Detection of algal bloom and factors influencing its formation in Taihu Lake from 2000 to 2011 by MODIS

Huang, Changchun; Li, Yunmei; Yang, Hao; Sun, Deyong; Yu, Zhaoyuan; Zhang, Zhuo; Chen, Xia; Xu, Long

doi:10.1007/s12665-013-2764-6

Cited by 81 publications

(41 citation statements)

References 39 publications

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“…However, FPAR M often produce misleading signals in GPP estimations due to the contamination by atmospheric characteristics [28,70]. Thus, compared with FPAR M among diverse seasons and different spatial biomes, realistic ground FPAR G could be either undervalued [34,71], overvalued [25,29,72], or had a closest agreement [17,34].…”

Section: Impacts Of Fpar On Gpp Estimationsmentioning

confidence: 99%

Tower-Based Validation and Improvement of MODIS Gross Primary Production in an Alpine Swamp Meadow on the Tibetan Plateau

Niu

Zhang

et al. 2016

Remote Sensing

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Alpine swamp meadow on the Tibetan Plateau is among the most sensitive areas to climate change. Accurate quantification of the GPP in alpine swamp meadow can benefit our understanding of the global carbon cycle. The 8-day MODerate resolution Imaging Spectroradiometer (MODIS) gross primary production (GPP) products (GPP_MOD) provide a pathway to estimate GPP in this remote ecosystem. However, the accuracy of the GPP_MOD estimation in this representative alpine swamp meadow is still unknown. Here five years GPP_MOD was validated using GPP derived from the eddy covariance flux measurements (GPP_EC) from 2009 to 2013. Our results indicated that the GPP_EC was strongly underestimated by GPP_MOD with a daily mean less than 40% of EC measurements. To reduce this error, the ground meteorological and vegetation leaf area index (LAI G ) measurements were used to revise the key inputs, the maximum light use efficiency (ε max ) and the fractional photosynthetically active radiation (FPAR M ) in the MOD17 algorithm. Using two approaches to determine the site-specific ε max value, we suggested that the suitable ε max was about 1.61 g C MJ´1 for this alpine swamp meadow which was considerably larger than the default 0.68 g C MJ´1 for grassland. The FPAR M underestimated 22.2% of the actual FPAR (FPAR G ) simulated from the LAI G during the whole study period. Model comparisons showed that the large inaccuracies of GPP_MOD were mainly caused by the underestimation of the ε max and followed by that of the undervalued FPAR. However, the DAO meteorology data in the MOD17 algorithm did not exert a significant affection in the MODIS GPP underestimations. Therefore, site-specific optimized parameters inputs, especially the ε max and FPAR G , are necessary to improve the performance of the MOD17 algorithm in GPP estimation, in which the calibrated MOD17A2 algorithm (GPP_MODR3) could explain 91.6% of GPP_EC variance for the alpine swamp meadow.

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Section: Impacts Of Fpar On Gpp Estimationsmentioning

confidence: 99%

Tower-Based Validation and Improvement of MODIS Gross Primary Production in an Alpine Swamp Meadow on the Tibetan Plateau

Niu

Zhang

et al. 2016

Remote Sensing

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“…The hourly scale maps of K d (490) can show the consecutive dynamic characteristics of K d (490) at both the spatial and the temporal scales. The high K d (490) values were mainly distributed in the northwestern and southwestern areas of Lake Taihu as affected by sediment resuspension and algal blooms in summer [22,29] …”

Section: Short-term Observation Of K D (490) From Gocimentioning

confidence: 99%

“…Each element (reference panel, water and sky) was measured after instrument optimization and calibrating for dark current. The radiances of reference panel (Lp(λ,0 + )), water (Lsw(λ,0 + )), sky (Lsky(λ,0 + )) and the panel again were measured ten times with the abnormal spectra being removed [22]. The rest of spectra were used to calculate Rrs according to the following formula:…”

Section: Introductionmentioning

confidence: 99%

Semi-Analytical Retrieval of the Diffuse Attenuation Coefficient in Large and Shallow Lakes from GOCI, a High Temporal-Resolution Satellite

Huang

Yao

2017

Remote Sensing

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Abstract:Monitoring the dynamic characteristics of the diffuse attenuation coefficient (K d (490)) on the basis of the high temporal-resolution satellite data is critical for regulating the ecological environment of lake. By measuring the in-situ K d (490) and the remote-sensing reflectance, a semi-analytical algorithm for K d (490) was developed to determine the short-term variation of K d (490). From 2006 to 2014, the data about 412 samples (among which 60 were used as match-up points, 282 for calibrating dataset and the remaining 70 for validating dataset) were gathered from nine expeditions to calibrate and validate the aforesaid semi-analytical algorithm. The root mean square percentage error (RMSP) and the mean absolute relative error (MAPE) of validation datasets were respectively 27.44% and 22.60 ± 15.57%, while that of the match-up datasets were respectively 34.29% and 27.57 ± 20.56%. These percentages indicate that the semi-analytical algorithm and Geostationary Ocean Color Imager (GOCI) data are applicable to obtain the short-term variation of K d (490) in the turbid shallow inland waters. The short-term GOCI-observed K d (490) shows a significant seasonal and spatial variation and a similar distribution to the matching Moderate Resolution Imaging Spectroradiometer (MODIS) which derived K d (490). A comparative analysis on wind (observed by buoys) and GOCI-derived K d (490) suggests that wind is a primary driving factor of K d (490) variation, but the lacustrine morphometry affects the wind force that is contributing to K d (490) variation.

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“…The lake has an average water depth of 1.9 m, a surface area of 2338 km Lake Tai, which is an important regional fresh water resource [18], is infamous for its problems with widespread algal blooms, which seriously degrade the water quality [19]. Across the lake area, the algal bloom is most serious in the two large, semi-enclosed bays, Zhushan Bay and Meiliang Bay [20], so, for the purposes of this study, Lake Tai was subdivided into two zones: Meiliang-Zhushan Bay and Main Lake (Figure 1).…”

Section: Site Descriptionmentioning

confidence: 99%

Evaluation of a Modified Monod Model for Predicting Algal Dynamics in Lake Tai

Wang

et al. 2015

Water

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Several modified versions of the Monod model have been proposed to simulate algal dynamics in lakes by keeping the parent model's advantages of simplicity and low data requirement. This study evaluated the performance of a widely-used modified Monod model in predicting algal dynamics at various time scales in Lake Tai, a typical shallow lake in east China, using multiple time series. Chlorophyll-a (Chl-a) concentration was used as a surrogate for algal (CyanoHABs: cyanobacterial harmful algal blooms) growth and the independent variables were total nitrogen (TN), total phosphorus (TP), and either water temperature or air temperature. The evaluation indicated that the model parameters could have distinctly different values, depending on whether or not constraints are imposed, time scales, and types of nutrients. The model performance varied in terms of time scales as well as magnitudes and fluctuations of Chl-a and TN or TP concentrations, achieving a relative OPEN ACCESSWater 2015, 7 3627 better performance for the monthly rather than three-day time scale and for the central part rather than bays of the study lake. The model with TP as the independent variable had a better performance than the model with TN as the independent variable, regardless of the time scale used. The temperature-nutrient interactions were important for algal growth when the temporal fluctuations of these two factors were large but the interactions could become minimal otherwise.

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Detection of algal bloom and factors influencing its formation in Taihu Lake from 2000 to 2011 by MODIS

Cited by 81 publications

References 39 publications

Tower-Based Validation and Improvement of MODIS Gross Primary Production in an Alpine Swamp Meadow on the Tibetan Plateau

Tower-Based Validation and Improvement of MODIS Gross Primary Production in an Alpine Swamp Meadow on the Tibetan Plateau

Semi-Analytical Retrieval of the Diffuse Attenuation Coefficient in Large and Shallow Lakes from GOCI, a High Temporal-Resolution Satellite

Evaluation of a Modified Monod Model for Predicting Algal Dynamics in Lake Tai

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