Remote Sensing Inversion of Water Quality Parameters and Their Correlation with Landscape Patterns in Haikou Changqin Lake Based on UAV Multispectral Data

doi:10.16258/j.cnki.1674-5906.2025.12.010

Ecology and Environmental Sciences ›› 2025, Vol. 34 ›› Issue (12): 1930-1943.DOI: 10.16258/j.cnki.1674-5906.2025.12.010

• Original article [Environmental Science] • Previous Articles Next Articles

Remote Sensing Inversion of Water Quality Parameters and Their Correlation with Landscape Patterns in Haikou Changqin Lake Based on UAV Multispectral Data

LI Qingyun¹(), XIAO Yimin¹, LIN Wei²^,⁴, LEI Jinrui²^,⁵^,^*(), WANG Yunlei³, KONG Meiman¹, YANG Yunfang¹

1. Zhejiang Guangsha Vocational and Technical University of construction, College of Urban and Rural Construction, Dongyang 322100, P. R. China
2. Hainan Academy of Forestry Sciences (Hainan Mangrove Research Institute), Haikou 571100, P. R. China
3. Chinese Academy of Tropical Agricultural Sciences, Spiced Beverage Research Institute, Wanning 571533, P. R. China
4. College of Tropical Agriculture and Forestry, Hainan University, Haikou 570228, P. R. China
5. Haikou Wetland Protection Engineering Technology Research and Development Center, Haikou 571100, P. R. China

Received:2025-07-08 Online:2025-12-18 Published:2025-12-10

基于无人机多光谱数据的海口长钦湖水质参数遥感反演及其与景观格局的相互关系

李清云¹(), 肖亦敏¹, 林卫²^,⁴, 雷金睿²^,⁵^,^*(), 王韫镭³, 孔玫蔓¹, 杨云芳¹

1.浙江广厦建设职业技术大学城乡建设学院，浙江东阳 322100
2.海南省林业科学研究院（海南省红树林研究院），海南海口 571100
3.中国热带农业科学院香料饮料研究所，海南万宁 571533
4.海南大学热带农林学院，海南海口 570228
5.海口市湿地保护工程技术研究开发中心，海南海口 571100

通讯作者: *E-mail：raykingre@163.com
作者简介:李清云（1987年生），女，博士研究生，研究方向为湿地生态与水环境科学。E-mail: lqy181268@126.com
基金资助:
海南省自然科学基金项目(723QN275);人才引进科研启动项目(2024ZX076);海南省财政科技计划项目(jscx202024)

Abstract

Abstract:

Land use and landscape patterns are key factors influencing the distribution of water quality in critical urban water bodies. Accurate and efficient water quality monitoring is essential for elucidating the correlation between water quality and landscape patterns, as well as for providing a scientific basis for water quality regulation and management. In this study, Changqin Lake in Haikou City was selected as the study area. By synchronously collecting water samples and UAV-based multispectral remote sensing data, optimal inversion models for total phosphorus (TP), total nitrogen (TN), turbidity (TUB), and chlorophyll-a (Chl-a) concentrations in wetland water during the dry and wet seasons were developed. Spatial distributions of these water quality parameters were generated, and their relationships with the landscape patterns were analyzed. The results showed that 1) For TP, TN, and Chl-a, the best was for TN in the dry season, and power function regression was optimal for TUB in the wet season. The inversion results suggested that Changqin Lake exhibited relatively low pollution levels and had good water quality. 2) Spatiotemporal variations were observed in water quality parameters. The average and maximum concentrations of TP, TN, and Chl-a were higher in the wet season than in the dry season, whereas TUB showed significantly higher values in the dry season. 3) A 120-meter buffer zone was identified as a critical threshold for landscape pattern changes in Changqin Lake. As the buffer zone expanded, the aggregation index (AI) and largest patch index (LPI) increased, whereas the contagion index (CONTAG), Shannon’s diversity index (SHDI), and Shannon’s evenness index (SHEI) decreased. The division index (DIVISION), patch density (PD), and landscape shape index (LSI) initially decreased before increasing. Beyond 120 m, landscape heterogeneity and fragmentation intensified with increasing radius. 4) During the dry season, water quality parameters were correlated with construction land, dry farmland, and LPI at specific scales. In the wet season, TP and TUB were not correlated with landscape types or pattern indices, whereas TN was significantly positively correlated with roads, PD, and SHDI, and Chl-a was positively correlated with constructed land. These findings provide scientific support for the efficient monitoring and conservation of the water quality in key urban wetlands.

Key words: water quality parameters, UAV, multispectral, remote sensing inversion, landscape pattern, correlation

摘要： 土地利用景观是影响城市重要水域水质分布的关键因素，精确、高效监测水域水质对解释水质与景观格局之间的相关性具有重要意义，同时也是水质监管与治理的重要基础。选取海口市长钦湖为研究区，通过同步采集水质样品与无人机多光谱遥感数据，建立旱、雨季湿地水体总磷（TP）、总氮（TN）、浊度（TUB）和叶绿素a（Chl-a）浓度的最优反演模型，生成水体水质参数的空间分布，并分析其空间变化与景观格局的相互关系。结果表明，1）长钦湖湿地旱、雨季TP、TN、Chl-a反演模型均以多项式模型呈现最优精度，在TN反演模型中旱季以线性回归模型精度最优，在TUB反演模型中雨季以幂函数回归模型精度最高，反演结果显示长钦湖水质污染程度轻、状况较好。2）长钦湖水质参数存在一定时空变异特性，TP、TN、Chl-a平均及最大浓度雨季大于旱季，TUB平均及最大浓度旱季显著大于雨季。3）缓冲区尺度120 m为长钦湖景观变化重要节点，随缓冲区扩大，AI、LPI值递增，CONTAG、SHDI、SHEI值递减，DIVISION、PD、LSI先减后增，120 m后景观异质性与破碎化程度随半径增大而升高。4）旱季在特定尺度内水质参数与建设用地、旱地、LPI具有相关性；雨季TP、TUB浓度与各景观类型及格局指数无相关性，TN浓度与道路、PD、SHDI显著正相关，Chl-a浓度与建设用地呈正相关。研究成果可为城市重点湿地水质的高效监测及保护工作提供科学层面的支撑。

关键词: 水质参数, 无人机, 多光谱, 遥感反演, 景观格局, 相关性

CLC Number:

X824
X832

LI Qingyun, XIAO Yimin, LIN Wei, LEI Jinrui, WANG Yunlei, KONG Meiman, YANG Yunfang. Remote Sensing Inversion of Water Quality Parameters and Their Correlation with Landscape Patterns in Haikou Changqin Lake Based on UAV Multispectral Data[J]. Ecology and Environmental Sciences, 2025, 34(12): 1930-1943.

李清云, 肖亦敏, 林卫, 雷金睿, 王韫镭, 孔玫蔓, 杨云芳. 基于无人机多光谱数据的海口长钦湖水质参数遥感反演及其与景观格局的相互关系[J]. 生态环境学报, 2025, 34(12): 1930-1943.

Figures/Tables 14

References 37

[1]	CHENG X, SONG J P, YAN J Z, 2023. Influences of landscape pattern on water quality at multiple scales in an agricultural basin of western China[J]. Environmental Pollution, 319: 120986. DOI URL
[2]	DOU J H, XIA R, CHEN Y, et al., 2022. Mixed spatial scale effects of landscape structure on water quality in the Yellow River[J]. Journal of Cleaner Production, 368: 133008. DOI URL
[3]	DE KEUKELAERE L, MOELANS R, KNAEPS E, et al., 2023. Airborne drones for water quality mapping in inland, transitional and coastal waters—MapEO water data processing and validation[J]. Remote Sensing, 15(5): 1345. DOI URL
[4]	GHOLIZADEH M H, MELESSE A M, REDDI L, 2016. A comprehensive review on water quality parameters estimation using remote sensing techniques[J]. Sensors, 16(8): 1298. DOI URL
[5]	HOU Y, ZHANG A, LV R, et al., 2023. Machine learning algorithm inversion experiment and pollution analysis of water quality parameters in urban small and medium-sized rivers based on UAV multispectral data[J]. Environmental Science and Pollution Research, 30(32): 78913-78932. DOI
[6]	HAN H J, YAN X, XIE H W, et al., 2023. Incorporating a new landscape intensity indicator into landscape metrics to better understand controls of water quality and optimal width of riparian buffer zone[J]. Journal of Hydrology, 625(Part B): 130088.
[7]	JONES E R, BIERKENS M F P, VAN P P J T M, et al., 2023. Sub-Saharan Africa will increasingly become the dominant hotspot of surface water pollution[J]. Nature Water, 1(7): 602-613. DOI
[8]	LUNT J, SMEED L, 2019. Turbidity alters estuarine biodiversity and species composition[J]. Journal of Marine Science, 77(1): 379-387.
[9]	LEE S, MCCARTT G W, MOGLEN G E, et al., 2020. Assessing the effectiveness of riparian buffers for reducing organic nitrogen loads in the Coastal Plain of the Chesapeake Bay watershed using a watershed model[J]. Journal of Hydrology, 585: 124779. DOI URL
[10]	LAURA S M, IZAGUIRRE I, ZAGARESE H, et al., 2023. Drivers of planktonic chlorophyll a in pampean shallow lakes[J]. Ecological Indicators, 146: 109834. DOI URL
[11]	MA T, SUN S A, FU G T, et al., 2020. Pollution exacerbates China’s water scarcity and its regional inequality[J]. Nature Communications, 11(1): 650. DOI
[12]	MISHRA A, ALNAHIT A, CAMPBELL B, 2021. Impact of land uses, drought, flood, wildfire, and cascading events on water quality and microbial communities: A review and analysis[J]. Journal of Hydrology, 596: 125707. DOI URL
[13]	SIRABAHENDA Z, ST-HILAIRE A, COURTENAY S C, et al., 2020. Assessment of the effective width of riparian buffer strips to reduce suspended sediment in an agricultural landscape using ANFIS and SWAT models[J]. Catena, 195: 104762. DOI URL
[14]	WANG J, FU Z, QIAO H, et al., 2019. Assessment of eutrophication and water quality in the estuarine area of Lake Wuli, Lake Taihu, China[J]. Science of the Total Environment, 650(Part 1): 1392-1402. DOI URL
[15]	XU Q Y, YAN T Z, WANG C Y, et al., 2023. Managing landscape patterns at the riparian zone and sub-basin scale is equally important for water quality protection[J]. Water Research, 229: 119280. DOI URL
[16]	ZHANG J, LI S, DONG R, et al., 2019. Influences of land use metrics at multi-spatial scales on seasonal water quality: A case study of river systems in the Three Gorges Reservoir Area, China[J]. Journal of Cleaner Production, 206: 76-85. DOI URL
[17]	ZHANG F, CHEN Y, WANG W W, et al., 2022. Impact of land-use/land-cover and landscape pattern on seasonal in-stream water quality in small watersheds[J]. Journal of Cleaner Production, 357: 131907. DOI URL
[18]	ZOU W, ZHU G, XU H, et al., 2022. Elucidating phytoplankton limiting factors in lakes and reservoirs of the Chinese Eastern Plains ecoregion[J]. Journal of Environmental Management, 318: 115542. DOI URL
[19]	ZHANG H X, CAO X H, HUO S, et al., 2023. Changes in China’s river water quality since 1980: Management implications from sustainable development[J]. npj Clean Water, 6(1): 45. DOI
[20]	范雅双, 于婉晴, 张婧, 等, 2021. 太湖上游水源区河流水质对景观格局变化的响应关系——以东苕溪上游为例[J]. 湖泊科学, 33(5): 1478-1489.
	FAN Y S, YU W Q, ZHANG J, et al., 2021. Response of water quality to landscape pattern change in the water source area of upper reaches of lake Taihu: A case study in the upper reaches of Dongtiaoxi River[J]. Journal of Lake Sciences, 33(5): 1478-1489. DOI URL
[21]	黄华, 李茂亿, 陈吟晖, 等, 2021. 基于PLSR的珠江口城市河流水质高光谱反演[J]. 水资源保护, 37(5): 36-42.
	HUANG H, LI M Y, CHEN Y H, et al., 2021. Water quality retrieval by hyperspectral for city rivers in Pearl River Estuary based on partial least squares regression[J]. Water Resources Protection, 37(5): 36-42.
[22]	胡艳芳, 范中亚, 陈昭婷, 等, 2021. 汕头市练江流域景观格局与水质的关联分析[J]. 中国环境监测, 37(3): 126133.
	HU Y F, FANG Z Y, CHEN Z T, et al., 2021. Correlation Analysis Between Landscape Pattern and Water Quality in the Lianjiang River Watershed in Shantou City[J]. Environmental Monitoring in China, 37(3): 126133.
[23]	胡丛巧, 迪丽努尔·阿吉, 李茹霞, 等, 2025. 博斯腾湖不同时空尺度下土地利用景观格局对水质的影响[J]. 水生态学杂志, 46(1): 34-44.
	HU C Q, DILINUER A, LI R X, et al., 2025. Effect of land use pattern on water quality in Bosten Lake at different temporal and spatial scales[J]. Journal of Hydroecology, 46(1): 34-44.
[24]	刘彦君, 夏凯, 冯海林, 等, 2019. 基于无人机多光谱影像的小微水域水质要素反演[J]. 环境科学学报, 39(4): 1241-1249.
	LIU Y J, XIA K, FENG H L, et al., 2019. Inversion of water quality elements in small and micro-size water region using multispectral image by UAV[J]. Acta Scientiae Circumstantiae, 39(4): 1241-1249.
[25]	李雪, 张婧, 于婉晴, 等, 2021. 京杭运河杭州段城市景观格局对河网水环境的影响[J]. 生态学报, 41(13): 5242-5253.
	LI X, ZHANG J, YU W Q, et al., 2021. Impact of the urban landscape pattern in the Hangzhou Section of the Beijing-Hangzhou Grand Canal on the river aquatic environment[J]. Acta Ecologica Sinica, 41(13): 5242-5253.
[26]	刘智琦, 潘保柱, 韩谞, 等, 2022. 青藏高原湖泊水环境特征及水质评价[J]. 环境科学, 43(11): 5073-5083.
	LIU Z Q, PAN B Z, HAN X, et al., 2022. Water environmental characteristics and water quality assessment of Lakes in Tibetan Plateau[J]. Environmental Science, 43(11): 5073-5083. DOI URL
[27]	刘小欢, 张文, 郑和松, 等, 2024. 1980-2020年长江中游阳新县湖群湿地景观格局演变及影响因素[J]. 西北林学院学报, 39(3): 239-247.
	LIU X H, ZHANG W, ZHENG H S, et al., 2024. Driving force analysis of wetland landscape pattern changes of the Lakes inYangxin in the middle reaches of Yangtze River from 1980 to 2020[J]. Journal of North west Forestry University, 39(3): 239-247.
[28]	时浩南, 梅琨, 吴宇鹏, 等, 2024. 温瑞塘河流域景观格局对水质影响的空间尺度效应[J]. 环境科学学报, 44(12): 390-402.
	SHI H N, MEI K, WU Y P, et al., 2024. The spatial scale effects of landscape pattern on water quality in the Wen-Rui Tang River watershed[J]. Acta Scientiae Circumstantiae, 44(12): 390-402.
[29]	宋继鹏, 2022. 景观格局与河流水质的关系研究——以重庆市龙溪河流域为例[D]. 重庆: 西南大学.
	SONG J P, 2020. Study on the relationship between landscape pattern and river water quality: A case study of the Longxi River Basin in Chongqing[D]. Chongqing: Southwest University.
[30]	邬建国, 2007. 景观生态学—格局、过程、尺度与等级[M]. 第2版. 北京: 高等教育出版社.
	WU J G, 2007. Landscape ecology:Pattern, process, scale and hierarchy[M]. Second Edition. .Beijing: Higher Education Press.
[31]	吴艾璞, 马春子, 霍守亮, 等, 2025. 河岸带景观格局对密云水库流域河流水质的影响[J]. 地理学报, 80(3): 724-741. DOI
	WU A P, MA C Z, HUO S L, et al., 2025. Impact of riparian landscape patterns on river water quality in the Miyun Reservoir Basin[J]. Acta Geographica Sinica, 80(3): 724-741. DOI
[32]	中华人民共和国环境保护部, 2002. GB 3838—2002中华人民共和国地表水环境质量标准[S]. 北京: 中国环境科学出版社出版.
	Ministry of Environmental Protection of the People’s Republic of China, 2002. GB 3838—2002 Environmental quality standards for surfacewater of the People’s Republic of China[S]. Being: China Environmental Science Press.
[33]	张乐, 雷金睿, 陈毅青, 等, 2023. 基于无人机多光谱数据的水质参数反演与评价——以海口市永庄水库为例[J]. 中国环境科学, 43(S1): 258-267.
	ZHANG L, LEI J R, CHEN Y Q, et al., 2023. Inversion and evaluation of water quality parameters based on UAV multispectral data: A case study of Yongzhuang Reservoir in Haikou[J]. China Environmental Science, 43(S1): 258-267.
[34]	章佩丽, 宋亮楚, 王昱, 等, 2022. 基于无人机多光谱的城市水体典型河道水质参数反演模型构建[J]. 环境污染与防治, 44(10): 1351-1356.
	ZHANG P L, SONG L C, WANG Y, et al., 2022. Establishment of Inversion model for water quality parameters in typical urban rivers based on unmanned aerial vehicle multispectral data[J]. Environmental Pollution & Control, 44(10): 1351-1356.
[35]	张伟燕, 马龙, 吉力力·阿不都外力, 等, 2019. 博尔塔拉河地表水重金属来源分析及其污染评价[J]. 干旱区资源与环境, 33(7): 100-106.
	ZHANG W Y, MA L, JILILI A, et al., 2019. Source analysis and pollution assessment of heavy metals in surface water of Bortala River[J]. Journal of Arid Land Resources and Environment, 33(7): 100-106.
[36]	周添红, 苏思霖, 马凯, 等, 2024. 典型区域土地利用/景观格局对黄河上游水体TN的影响[J]. 环境科学, 45(10): 5768-5776.
	ZHOU T H, SU S L, MA K, et al., 2024. Influence of typical regional land use/landscape pattern on water TN of the upper Yellow River[J]. Environmental Science, 45(10): 5768-5776.
[37]	朱云芳, 朱利, 李家国, 等, 2017. 基于GF-1 WFV影像和BP神经网络的太湖叶绿素a反演[J]. 环境科学学报, 37(1): 130-137.
	ZHU Y F, ZHU L, LI J G, et al., 2017. The study of inversion of chlorophyll a in Taihu based on GF-1 WFV image and BP neural network[J]. Acta Scientiae Circumstantiae, 37(1): 130-137.

光谱参数	计算公式	光谱参数	计算公式
V1	R₁	V9	(R₂+R₃)/R₅
V2	R₂	V10	(R₂+R₃)R₅
V3	R₃	V11	R₁/(R₅+R₂)
V4	R₄	V12	R₂+R₃
V5	R₅	V13	R₂+R₃+R₅
V6	R₃/R₂	V14	R₃/R₅
V7	R₂+R₅	V15	R₂/R₁
V8	(R₃+R₅)/R₂	V16	R₅/R₁

光谱参数	计算公式	光谱参数	计算公式
V1	R₁	V9	(R₂+R₃)/R₅
V2	R₂	V10	(R₂+R₃)R₅
V3	R₃	V11	R₁/(R₅+R₂)
V4	R₄	V12	R₂+R₃
V5	R₅	V13	R₂+R₃+R₅
V6	R₃/R₂	V14	R₃/R₅
V7	R₂+R₅	V15	R₂/R₁
V8	(R₃+R₅)/R₂	V16	R₅/R₁

水质要素	旱季模型	TP模型表达式	r²	RMSE	水质要素	雨季模型	模型表达式	r²	RMSE
TP	U_TP1	y=0.001x−0.296	0.0793	0.258	TP	U_TP15	y=1.166x−1.950	0.4767	0.080
	E_TP1	y=0.067e^0.001^x	0.0578	0.690		E_TP15	y=0.002e^2.652^x	0.4202	0.203
	P_TP1	y=0.153×10⁻³x^1.091	0.0330	0.699		P_TP15	y =0.009x^5.366	0.4152	0.204
	PL_TP1	y=9.204×10⁻⁶x²−0.019x+9.799	0.6686	0.156		PL_TP15	y=5.429x²−20.958x+20.568	0.5364	0.076
TN	U_TN8	y= −18.983x+27.059	0.6313	0.656	TN	U_TN4	y= −0.008x+10.210	0.4578	0.748
	E_TN8	y=1.515×10⁵e^−8.607^x	0.5964	0.320		E_TN4	y=63.535e^−0.004^x	0.4623	0.328
	P_TN8	y=40.683x^−11.327	0.6008	0.319		P_TN4	y=1.311×10⁹x^−2.952	0.4309	0.337
	PL_TN8	y=133.490x²−369.451x+256.818	0.7158	0.582		PL_TN4	y= −4.206×10⁻⁵x²+0.064x−20.474	0.5806	0.665
TUB	U_TUB14	y= −8.585x+24.670	0.1661	2.198	TUB	U_TUB5	y=0.048x−49.652	0.8828	1.824
	E_TUB14	y=1.162×10²e^−1.444^x	0.2347	0.298		E_TUB5	y=0.160e^0.003^x	0.8184	0.163
	P_TUB14	y=35.051x^−2.385	0.2066	0.303		P_TUB5	y=5.709×10⁻¹⁴x^4.601	0.8396	0.154
	PL_TUB14	y= −101.361x²+349.731x−290.720	0.6497	1.440		PL_TUB5	y= −2.591×10⁻⁵x²+0.119x−98.470	0.8885	1.798
Chl-a	U_CHL14	y=22.879x−37.235	0.4177	2.787	Chl-a	U_CHL9	y= −5.989x +36.235	0.3067	4.193
	E_CHL14	y=1.804×10⁻⁶e^7.790^x	0.3154	1.184		E_CHL9	y=148.488e^−0.680^x	0.1614	0.722
	P_CHL14	y=0.001x^13.567	0.3068	1.192		P_CHL9	y=1506.542x^−3.566	0.1819	0.713
	PL_CHL14	y=213.491x²−732.793x+629.278	0.7241	1.939		PL_CHL9	y=14.054x²−145.658x+380.245	0.6299	3.096

水质要素	旱季模型	TP模型表达式	r²	RMSE	水质要素	雨季模型	模型表达式	r²	RMSE
TP	U_TP1	y=0.001x−0.296	0.0793	0.258	TP	U_TP15	y=1.166x−1.950	0.4767	0.080
	E_TP1	y=0.067e^0.001^x	0.0578	0.690		E_TP15	y=0.002e^2.652^x	0.4202	0.203
	P_TP1	y=0.153×10⁻³x^1.091	0.0330	0.699		P_TP15	y =0.009x^5.366	0.4152	0.204
	PL_TP1	y=9.204×10⁻⁶x²−0.019x+9.799	0.6686	0.156		PL_TP15	y=5.429x²−20.958x+20.568	0.5364	0.076
TN	U_TN8	y= −18.983x+27.059	0.6313	0.656	TN	U_TN4	y= −0.008x+10.210	0.4578	0.748
	E_TN8	y=1.515×10⁵e^−8.607^x	0.5964	0.320		E_TN4	y=63.535e^−0.004^x	0.4623	0.328
	P_TN8	y=40.683x^−11.327	0.6008	0.319		P_TN4	y=1.311×10⁹x^−2.952	0.4309	0.337
	PL_TN8	y=133.490x²−369.451x+256.818	0.7158	0.582		PL_TN4	y= −4.206×10⁻⁵x²+0.064x−20.474	0.5806	0.665
TUB	U_TUB14	y= −8.585x+24.670	0.1661	2.198	TUB	U_TUB5	y=0.048x−49.652	0.8828	1.824
	E_TUB14	y=1.162×10²e^−1.444^x	0.2347	0.298		E_TUB5	y=0.160e^0.003^x	0.8184	0.163
	P_TUB14	y=35.051x^−2.385	0.2066	0.303		P_TUB5	y=5.709×10⁻¹⁴x^4.601	0.8396	0.154
	PL_TUB14	y= −101.361x²+349.731x−290.720	0.6497	1.440		PL_TUB5	y= −2.591×10⁻⁵x²+0.119x−98.470	0.8885	1.798
Chl-a	U_CHL14	y=22.879x−37.235	0.4177	2.787	Chl-a	U_CHL9	y= −5.989x +36.235	0.3067	4.193
	E_CHL14	y=1.804×10⁻⁶e^7.790^x	0.3154	1.184		E_CHL9	y=148.488e^−0.680^x	0.1614	0.722
	P_CHL14	y=0.001x^13.567	0.3068	1.192		P_CHL9	y=1506.542x^−3.566	0.1819	0.713
	PL_CHL14	y=213.491x²−732.793x+629.278	0.7241	1.939		PL_CHL9	y=14.054x²−145.658x+380.245	0.6299	3.096

水质要素	旱季模型	TP回归方程	r²	回归方程斜率	水质要素	雨季模型	TP回归方程	r²	回归方程斜率
TP	UTP1	y=0.072x+0.839	0.0374	0.0720	TP	UTP15	y=0.354x+0.267	0.5377	0.3539
	ETP1	y=0.018x+0.208	0.0610	0.0183		ETP15	y=0.398x+0.277	0.6183	0.3979
	PTP1	y=0.023x+0.330	0.0391	0.0232		PTP15	y=0.356x+0.259	0.6023	0.3557
	PLTP1	y=0.442x+0.215	0.5356	0.4421		PLTP15	y=0.472x+0.230	0.7627	0.4715
TN	UTN8	y=0.667x+0.577	0.7745	0.6677	TN	UTN4	y=0.533x+1.511	0.5917	0.5327
	ETN8	y=0.530x+0.744	0.7403	0.5298		ETN4	y=0.440x+0.756	0.5063	0.4403
	PTN8	y=0.529x+0.745	0.7345	0.5288		PTN4	y=0.525x+1.358	0.4906	0.5247
	PLTN8	y=0.545x+0.796	0.6691	0.5452		PLTN4	y=0.909x−0.284	0.6802	0.9085
TUB	UTUB14	y=0.262x+7.042	0.2844	0.2616	TUB	UTUB5	y=0.838x−0.888	0.9642	0.8376
	ETUB14	y=0.363x+5.800	0.2311	0.3634		ETUB5	y=0.729x−2.597	0.9809	0.7294
	PTUB14	y=0.330x+6.113	0.2135	0.3300		PTUB5	y=1.183x−5.147	0.9821	1.1825
	PLTUB14	y=0.671x+2.792	0.7670	0.6713		PLTUB5	y=0.759x−0.620	0.9414	0.7592
Chl-a	UCHL14	y=0.465x+2.120	0.6844	0.4654	Chl-a	UCHL9	y=0.467x+3.825	0.2836	0.4674
	ECHL14	y=0.493x+1.039	0.8304	0.4932		ECHL9	y=0.278x+3.650	0.3460	0.2784
	PCHL14	y=0.588x+1.432	0.8163	0.5877		PCHL9	y=0.294x+3.516	0.3669	0.2944
	PLCHL14	y=0.940x+0.461	0.8416	0.9395		PLCHL9	y=0.546x+2.479	0.6691	0.5456

Remote Sensing Inversion of Water Quality Parameters and Their Correlation with Landscape Patterns in Haikou Changqin Lake Based on UAV Multispectral Data

基于无人机多光谱数据的海口长钦湖水质参数遥感反演及其与景观格局的相互关系

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水质参数	季节	最大值	最小值	平均值	标准差
ρ_(TP)/ (mg·L⁻¹)	旱季	1.20	0.01	0.28	0.22
ρ_(TP)/ (mg·L⁻¹)	雨季	0.62	0.34	0.48	0.07
ρ_(TN)/ (mg·L⁻¹)	旱季	5.72	1.25	2.57	0.75
ρ_(TN)/ (mg·L⁻¹)	雨季	3.87	1.66	3.47	0.43
ρ_(TUB)/ (mg·L⁻¹)	旱季	10.95	1.85	9.07	1.55
ρ_(TUB)/ (mg·L⁻¹)	雨季	3.47	0.84	1.57	0.54
ρ_(Chl-a)/ (μg·L⁻¹)	旱季	20.79	0.46	4.83	3.48
ρ_(Chl-a)/ (μg·L⁻¹)	雨季	13.67	2.85	6.14	2.02

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