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Playas, as the flattest landforms in semiarid and arid regions, are extremely sensitive to climate changes, such as changes in the hydrologic regime of the landscape. The changes in these landforms due to irrigation, anthropogenic activities, and climate change could be a source of disasters. In this study, we explored the spatial-temporal changes of the Abarkuh Playa in Central Iran using the time series of the Sentinel-1 backscatter dataset in the three scales. Our results showed that the western area of the Abarkuh Playa has been changed to other landforms with different characteristics, which is clear from all backscatter maps. The spatial-temporal analysis of the time series of backscatter data using the independent component analysis and time series of precipitation revealed that the backscatter variations were associated with direct rainfall across the playa and the surface was reacting to changes in the soil moisture content. The results of the power scale showed that the boundary of the playa could successfully be recognized as the oscillating pattern from other landforms in the study area. Moreover, the spatial-temporal analysis of backscatter in the power scale showed that different polarizations could reveal different patterns of surface changes for the playa. [ABSTRACT FROM AUTHOR]

Groundwater resources are the most reliable freshwater supply in arid regions where many aquifers face dramatic depletion due to natural and anthropogenic causes. The annual average rate of decline of groundwater level is about 1.65 m. This research focuses on an aquifer that suffers from severe groundwater stress, and it aims to identify the main causes of the stress and to understand the effects of climate change and human activity. Monthly data on groundwater level, precipitation, temperature, river discharge, evapotranspiration, soil moisture, and vegetation cover were collected from 2000 to 2020. The results indicate that declining groundwater levels mainly resulted from the expansion of vegetation cover rather than changes in hydro-climatic variables. Finally, this work highlights how significant financial investment in improving irrigation efficiency in the absence of socio-economic plans, education, awareness, and monitoring programmes unproductively resulted in the expansion of agricultural activities rather than preserving groundwater storage. [ABSTRACT FROM AUTHOR]

Soil moisture is an influential parameter in land surface hydrology and precise soil moisture data that can help researcher to realize the climate changes and land-atmosphere interactions. A initial struggle for the utilize of soil moisture data from satellite sensors is their reliability. It is important to appraise the dependability of those data before they can be regularly used at a global or local scale. In this study, the satellite soil moisture data was evaluated from the Soil Moisture Active/Passive (SMAP) over Simineh-Zarrineh Catchment in Bokan region, NW of Iran. A total of 287 soil samples as ground-based observations in the time period of 03 April to 03 December 2017 were taken for SMAP data validation. Results showed that the satellite data and in situ observation has a good correlation, with a mean correlation (r) value of 0.63 in total. This correlation level showed that, commonly, the SMAP soil moisture products over Simineh-Zarrineh Catchment (Bokan) have great quality, and it would be valuable for versatile utilization, including monitoring of land surface, weather prediction, modeling of hydrological process, soil loess monitoring, and climate studies. The results reveal that the remotely sensed data demonstrate the good correlation with in situ observation across the dry land with mean correlation (r) values of 0.67 throughout the time period. Particularly, SMAP soil moisture reveal a constant structure for obtain the spatial distribution of surface soil moisture. Additional researches are necessary for well realizing the SMAP data. [ABSTRACT FROM AUTHOR]

The root zone soil moisture (RZSM) is essential for monitoring and forecasting agricultural, hydrological, and meteorological systems. Accordingly, researchers are determined to improve robust machine learning (ML) models to increase the accuracy of the RZSM predictions. This paper designed new complementary forecasting paradigms hybridizing Empirical Wavelet Transform (EWT) and two modern ensemble-based ML models, namely, extreme gradient boosting (XGBoost) and categorical boosting (CatBoost), to forecast long-term multi-step ahead daily RZSM in very cold and very warm semi-arid regions of Iran. For this purpose, the required datasets consisting of soil properties and meteorological information were extracted from the satellite datasets during 2005–2020 for Ardabil and Minab sites. Afterward, the significant lags of RZSM time series and optimal influence candidate inputs were sought based on the partial autocorrelation function (PACF) and mutual information techniques, respectively. Selected lagged components of RZSM time series were decomposed using EWT into different sub-sequences and consequently concatenated with candidate inputs to feed the ensemble ML models to forecast one-, three-, and seven-day-ahead RZSM at each case study. The performance of EWT-CatBoost and EWT-XGBoost and their counterpart standalone approaches was firstly evaluated in forecasting one-, three-, and seven-day-ahead RZSM using satellite data in this study and their accuracy were compared with a standalone kernel ridge regression (KRR) and complementary EWT-KRR models based on several statistical metrics (e.g., correlation coefficient (R), root mean square error (RMSE), Nash–Sutcliffe model efficiency coefficient (NSE)) and diagnostic analysis. The outcomes of testing phase in Ardabil site ascertained that the EWT-CatBoost (for RZSM(t + 1), R= 0.9979, RMSE= 0.0019, and NSE= 0.9985; for RZSM(t + 3), R= 0.9934, RMSE= 0.0035, and NSE= 0.9885; for RZSM(t + 7), R= 0.9489, RMSE= 0.0109, and NSE= 0.8634) outperformed the other models. On the other hand, the EWT-XGBoost model according to its best results (for RZSM(t + 1), R= 0.9911, RMSE= 0.0064, and NSE= 0.9805; for RZSM(t + 3), R= 0.9807, RMSE= 0.0092, and NSE= 0.9589; for RZSM(t + 7), R= 0.9680, RMSE= 0.0120, and NSE= 0.9309) yielded the most promising accuracy in forecasting multi-step ahead daily RZSM followed by the EWT-CatBoost, and EWT-KRR, respectively. [Display omitted] • Multi-step ahead forecasting the daily root zone soil moisture. • Developing two new complementary ensemble models; EWT-CatBoost and EWT-XGBoost. • Application of microwave satellite (SMAP) datasets for two regions of Iran. • Validating the models of RZSM in very cold and very warm semi-arid climates. [ABSTRACT FROM AUTHOR]

• We assessed the correlation between soil moisture proxies and high temperatures. • SPEI is better suited to approximate both subsoil and topsoil water content anomalies. • There is a significant correlation at most cases when AI ranges from 0.2 to 1. • ENSO impacts the associations particularly in semi-arid and humid regions. The coupling of soil moisture to climatic variables highly depends on soil moisture variability. Therefore, the activities affecting the soil moisture availability and dynamics such as land use/cover change and agricultural practices influence land-atmosphere interactions. Using soil moisture proxies, modeling studies have proven significant coupling of soil water variations to near-surface atmospheric temperature in some regions. However, these works have mostly applied the precipitation-based indices at a specific time scale. In the present study, the concurrent and lagged associations of the soil wetness variations and the high temperature index (HTI) across different climates in Iran were evaluated using Spearman's rank test. Standardized precipitation index (SPI) and Standardized Precipitation Evapotranspiration Index (SPEI) calculated at one-month and six-month scales were employed as soil moisture surrogates. When seasonal aridity index (AI) range was from 0.2 to 1.0 (transitional condition), SPEI1 and SPI1 were correlated significantly (p < 0.05) with HTI at 83.0 and 42.4 % of cases, respectively. However, when AI was below 0.2 (water-limited condition) or over 2.0 (energy-limited condition), the topsoil moisture indices showed an insignificant association with HTI at the majority of cases. A significant negative association existed between SPEI6 and HTI at most dry cases in which soil surface is dry but subsoil appears not to be fully depleted. Therefore, the topsoil and subsoil moisture variations are likely to affect high temperature occurrence under transitional and dry conditions, respectively. The insignificant lagged correlations found for most areas indicate poor linear relationship between high temperature occurrence and soil water dynamics in the preceding month over the study area. Furthermore, when AI exceed 0.2, ENSO-related soil drought is likely to cause a stronger soil moisture-temperature coupling. Overall, the role of evapotranspiration and soil moisture depth should be considered for assessing soil moisture dynamics and land-atmosphere interactions. Our findings may be helpful for adopting soil moisture-based adaptive measures to negate the impacts of high temperatures on agricultural products. [ABSTRACT FROM AUTHOR]

Soil moisture is an integral quantity parameter in hydrology and agriculture practices. Satellite remote sensing has been widely applied to estimate surface soil moisture. However, it is still a challenge to retrieve surface soil moisture content (SMC) data in the heterogeneous catchment at high spatial resolution. Therefore, it is necessary to improve the retrieval of SMC from remote sensing data, which is important in the planning and efficient use of land resources. Many methods based on satellite-derived vegetation indices have already been developed to estimate SMC in various climatic and geographic conditions. Soil moisture retrievals were performed using statistical and machine learning methods as well as physical modeling techniques. In this study, an important experiment of soil moisture retrieval for investigating the capability of the machine learning methods was conducted in the early spring season in a semi-arid region of Iran. We applied random forest (RF), support vector machine (SVM), artificial neural network (ANN), and elastic net regression (EN) algorithms to soil moisture retrieval by optical and thermal sensors of Landsat 8 and knowledge of land-use types on previously untested conditions in a semi-arid region of Iran. The statistical comparisons show that RF method provided the highest Nash–Sutcliffe efficiency value (0.73) for soil moisture retrieval covered by the different land-use types. Combinations of surface reflectance and auxiliary geospatial data can provide more valuable information for SMC estimation, which shows promise for precision agriculture applications. [ABSTRACT FROM AUTHOR]

S index as a soil physical quality is the slope of the soil water release curve on a mass base at its inflection point on a logarithmic matric potential scale. It is necessary to select the most influential soil properties on S index to predict it. In the current study, to select properties that influence S index, a hybrid algorithm: genetic algorithm (GA) in combination with an artificial neural network (ANN) was designed. The potential power of the GA-ANN algorithm in setting up a framework for identifying the most determinant parameters of S index in an Iranian semiarid region was also investigated. Results of GA-ANN algorithm for selecting best dataset showed that five properties including soil organic matter (SOM), sand, clay, carbonate calcium equivalent (CCE), and bulk density (BD) had the lowest error. The ANN method resulted in a higher model efficiency (R2 = 0.92, RMSE = 0.00065 and MAPE = 0.14% than MLR (multiple linear regression (approach (R2 = 0.0.016, RMSE = 2.72 and MAPE = 43. 16%). Results of sensitivity analysis for the ANN model showed that BD and CCE had the highest and the lowest effects on S index, respectively. Considering the sensitivity analysis results and also that fact that measurement of the S index is not cost-efficient, it is suggested that bulk density be used as an indicator of soil quality. • The best influential subset on S index was determined using GA-ANN. • A subset with 5 features was select as best subset. • After selecting the best subset, the S index was modeled using ANN and MLR. • ANN was more accurate than MLR in predicting soil CEC. • Results of sensitivity analysis showed BD had the highest effect on S index. [ABSTRACT FROM AUTHOR]

Irrigated agriculture is the principal consumer of fresh water resources. Most countries do not have a precise measurement of water consumption for irrigation. In this study, an innovative approach is proposed that allows for estimation of irrigation water use at the catchment scale based on satellite soil moisture data. To this end, the SM2RAIN algorithm, which had been originally developed for estimation of rainfall from the soil moisture observations, is adopted. The satellite soil moisture observations obtained from Advanced Microwave Scanning Radiometer 2 (AMSR2) along with different rainfall and evapotranspiration (ET) products in the period 2012–2015 are used as the input to the model. The methodology is tested in the agricultural plains of southern Urmia Lake, which is one of the main agricultural plains in Iran for which actual irrigation data is available. The results reveal that the proposed approach can capture the overall irrigation pattern, although; it is systematically overestimating irrigation volume compared to observed irrigation data. Thus the bias is calculated over largely non-irrigated pixels and used to modify the model estimates. The bias-corrected results show good agreement with the in situ irrigation data. In particular, the average model performance in the irrigated pixels in terms of R and RMSE (mm/month) are (0.86 and 12.895) respectively. Accuracy varied depending on the inputs, with improvement in order of 11% and 42% in R and RMSE depending on the inputs chosen. The method is also applied to less irrigated areas that result in obtaining significantly lower irrigation rates. The low spatial resolution of soil moisture products (i.e. ~50 km) makes it difficult to capture the irrigation water of small irrigated croplands. Unreliable rainfall and ET data can also lead to the over/underestimation of irrigation. In spite of the above limitations (particularly lack of reliable ET dataset), the proposed model can still capture the irrigation pattern, given that strong soil moisture signal from irrigation is detected by the satellite. • AMSR2 satellite soil moisture data is used to quantify irrigation amount. • SM2RAIN is able to capture irrigation pattern consistent with observations. • Nearly zero irrigation amount is estimated at the non-irrigated pixels. • Weekly signal are observed in soil moisture fluctuation at irrigated pixels. • Coarse resolution of microwave data hampers the usage of the model in small parcels. [ABSTRACT FROM AUTHOR]