LI S, XU L, JING Y, et al. High-quality vegetation index product generation: a review of ndvi time series reconstruction techniques[J]. International Journal of Applied Earth Observation and Geoinformation, 2021, 105: 102640. DOI:10.1016/j.jag.2021.102640.
卫星遥感归一化植被指数(NDVI)已经广泛应用于遥感领域,然而由于大气扰动、云覆盖、传感器故障等原因,NDVI时间序列中存在大量噪声,因此,NDVI产品在使用前进行去噪非常有必要。在过去的几十年里已经提出了大量的技术方法来缓解这个问题,但是目前没有对此进行系统性的总结和分析各种NDVI时间序列重建技术的现状。因此本文对当前NDVI重建方法进行了概括,解释了各种方法的原理、优缺点。目前常用的NDVI重建方法主要有以下三类:
介绍了NDVI重建质量评价方法,讨论了未来的发展趋势。
如下表所示,本表来自于论文原文,GIMMS NDVI目前已经发展到了V3版本,时间1981-2015,可以使用R语言gimms包获取,下面的链接有误。
Sensors | Products | Resolutions | Dates | Data Sources |
---|---|---|---|---|
MODIS Terra/Aqua | MOD13Q1 | 16-day, 250 m | 1999~ | https://ladsweb.modaps.eosdis.nasa.gov |
MOD13A1 | 16-day, 500 m | |||
MOD13A2 | 16-day, 1 km | |||
MOD13A3 | Monthly, 1 km | |||
MOD13C1 | 16-day, 0.05 deg | |||
MOD13C2 | Monthly, 0.05 deg | |||
VIIRS | VNP13A1 | 16-day, 500 m | 2011~ | https://ladsweb.modaps.eosdis.nasa.gov |
VNP13A2 | 16-day, 1 km | |||
VNP13A3 | Monthly, 1 km | |||
VNP13C1 | 16-day, 1 km | |||
VNP13C2 | Monthly, 1 km | |||
Landsat 5 TM | Landsat5 C1 | 16-day, 30 m | 1984 ~ 2012 | https://earthexplorer.usgs.gov |
Landsat 7 ETM | Landsat7 C1 | 16-day, 30 m | 1999~ | |
Landsat 8 OLI | Landsat8 C1 | 16-day, 30 m | 2013~ | |
Sentinel-2 | S2 L1C | 5-day, 10 m | 2015~ | https://earthexplorer.usgs.gov/ |
NOAA-AVHRR | GVI | 7-day, >=15 km | 1982~ | https://ladsweb.modaps.eosdis.nasa.gov |
GIMMS | 15-day, 8 km | 1981 ~ 2006 | https://ecocast.arc.nasa.gov/data/pub/gimms | |
SPOT | VGT-S10 | 10-day, 1 km | 1998~ | https://earth.esa.int/eogateway |
基于空间的方法是重建单幅遥感影像的最常用技术,基于临近像素的相关性,根据影像有效部分恢复丢失的部分像元。空间插值是典型代表,包括线性插值、双线性插值、克里金插值等,基于空间的方法对于少量丢失像元或简单纹理有较好的效果,但不适于时间序列重构。本文不过多讨论。
根据重构方法原理可以主要分为三类:
Category | Method | Advantage | Disadvantage | Scope |
---|---|---|---|---|
Temporal interpolation | MVC | Simplicity | Neglecting atmospheric interference | Data without long-term gaps |
IDR | Retaining the valid pixels | Overestimating during dormant period | ||
DA | Smoother temporal evolution | Poor performance at the onset of spring green | ||
BISE | Close to original time series | Bad for long-term decline trend | ||
Temporal filters | SG | Reaching the upper envelope | Bad for peak growth and crop harvest | Long-term accumulated data |
MVI | Good with uniform spatial–temporal distribution | Bad for areas with high interannual changes | ||
MA | Retaining the seasonal peak of the curve | Changing the width and height of the curve | ||
CW | Maintaining the shape and amplitude of the curve | Remaining residual noises | ||
Temporal function-fitting | AG | Extracting phenological parameters while smothing | Bad for very short growing seasons | Vegetation without suddenly explodes or perishes |
DL | Revealing the trend of NDVI time series | Remaining problematic with NDVI in winter | ||
Temporal deep learning models | Deep-STEP_FE | Good performance with a very large dataset | High computational complexity | Nearly all data |
Frequency-based methods | FFT | Smooth reconstructed curve | Poor performance for irregular time series | Vegetation without abruptly changes |
HANTS | Reduction of the amount of data | Over-parameterized | ||
WT | Preserving patterns of time series while smoothing | Reducing some reasonable high values | ||
Hybrid methods | TSF | Restoring the seasonal trends | Restriction of QC data | Nearly all data |
SFA-MOM | Good performance in data deficient scenario | Restriction of LULC data | ||
STSG | Available for local low values during harvest | Bad for the data with high spatial resolution | ||
ST-tensor | Available for spatio-temporal continuous gaps | Restriction of RI data |
质量评价从定性和定量两方面进行评价。
一种通过目视检查的主观评价,简单、直观,主要两种方式:
本文以湖北神农架地区的2011-2020年平均值NDVI作为基准数据,使用相关系数(Correlation Coefficient, CC)和均方根误差(Root Mean Square Error, RMSE)来对重建后的结果进行质量评定。
尽管目前已经提出了很多的NDVI重建技术,但仍有很多方面可以改进。未来的发展趋势包括:
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