescence stages. The TS-LUE model is inter-compared with state-of-the-art ${\varepsilon _{{\text{max}}}}$ –static moderate resolution imaging spectroradiometer-GPP, eddy-covariance-LUE, and vegetation production models. Validation results at 14 FLUXNET sites for five crop species showed that: (a) the TS-LUE model significantly reduced the large bias at high- and low-level GPP as produced by the three ${\varepsilon _{{\text{max}}}}$ –static LUE models for all crop types; and (b) the TS-LUE model generated daily GPP estimates in good agreement with in-situ measurements and was found to outperform the three ${\varepsilon _{{\text{max}}}}$ –static LUE models. Especially compared to the well-known moderate resolution imaging spectroradiometer-GPP, the TS-LUE model could remarkably decrease the root mean square error (in gC m ^−2 d ^−1 ) by 24.2% and 35.4% (from 3.84 to 2.91 and 2.48) and could increase the coefficient of determination by 14.7% and 20% (from 0.75 to 0.86 and 0.9) when the leaf area index (LAI) and infrared reflectance of vegetation (NIR _v ) were used to re-parameterize the ${\varepsilon _{{\text{max}}}}$ , respectively. The TS-LUE model ...
Keywords	gross primary production ; maximum light-use efficiency ; two-stage light-use efficiency model ; seasonal fluctuations ; agroecosystems ; Environmental technology. Sanitary engineering ; TD1-1066 ; Environmental sciences ; GE1-350 ; Science ; Q ; Physics ; QC1-999
Subject code	333
Language	English
Publishing date	2022-01-01T00:00:00Z
Publisher	IOP Publishing
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Precipitation regimes primarily drive the carbon uptake in the Tibetan Plateau

Lei He / Yaowen Xie / Jian Wang / Juanjuan Zhang / Menglin Si / Zecheng Guo / Changhui Ma / Qiang Bie / Zhao-Liang Li / Jian-Sheng Ye

Ecological Indicators, Vol 154, Iss , Pp 110694- (2023)

2023

Abstract: Warming and precipitation variations have significant impacts on carbon uptake in the Tibetan Plateau. However, which climatic variable or process primarily drives the inter-annual variations of carbon uptake is not clear. Using multiple gross primary ... ...

Abstract	Warming and precipitation variations have significant impacts on carbon uptake in the Tibetan Plateau. However, which climatic variable or process primarily drives the inter-annual variations of carbon uptake is not clear. Using multiple gross primary productivity (GPP) estimates, we study the controlling factors of the previous-year lagged effect and evaluate the effects of climate variables on GPP in the Tibetan Plateau. Results show that the lagged ecosystems in which productivity is significantly associated with previous-year precipitation are more sensitive to biotic and environmental factors than unlagged ones. In addition, previous-year precipitation as a whole has a positive impact on GPP over the Tibetan Plateau. Furthermore, precipitation regimes including precipitation intensity and dry-days fraction, and current-year precipitation amount primarily drive the GPP variabilities instead of temperature, vapor pressure deficit (VPD), and radiation at the grid scale. Future projections suggest that precipitation amount and intensity will increase, and dry-days fraction will decrease, which indicates that precipitation might have a more complicated impact on carbon uptake via variations in intra-distribution. Our study reveals the primary climatic factors that influence the variations of carbon uptake in the Tibetan Plateau, offering valuable insights for accurate carbon cycle modeling.
Keywords	Lagged precipitation effect ; Precipitation regimes ; Warming ; Gross primary productivity ; Tibetan Plateau ; Ecology ; QH540-549.5
Subject code	550
Language	English
Publishing date	2023-10-01T00:00:00Z
Publisher	Elsevier
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: Evaluation of two end-member-based models for regional land surface evapotranspiration estimation from MODIS data

Tang, Ronglin / Zhao-Liang Li

Agricultural and forest meteorology. 2015 Mar. 15, v. 202

2015

Abstract: The importance of accurate estimations of regional and global evapotranspiration (ET) has been recognized worldwide in the fields of hydrology, water resource management, meteorology, and global climate change. Given the different structures of the end- ... ...

Abstract	The importance of accurate estimations of regional and global evapotranspiration (ET) has been recognized worldwide in the fields of hydrology, water resource management, meteorology, and global climate change. Given the different structures of the end-member-based surface energy balance algorithm for land (SEBAL) and surface temperature–vegetation index (Ts–VI) triangle models, which nonetheless use essentially the same definitions of dry and wet pixels, questions arise regarding the nature of the differences between the two models’ estimated sensible heat fluxes and latent heat fluxes and what controls the heat flux differences. This study aims to investigate how the SEBAL model and the Ts–VI triangle method differ from each other in the regional evaporative fraction (EF) and ET estimates through analytical deduction and model applications. Because both the SEBAL model and the Ts–VI triangle model are of limited use when deep-layer soil moisture is moderately to significantly stressed, MODIS remote sensing data from 23 clear-sky overpass times between January 2010 and late October 2011, covering a wide range of soil moisture content and fractional vegetation cover conditions, are acquired to assess the two models over a non-water stressed study area on the North China plain. The results show that the SEBAL model is able to produce satisfactory sensible heat flux (H) and latent heat flux (LE) estimates compared with ground-based large aperture scintillometer measurements at the Yucheng station, with small biases of 4.1W/m2 and 2.3W/m2 and root mean square errors (RMSEs) of 46.4W/m2 and 48.6W/m2, respectively. However, the Ts–VI triangle model produces much worse H and LE estimates, with biases of 98.5W/m2 and −92.2W/m2 and RMSEs of 119.3W/m2 and 115.5W/m2, respectively. Variations of pixel-by-pixel surface available energy and momentum roughness length over the study area are responsible for the differences of the H and LE estimates between the two models. The SEBAL model produces larger EF and ET for most pixels than the Ts–VI triangle method when the same group of dry and wet pixels is applied, especially for pixels characterized by medium to high vegetation fractions or surface soil moisture contents. The Ts–VI triangle method is more sensitive to the surface temperatures of the dry and wet pixels than the SEBAL model. The findings from this study benefit the proper selection of end-member-based models for regional ET estimation and aid in quantifying the resultant uncertainties in the model-derived surface energy components.
Keywords	algorithms ; climate change ; energy balance ; evapotranspiration ; heat transfer ; meteorology ; models ; moderate resolution imaging spectroradiometer ; momentum ; roughness ; soil water ; soil water content ; spatial data ; surface temperature ; uncertainty ; vegetation cover ; water management ; China
Language	English
Dates of publication	2015-0315
Size	p. 69-82.
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	409905-9
ISSN	0168-1923
ISSN	0168-1923
DOI	10.1016/j.agrformet.2014.12.005
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: An Improved Spatio-Temporal Adaptive Data Fusion Algorithm for Evapotranspiration Mapping

Tong Wang / Ronglin Tang / Zhao-Liang Li / Yazhen Jiang / Meng Liu / Lu Niu

Remote Sensing, Vol 11, Iss 7, p

2019 Volume 761

Abstract: Continuous high spatio-temporal resolution monitoring of evapotranspiration (ET) is critical for water resource management and the quantification of irrigation water efficiency at both global and local scales. However, available remote sensing satellites ...

Abstract	Continuous high spatio-temporal resolution monitoring of evapotranspiration (ET) is critical for water resource management and the quantification of irrigation water efficiency at both global and local scales. However, available remote sensing satellites cannot generally provide ET data at both high spatial and temporal resolutions. Data fusion methods have been widely applied to estimate ET at a high spatio-temporal resolution. Nevertheless, most fusion methods applied to ET are initially used to integrate land surface reflectance, the spectral index and land surface temperature, and few studies completely consider the influencing factor of ET. To overcome this limitation, this paper presents an improved ET fusion method, namely, the spatio-temporal adaptive data fusion algorithm for evapotranspiration mapping (SADFAET), by introducing critical surface temperature (the corresponding temperature to decide soil moisture), importing the weights of surface ET-indicative similarity (the influencing factor of ET, which is estimated from remote sensing data) and modifying the spectral similarity (the differences in spectral characteristics of different spatial resolution images) for the enhanced spatial and temporal adaptive reflectance fusion model (ESTARFM). We fused daily Moderate Resolution Imaging Spectroradiometer (MODIS) and periodic Landsat 8 ET data in the SADFAET for the experimental area downstream of the Heihe River basin from April to October 2015. The validation results, based on ground-based ET measurements, indicated that the SADFAET could successfully fuse MODIS and Landsat 8 ET data (mean percent error: −5%), with a root mean square error of 45.7 W/m 2 , whereas the ESTARFM performed slightly worse, with a root mean square error of 50.6 W/m 2 . The more physically explainable SADFAET could be a better alternative to the ESTARFM for producing ET at a high spatio-temporal resolution.
Keywords	evapotranspiration ; fusion ; multi-source satellite data ; Landsat 8 ; MODIS ; SADFAET ; Science ; Q
Subject code	550
Language	English
Publishing date	2019-03-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article: A framework for the retrieval of all-weather land surface temperature at a high spatial resolution from polar-orbiting thermal infrared and passive microwave data

Duan, Si-Bo / Pei Leng / Zhao-Liang Li

Remote sensing of environment. 2017 June 15, v. 195

2017

Abstract: Land surface temperature (LST) is an important parameter associated with the land-atmosphere interface. Satellite remote sensing is the most effective method of measuring LST at regional and global scales. Satellite thermal infrared (TIR) measurements ... ...

Abstract	Land surface temperature (LST) is an important parameter associated with the land-atmosphere interface. Satellite remote sensing is the most effective method of measuring LST at regional and global scales. Satellite thermal infrared (TIR) measurements are widely used to retrieve LST with high accuracy and high spatial resolution but are limited to cloud-free conditions due to their inability to penetrate clouds. By contrast, satellite passive microwave (PMW) measurements are capable of penetrating clouds and providing data regardless of the cloud conditions. However, PMW measurements have limitations, such as a low spatial resolution and low temperature retrieval accuracy. Furthermore, temperature retrieval from PMW measurements yields the subsurface temperature, which differs from the LST retrieved from TIR measurements (skin temperature). This study proposes a framework for the retrieval of all-weather LST at a high spatial resolution by combining the advantages of TIR and PMW measurements. Compared to the MODIS LST product, the all-weather LST reflects the spatial variations in LST accurately. In situ LST measurements at four sites in an arid area of northwest China were used to evaluate the accuracy of the all-weather LST. The root mean square error of the LST under cloud-free conditions was approximately 2K, whereas that of the LST under cloudy conditions varied from 3.5K to 4.4K. The results indicate that the all-weather LST retrieval algorithm can provide an LST dataset with reasonable accuracy and a high spatial resolution under clear and cloudy conditions.
Keywords	algorithms ; data collection ; moderate resolution imaging spectroradiometer ; remote sensing ; satellites ; surface temperature ; China
Language	English
Dates of publication	2017-0615
Size	p. 107-117.
Publishing place	Elsevier Inc.
Document type	Article
ZDB-ID	431483-9
ISSN	0034-4257
ISSN	0034-4257
DOI	10.1016/j.rse.2017.04.008
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Remote Sensing of Spatiotemporal Changes in Wetland Geomorphology Based on Type 2 Fuzzy Sets

Hongyuan Huo / Jifa Guo / Zhao-Liang Li / Xiaoguang Jiang

Remote Sensing, Vol 9, Iss 7, p

A Case Study of Beidagang Wetland from 1975 to 2015

2017 Volume 683

Abstract: Few studies have considered the spatiotemporal changes in wetland land cover based on type 2 fuzzy sets using long-term series of remotely sensed data. This paper presents an improved interval type 2 fuzzy c-means (IT2FCM*) approach to analyse the ... ...

Abstract	Few studies have considered the spatiotemporal changes in wetland land cover based on type 2 fuzzy sets using long-term series of remotely sensed data. This paper presents an improved interval type 2 fuzzy c-means (IT2FCM) approach to analyse the spatial and temporal changes in the geomorphology of the Beidagang wetland in North China from 1975 to 2015 based on long-term Landsat data. Unlike traditional type 1 fuzzy c-means methods, the IT2FCM algorithm based on interval type-2 fuzzy set has an ability to better handle the spectral uncertainty. Four indexes were adopted to validate the separability of classes with the IT2FCM* algorithm. These four validity indexes showed that IT2FCM* obtained better results than traditional methods. Additionally, the accuracy of the classification results was assessed based on the confusion matrix and kappa coefficient, which were high for the analysis of wetland landscape changes. Based on the analysis of separability of classes with the IT2FCM* algorithm using four validity indexes, the classification results, and the membership value images, the long-term series of satellite datasets were processed using the IT2FCM* method, and the study area was classified into six classes. Because water resources and vegetation are two key wetland components, the water resource dynamics and vegetation dynamics, based on the normalized difference vegetation index (NDVI), were analysed in detail according to the spatiotemporal classification results. The results show that the changes in vegetation types have historically been associated with water resource variations and that water resources play an important role in the evolution of vegetation types.
Keywords	Landsat ; wetland ; fuzzy clustering ; spatiotemporal changes ; type-2 fuzzy set ; Science ; Q
Language	English
Publishing date	2017-07-01T00:00:00Z
Publisher	MDPI AG
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Determination of the optimal training principle and input variables in artificial neural network model for the biweekly chlorophyll-a prediction

Yu Liu / Du-Gang Xi / Zhao-Liang Li

PLoS ONE, Vol 10, Iss 3, p e

a case study of the Yuqiao Reservoir, China.

2015 Volume 0119082

Abstract: Predicting the levels of chlorophyll-a (Chl-a) is a vital component of water quality management, which ensures that urban drinking water is safe from harmful algal blooms. This study developed a model to predict Chl-a levels in the Yuqiao Reservoir ( ... ...

Abstract	Predicting the levels of chlorophyll-a (Chl-a) is a vital component of water quality management, which ensures that urban drinking water is safe from harmful algal blooms. This study developed a model to predict Chl-a levels in the Yuqiao Reservoir (Tianjin, China) biweekly using water quality and meteorological data from 1999-2012. First, six artificial neural networks (ANNs) and two non-ANN methods (principal component analysis and the support vector regression model) were compared to determine the appropriate training principle. Subsequently, three predictors with different input variables were developed to examine the feasibility of incorporating meteorological factors into Chl-a prediction, which usually only uses water quality data. Finally, a sensitivity analysis was performed to examine how the Chl-a predictor reacts to changes in input variables. The results were as follows: first, ANN is a powerful predictive alternative to the traditional modeling techniques used for Chl-a prediction. The back program (BP) model yields slightly better results than all other ANNs, with the normalized mean square error (NMSE), the correlation coefficient (Corr), and the Nash-Sutcliffe coefficient of efficiency (NSE) at 0.003 mg/l, 0.880 and 0.754, respectively, in the testing period. Second, the incorporation of meteorological data greatly improved Chl-a prediction compared to models solely using water quality factors or meteorological data; the correlation coefficient increased from 0.574-0.686 to 0.880 when meteorological data were included. Finally, the Chl-a predictor is more sensitive to air pressure and pH compared to other water quality and meteorological variables.
Keywords	Medicine ; R ; Science ; Q
Subject code	310
Language	English
Publishing date	2015-01-01T00:00:00Z
Publisher	Public Library of Science (PLoS)
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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