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Article

Estimating Regional Soil Moisture Distribution Based on NDVI and Land Surface Temperature Time Series Data in the Upstream of the Heihe River Watershed, Northwest China

by
Xiao Bai
1,
Lanhui Zhang
1,†,
Chansheng He
1,2,*,† and
Yi Zhu
1
1
Key Laboratory of West China’s Environmental System (Ministry of Education), Center for Dryland Water Resources Research and Watershed Science, College of Earth and Environmental Sciences, Lanzhou University, Lanzhou 730000, China
2
Department of Geography, Western Michigan University, Kalamazoo, MI 49008, USA
*
Author to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Remote Sens. 2020, 12(15), 2414; https://doi.org/10.3390/rs12152414
Submission received: 10 June 2020 / Revised: 21 July 2020 / Accepted: 24 July 2020 / Published: 28 July 2020
(This article belongs to the Section Remote Sensing in Geology, Geomorphology and Hydrology)
Browse Figures
Graphical abstract
"> Figure 1
<p>The in situ observation stations in the upstream of the Heihe River Watershed, Northwest China. The number shows 31 land use/land cover (LULC) soil elevation types (details in <a href="#remotesensing-12-02414-t001" class="html-table">Table 1</a>). (<b>a</b>) the study area in China; (<b>b</b>) the elevation of the study area, black circles represent the in situ sites and crimson circles represent the three automatic meteorological station sites; (<b>c</b>) land use types distribution in the study area; (<b>d</b>) soil types. The locations of in situ monitoring sites and meteorological stations are also indicated in (<b>b</b>), (<b>c</b>) and (<b>d</b>).</p> "> Figure 2
<p>Vegetation Index (NDVI) in the study area. (<b>a</b>) The NDVI distribution and the location of the in situ observation site D1; (<b>b</b>) the NDVI<sub>AG</sub> distribution; (<b>c</b>) the NDVI<sub>RES</sub> distribution; (<b>d</b>) the NDVI/NDVI<sub>AG</sub>/NDVI<sub>RES</sub> change at the site D1.</p> "> Figure 3
<p>Land Surface Temperature (LST) data in the study area. (<b>a</b>) the LST distribution and the location of the in situ observation site D1; (<b>b</b>) the LST change at the site D1. The temperature is in Kelvin.</p> "> Figure 4
<p>Temporal variability of soil moisture. (<b>a</b>) temporal variability of soil moisture in real value; (<b>b</b>) anomaly between each layer and the mean soil moisture.</p> "> Figure 5
<p>Spatial distribution of soil moisture during the 2014–2016 growth period. (<b>a</b>) 2014 growth period; (<b>b</b>) 2015 growth period; (<b>c</b>) 2016 growth period; (<b>d</b>) rainfall distribution pattern by kriging interpolation based on data obtained from the three automatic weather stations during the 2014–2016 period.</p> "> Figure 6
<p>Mean and coefficient of variation (CV) of soil moisture at different layers.</p> ">
Versions Notes

Abstract

:
Temporal and spatial variability of soil moisture has an important impact on hydrological processes in mountainous areas. Understanding such variability requires soil moisture datasets at multiple temporal and spatial scales. Remote sensing is a very effective method to obtain surface (~5 cm depth) soil moisture at the regional scale but cannot directly measure soil moisture at deep soil layers (>5 cm depth) currently. This study chose the upstream of the Heihe River Watershed in the Qilian Mountain Ranges in Northwest China as the study area to estimate the profile soil moisture (0–70 cm depth) at the regional scale using satellite Vegetation Index (NDVI) and Land Surface Temperature (LST) products. The study area was divided into 31 zones according to the combination of altitude, vegetation and soil type. Long-term in situ soil moisture observation stations were set up at each of the zones. Soil moisture probe, ECH2O, was used to collect soil moisture at five layers (0–10, 10–20, 20–30, 30–50 and 50–70 cm) continuously. Multiple linear regression equations of time series MODIS (Moderate-resolution Imaging Spectroradiometer) NDVI, LST and soil moisture were developed for each of the five soil layers at the 31 zones to estimate the soil moisture (0–70 cm) on a regional scale with a spatial resolution of 1 km2 and a temporal resolution of 16-d from October, 2013 to September, 2016. The correlation coefficient R of the regression equations was between 0.47 and 0.94, the RMSE was 0.03, indicating that the estimation method based on the MODIS NDVI and LST data was suitable and could be applied to alpine mountainous areas with complex topography, soil and vegetation types. The overall pattern of soil moisture spatial distribution indicated that soil moisture was higher in the eastern region than in the western region, and the soil moisture content in the whole study area was 14.5%. The algorithm and results provide novel applications of remote sensing to support soil moisture data acquisition and hydrological research in mountainous areas.

Graphical Abstract">

Graphical Abstract

1. Introduction

Soil moisture is an essential component of the terrestrial water cycle, and serves as a critical link between the precipitation, surface water, groundwater and vegetation water [1,2,3,4,5,6]. It plays an important role in hydrological processes and land surface–atmosphere interactions such as the Soil–Plant–Atmosphere Continuum (SPAC) [7]. Mountains are water towers of rivers, and understanding the distribution of soil moisture over the mountainous areas, is essential for hydrological modeling and water resources management, especially in arid areas, such as Northwest China [8]. Since its strong temporal and spatial variability, soil moisture has an important effect on the distribution of regional water resources, and ecosystem services [9,10,11]. Unfortunately, the impact of soil moisture on both hydrological processes and ecosystem services over the regional scale has been poorly understood due to the lack of long-term, large-scale soil moisture datasets, particularly in such mountainous areas [12].
Over the past decades, acquisition of soil moisture data has evolved from traditional gravimetric methods to the applications of semi-automatic and automatic monitoring of the neutron probe, time-domain reflectometry (TDR), frequency domain reflectometry (FDR). At the regional scale, remote sensing, particularly satellites, provides soil moisture estimates over large areas [13], which is mainly based on the measurement of electromagnetic radiation energy reflected or emitted from the land surface. Satellite soil moisture products mainly include AMSR (Advanced Microwave Scanning Radiometer) [14], ERS (European Remote Sensing Satellite) [15], ASCAT (The Advanced Scatterometer) [16,17], SMOS (Soil Moisture and Ocean Salinity) [18] and SMAP (Soil Moisture Active Passive) [19]. In addition, soil moisture data at the regional scale can also be obtained through model simulation and data assimilation [20]. Common weaknesses of these soil moisture products are that the accuracy and resolution in heterogeneous mountainous areas are still too coarse to meet the study requirements [21,22]. Temperature Vegetation Dryness Index (TVDI) [23] and Apparent Thermal Inertia (ATI) [24,25] are two common methods to estimate soil moisture by using visible light and near-infrared wavebands. However, both the TVDI and ATI methods mainly establish the correlation between the measured soil moisture on the ground and the TVDI or ATI indices to estimate the soil moisture distribution in the region. For alpine and heterogeneous areas, the complexity of the underlying surface leads to a series of uncertainties, such as the inconsistent linear relationship between soil moisture and ATI, resulting in large estimation errors. In addition to these deficiencies, current remote sensing methods mainly concentrate on the relationship between the surface reflection value and the in situ observations to estimate the surface soil moisture at the regional scale [23]. Few studies have estimated the profile soil moisture, particularly in the alpine areas. Li et al. [26] used remotely sensed surface soil moisture to calculate deep soil moisture by a flux model, and pointed out that remote sensing surface soil moisture in wet areas was suitable for estimating profile soil moisture, while the results in dry areas had large errors. Tobin et al. [27] downscaled AMSR-E and ERS-CCI (European remote sensing satellite-climate change initiative) soil moisture data using an exponential filter (ExpF) with soil moisture index derived from MODIS NDVI. Tian et al. [3] coupled the surface soil moisture and profile soil moisture, applied ExpF, artificial neural networks (ANN) and cumulative distribution function matching (CDF) methods in an alpine region to estimate the deep profile soil moisture at the regional scale. Lu et al. [28] investigated a nonlinear autoregressive neural network method with exogenous input (NARXnn) to estimate time series soil moisture by multiple remote sensing data. Estimation of 0–100 cm soil moisture by the principle of maximum entropy achieved better results than those obtained by the exponential decaying function in the Southeastern USA, a subtropical humid area [29]. With the measurement of the spectral characteristics of soil profile, Balet et al. [30] inferred soil moisture conditions based on the MARMIT (MultilAyer Radiative Transfer Model for soIl reflecTance) model. Over the past decades, MODIS LST and NDVI products have been widely used in agriculture, ecosystem and global change research [31,32]. The NDVI is an index that shows the difference between vegetation reflectance in the visible and near-infrared bands and the soil background [33]. Land Surface Temperature (LST) is a key parameter for agricultural drought monitoring, hydrological research and urban thermal environment [34,35]. Both MODIS LST and NDVI data are widely used for soil moisture estimation based on the TVDI and ATI methods (e.g., [21,36]). For example, Yang et al. [37] proposed a trapezoidal space defined by remote sensed vegetation cover and LST to estimate surface soil moisture. Compared with the aforementioned soil moisture products, both MODIS LST and NDVI have higher spatial resolutions in soil moisture monitoring. In addition, both MODIS LST and NDVI are usually used for downscaling the satellite soil moisture products [22,38].
In recent years, many scale transformation studies for soil moisture retrieval have been done. However, the accuracy of most of these works was poor in mountainous areas, and the temporal and spatial resolutions of profile soil moisture estimation were still too coarse to meet the needs of hydrological research in heterogeneous mountain areas. To improve the deficiency in estimating profile soil moisture distribution over mountainous areas, this study proposed a novel approach to estimate profile soil moisture by integrating remote sensing LST and NDVI products and in situ soil moisture observations. The upstream of the Heihe River Watershed in the Qilian Mountain Ranges in Northwest China was chosen as the study area to estimate the profile soil moisture (0–70 cm depth) at the regional scale by remote sensing. The study area was divided into 31 zones based on the combination of altitude, vegetation and soil type. In situ soil moisture observation stations were set up at each of the zones to collect soil moisture at five layers (0–10, 10–20, 20–30, 30–50 and 50–70 cm) continuously for the period of October, 2013 to September, 2016. Multiple time series MODIS NDVI, LST and soil moisture were fitted for each of the five soil layers at the 31 zones to estimate the profile soil moisture distribution on a regional scale. The study aims to provide a novel approach to estimate profile soil moisture distribution by remote sensing to support hydrological research in mountainous areas.

2. Study Area

The in situ soil moisture data for this study were obtained from the Soil Hydrological Heterogeneity Observation Network in the upstream of the Heihe River Watershed (97°46′–101°11′E, 38°12′–39°22′N) Northwest, China (Figure 1a), a 2.75 × 104 km2 study area with 30 tributaries, elevation ranging from 2000 m to 5580 m (above sea level, a.s.l) on the northeast edge of the Qinghai–Tibet Plateau. The spatial distribution of annual mean temperature ranges from –3.1 °C to 3.6 °C, and annual precipitation ranges from 200 mm to 700 mm [39]. Perennial snow cover and glaciers are present above 4000 m with permafrost extending down to 3700 m. Affected by mountain climate and terrain, there is a remarkable vertical zonal distribution of vegetation and soils in the study area. In the upstream of the Heihe River Watershed, the major vegetation types include coniferous forest (Picea crassifolia), shrub (Potentilla fruticosa), steppe (Stipa purpurea Griseb), alpine meadow (Kobresia pygmaea Clarke), alpine sparse vegetation (Saussurea medusa Maxim) and desert (Sympegma regelii Bunge) [22]. The main soil types include aeolian sandy soil, cold desert soil, alpine meadow soil and mountain swamp chestnut soil [8].

3. Data Sets

3.1. In Situ Soil Moisture Monitoring Network

3.1.1. Soil Moisture Observation Network

We established a Soil Hydrological Heterogeneity Observation Network comprising 31 stations (Figure 1) in the upstream of the Heihe River Watershed based on the combination of land use/land cover (LULC), soil type and elevation [39,40]. The distribution of soil and vegetation types is shown in Table 1. We used GPS (Garmin Oregon 550) to record latitude, longitude and altitude information for each station, the positional errors were within ±1 m and altitude error was in ±3–5 m. We also recorded soil profile characteristics, vegetation status, root depth, aspect and slope using a geological compass, and took pictures of the sites every summer since June, 2014.

3.1.2. Soil Moisture Measurement

ECH2O 5TE soil moisture probes (DECAGON Devices, USA) and EM50 datalogger were installed at each in situ observation site. The 5TE probe measures the soil volumetric water content by the dielectric constant of the soil, with a precision of ±3% and accuracy of ±1–2% [3,41]. The 5TE probes with a sensing depth of 2.5 cm were vertically centered in each of the sampled layers (0–10 cm, 10–20 cm, 20–30 cm, 30–50 cm and 50–70 cm) [42]. There is one probe in each layer for a total of five at each station. Datalogger EM50 was placed into a waterproof box sealed with high-strength glass glue and wrapped in a thick waterproof bag buried at a distance of at least 50 cm away from the 5TE probes. The system collected soil moisture content data at 30 min intervals. Regular site maintenance took place twice a year at the beginning of June and at the end of October [3].
However, the mountainous study area is topographically complex and hard to reach, and it is a real challenge to maintain the soil moisture in situ observation network in the study area. Wireless data transmission is not possible because there is no mobile communication network coverage in the study area. As a result, there are some gaps in the measurement datasets due to severe weather and unstable batteries or sensors and the sensor damage by livestock and rats [3].

3.2. Time Series Data of NDVI and LST

NDVI was used as the vegetation index for the upscaling of the in situ soil moisture in this study. Specifically, we acquired the 16-day composite NDVI dataset MOD13A2 V6 (https://e4ftl01.cr.usgs.gov/MOLT/MOD13A2.006/) of the sixth edition of terrestrial Level 3 standard data products for the period of October, 2013 to September, 2016, with a total of 69 scenes at a spatial resolution of 1 km2 [43]. We used the 8-d composite Land Surface Temperature (LST) data MOD11A2 V6 (https://e4ftl01.cr.usgs.gov/MOLT/MOD11A2.006/) for the same period of October, 2013 to September, 2016, for a total of 138 scenes with a resolution of 1 km2 [44]. Since the LST data were of 8-d composite, we used Maximum Value Composites (MVC) to process the LST dataset into a 16-d temporal resolution to match the temporal frequency of the NDVI dataset [45].

4. Methodology

The remote sensing vegetation index products contain a lot of noise caused by precipitation, cloud cover, human influence and the sensor itself in the data acquisition and process stages [46,47], and these factors can be collectively called random factors or residual parts.
The MOD13A2 V5 NDVI product was divided into the NDVI seasonal part and the NDVI residual part by using the Asymmetric Gaussian fitting method (AG) [48]. The multivariate linear regression fitting was used to establish multiple linear regression equations between the time series soil moisture data and the MODIS LST and NDVI. Subsequently, soil moisture upscaling equations were developed for every profile in the 31 vegetation–soil–elevation zones.
We installed only three automatic weather stations in the study region due to resource constraints. The limited number of precipitation observation stations cannot match the number of in situ soil moisture observations. Our in situ observations covered the three-year period (October, 2013 to September, 2016), and were able to represent the mean precipitation amount in the study region. Therefore, we only used MODIS LST and NDVI remote sensing data to estimate the spatial distribution of soil moisture in the study region.

4.1. Asymmetric Gaussian Function Fitting

Based on the noise information, NDVI data products can be divided into seasonal part, NDVIAG, and residual part, NDVIRES as shown in Equation (1). It needs to be reconstructed to remove the residual part [48].
NDVI = NDVI AG + NDVI RES
The time series NDVI data curve reconstructed by the AG algorithm, can well express the interannual variation characteristics of the vegetation, and identify all the abnormal values [31]. The AG algorithm is a nonlinear least-squares fitting algorithm based on the Asymmetric Gaussian function. The original NDVI data and NDVIAG were used to obtain the residual part of the time series Vegetation Index (NDVIRES). The shallow soil moisture was greatly affected by the residual part which was likely to be related to short-term weather. The deep soil moisture was mainly subject to the seasonal changes in weather conditions and plant growth and environmental factors.

4.2. Multiple Linear Regression Fittings

In this study, a multiple linear regression method was used to estimate the soil moisture using the in situ soil moisture observations, Vegetation Index (NDVI) and Land Surface Temperature (LST) data. The specific equation is as follows:
SMC = a 0 + a 1 * LST + a 2 * NDVI RES + a 3 * NDVI AG
In Equation (2), SMC is soil volumetric water content (m3 m −3), LST is the Land Surface Temperature (K), NDVIRES and NDVIAG are the residual part and seasonal part of the vegetation index, respectively, a0, a1, a2 and a3 are the coefficients to be determined, a1 is in m3 m −3 K –1, while a2 and a3 are dimensionless.
Following Zhang et al. [22], the regression equations were evaluated by complex correlation coefficient (R), the F-test value, the confidence level P and the root mean square error (RMSE). R describes the linear correlation of the dependent variable (soil moisture) and multiple explanatory variables in multivariate linear regression. The F-test was used to test whether the established regression equation is statistically significant. The larger the F-value, the better the fitted regression equation. Confidence level P indicated the significance level of the regression equation. The smaller the p-value, the more significant the equation. RMSE was used to measure the deviation between the predicted value and the true value. The smaller the RMSE value, the closer the simulated value to the observed value, the higher the accuracy of the regression equation. R and RMSE calculation formulas are as follows:
R = ∑ i = 1 n ( SMC obs , i - SMC obs ¯ ) ( SMC mod , i - SMC mod ¯ ) ∑ i = 1 n ( SMC obs , i - SMC obs ¯ ) 2 ∑ i = 1 n ( SMC mod , i - SMC mod ¯ ) 2
RMSE =   ∑ i = 1 n ( SMC obs , i - SMC mod , i ) 2 n
where, SMCobs,i is the measured soil moisture value, SMCmod,i is the multiple linear regression fitted soil moisture value, n is the number of observations [22].

5. Results and Discussions

5.1. Time Series NDVI and LST

The NDVI dataset was the MOD13A2 product from 16 October 2013 to 29 September 2016. The product was the 16-day composite with a total of 69 images. NDVIAG (Figure 2b) was obtained by reconstructing the NDVI time series data with the AG algorithm, and then the NDVIAG and the original NDVI data (Figure 2a) were used to derive the residual NDVIRES (Figure 2c). The reconstructed NDVI time series data (NDVIAG) based on the AG algorithm and the trend of the original NDVI time series data were basically the same (Figure 2d), and the reconstructed results were used to detect abnormally high and low values in the time series data, to correct the low values, and to reflect seasonal changes in the vegetation index [49]. The seasonal NDVIAG and the residual NDVIRES were consistent with the growth pattern of the vegetation [48].
The selected LST dataset was the MOD11A2 product from 16 October 2013 to 29 September 2016, which was an eight-day composite product with a total of 138 images. The eight-day LST dataset was converted to a 16-day LST images (Figure 3b) to match the NDVI temporal resolution by the MVC method [45].

5.2. Upscaling of In Situ Soil Moisture

The soil moisture time series data in the 31 in situ observation sites were processed to 16-day mean values corresponding to the NDVI and LST’s temporal resolution. Subsequently, multiple linear regressions were established between the in situ soil moisture observations and the NDVI and LST time series data. The five-layer soil moisture upscaling equation coefficients of the 31 in situ observation sites were shown in Table 2 and Table A1, Table A2, Table A3 and Table A4. For the 0–10 cm layer of the site D8, the soil moisture upscaling equation was significant at a 95% confidence level, and all the other models at the rest 31 in situ observation sites were significant at a 99% level. Among them, the correlation coefficient R of the 0–10 cm layer was 0.36–0.93, the RMSE was 0.03 (Table 2); the R and RMSE of 10–20 cm layer were 0.65–0.90, and 0.03 (Table A1), respectively; The R and RMSE of 20–30 cm layer were 0.51–0.95 and 0.03 (Table A2), respectively; the R and RMSE of 30–50 cm layer were 0.47–0.93, and 0.03 (Table A3), respectively; and the R and RMSE of 50–70 cm layer were 0.54–0.94, and 0.02, respectively (Table A4). The accuracy of scaling results was higher than that of Xu et al. [21]. In general, the five-layer RMSE mean values were close to the ECH2O data accuracy (0.03 cm3 cm−3), indicating that the accuracy of the in situ soil moisture estimation models was reasonable.
The soil moisture of each pixel (with spatial resolution of 1 km2) at the 31 zones from 16 October 2013 to 29 September 2016 was calculated by using the upscaling model to obtain the five-layer soil moisture dataset in the study area for the same study period. The dataset covered an area of 2.75 × 104 km2, with a temporal resolution of 16-day and a spatial resolution of 1 km2 (e.g., Figure 5).

5.3. Accuracy Evaluation of Upscaling Soil Moisture Models

To evaluate the accuracy of soil moisture upscaling models, the three sites—Biandukou, Dayekou and Kangle, which were not used to establish the regression equations, were used as validation sites, and their soil moisture data were used for correlation test and error analysis (Table 3). The results of the soil moisture upscaling in the different soil layers showed that the correlation coefficients were between 0.5410 and 0.8940, and the RMSE were between 0.0066 and 0.0549, all of which passed the F-test at 99% significant level. The results showed that the correlation between the upscaled data and the measured data were significant, and the soil moisture upscaling models well expressed the dry and wet conditions of the soil at the regional scale.
Comparing the validation results of soil moisture models of the five layers, we found that the R’s descending order was 20–30 cm > 10–20 cm > 0–10 cm > 30–50 cm > 50–70 cm, RMSE’s descending order was 0–10 cm > 10–20 cm > 30–50 cm > 50–70 cm > 20–30 cm. The trends of the two indices were not consistent, the main reason might be: 1) the variability of soil moisture with time decreased from the top to the bottom soil layer, and the 0–10 cm layer had the largest soil water fluctuation and the largest error, 2) the LST was mainly affected by the 0–10 cm layer of soil and overlying vegetation, 3) NDVI was mainly affected by the vegetation growth, and the root zone soil moisture was very important for vegetation growth, the main vegetation types of the three verification sites were all grassland, with their root system mainly distributed in the 20–30 cm. Therefore, the accuracy of the 20–30 cm soil moisture upscaling model was the highest, and the lowest was the 50–70 cm layer.
Comparison of the soil moisture upscaling results of the three verification sites showed that the RMSE values were getting smaller from the top to the deep soil layers, Kangle appeared to have the best fit. Topographically, the Kangle site was located at a plateau within 1 km2 pixels. The vegetation type was mainly grass (Stipa Steppe) with homogeneous distribution. The climate was dry, with smaller rainfall (annual 338 mm for Kangle, 428 mm for Dayekou and 702 mm for Biandukou during the 2013–2016 period), the soil moisture was relatively low and stable. Under these climate, topography and vegetation conditions, the LST and NDVI data were of relatively high quality, and the accuracy of the upscaling was high. The Biandukou and Dayekou sites were located on the mountain slope. The terrain was complex within 1 km2 pixels, and the vegetation types were diverse. In addition, there was a reservoir near the Dayekou site. These factors were likely to have affected the LST and NDVI quality of remote sensing products, and the accuracy of the upscaling model was not as good as that of the Kangle site.

5.4. Soil Moisture Variability at Different Temporal and Spatial Scales

5.4.1. Temporal Variability of Soil Moisture at the Regional Scale

Figure 4 shows the changes in soil moisture at different layers over time. As Figure 4a shows, the soil moisture was the lowest in December and the highest in July. The descending soil moisture order in all the layers in December were 20–30 cm > 50–70 cm> 10–20 cm > 30–50 cm > 0–10 cm. The descending soil moisture order in all the layers in July were 20–30 cm >10–20 cm > 0–10 cm > 30–50 cm > 50–70 cm. Among them, the 20–30 cm were the highest among all the layers of the soil profile. During the whole three years, the variation range of each layer was 0–10 cm (coefficient of variation, CV. 12.44%), 10–20 cm (CV. 12.27%), 20–30 cm (CV. 11.86%), 30–50 cm (CV. 10.40%), 50–70 cm (CV. 8.38%), respectively, which was closely related to the effects of precipitation and soil infiltration on soil moisture. Figure 4b shows that the 20–30 cm and 30–50 cm had a relatively high and low but stable variation. Among the four seasons, the precipitation and temperature were low in winter, leading to the decrease of liquid water in the soil, thus the soil moisture was the lowest in the winter. Summer rainfall was the highest and most frequent, therefore the soil moisture content was highest and most variable in the summer.

5.4.2. Spatial Distribution of Soil Moisture at the Regional Scale

The overall spatial distribution of soil moisture during the growing season from 2014 to 2016 are shown in Figure 5. The weighted mean soil moisture of the three years was 14.50% in the entire study region. Soil moisture was higher in most of the eastern, central and northwestern parts of the region. Overall, the soil moisture in the eastern area was generally higher than that in the central and western areas, and the soil moisture was the lowest in the western area. The spatial patterns were consistent with the precipitation distribution pattern shown in Figure 5d and Tian et al. [42]. The precipitation during the growth period of 2014 to 2016 was highest in the Biandukou, the second highest in the Dayekou, and the lowest in the Kangle automatic weather station, showing a spatial pattern of a declining trend from the eastern to the central and to the western parts of the region. Since the limited number of automatic weather stations and coarse spatial coverage, the precipitation was not incorporated into the regression analysis.

5.4.3. Soil Profile Moisture Heterogeneity at the Regional Scale

As shown in Figure 6, soil moisture and its variability at different layers were shown by the relationship between the mean and coefficient of variation (CV). The fitting curves indicated that the variability of soil moisture at the shallow layers was larger than deeper layers. Mean and CV had negative correlations in the 30–50 cm and 50–70 cm layers, indicating that the soil moisture variability was the highest in dry conditions and the lowest in humid conditions (e.g., summer), and the fitting curve between the 30–50 cm layers and the whole profile was close, indicating that the characteristics of the soil moisture change in the 30–50 cm was similar to that of the whole profile. As described in the above section, the most representative of the whole profile was 30–50 cm among the five layers.

6. Conclusions

Estimating spatial and temporal distribution of soil moisture is a challenge in high-elevation, data-scarce, and heterogeneous mountainous areas like the upstream of the Heihe River Watershed in Northwest China. We proposed a regression model based on the MODIS NDVI and LST to estimate profile soil moisture at the regional scale. Subsequently, we analyzed the spatial and temporal variability of soil moisture at the regional scale. Results showed that the multivariate linear regression method could be used to estimate high-resolution soil moisture products in alpine and cold mountainous areas at both shallow and deep soil layers. The soil moisture in the east of the upstream of the Heihe River Watershed was significantly higher than that in the west, and the average soil moisture in the whole region was 14.5% in the 0–70 cm depth. The soil moisture at the 30–50 cm soil layer could reasonably represent the 0–70 cm profile soil moisture. Soil moisture in the 0–10 cm layer had the highest variability while the 20–30 cm layer showed the lowest soil moisture variability among all the layers. The contribution of this study was to estimate profile soil moisture at the regional scale by readily available remote sensing products of NDVI and LST.
However, the microterrain features such as slope and aspect were not considered in the soil-vegetation-elevation sampling zone, which might affect the accuracy of soil moisture upscaling models. Satellite products with high spatial-temporal resolutions need to be integrated with in situ observations and other soil and vegetation datasets to improve the accuracy of estimating soil moisture at the regional scale, especially in data-scarce and topographically complex mountainous regions.

Author Contributions

Conceptualization, X.B., L.Z. and C.H.; field work: X.B. and Y.Z.; processing and analysis, X.B. and L.Z.; writing—original draft preparation, X.B. and L.Z.; review, editing, and finalization, C.H.; project administration, C.H. All authors have read and agreed to the published version of the manuscript.

Funding

The project is partially funded by the grants from National Natural Science Foundation of China (41530752, 41877148, 41501016 and 91125010).

Acknowledgments

We are grateful to the members of the Center for Dryland Water Resources Research and Watershed Science, Lanzhou University for their hard field work to collect and analyze the soil data in the high and cold, hard to reach mountainous areas over the past seven years. Meanwhile, we are deeply grateful to the anonymous reviewers and Section Managing Editor Muriel Zhang for their constructive comments and responsible work.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table
Table A1. Layer soil moisture estimation model (10–20 cm).
Table A1. Layer soil moisture estimation model (10–20 cm).
SiteIntercept-a0LST-a1NDVIRES-a2NDVIAG-a3F-ValueRRMSE
D1−0.04480.00030.12390.155247.1598 **0.83590.0082
D20.03560.00000.18100.174838.1284 **0.79850.0170
D3−0.22690.00080.49740.608145.2227 **0.82220.0224
D4−0.15020.00070.07250.046962.7791 **0.87980.0079
D5−0.45940.0020−0.05270.076125.6997 **0.73660.0294
D6−0.16410.0008−0.05930.149512.7288 **0.65800.0351
D7−0.00330.00000.27330.408344.2384 **0.81930.0271
D8−0.34410.00150.22530.221537.5842 **0.79640.0256
D9−0.37160.00160.06030.135641.7135 **0.81130.0313
D10−0.31280.00120.22420.449961.1032 **0.85920.0204
D11−0.07820.00030.29590.266060.4343 **0.85800.0276
D12−0.57180.00220.18280.099326.5246 **0.82620.0266
D13−0.62460.0028−0.22300.103725.5179 **0.73540.0561
D14−0.76400.0030−0.38910.078221.1450 **0.79470.0345
D15−3.14650.0121−0.3682−0.137949.6064 **0.83430.0655
D16−0.57560.0025−0.00850.108431.9732 **0.77210.0388
D17−0.62360.0024−0.00170.430938.3976 **0.85070.0587
D18−0.67640.0027−0.10420.040822.3538 **0.71260.0330
D19−0.47050.0020−0.02120.081336.9206 **0.79380.0313
D200.00720.00010.03090.242318.7568 **0.70170.0256
D210.0465−0.00030.25370.915419.9186 **0.72160.0205
D22−0.04610.00020.14330.421433.0572 **0.77720.0153
D23−0.85920.00320.05150.216868.1222 **0.87100.0283
D240.00020.00000.06610.113818.7331 **0.76040.0185
D25−0.42170.00180.38300.192949.7008 **0.83450.0243
D26−0.40720.00170.50100.121137.0421 **0.79430.0300
D27−0.41320.00160.06550.004115.6931 **0.64810.0243
D28−0.33280.00160.23320.169466.8502 **0.87400.0250
D29−1.43040.0057−0.14530.025837.4054 **0.82410.0436
D30−0.77210.00310.13550.090250.5135 **0.83660.0265
D31−0.88500.00340.01800.155795.4179 **0.90400.0253
** represents F-test significance level of 99%. The table keeps 4 decimal places, the calculation keeps 8 decimal places Intercept-a0, LST-a1, NDVIRES-a2 and NDVIAG-a3 represents the coefficients of upscaling Equation (2). F-value means the significance value in the F-test. R is the complex correlation coefficient. RMSE is the root mean square error.
Table
Table A2. Layer soil moisture estimation model (20–30 cm).
Table A2. Layer soil moisture estimation model (20–30 cm).
SiteIntercept-a0LST-a1NDVIRES-a2NDVIAG-a3F-ValueRRMSE
D1−0.00830.00020.07430.235441.1005 **0.82240.0111
D2−0.11530.00070.09310.119334.1421 **0.78220.0172
D3−0.07060.00040.02800.1362106.4698 **0.91150.0045
D4−0.14610.00070.04420.044281.4475 **0.91580.0058
D5−0.48630.0021−0.07690.064042.4492 **0.81370.0223
D6−0.16690.0008−0.05830.107210.6971 **0.62520.0315
D7−0.19600.00090.11180.225468.0018 **0.87910.0164
D8−0.28640.00130.23850.256173.0847 **0.87830.0181
D9−0.39680.00180.01370.091435.6686 **0.78870.0300
D10−0.19770.00090.14810.467297.0527 **0.90420.0148
D11−0.21590.00090.24040.223576.6659 **0.88300.0230
D12−0.45390.00190.18990.081730.0917 **0.84220.0211
D13−0.62810.0028−0.28280.139520.4940 **0.69720.0736
D14−1.20320.0047−0.54660.128128.3354 **0.83470.0474
D15−2.98200.0116−0.3259−0.195628.7933 **0.75540.0724
D16−0.53060.0023−0.07350.065415.8860 **0.65040.0431
D17−0.45740.00190.16210.343136.6091 **0.84500.0467
D18−0.42330.0018−0.18340.02187.6475 **0.51080.0373
D19−0.46010.0019−0.02530.073540.5132 **0.80720.0280
D200.0795−0.00010.10870.217725.5517 **0.75450.0182
D210.0384−0.00020.11390.883925.9263 **0.76540.0180
D22−0.02730.00020.15100.321815.9146 **0.65960.0162
D23−0.80160.00320.06690.104477.8980 **0.88450.0212
D24−0.20040.00070.00310.0648137.3034 **0.95370.0058
D25−0.47230.00190.41720.175147.0887 **0.84620.0224
D26−0.44830.00200.44540.087135.3780 **0.78750.0288
D27−0.27250.00110.04130.038023.8688 **0.72400.0174
D28−0.30720.00150.15350.148378.3862 **0.88960.0207
D29−1.71870.0068−0.21840.018530.9722 **0.79800.0554
D30−0.72170.00290.12860.093248.8410 **0.83230.0261
D31−0.93370.0034−0.01360.184851.1559 **0.84010.0385
** represents F-test significance level of 99%. The table keeps 4 decimal places, the calculation keeps 8 decimal places.
Table
Table A3. Layer soil moisture estimation model (30–50 cm).
Table A3. Layer soil moisture estimation model (30–50 cm).
SiteIntercept-a0LST-a1NDVIRES-a2NDVIAG-a3F-ValueRRMSE
D1−0.00140.0003−0.00910.01814.6020 **0.46520.0095
D2−0.03320.00030.05700.095326.3842 **0.74100.0140
D3−0.08420.00050.01720.0802147.0702 **0.93360.0036
D4−0.14550.00070.03160.033095.5330 **0.90280.0058
D5−0.45120.0020−0.12340.072848.8376 **0.83230.0213
D6−0.20330.0010−0.07370.070510.6289 **0.62400.0277
D7−0.35350.00140.14890.2710100.0371 **0.90660.0188
D8−0.26110.00130.01470.1866107.5899 **0.91230.0132
D9−0.32370.00130.19240.07616.2735 **0.48260.0567
D10−0.14690.00070.00540.3318103.2531 **0.90920.0108
D11−0.01800.00020.14370.215922.7155 **0.71540.0353
D12−0.13460.00080.09080.053714.4754 **0.73480.0149
D13−0.33980.0016−0.17590.128519.0697 **0.70470.0547
D14−0.71700.0030−0.32720.087818.6426 **0.77580.0381
D15−1.75130.0071−0.21160.045035.0906 **0.78630.0667
D16−0.38740.0017−0.10790.05676.9133 **0.49180.0526
D17−0.45760.00190.14250.497539.1722 **0.85300.0618
D18−0.32960.0016−0.08800.048912.7872 **0.60920.0322
D19−0.30360.0013−0.03530.073734.1784 **0.78230.0263
D20−0.05900.00040.07690.102325.2061 **0.75230.0131
D210.0503−0.00020.05020.664020.0755 **0.72300.0153
D22−0.04490.0002−0.02000.704631.0264 **0.76730.0259
D23−0.57550.00220.04300.165270.6508 **0.87480.0202
D24−0.21310.00080.01790.120770.9558 **0.91570.0128
D25−0.44550.00190.22080.138854.0855 **0.84500.0211
D26−0.41020.00190.46690.113830.2195 **0.76320.0331
D27−0.33710.00140.02870.052421.2821 **0.70390.0233
D28−0.08460.00070.12390.161760.3166 **0.87570.0202
D29−1.63360.0064−0.21810.027134.8192 **0.81450.0503
D30−0.65450.00260.09890.083942.3720 **0.81340.0249
D31−0.52940.00220.01220.232246.7052 **0.82850.0399
** represents F-test significance level of 99%. The table keeps 4 decimal places, the calculation keeps 8 decimal places.
Table
Table A4. The 50–70 cm layer soil moisture estimation model.
Table A4. The 50–70 cm layer soil moisture estimation model.
SiteIntercept-a0LST-a1NDVIRES-a2NDVIAG-a3F-ValueRRMSE
D10.02530.0001−0.03610.172619.3090 **0.69790.0123
D2−0.13750.00070.04250.102338.1098 **0.79850.0152
D3−0.08820.00050.02360.0765155.1495 **0.93670.0036
D4−0.14930.00070.02310.0354120.8715 **0.92090.0054
D5−0.34950.0016−0.12390.060133.9899 **0.78150.0208
D6−0.39340.0018−0.0707−0.001920.0979 **0.73940.0219
D7−0.11640.00060.06330.0714112.0099 **0.91540.0060
D8−0.07500.0008−0.05780.1556126.9255 **0.92420.0090
D9−0.23020.0012−0.13460.067220.0882 **0.69640.0278
D10−0.05240.0003−0.22370.352990.7162 **0.89980.0113
D110.01850.00020.12350.138019.0389 **0.68390.0255
D12−0.13240.00080.06540.017020.7468 **0.79190.0089
D13−0.35790.0017−0.07680.146023.4037 **0.72060.0563
D14−0.40180.0017−0.27100.04899.4123 **0.65790.0295
D15−0.50820.0024−0.04960.206527.0967 **0.74800.0667
D16−0.07810.0007−0.11160.10958.8637 **0.53880.0458
D17−0.24710.00100.15670.252424.4479 **0.79060.0395
D18−0.05610.00070.01700.060612.1061 **0.59870.0263
D19−0.23370.0011−0.05440.072032.4551 **0.77440.0250
D20−0.10910.00050.03690.107822.2550 **0.73150.0150
D210.0981−0.0004−0.17760.955712.3260 **0.63410.0281
D220.01500.0001−0.09150.568121.8802 **0.70880.0245
D23−0.54260.00210.03580.185051.7100 **0.83950.0238
D24−0.01460.00000.05400.05804.5765 **0.55400.0159
D25−0.27840.00120.00990.167540.9908 **0.80880.0210
D26−0.08080.00030.11090.034213.1810 **0.61500.0117
D27−0.14800.00070.01980.061026.5345 **0.74200.0142
D28−0.23070.00120.12680.103334.8308 **0.79220.0229
D29−0.41680.0020−0.07560.039520.0238 **0.72890.0274
D30−0.60830.00250.08640.069448.0417 **0.83020.0212
D31−0.44140.00190.01150.164843.6817 **0.82180.0308
** represents F-test significance level of 99%. The table keeps 4 decimal places, the calculation keeps 8 decimal places.

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Remotesensing 12 02414 g001 550
Figure 1. The in situ observation stations in the upstream of the Heihe River Watershed, Northwest China. The number shows 31 land use/land cover (LULC) soil elevation types (details in Table 1). (a) the study area in China; (b) the elevation of the study area, black circles represent the in situ sites and crimson circles represent the three automatic meteorological station sites; (c) land use types distribution in the study area; (d) soil types. The locations of in situ monitoring sites and meteorological stations are also indicated in (b), (c) and (d).
Figure 1. The in situ observation stations in the upstream of the Heihe River Watershed, Northwest China. The number shows 31 land use/land cover (LULC) soil elevation types (details in Table 1). (a) the study area in China; (b) the elevation of the study area, black circles represent the in situ sites and crimson circles represent the three automatic meteorological station sites; (c) land use types distribution in the study area; (d) soil types. The locations of in situ monitoring sites and meteorological stations are also indicated in (b), (c) and (d).
Remotesensing 12 02414 g001
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Figure 2. Vegetation Index (NDVI) in the study area. (a) The NDVI distribution and the location of the in situ observation site D1; (b) the NDVIAG distribution; (c) the NDVIRES distribution; (d) the NDVI/NDVIAG/NDVIRES change at the site D1.
Figure 2. Vegetation Index (NDVI) in the study area. (a) The NDVI distribution and the location of the in situ observation site D1; (b) the NDVIAG distribution; (c) the NDVIRES distribution; (d) the NDVI/NDVIAG/NDVIRES change at the site D1.
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Figure 3. Land Surface Temperature (LST) data in the study area. (a) the LST distribution and the location of the in situ observation site D1; (b) the LST change at the site D1. The temperature is in Kelvin.
Figure 3. Land Surface Temperature (LST) data in the study area. (a) the LST distribution and the location of the in situ observation site D1; (b) the LST change at the site D1. The temperature is in Kelvin.
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Figure 4. Temporal variability of soil moisture. (a) temporal variability of soil moisture in real value; (b) anomaly between each layer and the mean soil moisture.
Figure 4. Temporal variability of soil moisture. (a) temporal variability of soil moisture in real value; (b) anomaly between each layer and the mean soil moisture.
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Figure 5. Spatial distribution of soil moisture during the 2014–2016 growth period. (a) 2014 growth period; (b) 2015 growth period; (c) 2016 growth period; (d) rainfall distribution pattern by kriging interpolation based on data obtained from the three automatic weather stations during the 2014–2016 period.
Figure 5. Spatial distribution of soil moisture during the 2014–2016 growth period. (a) 2014 growth period; (b) 2015 growth period; (c) 2016 growth period; (d) rainfall distribution pattern by kriging interpolation based on data obtained from the three automatic weather stations during the 2014–2016 period.
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Figure 6. Mean and coefficient of variation (CV) of soil moisture at different layers.
Figure 6. Mean and coefficient of variation (CV) of soil moisture at different layers.
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Table
Table 1. Soil and vegetation types at the in situ observation sites and automatic meteorological stations.
Table 1. Soil and vegetation types at the in situ observation sites and automatic meteorological stations.
IDElevation Range (m)Soil TypeVegetation TypePercent (%) of Area
No.12000–2500Typical lime calcium soilMedium coverage grassland1.53
No.22000–2500Chestnut soilMedium coverage grassland0.91
No.32000–2500Sticky chestnut soilMedium coverage grassland1.34
No.42500–3000Typical chestnut soilMedium coverage grassland3.06
No.52500–3000Chestnut soilMedium coverage grassland2.87
No.63000–3500Chestnut soilMedium coverage grassland0.96
No.73000–3500Calcareous frozen calcium soilMedium coverage grassland2.48
No.83000–3500Saturated cold felt soilMedium coverage grassland3.79
No.93500–4000Saturated cold felt soilMedium coverage grassland7.57
No.103500–4000Calcareous frozen calcium soilMedium coverage grassland5.47
No.112500–3000Typical chestnut soilForest land3.22
No.122500–3000Typical gray cinnamon soilForest land1.34
No.132500–3000Peat type felt soilForest land0.97
No.142500–3000Chestnut soilForest land1.63
No.153000–3500Peat type felt soilForest land2.77
No.163000–3500Saturated cold felt soilForest land2.62
No.173500–4000Peat type felt soilForest land1.02
No.182500–3000Typical calcareous soilFarm land2.05
No.192500–3000Dryland calcareous soilFarm land1.66
No.202500–3000Calcareous cold calcareous soilBare land1.31
No.213000–3500Calcareous cold calcareous soilBare land1.74
No.223000–3500Saturated cold felt soilBare land1.04
No.233500–4000Typical frozen calcium soilBare land1.48
No.244000–4500Typical cold desert soilBare land9.33
No.254000–4500Saturated cold felt soilBare land3.54
No.262500–3000Typical chestnut soilHigh coverage grassland1.24
No.272500–3000Chestnut soilHigh coverage grassland1.01
No.283000–3500Typical chestnut soilHigh coverage grassland2.40
No.293000–3500Peat type felt soilHigh coverage grassland1.54
No.303000–3500Saturated cold felt soilHigh coverage grassland3.73
No.313500–4000Saturated cold felt soilHigh coverage grassland7.02
Biandukou3000–3500Peat type felt soilHigh coverage grassland
Kangle2000–2500Chestnut soilMedium coverage grassland
Dayekou2500–3000Chestnut soilForest land
Table
Table 2. Layer soil moisture estimation model (0–10 cm).
Table 2. Layer soil moisture estimation model (0–10 cm).
SiteIntercept-a0LST-a1NDVIRES-a2NDVIAG-a3F-ValueRRMSE
D1−0.14280.00060.48880.363827.1860 **0.75640.0230
D20.1053−0.00030.18830.188342.9037 **0.81510.0156
D3−0.09430.00040.53550.29249.1448 **0.54480.0264
D4−0.19030.00080.30470.079621.4239 **0.70510.0184
D5−0.38280.00170.03710.148224.7216 **0.73000.0402
D6−0.21110.0009−0.00790.172813.7290 **0.67210.0374
D7−0.18880.00070.12900.262941.9671 **0.81210.0224
D8−0.12690.00060.11960.08333.2826 *0.36270.0348
D9−0.24640.0012−0.11200.167228.5156 **0.75380.0384
D10−0.21470.00060.36320.695944.2519 **0.81930.0278
D11−0.11790.00030.27810.262444.5027 **0.82010.0316
D12−0.53450.00210.16400.119624.0405 **0.81300.0284
D13−1.36220.0055−0.14380.045226.5764 **0.74220.0693
D14−0.73880.0028−0.20800.084830.9407**0.84560.0284
D15−3.77970.0144−0.4288−0.137134.2043 **0.78240.0986
D16−0.49710.00210.04410.094640.0270 **0.80550.0294
D17−0.89970.00350.00510.330349.3673 **0.87800.0464
D18−0.86740.0032−0.19610.072020.9669 **0.72430.0426
D19−0.44600.0020−0.00380.083037.9425 **0.79780.0313
D20−0.03360.00020.02480.264814.7577 **0.65790.0326
D21−0.0005−0.00010.47190.853918.8724 **0.71220.0208
D22−0.05230.00030.19730.283913.0625 **0.61330.0183
D23−0.93140.00340.04340.250069.0881 **0.87250.0308
D24−0.13440.00050.13180.240991.0531 **0.93250.0194
D25−0.38500.00160.38140.245446.1557 **0.82490.0271
D26−0.37390.00170.51050.117731.1755 **0.76810.0319
D27−0.46180.00190.11240.080018.9346 **0.68290.0352
D28−0.43520.00210.15030.1258118.7521 **0.92290.0175
D29−0.92210.0038−0.05960.116455.6932 **0.87130.0366
D30−0.66800.00280.17400.128544.6734 **0.82690.0290
D31−0.91220.00330.07390.389856.3416 **0.85170.0589
* and ** represent F-test significance level of 95% and 99%, respectively. The table keeps 4 decimal places, the calculation keeps 8 decimal places. Intercept-a0, LST-a1, NDVIRES-a2 and NDVIAG-a3 represent the coefficients of upscaling Equation (2). F-value means the significance value in the F-test. R is the complex correlation coefficient. RMSE is the root mean square error.
Table
Table 3. Verification of the soil moisture upscaling equations at the three in situ observation sites.
Table 3. Verification of the soil moisture upscaling equations at the three in situ observation sites.
LayerSiteNRRMSEF-Value
0–10 cmBiandukou480.66450.037336.3749 **
Dayekou540.72470.041757.5060 **
Kangle540.67660.024743.9005 **
mean0.68860.034645.9271 **
10–20 cmBiandukou550.73660.025562.8749 **
Dayekou540.63100.054934.4064 **
Kangle490.79810.012782.4391 **
mean0.72190.031059.9068 **
20–30 cmBiandukou550.71720.025856.1386 **
Dayekou540.74550.027565.0525 **
Kangle540.83760.0106122.2273 **
mean0.76680.021381.1394 **
30–50 cmBiandukou550.54100.033921.9261 **
Dayekou540.59080.034227.8885 **
Kangle530.89400.0066203.0996 **
mean0.67530.024984.3048 **
50–70 cmBiandukou300.52540.033810.6759 **
Dayekou540.64410.024736.8675 **
Kangle540.81870.0094105.6833 **
mean0.66270.022651.0756 **
totalBiandukou0.63690.031337.5981 **
Dayekou0.66720.036644.3442 **
Kangle0.80500.0128111.4700 **
** means F-test at 99% significance. N is the observations.

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MDPI and ACS Style

Bai, X.; Zhang, L.; He, C.; Zhu, Y. Estimating Regional Soil Moisture Distribution Based on NDVI and Land Surface Temperature Time Series Data in the Upstream of the Heihe River Watershed, Northwest China. Remote Sens. 2020, 12, 2414. https://doi.org/10.3390/rs12152414

AMA Style

Bai X, Zhang L, He C, Zhu Y. Estimating Regional Soil Moisture Distribution Based on NDVI and Land Surface Temperature Time Series Data in the Upstream of the Heihe River Watershed, Northwest China. Remote Sensing. 2020; 12(15):2414. https://doi.org/10.3390/rs12152414

Chicago/Turabian Style

Bai, Xiao, Lanhui Zhang, Chansheng He, and Yi Zhu. 2020. "Estimating Regional Soil Moisture Distribution Based on NDVI and Land Surface Temperature Time Series Data in the Upstream of the Heihe River Watershed, Northwest China" Remote Sensing 12, no. 15: 2414. https://doi.org/10.3390/rs12152414

APA Style

Bai, X., Zhang, L., He, C., & Zhu, Y. (2020). Estimating Regional Soil Moisture Distribution Based on NDVI and Land Surface Temperature Time Series Data in the Upstream of the Heihe River Watershed, Northwest China. Remote Sensing, 12(15), 2414. https://doi.org/10.3390/rs12152414

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