*Article* **A Neural-Network Based Spatial Resolution Downscaling Method for Soil Moisture: Case Study of Qinghai Province**

**Aifeng Lv 1,2,\*, Zhilin Zhang <sup>3</sup> and Hongchun Zhu <sup>3</sup>**


**Abstract:** Currently, soil-moisture data extracted from microwave data suffer from poor spatial resolution. To overcome this problem, this study proposes a method to downscale the soil moisture spatial resolution. The proposed method establishes a statistical relationship between low-spatialresolution input data and soil-moisture data from a land-surface model based on a neural network (NN). This statistical relationship is then applied to high-spatial-resolution input data to obtain high-spatial-resolution soil-moisture data. The input data include passive microwave data (SMAP, AMSR2), active microwave data (ASCAT), MODIS data, and terrain data. The target soil moisture data were collected from CLDAS dataset. The results show that the addition of data such as the land-surface temperature (LST), the normalized difference vegetation index (NDVI), the normalized shortwave-infrared difference bare soil moisture indices (NSDSI), the digital elevation model (DEM), and calculated slope data (SLOPE) to active and passive microwave data improves the retrieval accuracy of the model. Taking the CLDAS soil moisture data as a benchmark, the spatial correlation increases from 0.597 to 0.669, the temporal correlation increases from 0.401 to 0.475, the root mean square error decreases from 0.051 to 0.046, and the mean absolute error decreases from 0.041 to 0.036. Triple collocation was applied in the form of [NN, FY3C, GEOS-5] based on the extracted retrieved soil-moisture data to obtain the error variance and correlation coefficient between each product and the actual soil-moisture data. Therefore, we conclude that NN data, which have the lowest error variance (0.00003) and the highest correlation coefficient (0.811), are the most applicable to Qinghai Province. The high-spatial-resolution data obtained from the NN, CLDAS data, SMAP data, and AMSR2 data were correlated with the ground-station data respectively, and the result of better NN data quality was obtained. This analysis demonstrates that the NN-based method is a promising approach for obtaining high-spatial-resolution soil-moisture data.

**Keywords:** soil moisture; neural network; downscaling; microwave data; MODIS data

#### **1. Introduction**

Moisture stored in surface soil accounts for less than 0.001% of total global freshwater by volume but plays an important role in connecting global terrestrial water, energy, and carbon cycling processes [1]. By influencing soil evaporation and transpiration, soil moisture (SM) strongly affects the interaction between the land surface and the atmosphere [2]. Thus, a thorough understanding of SM can contribute to efficient monitoring of the climate and environmental changes and provide valuable guidance for drought monitoring and flood forecasting in agriculture and forestry [3]. In addition, SM determines the distribution of precipitation infiltration and surface runoff, which controls plant growth [4]. Therefore, high-quality SM data is crucial in multiple technological fields, such as hydrology, meteorology, climatology, and water-resources management.

**Citation:** Lv, A.; Zhang, Z.; Zhu, H. A Neural-Network Based Spatial Resolution Downscaling Method for Soil Moisture: Case Study of Qinghai Province. *Remote Sens.* **2021**, *13*, 1583. https://doi.org/10.3390/rs13081583

Academic Editor: Nicolas Baghdadi

Received: 29 March 2021 Accepted: 16 April 2021 Published: 19 April 2021

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**Copyright:** © 2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Traditional methods to monitor SM usually rely on automatic or manual collection methods, which have the advantages of temporal continuity and guaranteed accuracy. However, these methods are unsatisfactory because, for starters, there are insufficient observation stations, which is especially serious because SM results are representative only of the soil near the given station. In addition to poor spatial representation, these methods are time-consuming and labor-intensive [5].

However, recent developments in remote-sensing methods have created the possibility to obtain large-scale, long-term soil-moisture data. In this field, microwave radiometers have become the most important source of global SM data due to their better temporal sampling features. In particular, microwave bands such as the L (0.5–1.5 GHz), C (4–8 GHz), and X (8–12 GHz) bands have been widely used to measure SM [6]. Currently, four passive microwave satellites and one active microwave satellite monitor SM globally. Four passive microwave sensors are currently in orbit: the microwave radiation imager (MWRI), which operates in the X-band, onboard the Fengyun-3 (FY3) satellite launched by the China National Space Administration (2008–present) [7], the Advanced Microwave Scanning Radiometer (AMSR2), which operates in the X and C bands, onboard the Global Change Observation Mission-Water (GCOM-W) satellite launched by the Japan Aerospace Exploration Agency (JAXA) (2012–present) [8], and two dedicated satellites equipped with L-band radiometers: the Soil Moisture and Ocean Salinity (SMOS) (2009–present) instrument launched by the European Space Agency (2010–present) [9] and the Soil Moisture Active Passive (SMAP) instrument launched by the National Aeronautics and Space Administration (NASA) (2015–present) [10]. Another contributor is the ASCAT (2007–present) instrument, which monitors active scatterer in the C band from the MEOP satellite launched by the ESA and is an important source of active microwave data [11]. These microwave radiometers have the advantages of providing a complete observation of the global land surface within two to three days and providing surface soil-moisture information on a large scale. Their major disadvantage, however, is the poor spatial resolution of the microwave radiometer, which is typically about 25–40 km. However, SM is subject to complex interactions between topography, soil, vegetation, and other meteorological factors, which leads to high spatial variability. Therefore, many regional hydrological and agricultural applications require SM data with a spatial resolution of several kilometers or even tens of meters. It is thus vital to develop techniques to obtain accurate, high-precision, soil-moisture data with high coverage.

The low spatial resolution of soil-moisture data extracted from passive microwave data is typically downscaled by combining it with other high-spatial-resolution data. Based on the combined data type, the following two categories emerge: (i) combinations of active and passive microwave data and (ii) combinations of visible, infrared, and microwave data. In previous work, Njoku et al. combined radar (active) and radiometer (passive) data to study SM under vegetated-terrain cover and analyzed the sensitivity with which multichannel low-frequency passive and active measurements can detect SM under different vegetation conditions [12]. In other work, Das et al. obtained a linear relationship between radar backscatter and soil-moisture data by merging coarse-scale radiometer SMAP SM data with the fine-scale backscatter coefficient to produce high-spatial-resolution (9 km) SM data [13]. Zhan et al. used a Bayesian method to merge relatively accurate 36-km radiometer brightness temperature with the relatively noisy 3-km radar backscatter coefficient and explored the potential for retrieving SM from these results. Their results prove that the Bayesian method produces better data than direct extraction of either the brightness temperature or radar backscatter [14]. To combine visible and infrared remote sensing with passive microwave data, Wilson et al. combined and weighted terrain maps and other spatial attributes according to the correlations to generate SM data [15]. Srivastava et al. used artificial neural networks (NN), support vector machines, relevance vector machines, and generalized linear models to combine MODIS surface temperature with SM retrieved by SMOS to conclude that the artificial NN produced better results than other methods [16]. Yang et al. estimated soil parameters by assimilating the brightness temperature data

simulated by the land surface model and the radiative transfer model. By minimizing the brightness temperature errors of AMSR2, they estimated the SM [17]. In researching SM downscaling, Chen et al. used dual Kalman filters to assimilate the brightness temperature of AMSR-E with the MODIS surface temperature [18]. Finally, Chauhan et al. used the universal triangle approach to link the high-resolution normalized difference vegetation index (NDVI), surface albedo, and land-surface temperature to SM data, thereby disaggregating low-spatial-resolution microwave SM into high-spatial-resolution SM [19]. The common idea behind these methods is to establish a statistical correlation or physical model between SM and auxiliary variables.

Qinghai Province is in the northeastern part of the Qinghai-Tibet Plateau, which is the source of the Yangtze River, the Yellow River, and the Lancang-Mekong River, and is an important water-conserving area in China and Asia [20]. In recent years, under the influence of global warming, the climate of Qinghai has been warming and humidifying, glaciers and snowfields are shrinking year by year, rivers, lakes, and wetlands are shrinking, soil erosion is expanding, and the water-conserving function is deteriorating seriously. Soil moisture is an important surface characteristic parameter and has an irreplaceable role in related land degradation, drought monitoring, and water conservation monitoring [21]. Therefore, an urgent need exists to systematically monitor the soil moisture information in Qinghai Province, which is an area that seriously lacks ground truth data, making the use of remote sensing data to retrieve SM in Qinghai Province of significant potential value.

The quality of remote sensing data largely determines the results of remote-sensing retrieval of soil moisture. Existing studies, such as those mentioned above, usually involve only a single passive microwave radiometer or a single passive microwave radiometer combined with a single active microwave radar for SM retrieval and do not involve the three bands L, C, and X simultaneously [22]. In this paper, we use the powerful multivariate and nonlinear fitting capability of NN to analyze the single-band as well as multi-band synergy in detecting soil moisture in the region of Qinghai Province for the three bands L, C, and X and select multiple microwave sensors (SMAP, AMSR2, FY3C, ASCAT) as data sources. The ability to detect SM information in Qinghai Province through multi-band synergy compensates for the shortcomings of insufficient information from a single sensor. At the same time, elevation and slope data are introduced to treat the complex topography of Qinghai Province to make the algorithm more universal [23]. Finally, we use MODIS data with high spatial resolution and topographic data to downscale SM experiments with the NN model trained with low-resolution data.

The paper is organized as follows: Section 2 explains the data and methods used in this study. Section 3 presents and discusses the main findings. Finally, Section 4 gives the main conclusions of the study.

#### **2. Materials and Methods**

*2.1. Data*

#### 2.1.1. Microwave Data

This study relies mainly on the passive microwave soil-moisture datasets SMAP, FY3C, and AMSR2 and the active microwave soil-moisture dataset of ASCAT as input microwave data. The datasets FY3C (10.65, 18.7, 23.8, 36.8, 89 GHz) and AMSR2 (6.925, 7.3, 10.65, 18.7, 23.8, 36.5, 89 GHz) have multiple frequencies, whereas SMAP (1.41 GHz) and ASCAT (5.3 GHz) have fixed frequencies; see Table 1 for details. In general, the L, C, and X bands are sensitive to SM, whereas other bands in other frequency ranges are not sensitive to SM. Therefore, this study uses the brightness temperature from the 1.41 GHz channel from SMAP, from the 6.9, 7.3, and 10.65 GHz channels of AMSR2, and from the 10.65 GHz channel of FY3C. Specifically, the SMAP radiometer uses a 24 MHz bandwidth centered at 1.41 GHz. The AMSR2 radiometer uses 0.35, 0.35, 0.10 GHz bandwidths centered at 6.9, 7.3, and 10.65 GHz, respectively. The FY3C radiometer uses a 180% ± 10% MHz bandwidth centered at 10.65 GHz. In addition, we use the backscatter coefficient (σ40) from the 5.3 GHz channel of the ASCAT active microwave data as the input variables for the NN. The specific spatial distribution of soil moisture is shown in Figure 1.


**Table 1.** Comparison of global soil-moisture products.

**Figure 1.** Daily average soil-moisture maps from (**a**) FY3C; (**b**) AMSR2; (**c**) SMAP; (**d**) CLDAS.
