江西省NPP估算及其与气候因子的关联分析-基于改进CASA模型

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Abstract This research increases the accuracy of Net Primary Productivity (NPP) estimation in the CASA (Carnegie-Ames-Stanford Approach) model by refining the calculation methods for solar radiation parameters and water stress coefficients. Based on the improvement, correlation and trend analysis of NPP and meteorological variables were carried out.Following model optimization, the correlation between NPP and field observation data improved to 0.62. From 2001 to 2022, the annual average NPP in Jiangxi Province increased steadily, with the average value exceeding 1000gC/(m²·a). The monthly NPP values were in the following seasonal order: autumn > summer > winter > spring, with July having the highest value. The highest and lowest annual NPP values were observed in 2018 and 2010, respectively. Trend analysis and correlation facts show that, despite a decline in solar radiation from 2001 to 2022, NPP changes were not considerably impacted. A least-squares regression model revealed that NPP increased with rising temperature and decreased with decreasing sun radiation. Despite recent increases in extreme events (2019~2022), there has been no notable decrease in NPP levels.

Key words： CASA model net primary productivity solar radiation climate change Jiangxi Province

Received: 12 June 2024

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X173

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	LU Tie-ding
	ZHANG Yuan
	ZENG Si-ting
	TAO Rui
	TENG Yue

Cite this article:

LU Tie-ding,ZHANG Yuan,ZENG Si-ting等. Estimation of net primary productivity and correlation study with climate parameters in Jiangxi Province using the enhanced CASA model[J]. CHINA ENVIRONMENTAL SCIENCECE, 2025, 45(1): 369-378.

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http://www.zghjkx.com.cn/EN/ OR http://www.zghjkx.com.cn/EN/Y2025/V45/I1/369

[1] Ruimy A, Saugier B, Dedieu G. Methodology for the estimation of terrestrial net primary production from remotely sensed data [J]. Journal of Geophysical Research: Atmospheres, 1994,99(D3):5263-5283.
[2] 朱文泉,潘耀忠,张锦水.中国陆地植被净初级生产力遥感估算 [J]. 植物生态学报, 2007,31(3):413-424. Zhu W, Pan Y, Zhang J. estimation of net primary productivity of chinese terrestrial vegetation based on remote sensing [J]. Chinese Journal of Plant Ecology, 2007,31(3):413-424.
[3] Rosswall T. The international geosphere-biosphere programme: A study of global change (IGBP) [J]. Environmental Geology and Water Sciences Environ., 1992,20:77-78.
[4] Canadell J G, Steffen W L, White P S. IGBP/GCTE terrestrial transects: Dynamics of terrestrial ecosystems under environmental change [J]. Journal of Vegetation Science, 2002,13(3):298-300.
[5] 张猛,陈淑丹,林辉,等.洞庭湖湿地净初级生产力估算研究 [J]. 遥感学报, 2023,27(6):1454-1466. Zhang M, Chen S D, Lin H, et al. Net primary productivity estimation of Dongting Lake wetland [J]. National Remote Sensing Bulletin, 2023,27(6):1454-1466.
[6] 权小丫,苏维词,勾容,等.FAST宁静区生态系统服务时空分异特征及权衡/协同关系分析 [J]. 中国环境科学, 2024,44(9):5145-5159. Quan X Y, Su W C, Gou R, et al. Analysis on spatio-temporal distribution characteristics and tradeoff-synergy of ecosystem services in the FAST Tranquility Zone [J]. China Environmental Science, 2024, 44(9):5145-5159.
[7] 洪长桥,金晓斌,陈昌春,等.集成遥感数据的陆地净初级生产力估算模型研究综述 [J]. 地理科学进展, 2017,36(8):924-939. Hong C, Jin X, Chen C, et al. Overview on estimation models of land net primary productivity integrating remote sensing data [J]. Progress in Geography, 2017,36(8):924-939.
[8] Wu C, Chen K, Chongyi E, et al. Improved casa model based on satellite remote sensing data: simulating net primary productivity of qinghai lake basin alpine grassland [J]. Geoscientific Model Development, 2022,15(17):6919-6933.
[9] 方浩玲,程先富,秦丽.安徽省植被净初级生产力估算-基于改进的CASA模型 [J]. 生态学报, 2024,44(4):1601-1612. Fang H L, Cheng X F, Qin L. Estimation of net primary productivity of vegetation in Anhui Province based on improved CASA Model [J]. Acta Ecologica Sinica, 2024,44(4):1601-1612.
[10] 彭少麟,郭志华,王伯荪.利用GIS和RS估算广东植被光利用率 [J]. 生态学报, 2000,20(6):903-909. Peng S, Guo Z, Wang B. Use of GIS and RS to estimate the light utilization efficiency of the vegetation in Guangdong, China [J]. Acta Ecologica Sinica, 2000,20(6):903-909.
[11] 苏胜涛,曾源,赵旦,等.中国陆地植被净初级生产力估算模型优化与分析-基于中国生态系统研究网络数据 [J]. 生态学报, 2022, 42(4):1276-1289. Su S T, Zeng Y, Zhao D, et al. Optimization of net primary productivity estimation model for terrestrial vegetation in China based on CERN data [J]. Acta Ecologica Sinica, 2022,42(4):1276-1289.
[12] Bao G, Bao Y, Qin Z, et al. Modeling net primary productivity of terrestrial ecosystems in the semi-arid climate of the mongolian plateau using lswi-based casa ecosystem model [J]. International Journal of Applied Earth Observations and Geoinformation, 2015,46:84-93.
[13] 姚云军,秦其明,赵少华,等.基于MODIS短波红外光谱特征的土壤含水量反演 [J]. 红外与毫米波学报, 2011,30(1):9-14,79. Yao Y, Qin Q, Zhao S, et al. Retrieval of soil moisture based on MODIS shortwave infrared spectral feature [J]. Journal Infrared Millimeter Waves, 2011,30(1):9-14.
[14] 朱文泉,潘耀忠,何浩,等.中国典型植被最大光利用率模拟 [J]. 科学通报, 2006,51(6):700-706. Zhu W Q, Pan Y Z, He H, et al. Simulation of maximum light use efficiency for some typical vegetation types in China [J]. Chinese Science Bulletin, 2006,51(6):700-706.
[15] Zheng Z, Zhu W, Zhang Y. Seasonally and spatially varied controls of climatic factors on net primary productivity in alpine grasslands on the Tibetan Plateau [J]. Global Ecology and Conservation, 2020,21,e00814.
[16] 徐勇,卢云贵,戴强玉,等.气候变化和土地利用变化对长江中下游地区植被NPP变化相对贡献分析 [J]. 中国环境科学, 2023,43(9): 4988-5000. Xu Y, Lu Y G, Dai Q Y, et al. Assessment of the relative contribution of climate change and land use change on net primary productivity variation in the middle and lower reaches of the Yangtze River Basin [J]. China Environmental Science, 2023,43(9):4988-5000.
[17] 张艳军,李子辉,官冬杰,等.2000~2020年成渝双城经济圈植被生态质量变化及其对极端气候因子的响应 [J]. 中国环境科学, 2023, 43(9):4876-4885. Zhang Y J, Li Z H, Guan D J, et al. Changes of vegetation ecological quality in the Chengdu-Chongqing economic circle from 2000 to 2020 and its response to extreme climatic factor [J]. China Environmental Science, 2023,43(9):4876-4885.
[18] 肖潇,邱新法,徐金勤.基于TerraClimate数据集的1960~2019年中国干湿气候变化特征 [J]. 长江科学院院报, 2023,40(2):27-33. Xiao X, Qiu X Xu J. Characteristics of dry and wet climate change in China from 1960 to 2019 based on TerraClimate Dataset [J]. Journal of Yangtze River Scientific Research Institute, 2023,40(2):27-33.
[19] 万华伟,李灏欣,高吉喜,等.我国植被生态系统固碳能力提升潜力空间格局研究 [J]. 生态学报, 2022,42(21):8568-8580. Wan H W, Li H X, Gao J X, et al. Spatial pattern analysis of carbon sequestration potential of vegetation ecosystem in China [J]. Acta Ecologica Sinica, 2022,42(21):8568-8580.
[20] 国家生态系统观测研究网络/中国生态系统研究网络.国家生态系统观测研究网络科技资源服务系统 [DB/OL]. 2015.http://www.cnern.org.cn. Chinese National Ecosystem Research Network/Chinese Ecosystem Research Network. Science and Technology Resources'Service System of Chinese National Ecosystem Research Network [DB/OL]. 2015. http://www.cnern.org.cn.
[21] Collalti A, Prentice I C. Is NPP proportional to GPP? Waring's hypothesis 20 years on [J]. Tree Physiol, 2019,39(8):1473-1483.
[22] 刘亮,关靖云,穆晨,等.2008~2018年伊犁河流域植被净初级生产力时空分异特征 [J]. 生态学报, 2022,42(12):4861-4871. Liu L, Guan J Y, Mu C, et al. Spatio-temporal characteristics of vegetation net primary productivity in the Ili River Basin from 2008 to 2018 [J]. Acta Ecologica Sinica, 2022,42(12):4861-4871.
[23] 徐新良.中国多年度省级行政区划边界数据[DS/OL].资源环境科学数据注册与出版系统.2023.[2024-5-15].http://www.resdc.cn/DOI. DOI:10.12078/2023010103. Xu X L.The administrative boundary data of multi-year for province level in China [DS/OL]. Resources and Environmental Science Data Registration&Publishing System. 2023, [2024-05-15]. http://www.resdc.cn/DOI.DOI: 10.12078/2023010103.
[24] Hou G, Wu S, Long W, et al. Quantitative analysis of the impact of climate change and oasification on changes in net primary productivity variation in mid-tianshan mountains from 2001 to 2020 [J]. Ecological Indicators, 2023,154:110820.
[25] ESRI. Area Solar Radiation [EB/OL]. [2024-5-15].https://desktop.arcgis.com/en/arcmap/latest/tools/spatial-analyst-toolbox/area-solar-radiation.htm.
[26] Fu P, Rich P M. A geometric solar radiation model with applications in agriculture and forestry [J]. Computers and Electronics in Agriculture, 2002,37(1):25-35.
[27] Wong m S, Zhu R, Liu Z, et al. Estimation of Hong Kong’s solar energy potential using GIS and remote sensing technologies [J]. Renewable Energy, 2016,99:325-335.
[28] Chai T, Draxler R. Root mean square error (RMSE) or mean absolute error (MAE)-Arguments against avoiding RMSE in the literature [J]. Geoscientific Model Development, 2014,7(3):1247-1250.
[29] Adler Jeremy, Parmryd Ingela. Quantifying colocalization by correlation: the pearson correlation coefficient is superior to the mander's overlap coefficient. [J]. Cytometry A, 2010,77(8):733-42.
[30] 董振,王萍萍,蔺跃付,等.基于地理加权回归模型的山东省临沂市结核病发病情况及其影响因素分析 [J]. 精准医学杂志, 2024,39(2): 170-174. Dong Z, Wang P, Lin Y et al. Incidence rate of tuberculosis and related influencing factors in Linyi City, Shandong Province, China: An analysis based on the geographical weighted regression model [J]. Journal of Precision Medicine, 2024,39(2):170-174.
[31] Mo K, Chen Q, Chen C, et al. Spatiotemporal variation of correlation between vegetation cover and precipitation in an arid mountain-oasis river basin in northwest china [J]. Journal of Hydrology, 2019,574: 138-147.
[32] 刘慧丽,陈浩,董廷旭,等.川渝地区NDVI动态特征及其对气候变化的响应 [J]. 生态学报, 2023,43(16):6743-6757. Liu H L, Chen H, Dong T X, et al. Variation characteristic of NDVI and its response to climate change in the Sichuan-Chongqing region [J]. Acta Ecologica Sinica, 2023,43(16):6743-6757.
[33] Lioyd-Hughes B, Saunders M A. A drought climatology for Europe [J]. International Journal of Climatology, 2002,22(13):1571-1592.