The amount of surface soil moisture (SSM) is a crucial ecohydrological natural resource that regulates important land surface processes. It affects critical land–atmospheric phenomena, including the division of energy and water (infiltration, runoff, and evaporation), that impacts the effectiveness of agricultural output (sensible and latent heat fluxes and surface air temperature). Despite its significance, there are several difficulties in making precise measurements, monitoring, and interpreting SSM at high spatial and temporal resolutions. The current study critically reviews the methods and procedures for calculating SSM and the variables influencing measurement accuracy and applicability under different fields, climates, and operational conditions. For laboratory and field measurements, this study divides SSM estimate strategies into (i) direct and (ii) indirect procedures. The accuracy and applicability of a technique depends on the environment and the resources at hand. Comparative research is geographically restricted, although precise and economical—direct measuring techniques like the gravimetric method are time-consuming and destructive. In contrast, indirect methods are more expensive and do not produce measurements at the spatial scale but produce precise data on a temporal scale. While measuring SSM across more significant regions, ground-penetrating radar and remote sensing methods are susceptible to errors caused by overlapping data and atmospheric factors. On the other hand, soft computing techniques like machine/deep learning are quite handy for estimating SSM without any technical or laborious procedures. We determine that factors, e.g., topography, soil type, vegetation, climate change, groundwater level, depth of soil, etc., primarily influence the SSM measurements. Different techniques have been put into practice for various practical situations, although comparisons between them are not available frequently in publications. Each method offers a unique set of potential advantages and disadvantages. The most accurate way of identifying the best soil moisture technique is the value selection method (VSM). The neutron probe is preferable to the FDR or TDR sensor for measuring soil moisture. Remote sensing techniques have filled the need for large-scale, highly spatiotemporal soil moisture monitoring. Through self-learning capabilities in data-scarce areas, machine/deep learning approaches facilitate soil moisture measurement and prediction.

This study was conducted to investigate the effects of various irrigation water (W) and nitrogen (N) levels on growth, root-shoot morphology, yield, and irrigation water use efficiency of greenhouse tomatoes in spring–summer and fall–winter. The experiment consisted of three irrigation water levels (W: 100% of crop evapotranspiration (ETc), 80%, and 60% of full irrigation) and three N application levels (N: 100%, 75%, and 50% of the standard nitrogen concentration in Hoagland’s solution treatments equivalent to 15, 11.25, 7.5 mM). All the growth parameters of tomato significantly decreased (p < 0.05) with the decrease in the amount of irrigation and nitrogen application. Results depicted that a slight decrease in irrigation and an increase in N supply improved average root diameter, total root length, and root surface area, while the interaction was observed non-significant at average diameter of roots. Compared to the control, W₈₀ N₁₀₀ was statistically non-significant in photosynthesis and stomatal conductance. The W₈₀ N₁₀₀ resulted in a yield decrease of 2.90% and 8.75% but increased irrigation water use efficiency (IWUE) by 21.40% and 14.06%. Among interactions, the reduction in a single factor at W₈₀ N₁₀₀ and W₁₀₀ N₇₅ compensated the growth and yield. Hence, W₈₀ N₁₀₀ was found to be optimal regarding yield and IWUE, with 80% of irrigation water and 15 mM of N fertilization for soilless tomato production in greenhouses.

Floods are brutal, catastrophic natural hazards which affect most human beings in terms of economy and life loss, especially in the large river basins worldwide. The Indus River basin is considered as one of the world’s large river basins, comprising several major tributaries, and has experienced severe floods in its history. There is currently no proper early flood warning system for the Indus River which can help administrative authorities cope with such natural hazards. Hence, it is necessary to develop an early flood warning system by integrating a hydrodynamic model, in situ information, and satellite imagery. This study used Hydrologic Engineering Center–River Analysis System (HEC–RAS) to predict river dynamics under extreme flow events and inundation modeling. The calibration and validation of the HEC–RAS v5 model was performed for 2010 and 2015 flood events, respectively. Manning’s roughness coefficient (n) values were extracted using the land use information of the rivers and floodplains. Multiple combinations of n values were used and optimized in the simulation process for the rivers and floodplains. The Landsat 5 Thematic Mapper (TM), Landsat 8 Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS) MOD09A1, and MOD09GA products were used in the analysis. The Normalized Difference Water Index (NDWI), Modified NDWI1 (MNDWI1), and MNDWI2, were applied for the delineation of water bodies, and the output of all indices were blended to produce standard flood maps for accurate assessment of the HEC–RAS-based simulated flood extent. The optimized n values for rivers and floodplains were 0.055 and 0.06, respectively, with significant satisfaction of statistical parameters, indicating good agreement between simulated and observed flood extents. The HEC–RAS v5 model integrated with satellite imagery can be further used for early flood warnings in the central part of the Indus River basin.

The ongoing global warming and changing patterns of precipitation have significant implications for crop yields. Process-based models are the most commonly used method to assess the impacts of projected climate changes on crop yields. In this study, the crop-environment resource synthesis (CERES)-Maize 4.6.7 model was used to project the maize crop yield in the Shaanxi Province of China over future periods. In this context, the downscaled ensemble projections of 17 general circulation models (GCMs) under four representative concentration pathways (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5) were used as input for the calibrated CERES-Maize model. Results showed a negative correlation between temperature and maize yield in the study area. It is expected that each 1.0 °C rise in seasonal temperature will cause up to a 9% decrease in the yield. However, the influence of CO₂ fertilization showed a positive response, as witnessed by the increase in the crop yield. With CO₂ fertilization, the average increase in the maize crop yield compared to without CO₂ fertilization per three decades was 10.5%, 11.6%, TA7.8%, and 6.5% under the RCP2.6, RCP4.5, RCP6.0, and RCP8.5 scenarios, respectively. An elevated CO₂ concentration showed a pronounced positive impact on the rain-fed maize yield compared to the irrigated maize yield. The average water use efficiency (WUE) was better at elevated CO₂ concentrations and improved by 7–21% relative to the without CO₂ fertilization of the WUE. Therefore, future climate changes with elevated CO₂ are expected to be favorable for maize yields in the Shaanxi Province of China, and farmers can expect further benefits in the future from growing maize.