Multi-temporal Analysis of Vegetation Dynamics in Arizona: A Remote Sensing Based Study (2014-2024)
亚利桑那州植被动态的多时相分析：基于遥感的研究（2014-2024）
1.Introduction
一、简介
Vegetation dynamics is an important observation of ecosystem response to environmental change. In the context of current climate change, it is important to study the response of vegetation to precipitation factors in arid regions (Huang et al., 2016, Zhao et al., 2011). Although some previous literature has examined vegetation changes in arid regions, few studies have compared multiple vegetation indices in different ecological regions within a single state for long-term analyses (McPhee, J.et al.,2004). The dynamics of vegetation in Arizona, a typical arid and semi-arid region, reflect the sensitivity of ecosystems to climate change. Studies have shown significant spatial and temporal correlations between vegetation and precipitation in arid regions; however, there are still gaps in the current understanding of the variability in the vegetation corresponding to precipitation in different regions of Arizona.
植被动态是生态系统对环境变化响应的重要观察。在当前气候变化的背景下，研究干旱地区植被对降水因子的响应具有重要意义( Huang et al., 2016； Zhao et al., 2011 ) 。尽管之前有一些文献考察了干旱地区的植被变化，但很少有研究比较单一州内不同生态区域的多个植被指数进行长期分析( McPhee, J. et al., 20 04 )。亚利桑那州是典型的干旱半干旱地区，植被动态反映了生态系统对气候变化的敏感性。研究表明，干旱地区植被与降水之间存在显着的时空相关性；然而，目前对亚利桑那州不同地区降水对应的植被变化的理解仍然存在差距。
This study explores dry-season vegetation dynamics in Arizona through a comparative analysis of the Normalised Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI). These two widely used vegetation indices have their own scope of application: the NDVI is more sensitive to areas of low vegetation cover and is suitable for reflecting large-scale vegetation trends (Huete et al., 2002, Sims D et al., 2006), while the EVI is mostly used in areas of high vegetation cover by optimising the soil background and atmospheric disturbances, but its applicability to sparse vegetation in arid zones is not well studied(Jamali et al., 2011). This study combines NDVI and EVI to comprehensively analyse the vegetation dynamics in arid zones, and to explore the differences in their performance in low vegetation cover areas and their relationship with precipitation. This combination not only exploits the sensitivity of NDVI in capturing sparse vegetation dynamics, but also exploits the potential of EVI in optimising disturbance conditions (Moura et al., 2012, Matsushita et al., 2007, Huete and Justice, 1999), providing a more comprehensive comparison and validation of vegetation monitoring methods in arid zones.
本研究通过对标准化植被指数（NDVI）和增强植被指数（EVI）的比较分析，探讨了亚利桑那州的旱季植被动态。这两种广泛使用的植被指数都有各自的适用范围：NDVI对植被覆盖度较低的地区比较敏感，适合反映大范围的植被变化趋势( Huete et al., 20 02; Sims D et al., 2006 ），而EVI主要通过优化土壤背景和大气扰动，应用于植被覆盖率高的地区，但其对干旱地区植被稀疏的适用性尚未得到很好的研究（ Jamali等，2011 ） ） 。本研究结合NDVI和EVI综合分析干旱区植被动态，探讨其在低植被覆盖地区表现的差异及其与降水的关系。这种组合不仅利用了 NDVI 在捕捉稀疏植被动态方面的敏感性，而且还利用了 EVI 在优化干扰条件方面的潜力（ Moura et al., 2012 Matsushita et al., 2007 Huete and Justice, 1999 ），提供了更全面的干旱区植被监测方法的比较和验证。
The overall objective of this study was how vegetation phenology, represented by NDVI and EVI, responds to interannual variability in precipitation in Arizona. The objectives of the study are as follows:
本研究的总体目标是以 NDVI 和 EVI 为代表的植被物候如何响应亚利桑那州降水的年际变化。研究目的如下：
1. analyse temporal trends in NDVI and EVI for Arizona and its sub-regions (northern, central and southern).
1. 分析亚利桑那州及其次区域（北部、中部和南部）NDVI 和 EVI 的时间趋势。
2. quantify the relationship between dry-season precipitation and vegetation index changes.
2.量化旱季降水与植被指数变化的关系。
3. assess the utility of multi-temporal MODIS data for continuous monitoring of vegetation phenology.
3. 评估多时相 MODIS 数据对植被物候连续监测的效用。
2. Methods
2 .方法
In this study, an integrated multi-index comparative approach was used to analyse vegetation dynamics and the relationship with precipitation in Arizona over a ten-year period from 2014 to 2024 using the Normalised Difference Vegetation Index (NDVI) and the Enhanced Vegetation Index (EVI) extracted from MODIS satellite data.
本研究采用综合多指数比较方法，利用归一化植被指数（NDVI）和增强植被指数（EVI）分析了亚利桑那州2014年至2024年十年间的植被动态及其与降水的关系。 ）从 MODIS 卫星数据中提取。
2.1 Study area and data sources
2.1 研究区域及数据来源
Arizona is an ideal study area because of its diverse ecological gradients and distinct seasonal patterns(Asner, 2010). Arizona has significant topographic and climatic gradients, with the northern, central, and southern regions having different ecological characteristics (McPhee et al., 2004). The study area was therefore divided into three distinct regions within Arizona: northern (34.5°N-37.0°N, 114.8°W-109.0°W), central (33.0°N-34.5°N, 114.8°W-109.0°W), and southern (31.3°N-33.0°N, 114.8°W-109.0°W). In this study, the whole state was first selected for macro-analysis to reveal the overall characteristics of regional vegetation dynamics. The northern region was then used as the focus study area because (1) the climate gradient varies significantly in this region; (2) the precipitation pattern in this region is typical(Zhang F et al.,2020); (3) the NDVI values are relatively stable (0.19-0.25), which makes it suitable to be used as an EVI validation area;
亚利桑那州因其多样化的生态梯度和独特的季节模式而成为理想的研究区域（ Asner ，2010） 。亚利桑那州具有显着的地形和气候梯度，北部、中部和南部地区具有不同的生态特征( McPhee等，2004 ) 。因此，研究区域在亚利桑那州内被分为三个不同的区域：北部（34.5°N-37.0°N，114.8°W-109.0°W），中部（33.0°N-34.5°N，114.8°W-109.0°W）和南部（31.3°N-33.0°N，114.8°W-109.0°W）。本研究首先选择全州进行宏观分析，以揭示区域植被动态的整体特征，然后将北部地区作为重点研究区，因为（1）该地区气候梯度变化显着； ( 2 )该区域降水格局典型( Zhang F et al.,2020)。 ( 3 )NDVI值相对稳定(0.19-0.25),适合作为EVI验证区;
Figure1. Remote sensing satellite imagery of Arizona in the study area
图1.研究区亚利桑那州遥感卫星图像
The primary data used are from the MODIS MOD13Q1 product with a spatial resolution of 250 m. This product provides 16 days of composite imagery and is able to minimise atmospheric and cloud interference. Other data include surface water extent from the JRC Global Surface Water Dataset and precipitation data from the CHIRPS database. These data sources provided the basis for long-term vegetation trend analyses and precipitation correlation analyses for this study.
使用的主要数据来自MODIS MOD13Q1产品，空间分辨率为250 m。该产品提供 16 天的合成图像，能够最大限度地减少大气和云的干扰。其他数据包括 JRC 全球地表水数据集的地表水范围和 CHIRPS 数据库的降水数据。这些数据来源为本研究的长期植被趋势分析和降水相关性分析提供了基础。
2.2 Data processing and quality control
2.2 数据处理和质量控制
This is a flow chart of the entire study.
这是整个研究的流程图。
Figure2. flow chart of this study
图2.本研究流程图
2.2.1 NDVI analysis
2.2.1 NDVI分析
The data were first pre-processed and quality controlled for the MODIS dataset using the summarQA band. Only pixels with qualitySummaryQA = 0 were retained for analysis, which excluded low quality pixels to ensure data reliability. Surface water pixels were then masked using the JRC Maximum Watershed Extent layer to minimise interference of water bodies with vegetation indices. Analyses in this study focused on the dry season (March to June, DOY 60-181), as this period is typically the most sensitive to environmental change for vegetation in arid regions. The calculated annual maximum NDVI values during the dry season were scaled by a factor of 0.0001 and converted to the standard NDVI range (-1 to 1) to ensure accuracy and readability of the data.
首先使用 summarQA 频段对 MODIS 数据集的数据进行预处理和质量控制。仅保留qualitySummaryQA = 0的像素进行分析，排除低质量像素以确保数据可靠性。然后使用 JRC Maximum Watershed Extent 图层屏蔽地表水像素，以尽量减少水体对植被指数的干扰。本研究的分析重点是旱季（3 月至 6 月，DOY 60-181），因为这一时期通常对干旱地区植被的环境变化最敏感。将计算出的旱季年度最大 NDVI 值按 0.0001 缩放并转换为标准 NDVI 范围（-1 至 1），以确保数据的准确性和可读性。
2.2.2 EVI time series analysis
2.2.2 EVI时间序列分析
For the EVI analysis, the northern region with relatively stable NDVI values was selected as the study area for additional analysis. The processing included
对于EVI分析，选择NDVI值相对稳定的北部地区作为研究区进行补充分析。处理过程包括
1. quality filtering (SummaryQA = 1) of MODIS EVI data to exclude low-quality pixels and screen data with large errors to ensure data reliability.
1. MODIS EVI数据的质量过滤（SummaryQA=1），排除低质量像素和误差较大的屏幕数据，保证数据可靠性。
2. using linear interpolation to fill in the time gaps at 8-day intervals to maintain consistency with the MODIS acquisition frequency; generating a smoothed EVI time series.
2、每隔8天使用线性插值法填补时间间隙，以保持与MODIS采集频率的一致性；生成平滑的 EVI 时间序列。
3. employ a Savitzky-Golay filter(Kim et al., 2014) using a third-order polynomial and a 64-day window to reduce the effects of noise and improve the continuity of the data while preserving the temporal trend.
3. 采用 Savitzky-Golay滤波器（ Kim et al., 2014 ），使用三阶多项式和 64 天窗口来减少噪声的影响并提高数据的连续性，同时保留时间趋势。
4. scaling the EVI values by 0.0001 to maintain standardised units.
4. 将 EVI 值缩放 0.0001 以维持标准化单位。
2.3 Statistical analyses
2.3 统计分析
2.3.1 Regional trend analysis
2.3.1 区域趋势分析
The Sen's slope(Sen,1968) estimation method was used in this study to quantify vegetation trends by calculating the median rate of change in vegetation greenness for the three regions of the north, centre and south. This method was chosen for its robustness to outliers and non-normal distributions(Dawood, 2017). The analyses were normalised to a base year (2014) and change was expressed as percentage difference from initial conditions to better compare areas with different baseline levels of vegetation.
本研究采用森氏斜率（ Sen，1968）估计方法，通过计算北、中、南三个区域植被绿度变化的中值率来量化植被趋势。选择这种方法是因为它对异常值和非正态分布具有鲁棒性（ Dawood，2017 ） 。将分析标准化为基准年（2014 年），并将变化表示为与初始条件的百分比差异，以便更好地比较具有不同植被基线水平的区域。
2.3.2 Precipitation correlations
2.3.2 降水相关性
The study analysed the correlation between vegetation indices and precipitation patterns at multiple time scales. The five-day precipitation data from CHIRPS were first aggregated and calculated on an annual basis to obtain the total precipitation for each year in the study area. The temporal resolution consistency of the precipitation data with the vegetation index data was ensured. Subsequently, the annual precipitation data obtained were combined with the percentage change in vegetation index calculated through Sen's Slope regression. The study area was divided according to three sub-regions: north, centre and south, and in each sub-region the median change in the net vegetation index of variation and the spatially weighted mean of the annual mean precipitation were calculated separately. The relationship between precipitation and vegetation change can be visualised by plotting the mean annual precipitation (x-axis) and the median percentage change in vegetation index (y-axis) on the same graph using a scatterplot.
该研究分析了多个时间尺度的植被指数与降水模式之间的相关性。首先将CHIRPS的五天降水数据按年进行汇总计算，得到研究区每年的总降水量。保证了降水数据与植被指数数据时间分辨率的一致性。随后，将获得的年降水量数据与通过森氏斜率回归计算出的植被指数变化百分比相结合。将研究区划分为北、中、南3个分区，分别计算各分区的植被净变异指数变化中值和年平均降水量的空间加权平均值。通过使用散点图在同一张图表上绘制年平均降水量（x 轴）和植被指数变化百分比中值（y 轴），可以直观地显示降水量和植被变化之间的关系。
2.4 Data visualisation and output
2.4 数据可视化与输出
The results were visualised:
结果可视化：
1. time series plots showing regional NDVI trends from 2014 to 2024
1. 2014年至2024年区域NDVI趋势的时间序列图
2. spatial maps of annual percentage change in vegetation greenness, including statistical significance masks
2. 植被绿度年度百分比变化的空间图，包括统计显着性掩模
3. scatter plots of vegetation change in relation to annual precipitation
3. 植被变化与年降水量关系的散点图
4. EVI time series filtered by Savitzky-Golay smoothing, highlighting seasonal patterns.
4. EVI 时间序列经过 Savitzky-Golay 平滑过滤，突出季节性模式。
The results of the final analyses were output in the form of CSV files and graphs containing regional statistics, as well as median vegetation change rates, confidence intervals, and their relationship with precipitation. The entire study process was implemented in Google earth engine platform through JavaScript API, which allows efficient processing of large-scale satellite data and reproducible analyses throughout the study area, providing a basis for understanding ecosystem responses to climate factors in different regions.
最终分析结果以 CSV 文件和图表的形式输出，其中包含区域统计数据、植被变化率中值、置信区间及其与降水的关系。整个研究过程通过JavaScript API在Google Earth Engine平台上实现，可以对整个研究区域的大规模卫星数据进行高效处理和可重复分析，为了解不同地区生态系统对气候因素的响应提供基础。
3 Results of the study
3 研究结果
A study of vegetation dynamics in Arizona from 2014 to 2024 showed that the three regions of the state have different spatial and temporal patterns of vegetation greenness. The study used two complementary approaches: a statewide Normalised Difference Vegetation Index (NDVI) analysis to understand broad regional patterns, and then EVI analysis targeting the northern region to obtain a more detailed picture of vegetation dynamics and response to precipitation.
对亚利桑那州 2014 年至 2024 年植被动态的研究表明，该州三个地区的植被绿度具有不同的时空格局。该研究使用了两种互补的方法：全州范围内的归一化植被指数 (NDVI) 分析，以了解广泛的区域模式；然后针对北部地区的 EVI 分析，以获得更详细的植被动态和对降水的响应。
3.1 Temporal trends in regional vegetation dynamics
3.1 区域植被动态的时间变化趋势
The dry season NDVI analyses showed large differences between the three regions, with significant and synchronised inter-annual fluctuations observed throughout the study period. The central region consistently had higher NDVI values and also had the highest fluctuations in interannual variability, with median values ranging between 0.21 and 0.38. The northern and southern regions had lower NDVI values, typically ranging from 0.16 to 0.28, reflecting their drier conditions. Notably, the north was the region with the least fluctuating NDVI values of the three regions, showing the most stable pattern.
旱季 NDVI 分析显示三个区域之间存在巨大差异，在整个研究期间观察到显着且同步的年际波动。中部地区的 NDVI 值始终较高，年际变化波动也最大，中值范围在 0.21 至 0.38 之间。北部和南部地区的 NDVI 值较低，通常在 0.16 至 0.28 之间，反映了其较为干燥的条件。值得注意的是，北部是三个地区中NDVI值波动最小的地区，表现出最稳定的格局。
Temporal analyses show several distinct periods(figure 3.4.5.6)
时间分析显示了几个不同的时期（图3.4.5.6）:
- 2014-2016: a gradual increase in NDVI values in all regions, with the most significant improvement in the central region, from 0.25 to 0.30
- 2014-2016年：各地区NDVI值逐渐上升，其中中部地区改善最为显着，从0.25上升至0.30
- 2016-2018: a period of slight decline, particularly noticeable in the central region, where NDVI declined by about 0.1 units
- 2016-2018年：小幅下降时期，中部地区尤其明显，NDVI下降约0.1个单位
- 2018-2020: period of sharp recovery with peak NDVI values (north: 0.26, centre: 0.38, south: 0.28)
- 2018-2020年：NDVI峰值急剧恢复时期（北部：0.26，中部：0.38，南部：0.28）
- 2020-2022: similar to the 2016-2018 period.
- 2020-2022年：与2016-2018年期间类似。
- 2022-2024: Gradual increase, reaching the previous peak level in the central-southern region, with a slight decrease in the northern region after 2023.
- 2022-2024年：逐步增加，中南部地区达到前期峰值水平，2023年后北部地区略有下降。
Figure3.Arizona Dry season NDVI trends 2014-2024 by region
图3 2014-2024 年按地区划分的亚利桑那州旱季 NDVI 趋势
Figure4.NDVI trends 2014-2024 for central Arizona
图4亚利桑那州中部 2014-2024 年 NDVI趋势
Figure5.NDVI trends 2014-2024 for northern Arizona
图5亚利桑那州北部 2014-2024 年 NDVI趋势
Figure6.NDVI trends 2014-2024 for southern Arizona
图6亚利桑那州南部 2014-2024 年 NDVI趋势
3.2 Relationship between the annual change pattern of NDVI and precipitation
3.2 NDVI年际变化格局与降水量的关系
The relationship between vegetation change and average annual precipitation was analysed by comparing scatter plots of regional median vegetation greenness change with annual precipitation. The scatterplots showed that the pattern of vegetation change varied across Arizona and was closely related to mean annual precipitation. The relationship between precipitation and vegetation change exhibits distinct regional characteristics(figure 7)
通过比较区域植被绿度中值变化与年降水量的散点图，分析植被变化与年平均降水量的关系。散点图显示，亚利桑那州各地的植被变化模式各不相同，并且与年平均降水量密切相关。降水量与植被变化的关系呈现出明显的区域特征（图7）:
- Annual precipitation varied from about 270 mm to 370 mm across regions
- 各地区年降水量从 270 毫米到 370 毫米不等
- The median annual change in vegetation greenness varies from region to region.
- 植被绿度的年变化中值因地区而异。
However, due to the limited number of data points, it is not immediately possible to make broad generalisations about the exact relationship between precipitation and vegetation change. The available data suggest a correlation between the two, but more detailed analyses are needed to establish a clear pattern of correlation.
然而，由于数据点数量有限，不可能立即对降水与植被变化之间的确切关系做出广泛的概括。现有数据表明两者之间存在相关性，但需要更详细的分析来建立清晰的相关性模式。
Figure7.yearly changes in dry-season vegetation greeness in relation to average annual rainfall
图7旱季植被绿度年变化与年平均降雨量的关系
3.3 Detailed EVI Analysis for Northern Arizona
3.3 亚利桑那州北部详细的 EVI 分析
Mean EVI values show clear seasonal fluctuations from 2014 to 2024, with EVI values generally fluctuating between 0.08 and 0.20; maxima typically reach 0.18-0.23 during the peak growing season; peaks typically occur during the summer monsoon season (July-September), and minima occur during the pre-monsoon dry season (April-June). The magnitude of these seasonal variations is large, but the overall trend shows relatively consistent seasonal fluctuations. During the dry period, EVI minima drop to about 0.08-0.10; EVI values are higher in 2022 and 2023, peaking at about 0.23.
2014年至2024年EVI平均值呈现明显的季节性波动，EVI值普遍在0.08至0.20之间波动；在生长旺季，最大值通常达到 0.18-0.23；峰值通常出现在夏季季风季节（七月至九月），最小值出现在季风前的旱季（四月至六月）。这些季节变化的幅度较大，但总体趋势表现出相对一致的季节波动。干旱期，EVI最低值降至0.08-0.10左右； 2022年和2023年EVI值较高，峰值约为0.23。
The Savitzky-Golay filtered EVI time series (EVI_sg) showed vegetation dynamics more clearly by reducing noise(figure8.9)
Savitzky-Golay 滤波的 EVI 时间序列（EVI_sg）通过降低噪声更清晰地显示了植被动态（图 8.9）:
- The smoothed curve shows a consistent seasonal pattern with peaks occurring every year
- 平滑曲线显示出一致的季节性模式，每年都会出现峰值
- Significant peaks occur in early 2020 (reaching ~0.17) and early 2021 (reaching ~0.20)
- 显着的峰值出现在 2020 年初（达到约 0.17）和 2021 年初（达到约 0.20）
- Minimums occur in mid-2018 (~0.07) and late 2019 (~0.08)
- 最小值出现在 2018 年中期 (~0.07) 和 2019 年末 (~0.08)
- The magnitude of seasonal variation remains relatively constant, typically ranging from 0.07-0.08 (minimum) to 0.15-0.20 (maximum).
- 季节性变化的幅度保持相对恒定，通常范围从 0.07-0.08（最小值）到 0.15-0.20（最大值）。
Figure8.MODIS EVI time series for northern Arizona
图8亚利桑那州北部的MODIS EVI 时间序列
Figure9.Savitzky-Golay Filtered EVI time series for northern Arizona
图9.亚利桑那州北部的 Savitzky-Golay 过滤 EVI 时间序列
3.4 Relationship between Precipitation and EVI in Northern Arizona
3.4 亚利桑那州北部降水量与EVI的关系
Comparison of precipitation time series with EVI patterns reveals several key relationships(figure9)
降水时间序列与EVI模式的比较揭示了几个关键关系（图9）:
- Significant precipitation events (>20 mm) occurred sporadically throughout the study period
- 在整个研究期间偶尔发生显着降水事件（>20 mm）
- The highest precipitation peaks occurred in
- 最高降水量峰值出现在
* 2019 (~27 mm)
* 2019 年（~27 毫米）
* 2021 (~23 mm)
* 2021 年（~23 毫米）
* 2022 (~25 mm)
* 2022 年（~25 毫米）
- EVI response to precipitation events typically shows a lag, with vegetation greenness increasing after major precipitation events.
- EVI 对降水事件的响应通常表现出滞后性，重大降水事件后植被绿度会增加。
-The most pronounced EVI response occurs in early 2020 and early 2021, corresponding to previous major precipitation events
-最明显的EVI响应发生在2020年初和2021年初，与之前的主要降水事件相对应
-Periods of low precipitation (e.g., mid-2018 and late 2019) coincided with lower EVI values
- 低降水期（例如 2018 年中期和 2019 年末）与较低的 EVI 值同时发生
The results suggest that there is a dynamic relationship between precipitation and vegetation response in northern Arizona, but that this relationship is not strictly linear due to factors such as lag effects and the influence of other environmental variables.
结果表明，亚利桑那州北部降水与植被响应之间存在动态关系，但由于滞后效应和其他环境变量的影响等因素，这种关系并不严格线性。
Figure9.Precitation time series for northern Arizona
图9.亚利桑那州北部的降水时间序列
These combined results suggest that vegetation dynamics in Arizona exhibit complex spatial and temporal patterns. Statewide NDVI analyses reveal broad regional differences in vegetation responses to climatic factors, while focused EVI analyses for the northern region reveal seasonal patterns and vegetation recovery processes in detail. The correlation between precipitation and vegetation indices reflects the sensitivity of vegetation to drought in the region. With this analysis, the study further validates the important role of precipitation patterns in driving vegetation dynamics in arid and semi-arid regions.
这些综合结果表明亚利桑那州的植被动态表现出复杂的空间和时间模式。全州范围内的 NDVI 分析揭示了植被对气候因素响应的广泛区域差异，而针对北部地区的重点 EVI 分析则详细揭示了季节模式和植被恢复过程。降水量与植被指数之间的相关性反映了该地区植被对干旱的敏感性。通过这一分析，该研究进一步验证了降水模式在驱动干旱和半干旱地区植被动态中的重要作用。
4.discussion
4.讨论
In this study, we conducted an in-depth analysis of vegetation dynamics in Arizona during 2014-2024 by integrating multi-temporal MODIS remote sensing data, and the results showed that the spatial and temporal changes in vegetation greenness had a relationship with precipitation, while exhibiting significant regional differences, revealing statewide vegetation trends and detailed local dynamics during the dry season.
本研究通过整合多时相MODIS遥感数据对亚利桑那州2014-2024年植被动态进行了深入分析，结果表明植被绿度的时空变化与降水量存在相关性，而植被绿度的时空变化与降水量存在相关性。表现出显着的区域差异，揭示全州植被趋势和旱季期间详细的当地动态。
Firstly, the annual NDVI fluctuation trends in different regions show similarity, which is influenced by regional large-scale climatic factors. However, the magnitude of changes varies from region to region, indicating the presence of local environmental moderators(King et al., 2008, Sohoulande Djebou et al., 2015). The study shows that the central region has the highest greenness of vegetation and the largest fluctuation, which may be related to its higher precipitation and better vegetation cover conditions(Bolander, 1981). The northern region, on the other hand, had a more stable vegetation greenness despite slightly lower precipitation, which may be related to its topography and vegetation type. In addition, significant recovery of vegetation was observed in the study during 2018-2020, which is consistent with the high precipitation during this period.
首先，不同地区NDVI年际波动趋势具有相似性，均受到区域大尺度气候因素的影响。然而，不同地区的变化幅度有所不同，这表明当地环境调节因素的存在（ King 等，2008； Sohoulande Djebou等，2015 ） 。研究表明，中部地区植被绿度最高且波动最大，这可能与其降水量较高、植被覆盖条件较好有关( Bolander，1981 ) 。而北部地区虽然降水量略少，但植被绿度较为稳定，这可能与其地形和植被类型有关。此外，研究在2018-2020年期间观察到植被显着恢复，这与该时期的高降水量一致。
Detailed EVI analyses in the northern region revealed a lagged response of vegetation to precipitation, a relationship that suggests that precipitation not only affects current vegetation growth, but also influences subsequent greenness recovery by replenishing soil moisture reserves(Dekker et al.,2007). However, the strength of the lag effect may vary by soil type, vegetation species, and management practices, factors that could not be quantified in depth in this study. In addition, the time-series smoothing of EVI demonstrated more clearly the seasonal dynamics of vegetation and its association with major precipitation events by reducing data noise. This suggests that multi-temporal satellite data combined with data smoothing techniques are valuable in monitoring vegetation health in dry areas.
北部地区详细的EVI分析揭示了植被对降水的滞后响应，这种关系表明降水不仅影响当前的植被生长，而且还通过补充土壤水分储备影响随后的绿化恢复（ Dekker等，2007） 。然而，滞后效应的强度可能因土壤类型、植被种类和管理实践而异，这些因素在本研究中无法深入量化。此外，EVI的时间序列平滑通过减少数据噪声，更清晰地展示了植被的季节动态及其与主要降水事件的关联。这表明多时相卫星数据与数据平滑技术相结合对于监测干旱地区的植被健康具有重要价值。
Although this study confirms the key role of precipitation on vegetation changes, the limited number of data points in the scatterplot analysis restricts a comprehensive understanding of the complex relationship between precipitation and vegetation dynamics. Although a clear positive correlation exists, the observed lag effect of vegetation response to significant precipitation events (>20 mm) suggests that other environmental factors are also involved. This non-linear relationship challenges simple precipitation-vegetation models and emphasises the need for more sophisticated monitoring methods.
尽管本研究证实了降水对植被变化的关键作用，但散点图分析中有限的数据点限制了对降水与植被动态之间复杂关系的全面理解。尽管存在明显的正相关性，但观察到的植被对重大降水事件（>20 mm）响应的滞后效应表明还涉及其他环境因素。这种非线性关系对简单的降水-植被模型提出了挑战，并强调需要更复杂的监测方法。
In addition, this study validates the critical role of multi-temporal satellite data in surface monitoring. With ten consecutive years of NDVI and EVI data, not only long-term trends but also seasonal dynamics can be revealed and captured. Compared with single-time-phase studies, multi-temporal data help to resolve complex spatial and temporal processes, especially in ecologically fragile regions such as arid and semi-arid zones, where the sensitivity to hydrological conditions is particularly obvious.
此外，本研究验证了多时相卫星数据在地表监测中的关键作用。通过连续十年的 NDVI 和 EVI 数据，不仅可以揭示和捕获长期趋势，还可以揭示和捕获季节性动态。与单时相研究相比，多时相数据有助于解决复杂的时空过程，特别是在干旱、半干旱地区等生态脆弱地区，对水文条件的敏感性尤为明显。
5.Conclusion
五、结论
The major findings of this study include:
这项研究的主要发现包括：
1. vegetation dynamics in Arizona is significantly spatial and temporal heterogeneous, with the highest and most fluctuating greenness in the central region, the most stable greenness in the northern region, and the most sensitive to precipitation in the southern region.
1. 亚利桑那州植被动态具有明显的时空异质性，中部地区绿度最高且波动最大，北部地区绿度最稳定，南部地区对降水最敏感。
2. The temporal patterns of vegetation change were correlated with precipitation dynamics but the relationship was complex, suggesting that water availability is a key driver of vegetation recovery in arid regions.
2. 植被变化的时间模式与降水动态相关，但关系复杂，表明水资源供应是干旱地区植被恢复的关键驱动因素。
3. Detailed EVI analyses in the northern region revealed seasonal fluctuations in vegetation and lagged responses to major precipitation events, further emphasising the importance of precipitation for ecosystem health.
3. 北部地区详细的EVI分析揭示了植被的季节性波动和对主要降水事件的滞后响应，进一步强调了降水对生态系统健康的重要性。
This study demonstrates the important role of multi-temporal satellite data in the monitoring of surface vegetation dynamics. By integrating NDVI and EVI analyses, this study not only provides a basis for long-term monitoring of vegetation dynamics in Arizona, but also provides scientific support for ecosystem management and restoration of arid zones on a global scale. However, there are some shortcomings of the study, such as the short study period, the limited number of data points, and the fact that only the effect of precipitation was considered without analysing other environmental variables that may affect vegetation. Therefore, future studies can combine high-resolution meteorological data, soil moisture, surface temperature and anthropogenic activity data to further improve the quantitative study of the relationship between vegetation dynamics and climatic factors in arid zones. In addition, the methodology of this study can be extended to other arid and semi-arid regions to verify its applicability on a global scale.
本研究论证了多时相卫星数据在地表植被动态监测中的重要作用。通过整合NDVI和EVI分析，本研究不仅为亚利桑那州植被动态的长期监测提供依据，也为全球范围内干旱区的生态系统管理和恢复提供科学支撑。但该研究也存在一些不足，如研究周期短、数据点数量有限、仅考虑降水的影响而没有分析其他可能影响植被的环境变量等。因此，未来的研究可以结合高分辨率气象数据、土壤湿度、地表温度和人类活动数据，进一步完善干旱区植被动态与气候因子关系的定量研究。此外，本研究的方法可以推广到其他干旱和半干旱地区，以验证其在全球范围内的适用性。
6.Reference
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7.GEE line
7.GEE线
https://code.earthengine.google.com/8aa7a8112b245d8c41e47a6c43c76c65
https://code.earthengine.google.com/04a2fc260a66caabde05281df0a0e3db