Paper Reading Note | Enhanced ATC

EATC(Ehanced Annual Temperature Cycles)

注意，此文章中运用此模型时候将Water部分给Mask去掉了！！！
水体范围的NDVI存在吗

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Abstract

ATC的改进版本，年温度周期，将离散LST温度拟合为逐日LST

LST（Land Surface Temperature）
SAT（Suface Air Temperature）

Validation Data : Aqua/MODIS

陌生单词

sampling style and the impenetrability of clouds
penetrate

v1

1.vt&vi 穿过，刺入，渗入
2.了解
3.vt.看穿

v2

synoptic conditions

1.Introduction

ATD、ATC、DTC

ATD(long time-series LSTs)

methods: Mann-Kendall、BFAST

DTC:

methods:the physical methods、 and statistical methods

ATC:

methods:

First, statistical time-series analysis methods

• HANTS(reconstructed LSTs are expected to be higher than the true LST under overcast conditions especially for the daytime)

Second, semi-physical methods

• Standard sinusoidal function(标准正弦波函数)过于圆滑对短期波动不敏感

Third, synthetic methods that combine the statistical and physical methods with/without additional data.

Issues

时间序列统计方法精度较高，但是比较依赖LST的数量，鲁棒性较强;与统计方法相比半物理的方法参数较少，所需要遥感影像数量也因此比较少，精度也因此相对较低！

单词

v1

sporadic manner

v2

diurnal temperature cycle 日间温度周期

v3

2.Materials and Methods

2.1 Study Area and Data

长江三角洲(Yangtze River Delta, YRD)
亚热带季风气候（ubtropical monsoon climate）
International Geosphere-Biosphere Programme (IGBP) classification scheme

MODIS 2010-2013 ,average for these four years

2.2 Method

2.2.1 Standard ATC Model

$$\begin{array}{}\text{(1)}& T(d)=f(T_0,A,\theta ,d)=T_0+A·sin(2\pi d/N+\theta )\end{array}$$

2.2.2 Ehanced ATC Model

$$\begin{array}{}\text{(2)}& T(d)=T_0+A·sin(2\pi d/N+\theta )+\mathrm{\Delta }{T}_{air}(d)·\gamma (d)\end{array}$$

$$\begin{array}{}\text{(3)}& \mathrm{\Delta }{T}_{air}(d)=T_{air}(d)-f(T_{0_{air}},A_{air},{\theta }_{air},d)\end{array}$$

$$\begin{array}{}\text{(4)}& \gamma (d)=\lambda (V_{max}-V_{min})/[V(d)-V_{min}+1]\end{array}$$

……

不多记录了，整体是这么个情况：
卫星LST数据通常是时间上不连续的，ATC模型用正弦函数模拟（依据两个假设，2.地表温度变化只由大气顶部太阳辐射引起，2.地表属性不会发生变化，这显然不够合理）
本文在ATC基础上引入了SAT和NDVI，提出了Ehanced ATC，
四个自由变量，也就是说这个模型至少需要4个ST数据，
经验证精度明显提高，但某些地方仍受热各向异性与城市热岛效应的影响……

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[1] ZOU Z, ZHAN W, LIU Z, et al. Enhanced Modeling of Annual Temperature Cycles with Temporally Discrete Remotely Sensed Thermal Observations [J]. Remote Sensing, 2018, 10(4).