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  • View in gallery

    Distribution of soil moisture stations (square indicates sand stations; triangle indicates clay stations). The station name and soil property for each station are listed in Table 1.

  • View in gallery

    Temporal variations of LAI for 14 stations (m2 m−2). The black and red thick solid lines are the LAIs averaged for the sand stations (stations 1–5 in Table 1) and clay stations (stations 6–14 in Table 1), respectively.

  • View in gallery

    Leading and lagging correlations between standardized SM anomalies for the top 50 cm and vegetation anomalies of the clay stations. The negative and positive values labeled in abscissa represent the leading and lagging months of vegetation to SM, respectively. The long dashed and dotted lines indicate the 0.01 and 0.05 levels of significance, respectively.

  • View in gallery

    Time series of standardized SM anomalies in the top 50 cm (black line) and LAI anomalies (red line) for the clay stations. July–October of each year is shown.

  • View in gallery

    As in Fig. 3, but for the sand stations.

  • View in gallery

    Time series of standardized LAI anomalies (red line), ET anomalies (blue line), and SM anomalies (black line) for the clay stations. July–October of each year is shown.

  • View in gallery

    Time evolutions of standardized SM anomalies (black line), 1-month-leading evapotranspiration anomalies (blue line), and 1-month-leading vegetation anomalies (red line) for the sand stations. Months on the horizontal axis without parentheses represent SM; those with parentheses represent LAI and ET.

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The Relationship between Soil Moisture and LAI in Different Types of Soil in Central Eastern China

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  • 1 Chinese Academy of Meteorological Sciences, and University of Chinese Academy of Sciences, Beijing, China
  • | 2 Chinese Academy of Meteorological Sciences, and CAS Center for Excellence in Tibetan Plateau Earth Sciences, Beijing, China
  • | 3 Chinese Academy of Meteorological Sciences, Beijing, China
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Abstract

As important parameters in the land–atmosphere system, both soil moisture (SM) and vegetation play a significant role in land–atmosphere interactions. Using observational data from clay and sand stations over central eastern China, the relationship between leaf area index (LAI) and SM (LAI–SM) in different types of soil was investigated. The results show that the LAI–SM correlation is significantly positive in clay but not significant in sand. The physical causes for the discrepant LAI–SM correlations in different types of soil were explored from the perspectives of evapotranspiration (ET) and soil water retention. In clay stations, increasing LAI is associated with greater soil-water-retention capacity. Although the increasing LAI corresponds to increasing ET, the impact of ET on SM is weak because of the small particle size of soil. Consequently, the LAI–SM relationship in clay is significantly positive. In sand stations, ET is negatively correlated with SM owing to the large soil particle size, resulting in a negative LAI–SM correlation in sand. However, soil water retention is weakened by the increased LAI, which may be an important factor causing the insignificant LAI–SM correlation in sand.

Publisher’s Note: This article was revised on 15 May 2017 to include the first author’s secondary affiliation, which was missing when originally published.

Corresponding author address: Dr. Renhe Zhang, Chinese Academy of Meteorological Sciences, No. 46 Zhong-Guan-Cun South Ave., Haidian District, Beijing 100081, China. E-mail: renhe@camscma.cn

Abstract

As important parameters in the land–atmosphere system, both soil moisture (SM) and vegetation play a significant role in land–atmosphere interactions. Using observational data from clay and sand stations over central eastern China, the relationship between leaf area index (LAI) and SM (LAI–SM) in different types of soil was investigated. The results show that the LAI–SM correlation is significantly positive in clay but not significant in sand. The physical causes for the discrepant LAI–SM correlations in different types of soil were explored from the perspectives of evapotranspiration (ET) and soil water retention. In clay stations, increasing LAI is associated with greater soil-water-retention capacity. Although the increasing LAI corresponds to increasing ET, the impact of ET on SM is weak because of the small particle size of soil. Consequently, the LAI–SM relationship in clay is significantly positive. In sand stations, ET is negatively correlated with SM owing to the large soil particle size, resulting in a negative LAI–SM correlation in sand. However, soil water retention is weakened by the increased LAI, which may be an important factor causing the insignificant LAI–SM correlation in sand.

Publisher’s Note: This article was revised on 15 May 2017 to include the first author’s secondary affiliation, which was missing when originally published.

Corresponding author address: Dr. Renhe Zhang, Chinese Academy of Meteorological Sciences, No. 46 Zhong-Guan-Cun South Ave., Haidian District, Beijing 100081, China. E-mail: renhe@camscma.cn

1. Introduction

As one of the most fundamental parameters in the land–atmosphere system, soil moisture (SM) has a substantial impact on the climate through its effects on surface albedo, thermal capacity, sensible heat, and latent heat (Delworth and Manabe 1988; Delworth et al. 1993; Ma et al. 2001). Many studies have shown that the slowly varying SM records past and present precipitation anomalies and then feeds back to the precipitation by affecting evapotranspiration (ET; e.g., Meehl 1994; Seneviratne et al. 2010). In general, SM anomalies could last for weeks or even months. This long-term “memory” (or persistence) makes SM a valuable “memory factor” for climate anomalies, which is as important as the sea surface temperature and has major implications for short-term climate predictions (Delworth and Manabe 1988; Vinnikov and Yeserkepova 1991; Entin et al. 2000; Koster and Suarez 2001; Wu et al. 2002; Lo and Famiglietti 2010; Seneviratne et al. 2012). The memory time of SM increases with soil depth in China. Specifically, the memory time of the top 1 m of SM varies from 1 month in southern China to 2.5 months in northern China (Entin et al. 2000). Previous studies have shown that the spring SM exhibits strong interannual variations (Zuo and Zhang 2009; Liu et al. 2014) and significantly influences the interannual variations of atmospheric circulation and precipitation in summer over eastern China (Zuo and Zhang 2007; Zhan and Lin 2011; Zhang and Zuo 2011; Meng et al. 2014; Zuo and Zhang 2016). These studies reported that when the spring soil is wetter over a vast region, that is, from the lower and middle reaches of the Yangtze River valley to northern China, northeastern China and the lower and middle reaches of the Yangtze River valley will have abnormally higher precipitation in summer, whereas the region south of the Yangtze River valley will have less precipitation.

As one of the most crucial components in the climate system, vegetation may have a substantial impact on climate variability, which has garnered increasing attention from researchers. Recent studies have shown that vegetation has a feedback effect on climate, both directly by affecting exchanges of heat, water, and momentum and indirectly through its effect on CO2 (Bonan 2002; Kaufmann et al. 2003). Because the persistence time of vegetation “greenness” is longer than 1–2 months (Liu et al. 2006), changes in vegetation could modify the local, regional, and global climate on diurnal, seasonal, and long-term scales (Bonan et al. 1992; Schwartz 1996; Kaufmann et al. 2003; Strengers et al. 2010; Jeong et al. 2011). It has been demonstrated that the leaf area index (LAI) anomalies have an influence on the ET and then cause the precipitation anomalies (Bounoua et al. 2000; Guillevic et al. 2002; Wang et al. 2006). Dallmeyer and Claussen (2011) showed that decreased vegetation in the Asian monsoon region results in increased precipitation in North Africa, whereas increased vegetation may lead to decreased precipitation in the Middle East. Zuo et al. (2011) reported that more vegetation around the southeastern Tibetan Plateau is significantly related to positive anomalies of precipitation in southern and northern China and negative anomalies in the area from the Yangtze River valley to the Yellow River valley and most of western China.

As two important parameters in the land system, vegetation and SM are strongly related. For example, SM plays an important role in vegetation because soil water is one of the key factors for vegetation growth. Increased SM may lead to increased vegetation (Wang et al. 2007). However, vegetation exhibits a more complex effect on SM. Kim and Wang (2007) reported that wet-soil-moisture-induced precipitation increase is reinforced during summer when LAI is included in CAM–CLM. Increased vegetation favors more precipitation through its impact on albedo and the Bowen ratio, which subsequently enhances SM (Bounoua et al. 2000; Buermann et al. 2001). At the same time, more vegetation may strengthen its function of conserving water, causing more SM to be retained. By contrast, more vegetation boosts the consumption of water itself, causing the depletion of SM (Pielke et al. 1998; Wang et al. 2006). Many factors affect the relationship between vegetation and SM. Scientists have investigated the importance of these factors. For example, Bounoua et al. (2000) found that both ET and precipitation P increase as a result of increases in global vegetation. However, ET increases more than P; thus, vegetation has a negative effect on SM. Notaro et al. (2006) reported that a vegetated surface, for example, corn and soybean, has a positive influence on P (SM), whereas the effect of winter wheat is negative. Wang et al. (2006) found that vegetation is negatively correlated with P (SM) over a 2-month period, whereas its relation is positive at the interannual time scale.

Therefore, studying the relationship between vegetation and SM is particularly important for understanding the land–atmosphere system and its influence. According to the existing literature, the relationship between vegetation and SM is complex. More vegetation may correspond to either increased (Bounoua et al. 2000; Buermann et al. 2001) or reduced SM (Pielke et al. 1998; Wang et al. 2006). This study investigates the relationship between LAI and SM in different types of soil using observational data obtained from central eastern China. Section 2 presents the data and methodology employed in the study. Section 3 discusses the relationships between LAI and SM in different types of soil. Sections 4 and 5 discuss the impacts of ET and the water-retention capacity of soil on the relationship between LAI and SM in different types of soil. The conclusions and discussion are given in section 6.

2. Data and methods

Observational data on SM, with a temporal resolution of 10 days and spanning from April 2001 to October 2010, were provided by the China Meteorological Administration (CMA). The SM in China is routinely observed on the eighth, eighteenth, and twenty-eighth day of each month, and the average of the three observations in a month is considered as the monthly mean. Strict quality control is applied to the CMA SM data (CMA 2003), and the data have been widely used in many previous studies (e.g., Ma et al. 2000; Guo et al. 2003; Sun et al. 2005; Zuo and Zhang 2007; Zhang and Zuo 2011; Liu et al. 2014). The temporal and spatial variability of SM in the top 10 cm is larger than at deeper depth (Entin et al. 2000), and the observational error in the top 10 cm is also larger (Liu and He 2012; Ma et al. 2015). In this study, the top 50 cm of SM is adopted, and more than 90% of the data from every station is considered valid. In fact, in central eastern China the root length density is mostly concentrated in the top 50 cm (Zhang et al. 2004; Ji et al. 2013; Wei et al. 2015).

Because of the difficulties in measuring actual ET, a reference ET has been used to estimate actual ET (e.g., Arnold et al. 1998; Cristea et al. 2013). As the most commonly used method for calculating reference ET (e.g., Duchemin et al. 2006; CMA 2007; Xing et al. 2014; Anderson et al. 2015), the FAO Penman–Monteith method (Allen et al. 1998) is applied in this study. The in situ data of sunshine time, temperature, relative humidity, and wind speed used in calculating ET are also obtained from meteorological stations provided by the CMA.

The LAI, which is defined as the one-sided green leaf area per unit of ground surface area, is used to measure the variation in vegetation during the study period from January 2001 to December 2010, with a temporal resolution of 8 days and a spatial resolution of 1 km. The LAI products from the Moderate Resolution Imaging Spectroradiometer (MODIS) may underestimate the values in collection 3 (referring to the MODIS version) because of clouds and other gaps (Gao et al. 2008; Bobée et al. 2012). However, the occasional lower-quality data in collection 3 are greatly improved in collection 4 (Fensholt et al. 2004; Yang et al. 2006), which also include extensive quality control (QC) information regarding cloud- and data-processing conditions. Yang et al. (2007) evaluated the data performance of LAI products using in situ measurements and concluded that the MODIS LAI could well represent the vegetation variations in central eastern China. The monthly LAI used in our study is defined as the average of values in one month. Given the spatial mismatch between CMA and MODIS data, the MODIS LAI data are spatially interpolated to the stations.

As one of the fundamental parameters of soil, soil texture is defined as the weight percent of sand, silt, and clay in soil. The particle sizes of sand, silt, and clay decrease in turn. The soil property in central eastern China is obtained from Shangguan et al. (2012) with a 30-arc-s resolution, which is sufficiently high to retrieve the sand and clay content using the spatial interpolation method. In our study, a sand station is defined on the basis of the “coarse” soil in FAO documentation, which is defined having more than 65% sand and less than 18% clay (FAO 2003). In the same manner, the clay station in our work is based on the “fine” soil of FAO, which is defined as having more than 35% clay. The particle size in the sand station is clearly larger than that in the clay station.

In our present study, 14 stations in central eastern China are selected from among the observational SM stations of CMA. The selection criterion is that these stations have the same vegetation type and same growing season but different types of soil. Additionally, the stations should be neighbors so that they are influenced by similar climate. Figure 1 shows the distribution of these 14 stations, which are located in the midlatitude range of 30°–40°N and longitude range of 100°–120°E over eastern China. The growing season of this area is from March to October, and the vegetation type is wheat before mid-May and corn afterward. Considering that the vegetation roots are mainly restricted within a depth of 50 cm, as noted above, the SM of the top 50 cm is selected. The soil of the 14 stations can be divided into two types: five stations in sand and nine stations in clay. The soil properties of the 14 stations are shown in Table 1.

Fig. 1.
Fig. 1.

Distribution of soil moisture stations (square indicates sand stations; triangle indicates clay stations). The station name and soil property for each station are listed in Table 1.

Citation: Journal of Hydrometeorology 17, 11; 10.1175/JHM-D-15-0240.1

Table 1.

Detailed information of observational SM stations.

Table 1.

Figure 2 shows the temporal variations of climatological mean LAI at each station, as well as the averages at the sand stations (stations 1–5 in Table 1) and clay stations (stations 6–14 in Table 1). When calculating the LAI values in Fig. 2, the climatological mean values for each month, from January to December, at each station are calculated first. Then, the climatological mean values for the same soil type are averaged for each month. The detailed methods are shown in Eqs. (1)(3). As shown in Eq. (1), for each station j (14 stations in total, j = 1, …, 14), represents the climatological mean in month i from January to December (i = 1, …, 12). Variables and in Eqs. (2) and (3) are the climatological mean LAI values for sand (j = 1, …, 5) and clay (j = 6, …, 14) stations, respectively:
e1
e2
e3
Fig. 2.
Fig. 2.

Temporal variations of LAI for 14 stations (m2 m−2). The black and red thick solid lines are the LAIs averaged for the sand stations (stations 1–5 in Table 1) and clay stations (stations 6–14 in Table 1), respectively.

Citation: Journal of Hydrometeorology 17, 11; 10.1175/JHM-D-15-0240.1

As shown in Fig. 2, the vegetation becomes green around March, reaches the first peak in April, and arrives at the harvesting time for wheat in mid-May, with the LAI rapidly decreasing. After that period, the LAI increases as a result of the implantation of corn and reaches the second peak in August, which is noticeably higher than the first one. The corn is harvested around October. Therefore, the growing season in our selected region is from March to October. In the present study, we focused on the relationship between SM and LAI from July to October. During this period, the LAI is larger and more stable (the LAI varies from 0.5 to 2.3 at clay stations and from 0.5 to 3.2 at sand stations); furthermore, the vegetation type is corn for all stations. Moreover, there is less missing SM data because this period is in the nonfrozen soil stage. Furthermore, based on the limited irrigation data we collected spanning from 2003 to 2010, the irrigation statistics for all selected stations show that there are 14 irrigation times in April, 5 times in May, 12 times in June, and 0 times from July to October. More irrigation times in spring in our study area are because of the lower P. More P occurs from July to October, and there is little irrigation during this period. Thus, selecting the period from July to October increases the reliability of our study. LAI and SM are averaged for the clay and sand stations to obtain their time series. The MODIS data are converted to station values to match the SM data using the aforementioned interpolating method.

To check if the selected sand and clay stations are influenced by the similar climate, we calculated the climatological precipitation and surface air temperature averaged over sand and clay stations from July to October. The precipitation in sand and clay stations is 324 and 390 mm, respectively, and air temperature is 21.56° and 21.26°C, respectively, indicating similar climatology for sand and clay stations in the period of July–October. As reported by Zuo and Zhang (2009), there is a significant long-term trend of SM over eastern China. In our study, we also calculated the linear trends in LAI and SM. The trend coefficients of LAI are 0.02 and −0.24 at the clay and sand stations, respectively. For SM, the trend coefficients are 0.26 at the clay stations and 0.27 at the sand stations. Because our study considered the interannual characteristics, the linear trend is removed in the time series of all data. Moreover, frequently used statistical methods were employed in this study, including correlation analysis and partial correlation analysis.

3. Relationship between vegetation and soil moisture

Figure 3 shows the leading and lagging correlation coefficients between standardized SM anomalies of the top 50 cm and LAI anomalies at clay stations. For convenience, LAI and LAI are adopted to represent the LAI leading 1 month and lagging 1 month, respectively. In this sense, LAI can be substituted with P, SM, or ET to express their leading or lagging relationships. As depicted in Fig. 3, the LAI is positively correlated with SM, but the correlation is insignificant when LAI is replaced by LAI or LAI . Specifically, the correlation coefficient of LAI or LAI with SM is 0.39, exceeding the 0.05 level of significance. The largest coefficient occurs in simultaneous LAI and SM, with a correlation coefficient of 0.48, exceeding the 0.01 level of significance. The time series of LAI and SM anomalies in Fig. 4 illustrate that their variations are consistent with each other. Therefore, SM increases (decreases) when LAI increases (decreases) at the clay stations.

Fig. 3.
Fig. 3.

Leading and lagging correlations between standardized SM anomalies for the top 50 cm and vegetation anomalies of the clay stations. The negative and positive values labeled in abscissa represent the leading and lagging months of vegetation to SM, respectively. The long dashed and dotted lines indicate the 0.01 and 0.05 levels of significance, respectively.

Citation: Journal of Hydrometeorology 17, 11; 10.1175/JHM-D-15-0240.1

Fig. 4.
Fig. 4.

Time series of standardized SM anomalies in the top 50 cm (black line) and LAI anomalies (red line) for the clay stations. July–October of each year is shown.

Citation: Journal of Hydrometeorology 17, 11; 10.1175/JHM-D-15-0240.1

Figure 5 shows the leading and lagging correlation between standardized SM anomalies of the top 50 cm and LAI anomalies at sand stations. As shown in the figure, the correlation coefficients are negative when vegetation leads and positive when SM leads. There is a weak negative simultaneous correlation. In contrast to clay stations, the correlation coefficients at sand stations are relatively small and vary from −0.2 to 0.2, failing to reach the 0.05 level of significance. Thus, vegetation has a significant positive correlation with SM at clay stations but not at sand stations.

Fig. 5.
Fig. 5.

As in Fig. 3, but for the sand stations.

Citation: Journal of Hydrometeorology 17, 11; 10.1175/JHM-D-15-0240.1

The above analysis on the relationship between SM and LAI is according to the averages for all clay or sand stations. To check if the results are stable across different stations within one soil type, the simultaneous correlation coefficients for each of the 14 stations are calculated and the results are shown in Table 2. The correlation coefficients between SM and LAI in clay stations are all positive and statistically significant exceeding the 0.1 or 0.05 level, except only one station (station 8). In sand stations the correlation coefficients are weak and not significant, except for one station (station 1) at the 0.1 level. Therefore, in general, the correlation between SM and LAI in clay and sand is quite different.

Table 2.

Correlation coefficients between soil moisture and LAI in each of the 14 stations. One and two asterisks represent the 0.1 and 0.05 levels of significance, respectively.

Table 2.

Based on the above analyses, although the 14 stations are not far from each other (in the midlatitude range of 30°–40°N and longitude range of 100°–120°E over eastern China), all of the vegetation types at the 14 stations are corn from July to October, and they are influenced by the similar climate, the relationships between LAI and SM exhibit different characteristics because of the difference in soil type. To investigate the reasons for this difference, we choose two factors, ET and the water-retention capacity of soil, which are directly related with soil type and have important impacts on SM, for further investigation.

4. Impacts of evapotranspiration

ET, that is, the sum of soil evaporation and vegetation transpiration, is a key parameter influencing SM and is also impacted by soil texture. The increased transpiration and decreased evaporation due to the increased vegetation have opposite effects on SM (Chen et al. 2006); thus, their combination could measure the contributions of ET to SM.

Figure 6 shows the time evolutions of standardized vegetation anomalies, ET anomalies, and SM anomalies for clay stations. As shown in the figure, the variations of LAI correspond well to the ET of clay stations. There is a significant positive correlation between them, with a correlation coefficient of 0.53, exceeding the 0.05 level of significance. Thus, the ET increases (decreases) when the LAI increases (decreases) in clay. However, the correlation coefficient between ET and SM is only −0.16, failing to reach significance and indicating that the ET at clay stations depends on the LAI, which is the main factor affecting SM.

Fig. 6.
Fig. 6.

Time series of standardized LAI anomalies (red line), ET anomalies (blue line), and SM anomalies (black line) for the clay stations. July–October of each year is shown.

Citation: Journal of Hydrometeorology 17, 11; 10.1175/JHM-D-15-0240.1

We calculate the leading and lagging correlations among ET, LAI, and SM for sand stations. Although all the leading and lagging correlation coefficients between LAI and SM of sand stations are insignificant (Fig. 5), the LAI is positively correlated with the ET , with a correlation coefficient of 0.59, exceeding the 0.01 level of significance. ET has a significant negative correlation with SM, with a correlation coefficient of −0.36, exceeding the 0.05 level of significance. Figure 7 shows the time series of standardized SM anomalies, ET anomalies, and LAI anomalies for sand stations. As shown in the figure, the LAI and ET exhibit similar variations as SM and ET shows the opposite variations of SM. Thus, at the sand stations, ET increases with LAI , causing SM to decrease noticeably. Therefore, the increase in antecedent ET is a main reason for the decrease in SM.

Fig. 7.
Fig. 7.

Time evolutions of standardized SM anomalies (black line), 1-month-leading evapotranspiration anomalies (blue line), and 1-month-leading vegetation anomalies (red line) for the sand stations. Months on the horizontal axis without parentheses represent SM; those with parentheses represent LAI and ET.

Citation: Journal of Hydrometeorology 17, 11; 10.1175/JHM-D-15-0240.1

To check the effect of precipitation on the ET–SM relationship, we calculated the partial correlation between ET and SM after the linear parts related to precipitation are removed. The partial correlation is still weak in clay stations, with the correlation coefficient being −0.19, and still statistically significant exceeding the 0.05 level in sand stations, with the correlation coefficient being −0.41. Therefore, the effect of precipitation on the relationship between ET and SM is very weak for both clay and sand stations.

The differences in ET effects between sand and clay may result from the soil texture. The particle size of sand is relatively large; thus, the pores are relatively large and numerous. Thereby, the soil suction is weak, allowing the vegetation at sand stations to draw soil water easily. Meanwhile, the soil water easily evaporates into the air because of the large pores of sand. This strong evaporation reinforces the loss of soil water, causing the significant negative correlation between ET and SM. By contrast, the particle size of clay is relatively small and the pores are relatively small; thus, the soil suction is strong. As a result, it is difficult for the water in clay to be drawn by vegetation and evaporated into the air. Consequently, the effect of ET in clay is insignificant, with a weak influence on soil water.

According to the above analysis, the physical explanation for the different relationships between LAI and SM in different types of soil can be proposed. ET is one of the main factors leading to the insignificant correlation between LAI and SM at sand stations. At sand stations, increased (decreased) LAI leads to increased (decreased) ET and thereby leads to the decrease (increase) in SM. Thus, the increase (decrease) in antecedent LAI does not correspond to the increase (decrease) in SM, resulting in the insignificant correlation between them. However, at clay stations, although the vegetation has a significant positive correlation with ET, the ET has only a slight impact on SM, leading to a significant positive correlation between LAI and SM.

The above analyses are focused on ET anomalies. To check the relationship between soil texture and ET itself, we chose two neighboring stations: a sand station Xin Xiang (35.31°N, 113.88°E; station 3 in Table 1) and a clay station Cao Xian (34.81°N, 115.55°E; station 11 in Table 1). The two stations are influenced by similar climate. The precipitation and surface air temperature averaged in July–October are 81.36 mm month−1 and 21.02°C, respectively, at Xin Xiang and 88.40 mm month−1 and 21.77°C, respectively, at Cao Xian. The ETs averaged from July to October at Xin Xiang and Cao Xian are 91.63 and 65.47 mm month−1, respectively, indicating the ET in the sand station is much larger than that in the clay station. Considering the two stations are neighboring and influenced by similar climate, and also have similar LAI, the difference in ET should be related with the soil texture. We also calculated the averaged ETs in the period of July–October for all sand and clay stations, which are 70.14 and 51.40 mm month−1, respectively. The result that the ET in all sand stations is much larger than that in all clay stations further indicates the importance of soil texture in ET.

Regarding soil texture, in the following section, we will further investigate the water-retention capacity of different soils and its influence on the relationship between LAI and SM.

5. Impacts of the water-retention capacity

The particle size of soil is a key factor that affects the water-retention capacity, which is the origination of SM maintenance (Gong et al. 2008). No appropriate methods for quantitatively calculating the water-retention capacity of soil have been presented to date. In the present study, the persistence time of SM anomalies (or SM memory), which is calculated using a 1-month-lag autocorrelation (Wu and Dickinson 2004), is used to analyze the water-retention capacity of different soil types.

To eliminate the spurious increment of SM memory induced by P, we calculate the partial correlation coefficients to analyze the water-retention capacity. First, the partial correlation between July and August SM anomalies of clay stations are calculated after removing the linear effect of P to characterize the July SM memory in clay. The calculated correlation coefficient is 0.45. In the same manner, the partial correlation between August and September is 0.71. Hence, in clay, the August SM memory (0.71) is larger than that of July (0.45). As shown in Fig. 2, the LAI in August is greater than that in July, namely, there is more vegetation in August, implying that the persistence of SM is stronger when the clay contains more vegetation. This may result from the soil porosity. The increased vegetation increases the porosity of clay because of the effects of vegetation roots, allowing the surface water to permeate more easily (Zhao et al. 2010; Xia et al. 2012). Additionally, the soil water does not permeate easily in clay because of the small particle size, which also plays a role in preserving moisture (Seneviratne et al. 2010). Therefore, the higher water-retention capacity of clay has an important influence on the significant positive correlation between LAI and SM.

As noted above, at sand stations, LAI has a significant positive correlation with ET , which is highly correlated with SM. Thus, we select the SM as a target to investigate the water-retention capacity of sand soil. In view of the LAI values for sand stations, as shown in Fig. 2, we compare the August SM memory with that of September for sand stations. After removing the linear effect of P, the partial correlation coefficient between August and September SM anomalies is 0.75, which decreases to 0.38 between the September and October SM anomalies, indicating that the SM memory in September is much weaker than that in August. Moreover, the LAI in August reaches a peak (Fig. 2), implying that the SM decreases more easily when the antecedent vegetation increases in sand. This may also be attributed to the effects of soil porosity. The relatively large particle size of sand leads to a larger porosity. Increasing vegetation further increases the porosity, and thus, the soil water permeates the sand easily. Therefore, the water-retention capacity of sand plays a negative role in the correlation between LAI and SM.

The above analysis indicates that vegetation plays a water-retention role in clay, whereas more vegetation in sand increases permeability because of the differences in water-retention capacity. Specifically, increased vegetation in clay enhances its permeability by increasing soil porosity, and the clay’s small particles make the soil water bind tightly to the soil, allowing the clay to retain more water. By contrast, the increased vegetation further increases the soil porosity because of the relative large particle size of sand, causing the porosity to be too large to hold the soil water. Thus, the positive correlation between LAI and SM is significant in clay but not significant in sand.

6. Conclusions and discussion

Using observed SM data and LAI from MODIS for the 2001–10 period, we analyzed the relationship between LAI and SM in central eastern China. The results show that the correlation between them differs for different types of soil: LAI is positively correlated with synchronous SM in clay, whereas there is no significant correlation between LAI and SM in sand.

Because ET and the water-retention capacity of soil influence SM in important ways, we analyzed their impacts on the correlation between LAI and SM in different types of soil. The analysis of ET indicates that LAI is positively correlated with synchronous ET in clay, whereas the SM does not decrease correspondingly because the greater “holding capacity” of clay prevents the soil water from draining. Namely, ET is not the main factor affecting SM in clay. Thus, LAI has a significant positive correlation with SM in clay. In sand, LAI is positively correlated with ET , and the increase (decrease) of ET leads to a decrease (increase) in SM, resulting in an insignificant correlation between LAI and SM. Thus, ET is one of the main factors affecting SM in sand.

Soil texture may also play an important role in the different effects of ET in sand and clay. The large particle size and large soil porosity of sand correspond to the weak suction of the soil matrix, causing water to be easily taken up by vegetation. At the same time, the soil water can easily evaporate into the air. This effect of ET aggravates the loss of soil water in sand. By contrast, the small particle size and low soil porosity of clay lead to strong suction of the soil matrix and difficulty in the drawing of water by vegetation. In addition, it is difficult for the soil water in clay to evaporate. Thus, ET in clay is weak and has only a slight influence on SM.

The analysis of the water-retention capacity of soil shows that the SM remains higher when there is more vegetation in clay. This may result from the small particle size and low soil porosity of clay: the permeated water from the surface increases with increased vegetation, and the low porosity ensures the water adsorption by soil, which is beneficial for maintaining the SM. This is also another main reason for the positive correlation between LAI and SM in clay. However, the persistence of SM is weaker when there is more antecedent vegetation in sand, possibly because sand has a large particle size and high porosity. As increases in vegetation further increase the porosity, the soil water can be easily lost. Thus, there is no significant correlation between SM and LAI. Therefore, the water-retention capacity of soil is also an important factor affecting the correlation between SM and LAI.

Overall, the positive correlation between LAI and SM in clay mainly results from the reinforcement of the water-retention capacity by vegetation. The increasing vegetation strengthens the water-retention capacity of soil, and thus, more water is held in the soil. In sand, the two factors of ET and water-retention capacity of soil make negative contributions to SM, resulting in the inconspicuous correlation between LAI and SM.

The effect of P was not taken into account in this study. Although P should strongly influence vegetation and SM, it is not directly connected with soil type, which was the focus of our present study, at such a spatial scale. To confirm that the impact of P on the correlation between SM and LAI is not important, partial correlation analysis was applied when the parts of SM and LAI linearly related to P are removed. For clay stations, the partial correlation coefficient between SM and LAI is 0.44 after the linear effect of P is removed, which is also statistically significant and only slightly smaller than the original coefficient of 0.48 (Fig. 4). For the sand stations, the correlation coefficient between SM and LAI changed from −0.14 (Fig. 5) to −0.26 after removing the linear effect of P , which was also not statistically significant. This implies that the positive correlation between SM and LAI is not affected mainly by P at clay stations and that P is also not the main factor affecting the correlation between LAI and SM at sand stations.

The cropping system as well as cultivation can significantly affect the LAI, which are not discussed in the present study. However, as seen in Fig. 2, the LAI in the 14 stations is quite similar and almost has the same variations for all stations, especially during our research period from July to October. Considering the similarity of LAI and its variation in sand and clay stations, the results obtained in this study should be robust. The relationship between SM and LAI revealed in the present study is based on the statistical methods. Although the possible physical mechanism is proposed from the aspect of evapotranspiration and soil water retention, it is important to further prove the results obtained in this study by numerical model.

Acknowledgments

The authors are grateful to three anonymous reviewers for their constructive comments. This study was supported by the China National 973 Project (2015CB453203), the National Basic Research Program of China (2016YFA0600602), the National Natural Science Foundation of China (Grant 41205059 and 41375092). We thank the CMA for providing the soil moisture data. The LAI data were obtained from the LP DAAC at https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod15a2.

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