Water clarity annual dynamics (1984–2018) dataset across China derived from Landsat images in Google Earth Engine

H. Tao, K. Song, Ge Liu, Qiang Wang, Z. Wen, P. Jacinthe, Xiaofeng Xu, Jia Du, Y. Shang, Sijia Li, Zongming Wang, L. Lyu, Junbin Hou, Xiang Wang, Dong Liu, Kun Shi, Baohua Zhang, H. Duan
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Abstract

Abstract. Water clarity provides a sensitive tool to examine spatial pattern and historical trend in lakes trophic status. Yet, this metric has insufficiently been explored despite the availability of remotely-sensed data. We used three Secchi disk depth (SDD) datasets for model calibration and validation from different field campaigns mainly conducted during 2004–2018. The red/blue band ratio algorithm was applied to map SDD for lakes (> 1 ha) based on the first SDD dataset, where R2 = 0.79, RMSE = 100.3 cm, rRMSE = 61.9 %, MAE = 57.7 cm. The other two datasets were used to validate the SDD estimation model, which were indicated the model had a stable performance of temporal transferability. The annual mean SDD of lakes were retrieved across China using Landsat top of air reflectance products in GEE from 1984 to 2018. The spatiotemporal dynamics of SDD were analysed at the five lake regions and individual lake scales, and the average, changing trend, lake number and area, and spatial distribution of lake SDDs across China were presented. In 2018, we found that the lakes with SDDs < 2 m accounted for the largest proportion (80.93 %) of the total lakes, but the total area of lakes with SDD between 0–0.5 m and > 4 m were the largest, accounting for 48.28 % of the total lakes. During 1984–2018, lakes in the Tibetan-Qinghai Plateau lake region (TQR) had the clearest water with an average value of 3.32 ± 0.38 m, while that in the Northeastern lake region (NLR) exhibited the lowest SDD (mean: 0.60 ± 0.09 m). Among the 10,814 lakes with SDD results more than 10 years, 55.42 % and 3.49 % of lakes experienced significant increasing and decreasing trends, respectively. At the five lake regions, except for the Inner Mongolia-Xinjiang lake region (MXR), more than half of the total lakes in every other lake region exhibited significant increasing trends. In the Eastern lake region (ELR), NLR and Yungui Plateau lake region (YGR), almost more than 50 % of the lakes that displayed an increase or decrease in SDD were mainly distributed in an area of 0.01–1 km2, whereas that in the TQR and MXR were primarily concentrated in large lakes (> 10 km2). Spatially, lakes located in the plateau regions generally exhibited higher SDD than those situated in the flat plain regions. The dataset can now be accessed through the website of the National Tibetan Plateau Data Center (http://data.tpdc.ac.cn): DOI: 10.11888/Hydro.tpdc.271571.
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基于谷歌地球引擎Landsat图像的中国水体净度年动态数据集(1984-2018
摘要水的净度是研究湖泊营养状况的空间格局和历史趋势的敏感工具。然而,尽管可以获得遥感数据,但对这一指标的探索还不够充分。我们使用了2004-2018年主要进行的不同野外活动的三个Secchi磁盘深度(SDD)数据集进行模型校准和验证。基于第1个SDD数据集,应用红蓝波段比算法绘制湖泊SDD (> 1 ha), R2 = 0.79, RMSE = 100.3 cm, rRMSE = 61.9%, MAE = 57.7 cm。另外两个数据集对SDD估计模型进行了验证,结果表明该模型具有稳定的时间可转移性。1984 - 2018年,利用Landsat卫星反演了中国湖泊的年平均SDD。在5个湖泊区域和单个湖泊尺度上分析了中国湖泊SDDs的时空动态,揭示了中国湖泊SDDs的平均值、变化趋势、湖泊数量和面积以及空间分布特征。2018年,我们发现sdd为4 m的湖泊最大,占湖泊总数的48.28%。1984-2018年,青藏高原湖区(TQR)湖水最清澈,平均为3.32±0.38 m,东北湖区(NLR)最低,平均为0.60±0.09 m。在10814个SDD结果大于10年的湖泊中,分别有55.42%和3.49%的湖泊呈显著的增加和减少趋势。在5个湖区,除内蒙-新疆湖区外,其余湖区湖泊总数均有一半以上呈显著增加趋势。在东部湖区、西北湖区和云贵高原湖区,近50%以上的SDD增减湖泊主要分布在0.01-1 km2的范围内,而在TQR和MXR, SDD增减湖泊主要集中在大于10 km2的大型湖泊。从空间上看,高原湖泊的SDD总体高于平原湖泊。该数据集现在可以通过国家青藏高原数据中心网站(http://data.tpdc.ac.cn): DOI: 10.11888/Hydro.tpdc.271571)访问。
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