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Research Article | Open Access

Spatiotemporal dynamics and geo-environmental factors influencing mangrove gross primary productivity during 2000–2020 in Gaoqiao Mangrove Reserve, China

Demei Zhaoa,b, Yinghui Zhanga,b, Junjie Wanga,c( ), Jianing Zhend, Zhen Shena,b, Kunlun Xiange, Haoli Xianga,b, Yongquan Wanga,b, Guofeng Wua,b( )
MNR Key Laboratory for Geo-Environmental Monitoring of Great Bay Area & Guangdong Key Laboratory of Urban Informatics & Shenzhen Key Laboratory of Spatial Smart Sensing and Services, Shenzhen University, Shenzhen, 518060, China
School of Architecture and Urban Planning, Shenzhen University, Shenzhen, 518060, China
College of Life Sciences and Oceanography, Shenzhen University, Shenzhen, 518060, China
Key Laboratory of Wetland Ecology and Environment, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, 130102, China
Guangdong Ecological Meteorology Center, Guangzhou, 510275, China
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Abstract

Background

Mangrove forests are a significant contributor to the global carbon cycle, and the accurate estimation of their gross primary productivity (GPP) is essential for understanding the carbon budget within blue carbon ecosystems. Little attention has been given to the investigation of spatiotemporal patterns and ecological variations within mangrove ecosystems, as well as the quantitative analysis of the influence of geo-environmental factors on time-series estimations of mangrove GPP.

Methods

This study explored the spatiotemporal dynamics of mangrove GPP from 2000 to 2020 in Gaoqiao Mangrove Reserve, China. A leaf area index (LAI)-based light-use efficiency (LUE) model was combined with Landsat data on Google Earth Engine (GEE) to reveal the variations in mangrove GPP using the Mann-Kendall (MK) test and Theil-Sen median trend. Moreover, the spatiotemporal patterns and ecological variations in mangrove ecosystems across regions were explored using four landscape indicators. Furthermore, the effects of six geo-environmental factors (species distribution, offshore distance, elevation, slope, planar curvature and profile curvature) on GPP were investigated using Geodetector and multi-scale geo-weighted regression (MGWR).

Results

The results showed that the mangrove forest in the study area experienced an area loss from 766.26 ​ha in 2000 to 718.29 ​ha in 2020, mainly due to the conversion to farming, terrestrial forest and aquaculture zones. Landscape patterns indicated high levels of vegetation aggregation near water bodies and aquaculture zones, and low levels of aggregation but high species diversity and distribution density near building zone. The mean value of mangrove GPP continuously increased from 6.35 ​g ​C·m−2·d−1 in 2000 to 8.33 ​g ​C·m−2·d−1 in 2020, with 23.21% of areas showing a highly and significantly increasing trend (trend value ​ > ​0.50). The Geodetector and MGWR analyses showed that species distribution, offshore distance and elevation contributed most to the GPP variations.

Conclusions

These results provide guidelines for selecting GPP products, and the combination of Geodetector and MGWR based on multiple geo-environmental factors could quantitatively capture the mode, direction, pathway and intensity of the influencing factors on mangrove GPP variation. The findings provide a foundation for understanding the spatiotemporal dynamics of mangrove GPP at the landscape or regional scale.

Keywords

Spatial heterogeneityGeodetectorMangrove GPPLUE modelMGWR

References

 

Ahmed, N., Cheung, W.W.L., Thompson, S., Glaser, M., 2017. Solutions to blue carbon emissions: shrimp cultivation, mangrove deforestation and climate change in coastal Bangladesh. Mar. Pol. 82, 68–75. https://doi.org/10.1016/j.marpol.2017.05.007.
Crossref Google Scholar
 

Akritas, M.G., Murphy, S.A., Lavalley, M.P., 1995. The Theil-Sen estimator with doubly censored-data and applications to astronomy. J. Am. Stat. Assoc. 90, 170–177. https://doi.org/10.2307/2291140.
Crossref Google Scholar
 

Alam, M.I., Yeasmin, S., Khatun, M.M., Rahman, M.M., Ahmed, M.U., Debrot, A.O., Ahsan, M.N., Verdegem, M.C.J., 2022. Effect of mangrove leaf litter on shrimp (Penaeus monodon, Fabricius, 1798) and color. Aquac. Rep. 25, 101185. https://doi.org/10.1016/j.aqrep.2022.101185.
Crossref Google Scholar
 

Alongi, D.M., 2014. Carbon cycling and storage in mangrove forests. Ann. Rev. Mar. Sci 6, 195–219. https://doi.org/10.1146/annurev-marine-010213-135020.
Crossref Google Scholar
 

Alvarado-Barrientos, M.S., López-Adame, H., Lazcano-Hernández, H.E., Arellano-Verdejo, J., Hernández-Arana, H.A., 2021. Ecosystem-atmosphere exchange of CO2, water, and energy in a basin mangrove of the northeastern coast of the Yucatan peninsula. J. Geophys. Research-Biogeo. 126 (2), e2020JG005811. https://doi.org/10.1029/2020JG005811.
Crossref Google Scholar
 

Arifanti, V.B., Kauffman, J.B., Hadriyanto, D., Murdiyarso, D., Diana, R., 2019. Carbon dynamics and land use carbon footprints in mangrove-converted aquaculture: the case of the Mahakam Delta, Indonesia. For. Ecol. Manag. 432, 17–29. https://doi.org/10.1016/j.foreco.2018.08.047.
Crossref Google Scholar
 

Arjasakusuma, S., Kusuma, S.S., Rafif, R., Saringatin, S., Wicaksono, P., 2020. Combination of Landsat 8 OLI and Sentinel-1 SAR time-series data for mapping paddy fields in parts of west and central Java provinces, Indonesia. ISPRS Int. J. Geo-Inf. 9 (11), 663. https://doi.org/10.3390/ijgi9110663.
Crossref Google Scholar
 

Barik, J., Mukhopadhyay, A., Ghosh, T., Mukhopadhyay, S.K., Chowdhury, S.M., Hazra, S., 2018. Mangrove species distribution and water salinity: an indicator species approach to Sundarban. J. Coast Conserv. 22, 361–368. https://doi.org/10.1007/s11852-017-0584-7.
Crossref Google Scholar
 

Braghiere, R.K., Quaife, T., Black, E., He, L., Chen, J.M., 2019. Underestimation of global photosynthesis in earth system models due to representation of vegetation structure. Global Biogeochem. Cycles 33, 1358–1369. https://doi.org/10.1029/2018GB006135.
Crossref Google Scholar
 

Brunsdon, C., Fotheringham, S., Charlton, M., 1998. Geographically weighted regression–modelling spatial non-stationarity. J. Roy. Stat. Soc. D-Sta. 47 (3), 431–443. https://doi.org/10.1111/1467-9884.00145.
Crossref Google Scholar
 

Chen, L., Msigwa, G., Yang, M.Y., Osman, A.I., Fawzy, S., Rooney, D.W., Yap, P.-S., 2022. Strategies to achieve a carbon neutral society: a review. Environ. Chem. Lett. 20, 2277–2310. https://doi.org/10.1007/s10311-022-01435-8.
Crossref Google Scholar
 

Cui, X.W., Liang, J., Lu, W.Z., Chen, H., Liu, F., Lin, G.X., Xu, F.H., Luo, Y.Q., Lin, G.H., 2018. Stronger ecosystem carbon sequestration potential of mangrove wetlands with respect to terrestrial forests in subtropical China. Agric. For. Meteorol. 249, 71–80. https://doi.org/10.1016/j.agrformet.2017.11.019.
Crossref Google Scholar
 

Drew, C.A., Eggleston, D.B., 2008. Juvenile fish densities in Florida Keys mangroves correlate with landscape characteristics. Mar. Ecol. Prog. Ser. 362, 233–243. https://doi.org/10.3354/meps07430.
Crossref Google Scholar
 

Fan, J.Y., Pan, J.Y., 2006. Convergence properties of a self-adaptive Levenberg-Marquardt algorithm under local error bound condition. Comput. Optim. Appl. 34, 47–62. https://doi.org/10.1007/s10589-005-3074-z.
Crossref Google Scholar
 

Ferreira, T.O., Otero, X.L., Vidal-Torrado, P., Macías, F., 2007. Effects of bioturbation by root and crab activity on iron and sulfur biogeochemistry in mangrove substrate. Geoderma 142, 36–46. https://doi.org/10.1016/j.geoderma.2007.07.010.
Crossref Google Scholar
 

Gebremichael, M., Barros, A.P., 2006. Evaluation of MODIS gross primary productivity (GPP) in tropical monsoon regions. Remote Sens. Environ. 100 (2), 150–166. https://doi.org/10.1016/j.rse.2005.10.009.
Crossref Google Scholar
 

Gnanamoorthy, P., Selvam, V., , 2020. Seasonal variations of net ecosystem (CO2) exchange in the Indian tropical mangrove forest of Pichavaram. Estuarin. Coast. Shelf. S. 243, 106828. https://doi.org/10.1016/jecss.2020.106828.
Crossref Google Scholar
 

Gorelick, N., Hancher, M., Dixon, M., Ilyushchenko, S., Thau, D., Moore, R., 2017. Google earth engine: planetary-scale geospatial analysis for everyone. Remote Sens. Environ. 202, 18–27. https://doi.org/10.1016/j.rse.2017.06.031.
Crossref Google Scholar
 

Griscom, B.W., Adams, J., Ellis, P.W., Houghton, R.A., Lomax, G., Miteva, D.A., Schlesinger, W.H., Shoch, D., Siikamäki, J.V., Smith, P., Woodbury, P., Zganjar, C., Blackman, A., Campari, J., Conant, R.T., Delgado, C., Elias, P., Gopalakrishna, T., Hamsik, M.R., Herrero, M., Kiesecker, J., Landis, E., Laestadius, L., Leavitt, S.M., Minnemeyer, S., Polasky, S., Potapov, P., Putz, F.E., Sanderman, J., Silvius, M., Wollenberg, E., Fargione, J., 2017. Natural climate solutions. Proc. Natl. Acad. Sci. U.S.A. 114 (44), 11645–11650. https://doi.org/10.1073/pnas.1710465114.
Crossref Google Scholar
 

Hamed, K.H., Rao, A.R., 1998. A modified Mann-Kendall trend test for autocorrelated data. J. Hydrol. 204, 182–196. https://doi.org/10.1016/S0022-1694(97)00125-X.
Crossref Google Scholar
 

Hilker, T., Hall, F.G., Coops, N.C., Black, A.T., Jassal, R., Mathys, A., Grant, N., 2014. Potentials and limitations for estimating daytime ecosystem respiration by combining tower-based remote sensing and carbon flux measurements. Remote Sens. Environ. 150, 44–52. https://doi.org/10.1016/j.rse.2014.04.018.
Crossref Google Scholar
 

Hu, L., Fan, W.J., Ren, H.Z., Liu, S.H., Cui, Y.K., Zhao, P., 2018. Spatiotemporal dynamics in vegetation GPP over the Great Khingan Mountains using GLASS products from 1982 to 2015. Rem. Sens. 10 (3), 488. https://doi.org/10.3390/rs10030488.
Crossref Google Scholar
 

Huang, Q., Qiu, F., Fan, W.L., Liu, Y.B., Zhang, Q., 2019. Evaluation of different methods for estimating the fraction of sunlit leaves and its contribution for photochemical reflectance index utilization in a coniferous forest. Rem. Sens. 11 (14), 1643. https://doi.org/10.3390/rs11141643.
Crossref Google Scholar
 

Ishtiaque, A., Myint, S.W., Wang, C.Y., 2016. Examining the ecosystem health and sustainability of the world's largest mangrove forest using multi-temporal MODIS products. Sci. Total Environ. 569, 1241–1254. https://doi.org/10.1016/j.scitotenv.2016.06.200.
Crossref Google Scholar
 

Jain, S., Sannigrahi, S., Sen, S., Bhatt, S., Chakraborti, S., Rahmat, S., 2020. Urban heat island intensity and its mitigation strategies in the fast-growing urban area. J. Urban Manag. 9 (1), 54–66. https://doi.org/10.1016/j.jum.2019.09.004.
Crossref Google Scholar
 

Javi, S.T., Malekmohammadi, B., Mokhtari, H., 2014. Application of geographically weighted regression model to analysis of spatiotemporal varying relationships between groundwater quantity and land use changes (case study: khanmirza Plain, Iran). Environ. Monit. Assess. 186, 3123–3138. https://doi.org/10.1007/s10661-013-3605-5.
Crossref Google Scholar
 

Jia, M.M., Wang, Z.M., Zhang, Y.Z., Mao, D.H., Wang, C., 2018. Monitoring loss and recovery of mangrove forests during 42 years: the achievements of mangrove conservation in China. Int. J. Appl. Earth. Obs. 73, 535–545. https://doi.org/10.1016/j.jag.2018.07.025.
Crossref Google Scholar
 

Kanniah, K.D., Kang, C.S., Sharma, S., Amir, A.A., 2021. Remote sensing to study mangrove fragmentation and its impacts on leaf area index and gross primary productivity in the south of Peninsular Malaysia. Rem. Sens. 13 (8), 1427. https://doi.org/10.3390/rs13081427.
Crossref Google Scholar
 

Koomen, E., Opdam, P., Steingrover, E., 2012. Adapting complex multi-level landscape systems to climate change. Landsc. Ecol. 27, 469–471. https://doi.org/10.1007/s10980-012-9721-8.
Crossref Google Scholar
 

Kovacs, J.M., Wang, J.F., Flores-Verdugo, F., 2005. Mapping mangrove leaf area index at the species level using IKONOS and LAI-2000 sensors for the Agua Brava Lagoon, Mexican Pacific. Estuar. Coast. Shelf Sci. 62, 377–384. https://doi.org/10.1016/j.ecss.2004.09.027.
Crossref Google Scholar
 

Lee, S.Y., Primavera, J.H., Dahdouh-Guebas, F., McKee, K., Bosire, J.O., Cannicci, S., Diele, K., Fromard, F., Koedam, N., Marchand, C., Mendelssohn, I., Mukherjee, N., Record, S., 2014. Ecological role and services of tropical mangrove ecosystems: a reassessment. Global Ecol. Biogeogr. 23 (7), 726–743. https://doi.org/10.1111/geb.12155.
Crossref Google Scholar
 

Lele, N., Kripa, M.K., Panda, M., Das, S.K., Nivas, A.H., Divakaran, N., Naik-Gaonkar, S., Sawant, A., Pattnaik, A.K., Samal, R.N., Thangaradjou, T., Saravanakumar, A., Rodrigues, B.F., Murthy, T.V.R., 2021. Seasonal variation in photosynthetic rates and satellite-based GPP estimation over mangrove forest. Environ. Monit. Assess. 193, 61. https://doi.org/10.1007/s10661-021-08846-0.
Crossref Google Scholar
 

Lewis, R.R., Milbrandt, E.C., Brown, B., Krauss, K.W., Rovai, A.S., Beever, J.W., Flynn, L.L., 2016. Stress in mangrove forests: early detection and preemptive rehabilitation are essential for future successful worldwide mangrove forest management. Mar. Pollut. Bull. 109 (2), 764–771. https://doi.org/10.1016/j.marpolbul.2016.03.006.
Crossref Google Scholar
 

Li, M.S., Lee, S.Y., 1997. Mangroves of China: a brief review. For. Ecol. Manag. 96 (3), 241–259. https://doi.org/10.1016/S0378-1127(97)00054-6.
Crossref Google Scholar
 

Li, X., Xiao, J.F., 2019. Mapping photosynthesis solely from solar-induced chlorophyll fluorescence: a global, fine-resolution dataset of gross primary production derived from OCO-2. Rem. Sens. 11 (21), 2563. https://doi.org/10.3390/rs11212563.
Crossref Google Scholar
 

Liu, J.G., Lai, D.Y.F., 2019. Subtropical mangrove wetland is a stronger carbon dioxide sink in the dry than wet seasons. Agric. For. Meteorol. 278, 107644. https://doi.org/10.1016/j.agrformet.2019.107644.
Crossref Google Scholar
 

Liu, M.L., Tian, H.Q., Lu, C.Q., Xu, X.F., Chen, G.S., Ren, W., 2012. Effects of multiple environment stresses on evapotranspiration and runoff over eastern China. J. Hydrol. 426, 39–54. https://doi.org/10.1016/j.jhydrol.2012.01.009.
Crossref Google Scholar
 

Liu, Z.J., Shao, Q.Q., Liu, J.Y., 2015. The performances of MODIS-GPP and -ET products in China and their sensitivity to input Data (FPAR/LAI). Rem. Sens. 7 (1), 135–152. https://doi.org/10.3390/rs70100135.
Crossref Google Scholar
 

Lovelock, C.E., Reef, R., 2020. Variable impacts of climate change on blue carbon. One Earth 3 (2), 195–211. https://doi.org/10.1016/j.oneear.2020.07.010.
Crossref Google Scholar
 

Lovelock, C.E., Simpson, L.T., Duckett, L.J., Feller, I.C., 2015. Carbon budgets for Caribbean mangrove forests of varying structure and with phosphorus enrichment. Forests 6 (10), 3528–3546. https://doi.org/10.3390/f6103528.
Crossref Google Scholar
 

Lu, W.Z., Xiao, J.F., Cui, X.W., Xu, F.H., Lin, G.X., Lin, G.H., 2019. Insect outbreaks have transient effects on carbon fluxes and vegetative growth but longer-term impacts on reproductive growth in a mangrove forest. Agric. For. Meteorol. 279, 107747. https://doi.org/10.1016/j.agrformet.2019.107747.
Crossref Google Scholar
 

Ma, C., Wang, C.Y., 2009. A nonsmooth Levenberg–Marquardt method for solving semi-infinite programming problems. J. Comput. Appl. Math. 230 (2), 633–642. https://doi.org/10.1016/j.cam.2009.01.004.
Crossref Google Scholar
 

Masnavi, M.R., Motedayen, H., Saboonchi, P., Hemmati, M., 2021. Analyses of landscape concept and landscape approach from theoretical to operational levels: a review of literature. Manzar 13 (57), 22–37. https://doi.org/10.22034/MANZAR.2021.283818.2128.
Crossref Google Scholar
 

Matin, N.H., Jalali, M., 2017. The effect of waterlogging on electrochemical properties and soluble nutrients in paddy soils. Paddy Water Environ. 15, 443–455. https://doi.org/10.1007/s10333-016-0562-y.
Crossref Google Scholar
 

Méndez-Alonzo, R., López-Portillo, J., Rivera-Monroy, V.H., 2008. Latitudinal variation in leaf and tree traits of the mangrove Avicennia germinans (Avicenniaceae) in the central region of the Gulf of Mexico. Biotropica 40 (4), 449–456. https://doi.org/10.1111/j.1744-7429.2008.00397.x.
Crossref Google Scholar
 

Mu, Q.Z., Heinsch, F.A., Zhao, M.S., Running, S.W., 2007. Development of a global evapotranspiration algorithm based on MODIS and global meteorology data. Remote Sens. Environ. 111 (4), 519–536. https://doi.org/10.1016/j.rse.2007.04.015.
Crossref Google Scholar
 

Mukherjee, N., Sutherland, W.J., Khan, M.N.I., Berger, U., Schmitz, N., Dahdouh-Guebas, F., Koedam, N., 2014. Using expert knowledge and modeling to define mangrove composition, functioning, and threats and estimate time frame for recovery. Ecol. Evol. 4 (11), 2247–2262. https://doi.org/10.1002/ece3.1085.
Crossref Google Scholar
 

Myneni, R.B., Hoffman, S., Knyazikhin, Y., Privette, J.L., Glassy, J., Tian, Y., Wang, Y., Song, X., Zhang, Y., Smith, G.R., Lotsch, A., Friedl, M., Morisette, J.T., Votava, P., Nemani, R.R., Running, S.W., 2002. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens. Environ. 83, 214–231. https://doi.org/10.1016/S0034-4257(02)00074-3.
Crossref Google Scholar
 

Myneni, R.B., Los, S.O., Asrar, G., 1995. Potential gross primary productivity of terrestrial vegetation from 1982–1990. Geophys. Res. Lett. 22 (19), 2617–2620. https://doi.org/10.1029/95GL02562.
Crossref Google Scholar
 

Myneni, R.B., Ramakrishna, R., Nemani, R., Running, S.W., 1997. Estimation of global leaf area index and absorbed par using radiative transfer models. IEEE Trans. Geosci. Rem. Sens. 35 (6), 1380–1393. https://doi.org/10.1109/36.649788.
Crossref Google Scholar
 

Narula, S.C., Wellington, J.F., 2007. Multiple criteria linear regression. Eur. J. Oper. Res. 181 (2), 767–772. https://doi.org/10.1016/j.ejor.2006.06.026.
Crossref Google Scholar
 

Patnaik, S., Biswal, B., 2020. Importance of nutrient loading and irrigation in gross primary productivity trends in India. J. Hydrol. 588, 125047. https://doi.org/10.1016/j.jhydrol.2020.125047.
Crossref Google Scholar
 

Pei, Y.Y., Dong, J.W., Zhang, Y., Yang, J.L., Zhang, Y.Q., Jiang, C.Y., Xiao, X.M., 2020. Performance of four state-of-the-art GPP products (VPM, MOD17, BESS and PML) for grasslands in drought years. Ecol. Inf. 56, 101052. https://doi.org/10.1016/j.ecoinf.2020.101052.
Crossref Google Scholar
 

Poulter, B., Heyder, U., Cramer, W., 2009. Modeling the sensitivity of the seasonal cycle of GPP to dynamic LAI and soil depths in tropical rainforests. Ecosystems 12, 517–533. https://doi.org/10.1007/s10021-009-9238-4.
Crossref Google Scholar
 

Rasquinha, D.N., Mishra, D.R., 2021. Tropical cyclones shape mangrove productivity gradients in the Indian subcontinent. Sci. Rep. 11 (1), 17355. https://doi.org/10.1038/s41598-021-96752-3.
Crossref Google Scholar
 

Raza, S., Zamanian, K., Ullah, S., Kuzyakov, Y., Virto, I., Zhou, J.B., 2021. Inorganic carbon losses by soil acidification jeopardize global efforts on carbon sequestration and climate change mitigation. J. Clean. Prod. 315, 128036. https://doi.org/10.1016/j.jclepro.2021.128036.
Crossref Google Scholar
 

Richardson, A.D., Black, T.A., Ciais, P., Delbart, N., Friedl, M.A., Gobron, N., Hollinger, D.Y., Kutsch, W.L., Longdoz, B., Luyssaert, S., Migliavacca, M., Montagnani, L., Munger, J.W., Moors, E., Piao, S.L., Rebmann, C., Reichstein, M., Saigusa, N., Tomelleri, E., Vargas, R., Varlagin, A., 2010. Influence of spring and autumn phenological transitions on forest ecosystem productivity. Philos. Trans. R. Soc. Lond. B Biol. Sci. 365, 3227–3246. https://doi.org/10.1098/rstb.2010.0102.
Crossref Google Scholar
 

Rodda, S.R., Thumaty, K.C., Fararoda, R., Jha, C.S., Dadhwal, V.K., 2022. Unique characteristics of ecosystem CO2 exchange in Sundarban mangrove forest and their relationship with environmental factors. Estuar. Coast. Shelf Sci. 267, 107764. https://doi.org/10.1016/j.ecss.2022.107764.
Crossref Google Scholar
 

Rodda, S.R., Thumaty, K.C., Jha, C.S., Dadhwal, V.K., 2016. Seasonal variations of carbon dioxide, water vapor and energy fluxes in tropical Indian mangroves. Forests 7 (2), 35. https://doi.org/10.3390/f7020035.
Crossref Google Scholar
 

Sazeides, C.I., Christopoulou, A., Fyllas, N.M., 2021. Coupling photosynthetic measurements with biometric data to estimate gross primary productivity (GPP) in Mediterranean pine forests of different post-fire age. Forests 12 (9), 1256. https://doi.org/10.3390/f12091256.
Crossref Google Scholar
 

Schloss, A.L., Kicklighter, D.W., Kaduk, J., Wittenberg, U., Intercomparison, P.P.N.M., 1999. Comparing global models of terrestrial net primary productivity (NPP): comparison of NPP to climate and the Normalized Difference Vegetation Index (NDVI). Global Change Biol. 5, 25–34. https://doi.org/10.1046/j.1365-2486.1999.00004.x.
Crossref Google Scholar
 

Su, J., Yin, B.C., Chen, L.Z., Gasparatos, A., 2022. Priority areas for mixed-species mangrove restoration: the suitable species in the right sites. Environ. Res. Lett. 17, 065001. https://doi.org/10.1088/1748-9326/ac6b48.
Crossref Google Scholar
 

Sun, Z.Y., Wang, X.F., Yamamoto, H., Tani, H., Zhong, G.S., Yin, S., Guo, E.L., 2018. Spatial pattern of GPP variations in terrestrial ecosystems and its drivers: climatic factors, CO2 concentration and land-cover change, 1982-2015. Ecol. Inf. 46, 156-165. https://doi.org/10.1016/j.ecoinf.2018.06.006.
Crossref Google Scholar
 

Tagliabue, G., Panigada, C., Dechant, B., Baret, F., Cogliati, S., Colombo, R., Migliavacca, M., Rademske, P., Schickling, A., Schuttemeyer, D., Verrelst, J., Rascher, U., Ryu, Y., Rossini, M., 2019. Exploring the spatial relationship between airborne-derived red and far-red sun-induced fluorescence and process-based GPP estimates in a forest ecosystem. Remote Sens. Environ. 231, 111272. https://doi.org/10.1016/j.rse.2019.111272.
Crossref Google Scholar
 

Turner, D.P., Ritts, W.D., Cohen, W.B., Gower, S.T., Zhao, M.S., Running, S.W., Wofsy, S.C., Urbanski, S., Dunn, A.L., Munger, J.W., 2003a. Scaling gross primary production (GPP) over boreal and deciduous forest landscapes in support of MODIS GPP product validation. Remote Sens. Environ. 88, 256–270. https://doi.org/10.1016/j.rse.2003.06.005.
Crossref Google Scholar
 

Turner, D.P., Urbanski, S., Bremer, D., Wofsy, S.C., Meyers, T., Gower, S.T., Gregory, M., 2003b. A cross-biome comparison of daily light use efficiency for gross primary production. Global Change Biol. 9 (3), 383–395. https://doi.org/10.1046/j.1365-2486.2003.00573.x.
Crossref Google Scholar
 

Valderrama, L., Troche, C., Rodriguez, M.T., Marquez, D., Vázquez, B., Velázquez, S., Vázquez, A., Cruz, M.I., Ressl, R., 2014. Evaluation of mangrove cover changes in Mexico during the 1970-2005 period. Wetlands 34, 747–758. https://doi.org/10.1007/s13157-014-0539-9.
Crossref Google Scholar
 

Valiela, I., Bowen, J.L., York, J.K., 2001. Mangrove forests: one of the world's threatened major tropical environments. Bioscience 51 (10), 807–815. https://doi.org/10.1641/0006-3568(2001)051[0807:MFOOTW]2.0.CO;2.
Crossref Google Scholar
 

Wang, L.C., Zhu, H.J., Lin, A.W., Zou, L., Qin, W.M., Du, Q.Y., 2017. Evaluation of the latest MODIS GPP products across multiple biomes using global eddy covariance flux data. Rem. Sens. 9 (5), 418. https://doi.org/10.3390/rs9050418.
Crossref Google Scholar
 

Xiao, X.M., Hollinger, D., Aber, J., Goltz, M., Davidson, E.A., Zhang, Q.Y., Moore, B., 2004. Satellite-based modeling of gross primary production in an evergreen needleleaf forest. Remote Sens. Environ. 89 (4), 519–534. https://doi.org/10.1016/j.rse.2003.11.008.
Crossref Google Scholar
 

Xie, X.Y., Li, A.N., Jin, H.A., Bian, J.H., Zhang, Z.J., Nan, X., 2021. Comparing three remotely sensed approaches for simulating gross primary productivity over mountainous watersheds: a case study in the Wanglang National Nature Reserve, China. Rem. Sens. 13 (18), 3567. https://doi.org/10.3390/rs13183567.
Crossref Google Scholar
 

Yan, S.R., Li, M., Sun, T., Liu, Q., Liang, L.Q., Wang, X., Li, C.H., 2021. The complex drought effects associated with the regulation of water-use efficiency in a temperate water-limited basin. J. Hydrol-Reg. Stud. 36, 100864. https://doi.org/10.1016/j.ejrh.2021.100864.
Crossref Google Scholar
 

Yang, Y.T., Guan, H.D., Shang, S.H., Long, D., Simmons, C.T., 2014. Toward the use of the MODIS ET product to estimate terrestrial GPP for nonforest ecosystems. Geosci. Rem. Sens. Lett. IEEE 11 (9), 1624–1628. https://doi.org/10.1109/LGRS.2014.2302796.
Crossref Google Scholar
 

You, Y.F., Wang, S.Y., Ma, Y.X., Wang, X.Y., Liu, W.H., 2019. Improved modeling of gross primary productivity of alpine grasslands on the Tibetan Plateau using the Biome-BGC Model. Rem. Sens. 11 (11), 1287. https://doi.org/10.3390/rs11111287.
Crossref Google Scholar
 

Yu, G.R., Zhang, L.M., Sun, X.M., Fu, Y.L., Wen, X.F., Wang, Q.F., Li, S.G., Ren, C.Y., Song, X., Liu, Y.F., Han, S.J., Yan, J.H., 2008. Environmental controls over carbon exchange of three forest ecosystems in eastern China. Global Change Biol. 14 (11), 2555–2571. https://doi.org/10.1111/j.1365-2486.2008.01663.x.
Crossref Google Scholar
 

Zeng, N., Jiang, K.J., Han, P.F., Hausfather, Z., Cao, J.J., Kirk-Davidoff, D., Ali, S., Zhou, S., 2022. The Chinese carbon-neutral goal: challenges and prospects. Adv. Atmos. Sci. 39, 1229–1238. https://doi.org/10.1007/s00376-021-1313-6.
Crossref Google Scholar
 

Zhang, Y.G., Guanter, L., Berry, J.A., Joiner, J., van der Tol, C., Huete, A., Gitelson, A., Voigt, M., Köhler, P., 2014. Estimation of vegetation photosynthetic capacity from space-based measurements of chlorophyll fluorescence for terrestrial biosphere models. Global Change Biol. 20 (12), 3727–3742. https://doi.org/10.1111/gcb.12664.
Crossref Google Scholar
 

Zhang, Y.J., Xu, M., Chen, H., Adams, J., 2009. Global pattern of NPP to GPP ratio derived from MODIS data: effects of ecosystem type, geographical location and climate. Global Ecol. Biogeogr. 18 (3), 280–290. https://doi.org/10.1111/j.1466-8238.2008.00442.x.
Crossref Google Scholar
 

Zhang, Y.L., Song, C.H., Band, L.E., Sun, G., 2019. No proportional increase of terrestrial gross carbon sequestration from the greening Earth. J. Geophys. Res-biogeo. 124 (8), 2540–2553. https://doi.org/10.1029/2018JG004917.
Crossref Google Scholar
 

Zhang, Y.S., Xiao, L., Guan, D.S., Chen, Y.J., Motelica-Heino, M., Peng, Y.S., Lee, S.Y., 2021. The role of mangrove fine root production and decomposition on soil organic carbon component ratios. Ecol. Indicat. 125, 107525. https://doi.org/10.1016/j.ecolind.2021.107525.
Crossref Google Scholar
 

Zhang, Y.H., Hu, Z.W., Wang, J.Z., Gao, X., Yang, C., Yang, F.S., Wu, G.F., 2023. Temporal upscaling of MODIS instantaneous FAPAR improves forest gross primary productivity (GPP) simulation. Int. J. Appl. Earth. Obs. 121, 103360. https://doi.org/10.1016/j.jag.2023.103360.
Crossref Google Scholar
 

Zhao, D.M., Zhen, J.N., Zhang, Y.H., Miao, J., Shen, Z., Jiang, X.P., Wang, J.J., Jiang, J.C., Tang, Y.Z., Wu, G.F., 2022. Mapping mangrove leaf area index (LAI) by combining remote sensing images with PROSAIL-D and XGBoost methods. Remote Sens. Ecol. Conserv. 9 (3), 370–389. https://doi.org/10.1002/rse2.315.
Crossref Google Scholar
 

Zhao, M.S., Heinsch, F.A., Nemani, R.R., Running, S.W., 2005. Improvements of the MODIS terrestrial gross and net primary production global data set. Remote Sens. Environ. 95 (2), 164–176. https://doi.org/10.1016/j.rse.2004.12.011.
Crossref Google Scholar
 

Zheng, Y., Zhang, L., Xiao, J.F., Yuan, W.P., Yan, M., Li, T., Zhang, Z.Q., 2018. Sources of uncertainty in gross primary productivity simulated by light use efficiency models: model structure, parameters, input data, and spatial resolution. Agric. For. Meteorol. 263, 242–257. https://doi.org/10.1016/j.agrformet.2018.08.003.
Crossref Google Scholar
 

Zheng, Y.H., Takeuchi, W., 2022. Estimating mangrove forest gross primary production by quantifying environmental stressors in the coastal area. Sci. Rep. 12, 2238. https://doi.org/10.1038/s41598-022-06231-6.
Crossref Google Scholar
 

Zhu, J.J., Yan, B., 2022. Blue carbon sink function and carbon neutrality potential of mangroves. Sci. Total Environ. 822, 153438. https://doi.org/10.1016/j.scitotenv.2022.153438.
Crossref Google Scholar
 

Zhu, X.D., Hou, Y.W., Zhang, Y.G., Lu, X.L., Liu, Z.Q., Weng, Q.H., 2021a. Potential of sun-induced chlorophyll fluorescence for indicating mangrove canopy photosynthesis. J. Geophys. Res-Biogeo. 126 (4), e2020JG006159. https://doi.org/10.1029/2020JG006159.
Crossref Google Scholar
 

Zhu, X.D., Sun, C.Y., Qin, Z.C., 2021b. Drought-induced salinity enhancement weakens mangrove greenhouse gas cycling. J. Geophys. Res-Biogeo. 126 (8), 18. https://doi.org/10.1029/2021JG006416.
Crossref Google Scholar
Forest Ecosystems
Volume 10 Issue 5,
October 2023
Article number: 100137
DOI: 10.1016/j.fecs.2023.100137
Cite this article:
Zhao D, Zhang Y, Wang J, et al. Spatiotemporal dynamics and geo-environmental factors influencing mangrove gross primary productivity during 2000–2020 in Gaoqiao Mangrove Reserve, China. Forest Ecosystems, 2023, 10(5): 100137. https://doi.org/10.1016/j.fecs.2023.100137

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Received: 21 June 2023
Revised: 31 August 2023
Accepted: 31 August 2023
Published: 07 September 2023
© 2023 The Authors.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
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