Soil Moisture Variability in India: Relationship of Land Surface–Atmosphere Fields Using Maximum Covariance Analysis

Climatological Changes in Soil Moisture during the 21st Century over the Indian Region Using CMIP5 and Satellite Observations

Climate data records of soil moisture (SM) are fundamental for improving our understanding of long-term dynamics in the coupled water, energy, and carbon cycles over land. However, many of these studies rely on models for which the errors are not yet fully understood over a region. This may have a considerable impact on the economic growth of the country if the model’s future predictions are used for studying long-term trends. Here we examined the spatial distribution of past, present, and future predictions of SM studied using the Coupled Model Intercomparison Project Phase5 (CMIP5) simulations for the historical period (1850–2005) and future climate projections (2006–2099) based on Representative Concentration Pathways (RCP-RCP2.6, RCP4.5, RCP6.0, and RCP8.5). Furthermore, the performance of modeled SM with the satellite AMSR-E (Advanced Microwave Scanning Radiometer-Earth observation system) was studied. The modeled SM variations of 38 Global Climate Models (GCMs) show discreteness but still we observed that CESM1-CM5, CSIRO-MK3-6-0, BCC-CSM1-1, and also BCC-CSM1-1-M, NorESM1-M models performed better spatially as well as temporally in all future scenarios. However, from the spatial perspective, a large deviation was observed in the interior peninsula during the monsoon season from model to model. In addition, the spatial distribution of trends was highly diversified from model to model, while the Taylor diagram presents a clear view of the model’s performance with observations over the region. Skill score statistics also give the accuracy of model predictions in comparison with observations. The time series was estimated for the future trend of the SM along with the past few decades, whereas the preindustrial and industrial period changes were involved. Significant positive anomaly trends are noticed in the whole time series of SM during the future projection period of 2021–2099 using CMIP5 SM model datasets.

Soil Moisture Retrieval Using Microwave Remote Sensing Data and a Deep Belief Network in the Naqu Region of the Tibetan Plateau

Soil moisture plays an important role in the land surface model. In this paper, a method of using VV polarization Sentinel-1 SAR and Landsat optical data to retrieve soil moisture data was proposed by combining the water cloud model (WCM) and the deep belief network (DBN). Since the simple combination of training data in the neural network cannot effectively improve the accuracy of the soil moisture inversion results, a WCM physical model was used to eliminate the effect of vegetation cover on the ground backscatter, in order to obtain the bare soil backscatter coefficient. This improved the correlation of ground soil backscatter characteristics with soil moisture. A DBN soil moisture inversion model based on the bare soil backscatter coefficients as the foundation training data combined with radar incidence angle and terrain factors obtained good inversion results. Studies in the Naqu area of the Tibetan Plateau showed that vegetation cover had a significant effect on the soil moisture, and the goodness of fit (R2) between the backscatter coefficient and soil moisture before and after the elimination of vegetation cover was 0.38 and 0.50, respectively. The correlation between the backscatter coefficient and the soil moisture was improved after eliminating the vegetation cover. The inversion results of the DBN soil moisture model were further improved through iterative parameters. The model prediction reached its highest level of accuracy when the restricted Boltzmann machine (RBM) was set to seven layers, the bias and R were 0.007 and 0.88, respectively. Ten-fold cross-validation showed that the DBN soil moisture model performed stably with different data. The prediction was further improved when the bare soil backscatter coefficient was used as the training data. The mean values of the root mean square error (RMSE), the inequality coefficient (TIC), and the mean absolute percent error (MAPE) were 0.023, 0.09, and 11.13, respectively.

Soil Moisture Retrievals Using Multi-Temporal Sentinel-1 Data over Nagqu Region of Tibetan Plateau

This paper presents an approach for retrieval of soil moisture in Nagqu region of Tibetan Plateau using VV-polarized Sentinel-1 SAR and MODIS optical data, by coupling the semi-empirical Oh-2004 model and the Water Cloud Model (WCM). The Oh model is first used to estimate the surface roughness parameter based on the hypothesis that the roughness is invariant among SAR acquisitions. Afterward, the vegetation water content (VWC) in the WCM is calculated from the daily MODIS NDVI data obtained by temporal interpolation. To improve the performance of the model, the parameters A, B, and α of the WCM are analyzed and optimized using randomly selected half of the sampled dataset. Then, the soil moisture is retrieved by minimizing a cost function between the simulated and measured backscattering coefficients. The comparison of the retrieved soil moisture with the ground measurements shows the determination coefficient R2 and the Root Mean Square Error (RMSE) are 0.46 and 0.08 m3/m3, respectively. These results demonstrate the capability and reliability of Sentinel-1 SAR data for estimating the soil moisture over the Tibetan Plateau.

Multi-Year NDVI Values as Indicator of the Relationship between Spatiotemporal Vegetation Dynamics and Environmental Factors in the Qaidam Basin, China

The Qaidam Basin is a unique and complex ecosystem, wherein elevation gradients lead to high spatial heterogeneity in vegetation dynamics and responses to environmental factors. Based on the remote sensing data of Moderate Resolution Imaging Spectroradiometer (MODIS), Tropical Rainfall Measuring Mission (TRMM) and Global Land Data Assimilation System (GLDAS), we analyzed the spatiotemporal variations of vegetation dynamics and responses to precipitation, accumulative temperature (AT) and soil moisture (SM) in the Qaidam Basin from 2001 to 2016. Moreover, the contribution of those factors to vegetation dynamics at different altitudes was analyzed via an artificial neural network (ANN) model. The results indicated that the Normalized Difference Vegetation Index (NDVI) values in the growing season showed an overall upward trend, with an increased rate of 0.001/year. The values of NDVI in low-altitude areas were higher than that in high-altitude areas, and the peak values of NDVI appeared along the elevation gradient at 4400–4600 m. Thanks to the use of ANN, we were able to detect the relative contribution of various environmental factors; the relative contribution rate of AT to the NDVI dynamic was the most significant (35.17%) in the low-elevation region (<2900 m). In the mid-elevation area (2900–3900 m), precipitation contributed 44.76% of the NDVI dynamics. When the altitude was higher than 3900 m, the relative contribution rates of AT (39.50%) and SM (38.53%) had no significant difference but were significantly higher than that of precipitation (21.97%). The results highlight that the different environmental factors have various contributions to vegetation dynamics at different altitudes, which has important theoretical and practical significance for regulating ecological processes.

Monitoring Residual Soil Moisture and Its Association to the Long-Term Variability of Rainfall over the Upper Blue Nile Basin in Ethiopia

Monitoring soil moisture and its association with rainfall variability is important to comprehend the hydrological processes and to set proper agricultural water use management to maximize crop growth and productivity. In this study, the European Space Agency’s Climate Change Initiative (ESA CCI) soil moisture product was applied to assess the dynamics of residual soil moisture in autumn (September to November) and its response to the long-term variability of rainfall in the Upper Blue Nile Basin (UBNB) of Ethiopia from 1992 to 2017. The basin was found to have autumn soil moisture (ASM) ranging from 0.09–0.38 m3/m3, with an average of 0.26 m3/m3. The ASM time series resulted in the coefficient of variation (CV) ranging from 2.8%–28% and classified as low-to-medium variability. In general, the monotonic trend analysis for ASM revealed that the UBNB had experienced a wetting trend for the past 26 years (1992–2017) at a rate of 0.00024 m3/m3 per year. A significant wetting trend ranging from 0.001 to 0.006 m3/m3 per year for the autumn season was found. This trend was mainly showed across the northwest region of the basin and covers about 18% of the total basin area. The spatial patterns and variability of rainfall and ASM were also found to be similar, which implies the strong relationship between rainfall and soil moisture in autumn. The spring and autumn season rainfall explained a considerable portion of ASM in the basin. The analyses also signified that the rainfall amount and distribution impacted by the topography and land cover classes of the basin showed a significant influence on the characteristics of the ASM. Further, the result verified that the behavior of ASM could be controlled by the loss of soil moisture through evapotranspiration and the gain from rainfall, although changes in rainfall were found to be the primary driver of ASM variability over the UBNB.

Greening Implication Inferred from Vegetation Dynamics Interacted with Climate Change and Human Activities over the Southeast Qinghai–Tibet Plateau

Vegetation dynamics are sensitive to climate change and human activities, as vegetation interacts with the hydrosphere, atmosphere, and biosphere. The Yarlung Zangbo River (YZR) basin, with the vulnerable ecological environment, has experienced a series of natural disasters since the new millennium. Therefore, in this study, the vegetation dynamic variations and their associated responses to environmental changes in the YZR basin were investigated based on Normalized Difference Vegetation Index (NDVI) and Global Land Data Assimilation System (GLDAS) data from 2000 to 2016. Results showed that (1) the YZR basin showed an obvious vegetation greening process with a significant increase of the growing season NDVI (Zc = 2.31, p < 0.05), which was mainly attributed to the wide greening tendency of the downstream region that accounted for over 50% area of the YZR basin. (2) Regions with significant greening accounted for 25.4% of the basin and were mainly concentrated in the Nyang River and Parlung Tsangpo River sub-basins. On the contrary, the browning regions accounted for <25% of the basin and were mostly distributed in the urbanized cities of the midstream, implying a significant influence of human activities on vegetation greening. (3) The elevation dependency of the vegetation in the YZR basin was significant, showing that the vegetation of the low-altitude regions was better than that of the high-altitude regions. The greening rate exhibited a significantly more complicated relationship with the elevation, which increased with elevated altitude (above 3500 m) and decreased with elevated altitude (below 3500 m). (4) Significantly positive correlations between the growing season NDVI and surface air temperature were detected, which were mainly distributed in the snow-dominated sub-basins, indicating that glaciers and snow melting processes induced by global warming play an important role in vegetation growth. Although basin-wide non-significant negative correlations were found between precipitation and growing season NDVI, positive influences of precipitation on vegetation greening occurred in the arid and semi-arid upstream region. These findings could provide important information for ecological environment protection in the YZR basin and other high mountain regions.

Editorial for the Special Issue “Assimilation of Remote Sensing Data into Earth System Models”

This Special Issue is a collection of papers reporting research on various aspects of coupled data assimilation in Earth system models. It includes contributions presenting recent progress in ocean–atmosphere, land–atmosphere, and soil–vegetation data assimilation.