Monitoring Spatio-Temporal Changes of Terrestrial Ecosystem Soil Water Use Efficiency in Northeast China Using Time Series Remote Sensing Data

Soil water use efficiency (SWUE) was proposed as an effective proxy of ecosystem water use efficiency (WUE), which reflects the coupling of the carbon–water cycle and function of terrestrial ecosystems. The changes of ecosystem SWUE at the regional scale and their relationships with the environmental and biotic factors are yet to be adequately understood. Here, we aim to estimate SWUE over northeast China using time-series Moderate Resolution Imaging Spectroradiometer (MODIS) gross primary productivity data and European Space Agency climate change initiative (ESA CCI) soil moisture product during 2007–2015. The spatio-temporal variations in SWUE and their linkages to multiple factors, especially the phenological metrics, were investigated using trend and correlation analysis. The results showed that the spatial heterogeneity of ecosystem SWUE in northeast China was obvious. SWUE distribution varied among vegetation types, soil types, and elevation. Forests might produce higher photosynthetic productivity by utilizing unit soil moisture. The seasonal variations of SWUE were consistent with the vegetation growth cycle. Changes in normalized difference vegetation index (NDVI), land surface temperature, and precipitation exerted positive effects on SWUE variations. The earlier start (SOS) and later end (EOS) of the growing season would contribute to the increase in SWUE. Our results help complement the knowledge of SWUE variations and their driving forces.


Introduction
Soil moisture (SM) is usually expressed as a percentage of soil water content in dry soil weight. As one of the main driving forces of the water, energy, and carbon cycles in land surface and atmosphere [1], soil moisture plays an important role in promoting photosynthesis and ecosystem dynamics [2]. Water use efficiency (WUE) is defined as the ratio of gross primary productivity (GPP) to evapotranspiration (ET) of plants for the same period [3]. WUE is an indicator of the adjustment of vegetation photosynthesis to water loss [4], and quantitatively characterizing WUE can help us understand the interaction between the carbon and the water cycles of terrestrial ecosystems [5]. Soil moisture is affected by evapotranspiration, runoff, groundwater, etc. [6]. In arid grassland and shrubland, strong linear relationships were found between evapotranspiration and soil moisture [7]. During the late growing season, GPP is mainly affected by the soil moisture [8]. GPP is highly sensitive to the variation of surface soil moisture, especially in dry areas [9]. Soil moisture could be a good proxy of the rate of evapotranspiration at a large regional scale; therefore, it could be used to analyze the WUE of an ecosystem. Soil moisture use efficiency (SWUE), defined as the ratio between GPP and SM, was proposed as a new measure of WUE [9]. SWUE may depict the rate of carbon assimilation

Study Area
Northeast China was chosen as the study area, located between 38° north (N) and 53° N and between 115° east (E) and 135° E, covering an area of about 1,240,000 km 2 ( Figure 1). It includes Heilongjiang Province, Jilin Province, Liaoning Province, and the east of the Inner Mongolia Autonomous Region. Northeast China belongs to a temperate continental monsoon climate, characterized by four distinct seasons with short, warm summers and long, cold winters. The precipitation is mainly concentrated in the summer, and the annual precipitation decreases from southeast to northwest. The study area is surrounded by mountain ranges along three directions, including the Greater Khingan Mountains in the northwest, the Lesser Khingan Mountains in the northeast, and the Changbai Mountains in the southeast. The Liao River Plain, Songnen Plain, and Sanjiang Plain are located in the central and southern parts and the northeastern corner, respectively. The Hulun Buir Plateau is located in the western tip of the region. There are hills and tablelands situated between mountains and plains. Phaeozem is the main type of soil. The main vegetation types include deciduous broadleaf forests, deciduous coniferous forests, coniferous, and broadleaf mixed forests and grassland. Throughout the past few decades, northeast China suffered from eco-environmental problems, such as serious soil erosion, decrease of available cropland, serious flood disaster, deterioration of ecological environment, and decrease of grain yield and quality [26]. Several ecological function protection areas were established in the study area, including (1)

CCI Soil Moisture Product
CCI soil moisture product data were the basis of this study. The CCI project was a part of the ESA global essential climate variable monitoring project. The product consists of three surface soil moisture data sets: active products, passive products, and combined products. The active and passive soil moisture products were derived from two microwave scattermeters (ASCAT and European Remote Sensing (ERS) Active Microwave Instrument (AMI)) and four microwave radiometers (Tropical Rainfall Measuring Mission Microwave imager (TMI), Scanning Multichannel Microwave Radiometer (SMMR), Special Sensor Microwave Imager (SSM/I), and Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E)), respectively. The combined products were merged by the first two datasets [27]. The spatial resolution of this daily soil moisture data was 0.25 • with a reference time of 12:00 a.m. UTC (Coordinated Universal Time). These products measure the soil moisture at 0.5-2-cm depth. The soil moisture data of passive and combined products are in volume units (m 3 ·m −3 ), while the active products are expressed as a percentage of saturation (%) [28]. In this study, daily soil moisture data from April to October for the period of 2007-2015 were accessed at https://www.esa-soilmoisture-cci.org/. Then, eight-day soil moisture time series were generated using the maximum value composite (MVC) method. The images were subsets of the study area in ArcGIS.

MODIS Time-Series Data
We selected MODIS products from 2007 to 2015 as the other main source of data, which were obtained from the level 1 and Atmosphere Archive and Distribution System (LAADS) Distributed Active Archive Center (DAAC), located in the Goddard Space Flight Center in Greenbelt, Maryland (https://ladsweb.modaps.eosdis.nasa.gov). The MODIS land surface temperature (LST) product (MOD11A2) provides daytime temperature with a spatial resolution of 1 km and temporal resolution of eight days [29]. We used the red and near-infrared reflectance bands of MOD09A1 product with a spatial resolution of 500 m to obtain eight-day NDVI (Normalized Difference Vegetation Index) time series [30]. NDVI data with a time resolution of 16 days were obtained from MOD13A2 with a spatial resolution of 1 km [31]. Datasets from MODIS eight-day GPP (MOD17A2H) at 500-m spatial resolution were collected [32]. The bilinear interpolation method was used to resample the MODIS data to a 0.25 • resolution, which was consistent with that of SM data.
Daily precipitation for 2007-2015 at a 0.5 • spatial resolution was obtained from the National Meteorological Information Center of China (http://data.cma.cn/). The gridded datasets were produced through the thin-plate spline (TSP) interpolation method in ANUSPLIN software using the meteorological data from 2472 national meteorological stations across China. We applied the MVC method to generate eight-day precipitation data.

Calculation of Soil Water Use Efficiency (SWUE)
SWUE describes the rate of carbon assimilation per unit of soil water loss, which links the photosynthetic productivity of ecosystems to soil water. In this study, the eight-day SWUE of each pixel in northeast China was calculated by soil moisture (SM) and gross primary productivity (GPP), expressed by the following formula: The units of GPP, SM, and SWUE were gC/m 2 , m 3 ·m −3 , and gC/kg H 2 O, respectively.

Spatial Change Trend Analysis
The coefficient of variation (CV) can measure the dispersion degree of data. In this study, we calculated CV of each pixel's SWUE from 2007 to 2015 to characterize the spatial distribution and variations of ecosystem surface SWUE in northeast China. The formulas are as follows: Spatial trends of SWUE were explored using a linear regression model with time as the independent variable and SWUE as the dependent variable [34]. The slope of the fitted regression line at each pixel was calculated using Equation (3).
where n represents the number of years, SWUE i is the summed SWUE in growing season for year i, SWUE represents average SWUE during n years, and SD is the standard deviation. As the CV becomes smaller, the value of SWUE becomes more stable. The F-statistic was used to determine the significance of the linear regression model. Based on the slope value and significant levels, the change trends were classified into five grades: extremely significant increase (slope > 0, 0 < p ≤ 0.05), significant increase (slope > 0, 0.05 < p ≤ 0.1), no significant change (p > 0.1), significant decrease (slope < 0, 0.05 < p ≤ 0.1), and extremely significant decrease (slope < 0, 0 < p ≤ 0.05).

Phenological Metrics Extraction
The phenological metrics were extracted from MOD13A2 NDVI data. Zhao et al. reported that there was little difference among Gaussian, logistic, and Savitzky-Golay fitting methods in the TIMESAT tool [23]. We used TIMESAT software in MATLAB R2015b (The Mathworks, Inc., Natick, MA, USA) with a seasonal parameter of 0.5, an adaptation strength of 2.0, a Savitzky-Golay window size of 2, and an amplitude of 20% to calculate the start of season (SOS), the end of season (EOS), and the length of season (LOS) in northeast China from 2007 to 2015.

Correlation Analysis
Pearson correlation is regarded as a standard method to analyze the relationship between two variables. To examine the response of SWUE variations to vegetation activity, land surface temperature, and precipitation dynamics, the correlation coefficients between eight-day time series SWUE and NDVI, LST, and precipitation were calculated for each pixel, respectively. To investigate the role of photosynthetic phenological factors affecting SWUE, we analyzed the Spearman partial correlations between the accumulated SWUE in the growing season and the SOS and EOS. The equations of the correlation coefficient and partial correlation coefficient were as follows: r xy,z = r xy − r xz r yz where r xy represents the correlation coefficient between x and y, ranging from −1 to 1, and r xy,z means the partial correlation coefficient between x and y when we controlled z values. If r < 0, x is negatively correlated with y. If r > 0, there is a positive correlation between x and y. Furthermore, x, y represent the mean values of x i and y i , respectively. The absolute value of r indicates the relevance of two variables. The greater the value of |r| is , the closer the two variables are and vice versa. In generally, 0 < |r| < 0.3 indicates a weak correlation between two variables, while 0.3 ≤ |r| < 0.6 means moderate correlation, and strong correlation exists in two variables when 0.6 ≤ |r| < 1. The significance of the results was examined using a t-test.        As shown in Figure 3d, SWUE of vegetation was generally high in the ecological function protection areas for water conservation in northeast China. Average SWUE values in the Changbai Mountains and the Great Khingan Mountains were 3.388 gC/kg H2O and 3.073 gC/kg H2O, respectively. The Sanjiang and Songnen Plain wetland ecological function protection areas play important roles in flood regulation, with average SWUE values of 2.669 gC/kg H2O and 2.100 gC/kg H2O, respectively. As the only ecological function protection area of species resources in northeast China, average SWUE in the Liaohe River Delta Wetlands reached 2.179 gC/kg H2O. In Horqin Sandy Land, the ability and role of ecosystems to prevent land desertification and reduce the risk of sandstorm is mainly emphasized. However, vegetation SWUE in this area was the lowest (1.945 gC/kg H2O).     Figure 5a illustrates the spatial distribution of the CV of SWUE. The CV values greater than 0.2 accounted for 0.9% of the studied area, and CV ranged from 0 to 0.2 in the remaining areas. From Figure 5b, the slope values of SWUE were negative in 49% of the total land, whereas positive slopes were found in 51% of the study area. Spatially, there were no significant variation trends of SWUE (p > 0.2). The results of the significance test showed that the accumulated SWUE in the growing season was relatively stable in northeast China for the period of 2007-2015.   Figure 5a illustrates the spatial distribution of the CV of SWUE. The CV values greater than 0.2 accounted for 0.9% of the studied area, and CV ranged from 0 to 0.2 in the remaining areas. From Figure 5b, the slope values of SWUE were negative in 49% of the total land, whereas positive slopes were found in 51% of the study area. Spatially, there were no significant variation trends of SWUE (p > 0.2). The results of the significance test showed that the accumulated SWUE in the growing season was relatively stable in northeast China for the period of 2007-2015.   Figure 5a illustrates the spatial distribution of the CV of SWUE. The CV values greater than 0.2 accounted for 0.9% of the studied area, and CV ranged from 0 to 0.2 in the remaining areas. From Figure 5b, the slope values of SWUE were negative in 49% of the total land, whereas positive slopes were found in 51% of the study area. Spatially, there were no significant variation trends of SWUE (p > 0.2). The results of the significance test showed that the accumulated SWUE in the growing season was relatively stable in northeast China for the period of 2007-2015.

Effects of NDVI, LST, and Precipitation Changes on SWUE Variability
To reveal the response of SWUE changes to vegetation dynamics in northeast China, we calculated the correlation coefficients between SWUE and NDVI in the period of 2007-2015 (Figure 6a). We found that 98.7% of the study area showed strong correlation (0.6 ≤ r < 1, p < 0.05), while weak to moderate correlation was observed in 1.3% of the landscape. SWUE showed a strong and positive correlation with NDVI. In the region with greater NDVI, which indicated enhanced vegetation activity, the SWUE of vegetation might also have improved.

Effects of NDVI, LST, and Precipitation Changes on SWUE Variability
To reveal the response of SWUE changes to vegetation dynamics in northeast China, we calculated the correlation coefficients between SWUE and NDVI in the period of 2007-2015 ( Figure  6a). We found that 98.7% of the study area showed strong correlation (0.6 ≤ r < 1, p < 0.05), while weak to moderate correlation was observed in 1.3% of the landscape. SWUE showed a strong and positive correlation with NDVI. In the region with greater NDVI, which indicated enhanced vegetation activity, the SWUE of vegetation might also have improved. Figure 6. The spatial patterns of correlation coefficients between soil water use efficiency (SWUE) and NDVI (a), land surface temperature (LST) (b), and precipitation (c).
In Figure 6b, the correlation between SWUE and LST in northeast China from 2007 to 2015 is presented. SWUE was highly and moderately correlated with LST, accounting for 19.0% and 63.1% of the land that passed the significance test (taking up 96.8% of the study area, p < 0.05), respectively. The area showing weak correlation occupied only 17.9% of the study area. Overall, LST in the growing season had positive effects on SWUE variations in the study area. As LST increased, SWUE might have improved.
As a supply of soil moisture, precipitation affects vegetation growth and the maintenance of ecosystem services, especially in arid and semi-arid areas. We resampled the time-series SWUE data to a 0.5° spatial resolution to match the precipitation data, using the bilinear interpolation method. The correlation coefficient between eight-day SWUE and precipitation in the growing season was then calculated (Figure 6c). It was found that SWUE was significantly positive correlated with precipitation in 84% of the study area (r > 0, p < 0.05), with 14.2% and 85.8% of the area presenting moderate and weak correlation, respectively. In the last nine years, the vegetation SWUE was positively correlated with the precipitation in northeast China, although the correlation was not strong. With the increase of precipitation, the value of SWUE increased.  In Figure 6b, the correlation between SWUE and LST in northeast China from 2007 to 2015 is presented. SWUE was highly and moderately correlated with LST, accounting for 19.0% and 63.1% of the land that passed the significance test (taking up 96.8% of the study area, p < 0.05), respectively. The area showing weak correlation occupied only 17.9% of the study area. Overall, LST in the growing season had positive effects on SWUE variations in the study area. As LST increased, SWUE might have improved.

Response of SWUE to Phenological Variation
As a supply of soil moisture, precipitation affects vegetation growth and the maintenance of ecosystem services, especially in arid and semi-arid areas. We resampled the time-series SWUE data to a 0.5 • spatial resolution to match the precipitation data, using the bilinear interpolation method. The correlation coefficient between eight-day SWUE and precipitation in the growing season was then calculated (Figure 6c). It was found that SWUE was significantly positive correlated with precipitation in 84% of the study area (r > 0, p < 0.05), with 14.2% and 85.8% of the area presenting moderate and weak correlation, respectively. In the last nine years, the vegetation SWUE was positively correlated with the precipitation in northeast China, although the correlation was not strong. With the increase of precipitation, the value of SWUE increased.   Figure 8a shows the spatial distribution of partial correlation coefficients between SWUE and SOS in the growing season. In 59.3% of the study area, r was less than 0, while 40.7% of the area showed positive partial correlation coefficients. Overall, there existed significant correlation between SOS and SWUE in 2.4% of the total area (p < 0.05). In these areas, the proportions of pixels that were negatively or positively correlated with SWUE were 67.2% (r < 0) and 32.8% (r > 0), respectively. It may be concluded that vegetation SWUE was higher with earlier SOS in northeast China during the last nine years. From Figure 8b, in 51.8% of the study area, SWUE was positively associated with EOS, while a negative correlation between SWUE and EOS was found in the remaining area (taking up 48.2% of the total land). About 1.9% of the study area passed the significance test (p < 0.05). The areas showing positive and negative correlations between EOS and SWUE accounted for 73.3% and 26.7%, respectively. Thus, the later EOS is, the greater the SWUE of vegetation could be.  Figure 8a shows the spatial distribution of partial correlation coefficients between SWUE and SOS in the growing season. In 59.3% of the study area, r was less than 0, while 40.7% of the area showed positive partial correlation coefficients. Overall, there existed significant correlation between SOS and SWUE in 2.4% of the total area (p < 0.05). In these areas, the proportions of pixels that were negatively or positively correlated with SWUE were 67.2% (r < 0) and 32.8% (r > 0), respectively. It may be concluded that vegetation SWUE was higher with earlier SOS in northeast China during the last nine years.   Figure 8a shows the spatial distribution of partial correlation coefficients between SWUE and SOS in the growing season. In 59.3% of the study area, r was less than 0, while 40.7% of the area showed positive partial correlation coefficients. Overall, there existed significant correlation between SOS and SWUE in 2.4% of the total area (p < 0.05). In these areas, the proportions of pixels that were negatively or positively correlated with SWUE were 67.2% (r < 0) and 32.8% (r > 0), respectively. It may be concluded that vegetation SWUE was higher with earlier SOS in northeast China during the last nine years. From Figure 8b, in 51.8% of the study area, SWUE was positively associated with EOS, while a negative correlation between SWUE and EOS was found in the remaining area (taking up 48.2% of the total land). About 1.9% of the study area passed the significance test (p < 0.05). The areas showing positive and negative correlations between EOS and SWUE accounted for 73.3% and 26.7%, respectively. Thus, the later EOS is, the greater the SWUE of vegetation could be. From Figure 8b, in 51.8% of the study area, SWUE was positively associated with EOS, while a negative correlation between SWUE and EOS was found in the remaining area (taking up 48.2% of the total land). About 1.9% of the study area passed the significance test (p < 0.05). The areas showing positive and negative correlations between EOS and SWUE accounted for 73.3% and 26.7%, respectively. Thus, the later EOS is, the greater the SWUE of vegetation could be.

Spatial and Temporal Characteristics of SWUE in the Growing Season
The average growing season SWUE in northeast China illustrated a clear spatial heterogeneity during the past nine years. SWUE ranged between 0.341 and 6.173 gC/kg H 2 O. The SWUE values of cropland and grassland in the Songnen Plain, Liaohe Plain, and Hulun Buir Plateau were relatively lower compared to the regional average. According to our statistics, the GPP values of cropland and grassland were relatively low. The SM in grassland was lowest, but SM was higher in cropland. The lower SWUE values were possibly due to more consumption of soil water by soil evaporation in the ecosystems. Relatively high SWUE was observed in the Changbai Mountains, Greater Khingan Mountains, and Lesser Khingan Mountains, indicating that forests might produce greater photosynthetic productivity per unit of soil water consumption. Xiao et al. also found that WUE was higher in forest, and that the WUE of grassland and cropland was relatively low in China using site data [3].
During the growing season from 2007 to 2015, SWUE fluctuated to a certain extent in northeast China. SWUE increased continuously from April to August, suggesting better efficiency of ecosystems in assimilating carbon by soil water consumption in spring and summer. The lowest SWUE mainly appeared at the end of October, showing the weak capability of photosynthetic productivity of the ecosystem generated by the use of limited soil water supply in autumn. This trend was consistent with the vegetation growth cycle. Note that SWUE variations in northeast China were more consistent with GPP than soil moisture, because the range of annual cumulative GPP (96.304-1343.26 gC/m 2 ) was larger than that of SM (3.001-9.431 m 3 ·m −3 ). The interannual variation of SWUE was generally stable in northeast China from 2007 to 2015. Spatially, SWUE exhibited insignificantly change trends, possibly because our study period was relatively short. He et al. also found that the distribution of SWUE was spatially heterogeneous at a global scale, but the interannual variations of SWUE were not illustrated in their study [9].

Relationship between SWUE, Climate, Geography, and Phenology Factors
To date, few studies on SWUE and its driving forces were conducted. Similar to WUE, SWUE reflects the coupling of the carbon-water cycle of terrestrial ecosystems and may be affected by environmental and biotic factors such as climate factors, soil features, and phenological factors [9]. In this current study, we found that the growing season SWUE tended to increase with the increase of NDVI, LST, and precipitation in northeast China. Since April, vegetation gradually developed from seedling to maturity, while NDVI and LST values kept increasing, and precipitation also increased. Good hydrothermal conditions during the summer were favorable for vegetation growth. Some previous studies indicated that, when leaf area of vegetation becomes larger, vegetation will reduce the loss of soil moisture by preventing evaporation, thereby potentially increasing SWUE [9,35]. The SWUE showed relatively weak and positive correlations with precipitation in the study area, which was related to the decreasing water supply from precipitation due to the surface runoff. Some similar researches also showed that high WUE is related to high temperature, high precipitation, and high NDVI [3,36].
At the biome level, SWUE showed large differences. In our study, higher SWUE in the growing season was observed in coniferous and broadleaf mixed forests, broadleaf forests, and coniferous forests in northeast China. The cold temperate coniferous forests are mainly distributed in the north of the Great Khingan Mountains, including evergreen or deciduous coniferous forests with frost resistance. Although there is lower precipitation and evaporation, the coniferous forests with cold and drought tolerance can grow better than other vegetation. The warm temperate deciduous broadleaf forests are mainly distributed in the south of Liaoning Province. The coniferous and broadleaf mixed forests are composed of evergreen coniferous, deciduous coniferous, and deciduous broadleaf forests, which are distributed in the south of the Great Khingan Mountains and the Lesser Khingan Mountains and the Changbai Mountains. The wide leaf area of broadleaf forest could reduce soil moisture loss; thus, SWUE was relatively high. He et al. found that evergreen broadleaf forests had the highest mean annual SWUE, while shrubs had the lowest value at a global scale for the period of 2000-2014 [9]. Both findings suggested that forests could generate higher productivity per unit of soil water consumption. The differences in SWUE values might be because the researches were conducted at a global scale and regional scale, respectively, and the classification schemes of forests were somewhat different.
The SWUE distribution varied among soil types. Coniferous and broadleaf mixed forests are mainly distributed on dark-brown forest soils, where the highest SWUE values in the growing season were observed. Lowest SWUE was identified for solonetz in arid and semi-arid areas in northeast China, which was in agreement with He et al.'s result of lower annual SWUE in arid ecosystems. Topography directly affects the material flow and energy transformation of the land surface. During the study period, vegetation SWUE showed a certain regularity with elevation change in northeast China. Below 1000 m, SWUE presented a rising trend. The cropland was mainly distributed below 400 m, where SWUE was marginally associated with the greater anthropogenic influence. Coniferous forests and broadleaf forests were mainly distributed between 800 and 1000 m, where vegetation SWUE reached the maximum. When the elevation was more than 1000 m, the SWUE decreased gradually as elevation increased. This was related to the decrease of GPP in vegetation with the elevation increasing.
The variations of growing season length have important effects on vegetation growth. The results of this study show that SWUE increased with earlier SOS and later EOS in northeast China. Similarly, Jin et al. found that WUE is correlated to SOS and EOS, whereby the increase of WUE also occurred with earlier SOS and later EOS [18]. Xiao et al.'s study indicated that the growing season length would affect the annual WUE [3]. Early SOS might make the growing season longer and result in the accumulation of GPP increase. The germination and growth of leaves could be promoted [37]. Leaf area increased gradually, and the photosynthesis of vegetation was enhanced. In spring, soil evaporation was not very large due to temperature restriction, and increasing vegetation coverage could prevent the evaporation of soil moisture [38]. Thus, SWUE increased with early SOS. Soil water deficit often occurred in autumn, and later EOS also led to the increase in growing season. Therefore, with the accumulation of GPP increasing, soil water use efficiency of the vegetation was higher.

Uncertainty
It should be noted that the lower spatial resolution of the available soil moisture product (0.25 • ) unavoidably affected the accuracy of SWUE, especially for highly heterogeneous ecosystems. The ECV-SM data only represented the surface soil moisture condition [9]; thus, the estimation of SWUE might ignore deeper soil water used by plants. In addition, ECV-SM products cannot accurately capture the soil moisture dynamics in dense vegetation areas, which limits the reliability of SWUE. There still exists the problem of missing data in the CCI soil moisture product. In this study, due to the lack of CCI soil moisture data, especially before 2007, we could not obtain and analyze longer time-series SWUE in northeast China. NDVI, LST, precipitation, and land surface phenology parameters were chosen as the representative factors when evaluating the relationship among SWUE, and environmental and phenological changes. Some other variables such as soil evaporation and irrigated water supplies may also be associated with the ecosystem SWUE. Moreover, the correlation analyses between SWUE and the above impact factors were preliminary and better-fitting mathematical methods should be explored in further study.
In addition, the land surface phenology results varied because different data sources and models were adopted. In many previous studies, the analysis of LSP would not include the cropland because it was highly affected by human activity. Due to the large area of cropland and the low resolution of estimated SWUE data, this study was conducted on the whole region including cropland. The phenological parameters were compared with the studies of Zhao et al. [23] (used Global Inventory Modeling and Mapping Studies (GIMMS) NDVI3g data), Wang et al. [39] (used Advanced Very High Resolution Radiomete (AVHRR) NDVI data), and Luo et al. [40] (Table 1). The deviations may be attributed to human interference and variations in natural conditions. The results of this study were reasonable.

Conclusions
Soil moisture can regulate vegetation productivity and affect terrestrial carbon uptake. Soil moisture-based water use efficiency may promote the understanding of soil water use in various ecosystems and ecological functions. This study investigated spatial and temporal patterns of SWUE in northeast China, integrating time series of MODIS GPP products and daily soil moisture data (ECV-SM) derived from microwave sensors. The spatial distribution of average SWUE was consistent with that of GPP. We identified that the variations of SWUE in northeast China were obvious within a year. There was an increasing trend of SWUE between April and August, but SWUE decreased gradually from August to October. The annual cumulative SWUE showed insignificant change trends from 2007 to 2015. The SWUE values showed appreciable differences among different elevations, biomes, soil types, and ecological function protection areas. Above 1000 meters, SWUE decreased with elevation. Highest SWUE values were observed in coniferous and broadleaf mixed forests