Appraisal of Climate Response to Vegetation Indices over Tropical Climate Region in India

Abstract

Extreme climate events are becoming increasingly frequent and intense due to the global climate change. The present investigation aims to ascertain the nature of the climatic variables association with the vegetation variables such as Leaf Area Index (LAI) and Normalized Difference Vegetation Index (NDVI). In this study, the impact of climate change with respect to vegetation dynamics has been investigated over the Indian state of Haryana based on the monthly and yearly time-scale during the time period of 2010 to 2020. A time-series analysis of the climatic variables was carried out using the MODIS-derived NDVI and LAI datasets. The spatial mean for all the climatic variables except rainfall (taken sum for rainfall data to compute the accumulated rainfall) and vegetation parameters has been analyzed over the study area on monthly and yearly basis. The liaison of NDVI and LAI with the climatic variables were assessed at multi-temporal scale on the basis of Pearson correlation coefficients. The results obtained from the present investigation reveals that NDVI and LAI has strong significant relationship with climatic variables during the cropping months over study area. In contrast, during the non-cropping months, the relationship weakens but remains significant at the 0.05 significance level. Furthermore, the rainfall and relative humidity depict strong positive relationship with NDVI and LAI. On the other, negative trends were observed in case of other climatic variables due to the limitations of NDVI viz. saturation of values and lower sensitivity at higher LAI. The influence of aerosol optical depth was observed to be much higher on LAI as compared to NDVI. The present findings confirmed that the satellite-derived vegetation indices are significantly useful towards the advancement of knowledge about the association between climate variables and vegetation dynamics.

1. Introduction

India, with a population of over 1.4 billion people, is expected to overtake China and become the most populous country in the world by the year 2023. This rapid population increase entails new challenges in India’s growing food demand. To this end, India’s agricultural sector has undergone a remarkable expansion resulting in a significant boost in its grain production, achieving nearly a five-fold increase in grain yield when compared to the previous 50 years [1].

Despite those efforts, climate change is endangering India’s food adequacy. Temperature and rainfall play a vital role in resource management, food security, agricultural growth, and the carbon budget cycle [2]. As droughts and heat waves significantly impact crop productivity, climate change will inevitably result in notable alterations to the crop growth cycle [3,4,5]. The shift in the physiological process of crops is responsible for the decline in radiation absorption and biomass production, which is directly linked with higher mean temperatures [6]. Gupta et al. [7] demonstrated that the productivity of wheat crops in India decreased by approximately 5.2% during the period of 1981 to 2009 due to global warming. In another study, Sonkar et al. [8] provided significant insight into the effect of climate change on wheat yield across the various agro-climatic zones of India from 1986 to 2015. They examined how variations in the maximum and minimum temperature, precipitation, and aerosol loading affected the yield of the wheat crop in India. According to this study, a 1 °C increase in maximum temperature led to a 7% decrease in wheat yield, while the opposite effect was observed with an increase in minimum temperature, which resulted in an increase in yield.

Thus, considering the impact of climate change on crop productivity, it is of key importance to develop a deep understanding of climate anomalies that can support agriculture risk management and support future vegetation evolution [9,10]. During this phase, a significant number of climatic factors have been examined, which showed the impact on the functioning and dynamics of vegetation [11,12,13]. Numerous studies are currently being carried out globally to understand the characteristics of vegetation cover under the current climatic scenario and the potential changes are being assessed at a global/regional/local scale [14,15].

With the advancement of space technologies, satellite-based monitoring of vegetation health using vegetation indices and other phenological parameters has flourished worldwide [16,17,18]. Over the last few decades, various studies have resulted in the development of numerous remote-sensing-based vegetation indices for monitoring and assessing the condition of vegetation cover globally. National Aeronautics and Space Administration (NASA) and the European Space Agency (ESA), have provided various satellite-based operational products to monitor vegetation health viz. NDVI, EVI, LAI, and FAPAR on a daily basis at their various spatial extents [19,20] for continuous monitoring of natural resources and atmosphere. Those data products are commonly used as input to various climate models to analyze how vegetation responds to climate dynamics [21,22].

Various researchers have examined the nature and impact of climatic variables with respect to vegetative dynamics/variability, focusing primarily on drought and flooding events [23,24,25,26,27,28]. According to Revadekar et al. [29], between the period of 1981 and 2010, the seasonal variability in NDVI values was predominantly caused by precipitation and rainfall. They implied that NDVI values tend to rise throughout the summer monsoon season (June to September), notably across the southern parts of India, due to the precipitation received by monsoonal rainfall, whereas NDVI tend to decrease during the hot weather season (March to May). Sarkar and Kafatos [27] investigated the vegetation variability using 18-year (1982–2000) monthly NDVI data with the empirical orthogonal function and wavelet decomposition techniques over the Indian sub-continent. Their results revealed that the monsoon rainfall and land surface temperature have a significant impact on the vegetation distribution. They also highlighted the importance of aerosol and raised the necessity to measure it over the Indian sub-continent. Ichii et al. [30] used NDVI, temperature and precipitation datasets pertaining to northern and southern semiarid regions of the globe for a period of 8 years between 1982 and 1990 and found a strong relationship between them. Similarly, Song and Ma [31] assessed the relationship between NDVI and meteorological parameters, during the period of the years 1982–2005 in China, and exemplified the extraordinary influence of each meteorological parameter with NDVI. Furthermore, the relationship between the leaf area index and land surface temperature at global and local scales was investigated by Rasul et al. [32]. They discovered that the vegetation cover and leaf area index (LAI) of the vegetation tends to significantly influence the land surface temperature.

In light of the above, it is evident that numerous attempts have been made by researchers globally to assess the association between the vegetation cover and climatic variables. However, to our knowledge, very few studies have been carried out so far towards establishing the relationship between LAI and climate factors or a combination of both NDVI and LAI. Therefore, in the present study, an attempt is made to uncover the nature and magnitude of association between the satellite-derived vegetation parameters viz. NDVI and LAI with climatic variables over the Indian state of Haryana. The present investigation findings are expected to assist in a better understanding towards the management, practices and necessary measures required concerning climate change and the upsurge in the crop productivity dynamics at the regional scale.

2. Data and Methodology

The geographical and climatic description of the study area is required in remote sensing studies because the optical satellite geophysical retrieval techniques are influenced by the natural and manmade structures in the different parts of the globe. The study area description is provided in the next subsection. The climate and vegetation datasets, with their source, spatial and temporal resolutions, are made available in Section 2.2. The methods adopted for the data processing to establish the relationship between LAI and climate factors or combination of both NDVI and LAI are also furnished in Section 2.2.

2.1. Study Area

Haryana is one of the most agriculture-dominated Indian states located in the northwestern region (semi-arid and arid climate) of India. Geographically, it extends from 27°37′ to 30°35′ latitude, and 74°28′ to 77°36′ longitude (Figure 1). The elevation of the study area ranges from 700 to 3600 feet above the mean sea level. The state receives the annual precipitation, ranging from 344 mm in the Fatehabad district to 1108 mm in the Yamunanagar district, with approximately 80% rainfall occurring during the monsoon season (June to September months) of every year. The dominant crops cultivated in the state consist of paddy and cotton during the Kharif season, and wheat and mustard during the Rabi season. Being situated in the Indo-Gangetic plains, the state has a water surplus with irrigation facilities and the weather of the study area varies from hot (April to June) to cold (December to January) [33].

2.2. Materials and Methods

The daily rainfall and temperature datasets used in this study were procured from the Indian Meteorological Department (IMD). Other climatic variables, namely relative humidity and wind speed, were obtained at a scale of 0.5° × 0.5° from NASA’s Prediction of Worldwide Energy Resources (NASA power), and the daily aerosol optical depth datasets for the wavelength of 550 nm, gridded at 1° × 1°, was downloaded from GIOVANNI (Goddard Earth Sciences Data and Information Services Centre).

NASA’s MODIS product (MYD13A1), which provides two vegetation indices, namely NDVI and EVI (Enhanced Vegetation Index), composite of 8 days, the MCD15A2H product providing fraction of photosynthetically active radiation (FAPAR), and leaf area index (LAI) composite of 16 days at a spatial resolution of 500 m were considered for the present study. Leaf area index (LAI) is a dimensionless entity which signifies the one-sided leaf area of plant canopies. It is defined as the projected area of leaves over a unit of land, and it is used for the computation of energy, carbon, and water cycle processes, and vegetation bio-geochemistry. Those vegetation cover parameters are produced by the satellite sensors using the spectral wavelengths in the red, near-infrared, and blue regions of electromagnetic spectrum [30]. NDVI and EVI are used as proxies to estimate vegetation health, growth and biomass. NDVI’s formula can be computed as follows:

$$NDVI=\frac{NIR-RED}{NIR+RED}$$

(1)

NDVI values vary from −1 to 1, where values close to 1 signify good vegetation health, whereas values close to 0 show no vegetation presence.

The climate data used in the present investigation is available on a daily basis at various spatial scales. However, the vegetation parameter (LAI and NDVI) datasets used herein are available for 16- and 8-day composites of the respective time-period. In order to perform the analysis, the rasterization of the datasets was carried out followed by the rescaling process to compute the monthly and annual mean layers of the various climatic variables at 500 m spatial scale.

Figure 2a shows the annually accumulated value of rainfall (RF) for the time period of the years 2010 to 2020. Figure 2b,c depicts the annual average of maximum temperature (TMAX) and minimum temperature (TMIN), respectively. Moreover, Figure 2d–e shows the annual mean relative humidity, wind speed and aerosol optical depth of the study area. The complete details concerning the optical aerosol depth can be found in [34,35]. Figure 3a,b depicts the spatial variation in the mean annual NDVI and LAI values in the study area.

The temporal datasets pertaining to various vegetation and climatic variables were derived using mean grid and spatial values over the study area. The temporal dynamics of vegetations indices and climate variables at monthly and yearly scales were considered for the assessment of climate parameters influence on NDVI and LAI. The deviation in the monthly and yearly values of vegetation indices and climate variables from mean were assessed using standard deviations. The correlation coefficients between vegetation indices and climate variables at monthly and yearly scales were computed to assess the nature and magnitude of association between vegetation indices climate variables, respectively, at the significance level of 5%. The Pearson correlation coefficients [36,37] can be computed using Equation (2) as follows:

$$r_{xy}=\frac{\sum _{i=1}^n(x_i-\overline{x})(y_i-\overline{y})}{\sqrt{{(x_i-\overline{x})}^2{(y_i-\overline{y})}^2}}$$

(2)

where $r_{xy}$ represents the value of Pearson correlation coefficients between variables $x$ (vegetation variable) and $y$ (climate variable) with sample size $n$. The range of $r_{xy}$ extends from −1 to +1; $i$ represents the month, and year, respectively.

3. Results

The vegetation and climate variables are associated with each other at seasonal and annual scales in a region. The MODIS vegetation data products released twice or more times in a month at a global scale enable the monitoring of vegetation growth and dynamics at different spatial and temporal scales [38]. The Indian Meteorological Department (IMD) has released gridded weather data (rainfall, TMAX and TMIN) at various temporal scales over the Indian region. NASA POWER provides global weather data with various temporal scale. The foreword sections of the present investigation demonstrate the results obtained from the various analysis pertaining to the spatio-temporal distribution of weather and vegetation parameters at monthly and yearly scales over the Haryana state of India during the period from 2010 to 2020.

3.1. Temporal Variations of the Climate Variables

3.1.1. Monthly and Annual Variation of Rainfall

Cultivation of crops in India is largely rainfed since the irrigation networks are not available in all the parts of the country. Therefore, it is vital to assess the spatial and term-poral pattern of the rainfall for the better crop production. Figure 4a presents the monthly accumulated rainfall in the respective years; in contrast, Figure 4b shows the mean monthly and annual rainfall in the respective months and year. The IMD has categorized the time duration of a year in various seasons viz. winter (January to February), pre-monsoon (March to May), monsoon (June to September) and post-monsoon (October to December). In Figure 4a, it can be observed that the net accumulated rainfall over the Haryana state from 2010 to 2020 varies significantly with occurrence of maximum rainfall during the monsoon months [38,39]. The multi-year accumulated sum value of rainfall is estimated to be 14423.63 mm (yearly summation of rainfall). The lowest accumulation value of 950.14 mm is observed in 2012, whereas the highest (1668.34 mm) is observed during the year 2020. The pattern of rainfall tends to vary every alternate year, with the highest intensity being recorded during the period of June to September, and the lowest during the months of December to February.

From Figure 4b, it can be observed that during years 2011, 2012, 2015 and 2017, the standard deviation is on the higher side compared to the mean with a significant amount of deviation recorded in the year 2012. The observation signifies the occurrence of drought during 2012 and lesser amount of rainfall from average during the respective years. Moreover, it can also be observed that during the period from 2018 to 2020, the standard deviation is significantly low compared to the mean, indicating excess to surplus occurrence of rainfall. In the rest of the year, a sufficient amount of rainfall is observed.

3.1.2. Analysis of Monthly and Annual Variation of Maximum and Minimum Temperatures

Figure 5a presents the pattern of monthly maximum temperature from 2010 to 2020 and Figure 5b depicts the mean maximum temperature on monthly and yearly bases. Furthermore, the pattern of monthly minimum temperature is depicted in Figure 6a and the mean minimum temperature on monthly and yearly bases is presented in Figure 6b. The analysis of results obtained from Figure 6 reveals that the mean maximum and minimum temperatures over the study area tend to change each year, with the maximum temperature being observed during the month of May, while the minimum is observed during December and January [40]. The multi-year mean maximum temperature and minimum temperature are found to be 31.10 °C and 17.82 °C, respectively, from 2010 to 2020. The highest values of maximum temperature and minimum temperature are observed to be 31.84 °C and 18.40 °C in the year 2010. In contrast, the lowest values of the maximum and minimum temperatures are found to be 29.98 °C and 16.97 °C, corresponding to the year 2020. It is noticeable that the highest and lowest temperatures in the study area tend to change every year, with moderate-to-high values occurring during the period from March to October, and the lowest from December to February.

In case of maximum and minimum temperatures, it can be observed that the standard deviation value of maximum and minimum temperatures is found to be higher than the mean value of the maximum temperature in the years 2012, 2019 and 2020, as well as in the months of January, February and December. This anomaly is observed in the same months of the respective years under consideration (2010–2020).

3.1.3. Analysis of Monthly and Annual Variation of Relative Humidity

Figure 7a depicts the monthly relative humidity pattern for the time period from 2010 to 2020, and Figure 7b demonstrates the monthly and yearly pattern of mean relative humidity. The analysis of Figure 7a shows that the relative humidity of the study area is high during the period from July to September as compared to other months in all the respective years. Moreover, the uneven pattern can be observed in the case of mean relative humidity from Figure 7b [41,42]. The changes in mean relative humidity over the region have occurred every year, with the maximum mean relative humidity being registered from June to October. The multi-year mean relative humidity is found to be 41.870, whereas the lowest (6.780) and highest (91.630) values are observed in 2015 and 2010, respectively.

3.1.4. Analysis of Monthly and Annual Variation of Wind Speed

Wind speed is also an important climatic variable which affects the vegetation growth, as turbulence in the atmosphere results in a carbon dioxide upsurge, which subsequently leads to a higher photosynthesis rate [43]. The monthly mean wind speed pattern during the months of the respective years is depicted in Figure 8a. Furthermore, Figure 8b demarcates the monthly and yearly mean wind speed pattern. The multi-year mean wind speed is estimated to be 2.802 m/s, whereas the highest (3.176) and lowest (2.666) are observed in 2012 and 2020, respectively. The intensity of the mean wind speed varies alternately every year, with peaks from April to June, and it deepens from October to February.

3.1.5. Analysis of Monthly and Annual Variation of Aerosol Optical Depth

The temporal pattern of mean aerosol optical depth from 2010 to 2020 is depicted in Figure 9a. Figure 9b represents the mean aerosol optical depth on monthly and yearly scales. The multi-year mean aerosol optical depth is estimated to be 0.802, with the lowest (0.751) registered in 2020 and highest (0.860) in 2016. It can be observed that the mean aerosol value is found to be significantly low in 2010, 2011, 2018 and 2020. The observation can also be attributed to the high humidity and rainfall. Particularly, during the year 2020, a nationwide lockdown due to the SARS-CoV 2 pandemic is also responsible for the lower value of aerosol [44]. Moreover, the AOD value is significantly high from July to August and from November to February. The increase in values can be due to the high humidity and water vapor during the monsoon and winter months [45]. Crop residue burning is also a major contributor for the increment in aerosols during the winter months [46].

3.1.6. Analysis of Monthly and Annual Variation of NDVI

From the above analysis of the obtained climatic variables results, it can be ascertained that the pattern of climatic variables changes with time and season. Since the weather and climatic pattern are significantly influenced by the land use and land cover of the region, here, we tend to assess the impact of green cover on the weather parameters. The monthly pattern of mean NDVI at different years from 2010 to 2020 is depicted in Figure 10a. In addition, Figure 10b represents the mean NDVI on monthly and yearly bases. The multi-year mean NDVI is estimated to be 0.444, with the lower limit of 0.414 in 2010, and extending up to 0.481 in 2019. The mean NDVI values show the continuous increasing trend from 2012 to 2019, and these decreases slightly in the year 2020. The NDVI values attain their highest value twice in a year (February and August months) in the Haryana state due to the crops grown in mainly two crop-seasons, which are the Rabi (mid-November to mid-May) and Kharif seasons (mid-July to mid-October). The decreasing trend of the NDVI values is found from the beginning of the pre-monsoon season (March–May), which it shows an increasing trend after the beginning of monsoon season over Haryana state. The unprecedented dip of mean NDVI values occurred in the year 2012 due to drought conditions in the Haryana state [47]. The blue strip in Figure 10a represents the lowering of mean NDVI values from April to June of every year due to the hot summer season.

3.1.7. Analysis of Monthly and Annual Variation of LAI

The temporal trends of LAI over the Haryana state have been assessed on annual and monthly bases from 2010 to 2020. The monthly mapping of mean LAI at different years during 2010 to 2020 is shown in Figure 11a. Figure 11b represents the mean LAI on monthly and yearly basis. The multi-year mean LAI is estimated to be 9.430, with a lowermost value of 8.223 in 2010, and uppermost value of 11.014 in 2020. The temporal trend of the LAI is almost following the NDVI temporal trend. The mean LAI values show an increasing trend from 2012 to 2020, continuously, with a saturation effect from the year 2013 to 2015. The LAI values attain their highest values twice in a year (February and September months) in the Haryana state due to the crop grown in mainly two seasons, namely the Rabi and Kharif seasons. The decreasing trend of the LAI values is found from the beginning of February to June; afterwards, it starts an increasing trend due to the beginning of monsoon season over the Haryana state. From February to June, a hot weather condition exists.

3.2. Correlation between NDVI and Climate Variables

Figure 12 depicts the values pertaining to the regression coefficient analysis performed between NDVI and climate variables during the months from 2010 to 2020. The monthly scale analysis of association reveals that rainfall and relative humidity have a positive linear association with NDVI. In contrast, the maximum and minimum temperatures show a negative trend. The AOD does not show any significant pattern as a significant amount of inconsistency and discontinuity is observed in relation with NDVI. The results also reveal that during the Rabi months from November to May, the association of rainfall and relative humidity with NDVI is largely positive and the strongest relationship is observed during the month of April, which weakens in the subsequent months. The coefficients of regression obtained from NDVI-maximum temperature, NDVI-minimum temperature, and NDVI-wind speed show strong linearly negative association for the period from November to May. Figure 13 depicts the coefficient of regression obtained between LAI and climate variables at a monthly scale for a decade (2010–2020). The analysis also reveals a strong positive association between LAI-rainfall and LAI-relative humidity from November to May (Rabi season). The association reaches its peak during the month of April and subsequently tends to weak in the later months. The relationship between the other remaining climate variables (LAI-TMAX, LAI-TMIN, LAI-WS, and LAI-AOD) was found to be strongly negative, with the strongest being during the month of May, showing a negative decreasing trend after.

Figure 14 depicts the Pearson’s correlation coefficients between the monthly NDVI and climate variables from 2010 to 2020. The results obtained reveal that the correlation coefficients values vary for NDVI-rainfall (0.037 to 0.366), NDVI-TMAX (−0.624 to −0.505), NDVI-TMIN (−0.428 to −0.256), NDVI-RH (0.352 to 0.736), NDVI-WS (−0.714 to −0.087), and NDVI-AOD (−0.442 to 0.187) for different combinations of the NDVI and climate variables in various years from 2010 to 2020. Figure 15 shows the regression coefficients between the LAI and the various climatic variables from 2010 to 2020. The values of the correlation coefficients vary for LAI-rainfall (0.0171 to 0.302), LAI-TMAX (−0.398 to −0.246), LAI-TMIN (−0.320 to −0.105), LAI-RH (0.099 to 0.547), LAI-WS (−0.733 to −0.099), and LAI-AOD (−0.642 to 0.019) for different combinations of the LAI and climate variables in various years from 2010 to 2020. The positive value of the correlation coefficient is found for NDVI-rainfall, NDVI-relative humidity, LAI-rainfall and LAI-relative humidity. The negative values of the correlation coefficients have been estimated for the remaining climate variables. The analysis of Figure 14 reveals a nearly equal influence of rainfall, maximum and minimum temperatures, relative humidity, and wind speed, on the NDVI. However, the aerosol optical depth imposed a similar impact on NDVI in the years 2010, 2011, 2012, 2013, 2017 and 2018, while in the remaining years, its lower impact is observed on NDVI. Furthermore, the influence of rainfall and relative humidity on the LAI is significantly low as compared to NDVI. The above observation drawn from the analysis can be attributed to the lower growth of the leaf area as compared to greenness content in relation to rainfall and humidity. Moreover, the climatic variables show a small impact towards the leaf area as compared to plant greenness content.

Table 1. The Pearson correlation coefficients (r) among climate variables and vegetation indices at the significance level 0.05% on monthly and yearly bases.

Vegetation Indices →	NDVI		LAI
Climate Variables ↓	Monthly (r)	Yearly (r)	Monthly (r)	Yearly (r)
Rainfall	0.215	0.480	0.039	0.454
Maximum temperature	−0.551	−0.416	−0.317	−0.501
Minimum temperature	−0.327	−0.201	−0.203	−0.259
Relative humidity	0.559	0.582	0.348	0.612
Wind speed	−0.599	−0.431	−0.444	−0.498
Aerosol optical depth	−0.261	0.104	−0.468	−0.019

demonstrates the cumulative impact of climate variables viz. RF, TMAX, TMIN, RH, WS and AOD in relation to NDVI and LAI. The analysis was carried out by computing the monthly and yearly averages of entire datasets, individually. The regression analysis was performed using the Pearson correlation coefficients between monthly mean vegetation indices and climate variables on monthly basis; moreover, the regression analysis was also carried out between the yearly mean of the vegetation indices and climate variables on a yearly basis.

3.3. Cumulative Effect of the Climate Variables to NDVI and LAI on Yearly and Monthly Bases

The multiple regression analysis is carried out for the vegetation parameters, namely NDVI and LAI, with the climate variables to study the combined effect of the climate variables on the vegetation parameters.

Table 2. Results of multiple regression analysis among the vegetation variables and climate variables on monthly and yearly bases.

Multiple Regression Equation	NDVI = a × TMAX + b × TMIN + c × RH +d × WS + e × RF + f × AOD + g		LAI = a × TMAX + b × TMIN + c × RH + d × WS + e × RF + f × AOD + g
Yearly basis	Slope	a = 0.028, b = −0.056, c = 0.006, d = −0.026, e = −0.001, f = 0.453	a = −0.064, b = −0.964, c = 0.128, d = −2.961, e = −0.001, f = 7.482
	Intercept	g = 0.052	g = 26.779
	Correlation coefficient	0.683	0.684
	RMSE	0.079	2.338
Monthly basis	Slope	a = −0.001, b = 0.003, c = 0.071, d = −0.001, e = 0.031, f = −0.001	a = −0.008, b = 0.007, c = −0.001, d = −0.044, e = 0.268, f = −0.073
	Intercept	g = 2.148	g = 120.087
	Correlation coefficient	0.890	0.781
	RMSE	0.210	6.359

portrays the outcomes of the multiple regression analysis between the vegetation indices and climate variables at monthly and yearly scales. From the results, it can be observed that there exists a strong (0.890) to moderate (0.684) association between the monthly and yearly scales, respectively. The significant multiple correlation is found between NDVI and climate variables (0.890 and 0.683) on monthly and yearly scales, respectively. Moreover, a relatively significantly strong relationship between LAI and climate variables is found (0.781 and 0.684) on monthly and yearly bases, respectively. Significantly good correlation is found between vegetation and climate variables in multiple regression analysis, indicating the significant impact of climate variables. A significant relationship with the combined impact of weather and aerosol variables exists due to the cropland is largely dominated over the study area and the cultivation of crops depends on the weather pattern of the region. In view of the above observations made from the results obtained, it is clear that the climatic factors and the land use of the region are interconnected and the areas where the vegetation is high will show a strong positive correlation with rainfall, relative humidity and mean temperature, whereas areas dominated by built-up areas will show a strong relation with maximum temperatures and high aerosol content.

3.4. Discussion

The growth of vegetation during the growing season is primarily influenced by climatic conditions. The favorable weather conditions usually yield a high vegetation index. Thus, the association of climate variables with the vegetation is well-known. In spite of that, the role of climate variability with respect to NDVI and LAI at monthly and yearly scales over the Indian state of Haryana from 2010 to 2020 has been analyzed in the present investigation. The primary aim of the investigation focused on the exploration of nature and the magnitude of climatic variables, along with their influence on NDVI and LAI, considering the high degree of the liaison of climatic variables with vegetation cover and, subsequently, vegetation indices.

The observations from the analysis indicated that certain weather conditions are favorable for the increases in NDVI and LAI. The values of NDVI and LAI are at a maximum twice a year due to the two crop-growing seasons that exist in the Haryana state, namely Rabi and Kharif. The values of NDVI and LAI have a decreasing trend during the hot weather (March–May). However, the values of the NDVI and LAI have a maximum in February during the Rabi crop season, while during the Kharif season, the maximum NDVI value is found in August and September for the LAI.

The association considering the Pearson correlation analysis has been carried out between the climate variables and vegetation indices (NDVI and LAI) to study the impact of climate variables in relation to NDVI and LAI at monthly and yearly scales for a period of 10 years from 2010 to 2020 over the Haryana state of India. The analysis reveals the distinct behavior during the cropping and non-cropping months. The higher values of the correlation coefficients are found between NDVI-climate and LAI-climate within the crop-growing season (for crop-growing months), while a significant correlation is found for the inter-crop-growing season (for the remaining months of a year). The regression analysis results demonstrated a significantly strong positive impact of climatic variables on the vegetation cover parameters (NDVI and LAI), specifically rainfall and relative humidity.

In contrast, the maximum and minimum temperatures, along with wind speed, depict a significantly moderate amount of negative association both at monthly and yearly scales. Moreover, the AOD illustrated a negative yet significant impact on vegetation parameters during the cropping months. However, during the non-crop growing months, the values of correlation coefficients between aerosol optical depth and NDVI or LAI are reduced. A higher correlation is found between the LAI and NDVI due to the lower sensitivity of NDVI at higher LAI values. It is also required to examine the relationship of NDVI-climate and LAI-climate with all the remaining regional climate parameters at various spatial and time scales. The multiple regression analysis implies the significant impact of the climate variables on the satellite-derived vegetation parameters. The values of correlation coefficients (0.683 for yearly and 0.890 for monthly) for the NDVI-climate variables in multiple linear regression analysis show the strong association between the NDVI and climate variables on a yearly and monthly basis. However, the multiple linear regression analysis also indicated a strong association between the LAI and climate variables on the basis of the values of the correlation coefficients (0.684 for yearly and 0.781 for monthly). Overall, this analysis technique may be more realistic for reflecting the associated response of the NDVI and LAI to climate change in tropical climate conditions. Most of the researchers have carried out the association of NDVI with the climate variables (mainly rainfall and temperature) over various climate regions [29,48]. Few studies have been published for the association of LAI with climatic variables or the combination of both (NDVI and LAI) [32]. The association of vegetation indices (NDVI and LAI) with the various climatic variables (rainfall, minimum and maximum temperature, relative humidity, wind speed) along with AOD has been assessed in the present study, which was uncovered in the previous studies over the tropical climate region.

4. Conclusions

The present study provides important insights considering the role of climate variability with respect to changes in the NDVI and LAI at monthly and yearly scales in the Indian state of Haryana from 2010 to 2020. The observations from the analysis indicated that the NDVI and LAI obtain maximum values twice in a year due to the two crop-growing seasons (Rabi and Kharif) and favorable climate conditions that exist, whereas it decreases during the hot weather (March–May). The association considering the Pearson correlation analysis has been carried out between the climate variables and vegetation indices (NDVI and LAI) at monthly and yearly scales, which reveals the distinct behavior for cropping and non-cropping months. The higher values of correlation coefficients are found between NDVI-climate and LAI-climate within the crop-growing season (for crop-growing months), while a significant correlation is found for the inter-crop-growing season (for the remaining months of a year). However, the AOD illustrated a negative yet significant impact on vegetation parameters during the crop-growing months, while during the non-crop-growing month, the values of the correlation coefficients are reduced. The multiple regression analysis found a significant association of the climate variables on NDVI and LAI with strong values of the correlation coefficients. It is also required to examine the relationship of NDVI-climate and LAI-climate with all the remaining reginal climate parameters at various spatial and time scales. The current analysis provides a broad understanding of the association of climate variability with the vegetation indices, namely NDVI and LAI, for a limited number of climate variables over tropical climate conditions. To better comprehend the studied dynamics over the investigated area, it is crucial to consider the association of these vegetation parameters with all local weather variables. In-depth research must be carried out on the effects of large-scale atmospheric systems such as the Southern Oscillation, North Atlantic Oscillation, etc., on the NDVI and LAI. Lastly, the research methodological framework presented herein may reflect the NDVI and LAI association with climate change more accurately in tropical climate conditions; this is subject of future research to be carried out.