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An important uncertainty in the modeling of methane (CH4) emissions from natural wetlands is the wetland area. It is difficult to model wetlands’ CH4 emissions because of several factors, including its spatial heterogeneity on a large range of scales. In this study, we investigate the impact of model resolution on the simulated wetland methane emission for the Fennoscandinavian Peninsula. This is carried out using a high-resolution wetland map (100 × 100 m2) and soil carbon map (250 × 250 m2) in combination with a highly simplified CH4 emission model that is coarsened in five steps from 0.005° to 1°. We find a strong relation between wetland emissions and resolution, which is sensitive, however, to the sub-grid treatment of the wetland fraction. In our setup, soil carbon and soil moisture are positively correlated at a high resolution, with the wetland location leading to increasing CH4 emissions with increasing resolution. Keeping track of the wetland fraction reduces the impact of resolution. However, uncertainties in CH4 emissions remain high because of the large uncertainty in the representation of wetland the area, as demonstrated using the output of the WetChimp intercomparison over our study domain. Because of wetland mapping uncertainties, existing models are unlikely to realistically represent the correlation between soil moisture and soil carbon availability. The correlation is positive in our simplified model but may be different in more complex models depending on their method of representing substrate availability. Therefore, depending on the correlation, CH4 emissions may be over- or underestimated. As increasing the model resolution is an effective approach to mitigate the problem of accounting for the correlation between soil moisture and soil carbon and to improve the accuracy of models, the main message of this study is that increasing the resolution of global wetland models, and especially the input datasets that are used, should receive high priority.

Drought poses large risks to ecosystem services and agricultural production because of their devastating impacts on the environment, economy, and society (Zhang, Keenan & Zhou, 2021). Generally, drought classied into four types including meteorological drought, soil drought, hydrological drought, and social-economic drought (Wilhite & Pulwarty, 2017), which are strongly-correlated with sustained precipitation deficit but various elements of hydrosphere response to drought in different degrees (AghaKouchak et al., 2015). The combination of interacting physical processes across several related droughts is referred to as the compound droughts

Publisher Copyright: Author A lightning strike is considered one of the most risky natural phenomena that can lead to human harmful and the surrounding soil layers. To tackle this issue, this article investigates the influence of direct lightning characteristics in terms of human body safety. Specifically, such investigation is carried out on the effect of resistivities of two-layer soils on human safety when lightning stroke hits the towers of the high voltage transmission lines (HVTLs). The merit of the proposed study is that the soil ionization phenomenon is taken into consideration. Further, the study focuses on the current passing through the human heart, when step and touch (contact) voltages are generated by grounding potential rise, caused by direct lightning strikes transmission tower and the produced potential rise that a person could be exposed. Also, studying the effects of peak current and time of lightning strokes are investigated. Additionally, the paper presents the effect of different reflection factors on human safety.For validation purposes, the ATP program is used in the simulation of the grounding system as well as the human body model. Numerous simulations were accomplished in order to examine the behavior of the current passing through with the human heart. Based on the simulation results, it was concluded that the soil characteristics have superior influences on the contact and step potentials and, accordingly, the survival threshold.

The study was conducted to investigate the effect of Black Plastic Mulch (BPM) and different rates of NPK (15:15:15) fertilizer on the growth and yield of three chili cultivars under field and protected cultivation. Two levels of mulch (BPM and no mulch), three levels of NPK (0, 200 and 300 kg/ha), and three cultivars “Demond F1” (DF1), “Legon 18” (L18), and “Shito Adope”, (SA) were used. Split application of half of each of the 200 and 300 kg/ha rates were applied two and four weeks after transplanting. Vegetative growth indices, soil moisture, and soil temperature were measured at 2, 4, 6, 8, and 10 weeks after transplanting. The yield was calculated using AVRDC (1990) formula for chili pepper. The study revealed an increase in soil temperature and moisture under BPM for both studies. Also, 200 kg/ha NPK and BPM led to an increase in vegetative growth and yield for DF1 and SA. However, 300 kg/ha NPK decreased vegetative growth. In addition, there was no significant influence of BPM on the number of fruits harvested. However, for the pot study, BPM led to an increase in the number of fruits for “Legon 18” as compared to the other cultivars. The study, therefore, concludes that 200 kg/ha NPK was the best when cultivating DF1 and SA, for chili growth and development in the savannah ecological zone.

Refereed/Peer-reviewed When choosing sites for monitoring of soil moisture for hydrological purposes, a suitable process that considers the factors influencing soil moisture level should be followed. In this study, two multi-criteria decision-making (MCDM) methods, the multi-influencing factor (MIF) method and the analytical hierarchy process (AHP) method, were used to identify the optimal soil moisture monitoring (SMM) sites in the Dry Creek Catchment in South Australia. The most representative areas for nine SMM sites were obtained using the MIF method, considering the factors of rainfall, soil type, land use, catchment slope, elevation, and upslope accumulated area (UAA). The AHP method was used to select the optimal sites using the site-specific criteria. 30.3% of the catchment area in the Australian Water Resources Assessment Landscape (AWRA-L) Grid_DC2 can be considered acceptable as representative area with the MIF method. Four potential sites were evaluated for each AWRA-L grid using the relative weights of the site-specific criteria with the AHP method. The Grid_DC2 required two sites that had the highest overall weight chosen with the AHP analysis. The procedure was repeated for the remaining four AWRA-L grids within the study area to select the required SMM sites.

Plants need water to grow well, so watering must follow the plant's needs. Parameters that affect the process of watering plants are air temperature and soil moisture, thus, we need a control system that can make decisions in the form of watering time to meet plant water needs according to their needs, namely an automatic watering system. Fuzzy method can be used for human-like decision making in a control system. This study uses the Sugeno fuzzy method which is integrated into the Microcontroller as the controller. Testing of this system is carried out on eggplant plants which are regulated based on how long the watering time is so that the air temperature and soil moisture are following the plant's needs. The test resulted that the effective watering time interval so that the eggplant soil moisture was maintained at 40-60% was 21 seconds. It can be concluded that the fuzzy inference embedded in the design has been able to overcome the problem of watering eggplant plants, where the test shows that after the watering process, the soil moisture value can be maintained at an average value of 55% so that the soil moisture is following the needs of the plant.

Agriculture is the most water-demanding economic activity. Nevertheless, the monitoring of agricultural production systems can improve the soil water condition and contribute to soil conservation, as well as increase irrigation efficiency through quick and assertive decision-making. Thus, the objective of this work was to carry out a real-time evaluation of the wetting front (WF), the water infiltration rate in the soil, and to verify whether the system performance can affect infiltration test data in a Red Latosol with clayey and very clayey textures. The public domain prototype system consisted of a permeameter, and 10 soil moisture sensors that were calibrated by the oven drying method and inserted into a PVC pipe from 10 cm to 100 cm depth. The equipment allowed the evaluation of the wetting front and calculation of the infiltration rate and water retention and variations along the soil profile. Data were analyzed using descriptive statistics using RStudio and Excel. The results showed that the prototype system is effective to simulate the water infiltration rate in the two soil textures analyzed at low cost. Agriculture is the most water-demanding economic activity. Nevertheless, the monitoring of agricultural production systems can improve the soil water condition and contribute to soil conservation, as well as increase irrigation efficiency through quick and assertive decision-making. Thus, the objective of this work was to carry out a real-time evaluation of the wetting front (WF), the water infiltration rate in the soil, and to verify whether the system performance can affect infiltration test data in a Red Latosol with clayey and very clayey textures. The public domain prototype system consisted of a permeameter, and 10 soil moisture sensors that were calibrated by the oven drying method and inserted into a PVC pipe from 10 cm to 100 cm depth. The equipment allowed the evaluation of the wetting front and calculation of the infiltration rate and water retention and variations along the soil profile. Data were analyzed using descriptive statistics using RStudio and Excel. The results showed that the prototype system is effective to simulate the water infiltration rate in the two soil textures analyzed at low cost.

Dirre Sheikh Hussein is a religious complex in Ethiopia that dates from the 12th century or earlier. An important focus for pilgrimage, it lies in an isolated part of Oromia State on poorly drained semi-arid lowlands. Significant architecturally and culturally, recent expansion and refurbishment of the major buildings led to structural issues, though these are now largely overcome through consolidation of the prayer hall roof, laying a stone walkway, digging drainage ditches etc. Current deterioration of the buildings can be seen as: (i) fading of colours, (ii) cracks in walls, roofs, and festive places, (iii) deposits of sand around walls and (iv) staining and drainage marks on the white painted surfaces. Heavy falls of rain overwhelm drainage and overfill storage pools. Meteorological observations are infrequent locally, so those made some 70–100 km away were used, along with regridded historical data, reanalysis and CMIP6 projections. These revealed increases in temperature, precipitation and humidity, and provided indications of long-term climate pressures at the site. Changing patterns of future precipitation, particularly heavy rain, may threaten the site. The relative humidity changes are small, though an increasing Scheffer index suggests potentially enhanced wood decay. Changes in soil moisture have the potential to disrupt the foundations. Very hot days may become a problem for the two annual celebrations at the site.

It is a priority develop intelligent irrigation systems to save water. Using optimal control formulations and techniques, the water consumption can be made to follow more closely the hydrological needs of the crop, taking into account current weather conditions. Here, the mathematical model presented by the authors in previous publications is improved. This new model incorporates new features like the slope of the soil, the possibility to include a percentage of water losses due to runoff, and a percentage of water losses if the soil is on the field capacity. A new and efficient replan strategy is applied tacking into account the data measured from moisture sensors, to ensure that hydric needs of the crop is fulfilled. A new approach to deal with multiple irrigation points is also proposed. It allows to redistribute the available water in the case an irrigation point is not able to provide the water needed.

In preparation for active microwave-based data assimilation into a crop modeling system, the mapping of daily 1-km AquaCrop model (v6.1) biomass and surface soil moisture to backscatter was optimised, using two forward operators, i.e. the Water Cloud Model (WCM) and the Support Vector Regression (SVR). Both forward operators were calibrated (2014–2018) with 1-km Sentinel-1 backscatter (ϒ°) observations in VV and VH polarisation, for three different study domains in Europe. For the validation period (2019–2021), the ϒ° simulations showed reasonable performances around Czech Republic and the Iberian Peninsula, to good performances over Belgium, but with strong variations within each domain. The domain-averaged root mean square difference between the model and Sentinel-1 ϒ° remained below 2 dB for both forward operators and all three study domains, and the mean bias for VV remained close to 0 dB, and close 0.5 dB for the VH polarisation. The WCM and SVR performed better in VV than VH and overall the SVR performed slightly better in mapping the AquaCrop soil moisture and vegetation to backscatter than the WCM. Additionally, the assumed linear relationship in the WCM between soil moisture and soil ϒ° holds better for VV than for VH. The remaining differences between WCM or SVR simulations and Sentinel-1 observations are mainly caused by AquaCrop model errors. ispartof: Remote Sensing Of Environment vol:294 status: published

The process of growing peppers requires extra attention important to maintaining soil moisture. The intensity of temperature and humidity in chili plants can make it easier for farmers to measure soil conditions and make it easier for farmers to monitor the quality of the chili they grow. In this study, the progression of an Internet of Things (IoT)-based system was performed out. To monitor and find out the temperature and humidity and can carry out the plant watering system automatically. NodeMcu ESp32 microcontroller as a data center, with temperature values taken from DHT11 sensors and humidity values from Soil Moisture sensors. This soil moisture control system works at temperatures of more than 20°C and humidity levels of more than 70% so that the system can control the water pump according to the measured level of soil moisture. From the results of experiments that have been carried out testing chili plants with a Soil Moisture sensor, the dry soil output value is >=70% with a temperature from the DHT11 sensor of 33.30 °C and a wet soil output value of

The presence of litter and winter snow cover can affect the decomposition of organic matter in forest soils and changes in δ13C values of soil-respired carbon dioxide (CO2). However, limited information is available on the responses of CO2 emissions from forest soils and their δ13C values to snow cover and litter addition over the year. We experimentally manipulated snow cover to study the impacts of light and heavy artificial snow cover on soil heterotrophic respiration and its δ13C values, using undisturbed large soil columns collected from two typical temperate forests in Northeastern China. Based on the average temperatures of surface forest soils in four seasons of the year in this study region, the simulations of autumn freeze–thaw, winter freeze, spring freeze–thaw, and the growing season were sequentially carried out under laboratory conditions. A set of novel analysis systems, including automated chamber equipment and laser spectroscopy analysis with high-frequency measurements for CO2 concentrations and the 13C/12C isotopic ratios in CO2, was used to study the effects of artificial snow cover and the presence of litter on soil heterotrophic respiration and its δ13C values. During the autumn freeze–thaw simulation, there were larger CO2 emissions and less negative δ13C values of soil-respired CO2 upon heavy snow cover than upon light snow cover, indicating that the presence of increased snow cover prior to winter freeze can increase the decomposition of organic C in subsurface soils under temperate forests. The δ13C values of soil-respired CO2 in all treatments were, on average, less negative as the simulated spring freeze–thaw proceeded, which was contrary to the variations of the δ13C during the autumn freeze–thaw simulation. Soil heterotrophic respiration and its δ13C values during the spring freeze–thaw simulation were, on average smaller upon heavy snow cover than upon light snow cover, which differed from those during the autumn freeze–thaw and growing season simulations, respectively. Taken together, the results highlight that the effects of snow cover on soil heterotrophic respiration and its δ13C values under temperate forests may vary with different seasons of the year and the presence of litter.

This paper investigates the use of soil moisture data from satellites and a hydrological model as inputs to a simplified CH4 emission model (MeSMOD) for estimating CH4 emissions from boreal and pan-Arctic regions between 2015 and 2021. MeSMOD is calibrated using FLUXNET—CH4 sites and the predictive performance is evaluated using several metrics, including the Nash-Sutcliffe efficiency (NSE). Using satellite soil moisture with 100 m resolution, MeSMOD has the highest performance (NSE = 0.63) compared with using satellite soil moisture of 10 km and hydrological model soil moisture of 10 km and 50 km (NSE = 0.59, 0.56, and 0.53, respectively) against site-level CH4 flux. This study has upscaled the estimates to the pan-Arctic region using MeSMOD, resulting in comparable mean annual estimates of CH4 emissions using satellite soil moisture of 10 km (33 Tg CH4 yr−1) and hydrological model soil moisture of 10 km (39 Tg CH4 yr−1) compared with previous studies using random forest technique for upscaling (29.5 Tg CH4 yr−1), LPJ-wsl process model (30 Tg CH4 yr−1), and CH4 CAMS inversion (34 Tg CH4 yr−1). MeSMOD has also accurately captured the high methane emissions observed by LPJ-wsl and CAMS in 2016 and 2020 and effectively caught the interannual variability of CH4 emissions from 2015 to 2021. The study emphasizes the importance of using high-resolution satellite soil moisture data for accurate estimation of CH4 emissions from wetlands, as these data directly reflect soil moisture conditions and lead to more reliable estimates. The approach adopted in this study helps to reduce errors and improve our understanding of wetlands’ role in CH4 emissions, ultimately reducing uncertainties in global CH4 budgets.

To investigate the different responses of crops to drought stress under field conditions of Central European Climate for selected crop rotations, a field experiment was conducted at a test site in the Czech Republic from 2014 to 2021. Depending on the crop, rainout shelters were placed in late spring and early summer to study the effects of drought in the final stages of crop development. Due to these rainout shelters and the associated lower water availability for the crops during the summer, a reduction in leaf area index, biomass and yield was observed. For example, a yield decrease of more than 30% was observed for spring barley, winter rape and winter wheat compared to conditions without rainout shelters. The reduction was 25% and 18% for winter rye and silage maize, respectively, under rainout shelters. Soil moisture played a significant role in yield, where a predictive model based on monthly soil moisture explained up to 79% (winter rape) of the yield variance.

New sensing technologies are at the cusp of providing state-of-the-art infrastructure to precisely monitor crop water requirements spatially so as to optimize irrigation scheduling and agricultural productivity. This project aimed to develop a new smart irrigation system that uses an L-band radiometer in conjunction with an irrigation boom, allowing for a precision water delivery system using derived high-resolution soil moisture information. A potato farm was selected due to its sensitivity to water and an existing irrigation system where the radiometer could be mounted. A field experiment was conducted to capture the soil moisture variation across the farm using the radiometer. A greenhouse trial was also conducted to mimic the actual growth of potatoes by controlling the soil moisture and exploring the impact on their growth. It was found that 0.3 cm3/cm3 was the optimal moisture level in terms of productivity. Moreover, it was demonstrated that on-farm soil moisture maps could be generated with an RMSE of 0.044 cm3/cm3. It is anticipated that through such technology, a real-time watering map will be generated, which will then be passed to the irrigation software to adjust the rate of each nozzle to meet the requirements without under- or over-watering.

An effective soil moisture retrieval method for FY-3E (Fengyun-3E) GNOS-R (GNSS occultation sounder II-reflectometry) is developed in this paper. Here, the LAGRS model, which is totally oriented for GNOS-R, is employed to estimate vegetation and surface roughness effects on surface reflectivity. Since the LAGRS (land surface GNSS reflection simulator) model is a space-borne GNSS-R (GNSS reflectometry) simulator based on the microwave radiative transfer equation model, the method presented in this paper takes more consideration on the physical scattering properties for retrieval. Ancillary information from SMAP (soil moisture active passive) such as the vegetation water content and the roughness coefficient are investigated for the final algorithm’s development. At first, the SR (surface reflectivity) data calculated from GNOS-R is calculated and then calibrated, and then the vegetation roughness factor is achieved and used to eliminate the effects on both factors. After receiving the Fresnel reflectivity, the corresponding soil moisture estimated from this method is retrieved. The results demonstrate good consistency between soil moisture derived from GNOS-R data and SMAP soil moisture, with a correlation coefficient of 0.9599 and a root mean square error of 0.0483 cm3/cm3. This method succeeds in providing soil moisture on a global scale and is based on the previously developed physical LAGRS model. In this way, the great potential of GNOS-R for soil moisture estimation is presented.

The current study employs the Soil-Water-Atmosphere-Plant (SWAP) model to simulate soil moisture and evaluate the influence of extreme meteorological conditions on soil in six locations in Lithuania. Soil moisture is an important component of the environment, and when measurements are unavailable, the most reliable method of evaluation is modelling. The results of the study revealed that the model output data closely matched the results of the soil moisture measurements, so the model may be useful for evaluating climate change impact and predicting the effects of extreme conditions on soils. The results also showed that satellite-based soil moisture products cannot replace in situ measurements, which is why modelling is the better alternative. Between 1951 and 2020, soil moisture decreased each month at the beginning of the vegetation season except for July. These changes corresponded partly to changes and redistribution of precipitation during the warm season. Current climate predictions do not reveal clear trends in average soil moisture level, though it is expected that both extremely wet and dry conditions will become more prevalent. The present study demonstrates that modelled soil moisture can be used to identify extremely dry and wet soil conditions, though these may not always correspond to droughts and long rainy periods. Evaluating extreme conditions using soil moisture measurements may provide additional information on the impact of the weather on soils and crops.

Global Navigation Satellite System interferometric reflectometry (GNSS-IR), as a new remote sensing detection technology, can retrieve surface soil moisture (SM) by separating the modulation terms from the effective signal-to-noise ratio (SNR) data. However, traditional low-order polynomials are prone to over-fitting when separating modulation terms. Moreover, the existing research mainly relies on prior information to select satellites for SM retrieval. Accordingly, this study proposes a method based on empirical modal decomposition (EMD) and cross-correlation satellite selection (CCSS) for SM retrieval. This method intended to adaptively separate the modulation terms of SNR through the combination of EMD and an intrinsic mode functions (IMF) discriminant method, then construct a CCSS method to select available satellites, and finally establish a multisatellite robust estimation regression (MRER) model to retrieve SM. The results indicated that with EMD, the different feature components implied in the SNR data of different satellites could be adaptively decomposed, and the trend and modulation terms of the SNR could more accurately be acquired by the IMF discriminant method. The available satellites could be efficiently selected through CCSS, and the SNR quality of different satellites could also be classified at different accuracy levels. Furthermore, MRER could fuse the multisatellite phases well, which enhanced the accuracy of SM retrieval and further verified the feasibility and effectiveness of combining EMD and CCSS. When rm=0.600 and rn=0.700, the correlation coefficient (r) of the multisatellite combination reached 0.918, an improvement of at least 40% relative to the correlation coefficient of a single satellite. Therefore, this method can improve the adaptive ability of SNR decomposition, and the selection of satellites has high flexibility, which is helpful for the application and popularization of the GNSS-IR technology.

Available soil moisture is thought to be the limiting factor for most ecosystem processes in the cold polar desert of the McMurdo Dry Valleys (MDVs) of Antarctica. Previous studies have shown that microfauna throughout the MDVs are capable of biological activity when sufficient soil moisture is available (~2–10% gravimetric water content), but few studies have attempted to quantify the distribution, abundance, and frequency of soil moisture on scales beyond that of traditional field work or local field investigations. In this study, we present our work to quantify the soil moisture content of soils throughout the Fryxell basin using multispectral satellite remote sensing techniques. Our efforts demonstrate that ecologically relevant abundances of liquid water are common across the landscape throughout the austral summer. On average, the Fryxell basin of Taylor Valley is modeled as containing 1.5 ± 0.5% gravimetric water content (GWC) across its non-fluvial landscape with ~23% of the landscape experiencing an average GWC > 2% throughout the study period, which is the observed limit of soil nematode activity. These results indicate that liquid water in the soils of the MDVs may be more abundant than previously thought, and that the distribution and availability of liquid water is dependent on both soil properties and the distribution of water sources. These results can also help to identify ecological hotspots in the harsh polar Antarctic environment and serve as a baseline for detecting future changes in the soil hydrological regime.

The soil water storage capacity is critical for soil management as it drives crop production, soil carbon sequestration, and soil quality and health. It depends on soil textural class, depth, land-use and soil management practices; therefore, the complexity strongly limits its estimation on a large scale with conventional-process-based approaches. In this paper, a machine learning approach is proposed to build the profile of the soil water storage capacity. A neural network is designed to estimate the soil moisture from the meteorology data input. By taking the soil moisture as a proxy in the modelling, the training captures those impact factors of soil water storage capacity and their nonlinear interaction implicitly without knowing the underlying soil hydrologic processes. An internal vector of the proposed neural network assimilates the soil moisture response to meteorological conditions and is regulated as the profile of the soil water storage capacity. The proposed approach is data-driven. Since the low-cost soil moisture sensors have made soil moisture monitoring simple and the meteorology data are easy to obtain, the proposed approach enables a convenient way of estimating soil water storage capacity in a high sampling resolution and at a large scale. Moreover, an average root mean squared deviation at 0.0307m3/m3 can be achieved in the soil moisture estimation; hence, the trained model can be deployed as an alternative to the expensive sensor networks for continuous soil moisture monitoring. The proposed approach innovatively represents the soil water storage capacity as a vector profile rather than a single value indicator. Compared with the single value indicator, which is common in hydrology, a multidimensional vector can encode more information and thus has a more powerful representation. This can be seen in the anomaly detection demonstrated in the paper, where subtle differences in soil water storage capacity among the sensor sites can be captured even though these sensors are installed on the same grassland. Another merit of vector representation is that advanced numeric methods can be applied to soil analysis. This paper demonstrates such an advantage by clustering sensor sites into groups with the unsupervised K-means clustering on the profile vectors which encapsulate soil characteristics and land properties of each sensor site implicitly.

This study uses Sentinel-3 SLSTR data to analyze short-term drought events between 2019 and 2021. It investigates the crucial role of vegetation cover, land surface temperature, and water vapor amount associated with drought over Kenya’s lower eastern counties. Therefore, three essential climate variables (ECVs) of interest were derived, namely Land Surface Temperature (LST), Fractional Vegetation Cover (FVC), and Total Column Water Vapor (TCWV). These features were analyzed for four counties between the wettest and driest episodes in 2019 and 2021. The study showed that Makueni and Taita Taveta counties had the highest density of FVC values (60–80%) in April 2019 and 2021. Machakos and Kitui counties had the lowest FVC estimates of 0% to 20% in September for both periods and between 40% and 60% during wet seasons. As FVC is a crucial land parameter for sequestering carbon and detecting soil moisture and vegetation density losses, its variation is strongly related to drought magnitude. The land surface temperature has drastically changed over time, with Kitui and Taita Taveta counties having the highest estimates above 20 °C in 2019. A significant spatial variation of TCWV was observed across different counties, with values less than 26 mm in Machakos county during the dry season of 2019, while Kitui and Taita Taveta counties had the highest estimates, greater than 36 mm during the wet season in 2021. Land surface temperature variation is negatively proportional to vegetation density and soil moisture content, as non-vegetated areas are expected to have lower moisture content. Overall, Sentinel-3 SLSTR products provide an efficient and promising data source for short-term drought monitoring, especially in cases where in situ measurement data are scarce. ECVs-produced maps will assist decision-makers with a better understanding of short-term drought events as well as soil moisture loss episodes that influence agriculture under arid and semi-arid climates. Furthermore, Sentinel-3 data can be used to interpret hydrological, ecological, and environmental changes and their implications under different environmental conditions.

Spaceborne GNSS-R technology is a new remote sensing method for soil moisture monitoring. Focusing on the significant influence of water on the surface reflectivity of CYGNSS, this paper improved the removal method of water influence according to the spatial resolution of CYGNSS data. Due to the disturbance effect of the incident angle, microwave frequency and soil type on the Fresnel reflection coefficient in surface reflectivity, a normalization method of Fresnel reflection coefficient was proposed after analyzing the data characteristics of variables in the Fresnel reflection coefficient. Finally, combined with the soil moisture retrieval method of linear equation, the accuracy was compared and verified by using measured data, SMAP products and official CYGNSS products. The results indicate that the normalization method of the Fresnel reflection coefficient could effectively reduce the influence of relevant parameters on the Fresnel reflection coefficient, but the normalization effect became worse at large incident angles (greater than 65°). Compared with the official CYGNSS product, the retrieval accuracy of optimized soil moisture was improved by 10%. The method proposed in this paper will play an important reference role in the study of soil moisture retrieval using spaceborne GNSS-R data.

In this work, superficial soil moisture is estimated from SAR data at the field scale on agricultural fields over which the relationship between the co-polarized backscattering coefficient (γ0VV) and the measured soil moisture (SSMv) is both direct and inverse. An inversion algorithm is adapted to the charateristics of the single field and applied to SAR signal differences. The differences of SAR signal are obtained from a change detection (CD) method applied on the VV band of the Sentinel-1 SAR mission. In the CD method, the variations of the total backscattered signal due to sharp changes in vegetation and soil roughness are excluded from the dataset by using a machine learning algorithm. The retrieval method is applied on a low vegetated agricultural area in Spain, characterized by a semi-arid mediterranean climate and where in situ soil moisture data are available. Good results are obtained not only over fields characterized by direct γ0VV/SSMv relationship, reaching values of correlation coefficient and RMSE up to r=0.89 and RMSE=0.042 m3/m3, but also over fields with inverse relationship, obtaining in this case values up to r=0.84 ad RMSE=0.026 m3/m3. Although the inverse relationship between the backscattering coefficient and the measured soil moisture is not yet well understood in the field of soil moisture estimation from radar data, for the present case, checking the nature of this relationship was fundamental in order to accordingly adapt the soil moisture retrieval algorithm to the dataset characteristics.

With the increasing availability and diversity of satellite imagery, the multisensor fusion of data can more effectively address the improved monitoring of temporary water bodies. This study supports the attempt to apply well established methods to detect spatial and temporal changes in ephemeral shallow lakes in lowland karst terrain, as well as to improve the understanding concerning the dynamics of water storage and hydrological mechanisms during extreme precipitation events. Based on the joint analysis of Copernicus Sentinel SAR and optical mission data, as well as soil moisture and EO-based rainfall observations over the period of 2015–2020, we demonstrated the control of the karst system on the ephemeral lake appearances in the broader area of Chalkida (Evvia, Greece). A connection between the prolonged and extended water coverage in the ephemeral lakes and flooding in the area is documented. Our EO-supported findings may serve as indicators for flood alerts in future extreme precipitation events, improving responses in cases of emergencies.

Soil moisture (SM) exists at the land-atmosphere interface and serves as a key driving variable that affects global water balance and vegetation growth. Its importance in climate and earth system studies necessitates a comprehensive evaluation and comparison of mainstream global remote sensing/reanalysis SM products. In this study, we conducted a thorough verification of ten global remote sensing/reanalysis SM products: SMAP DCA, SMAP SCA-H, SMAP SCA-V, SMAP-IB, SMOS IC, SMOS L3, LPRM_C1, LPRM_C2, LPRM_X, and ERA5-Land. The verification was based on ground observation data from the International SM Network (ISMN), considering both static factors (such as climate zone, land cover type, and soil type) and dynamic factors (including SM, leaf area index, and land surface temperature). Our goal was to assess the accuracy and applicability of these products. We analyzed the spatial and temporal distribution characteristics of global SM and discussed the vegetation effect on SM products. Additionally, we examined the global high-frequency fluctuations in the SMAP L-VOD product, along with their correlation with the normalized difference vegetation index, leaf area index, and vegetation water content. Our findings revealed that product quality was higher in regions located in tropical and arid zones, closed shrubs, loose rocky soil, and gray soil with low soil moisture, low leaf area index, and high average land surface temperature. Among the evaluated products, SMAP-IB, SMAP DCA, SMAP SCA-H, SMAP SCA-V, and ERA5-Land consistently performed better, demonstrating a good ability to capture the spatial and temporal variations in SM and showing a correlation of approximately 0.60 with ISMN. SMOS IC and SMOS L3 followed in performance, while LPRM_C1, LPRM_C2, and LPRM_X exhibited relatively poor results in SM inversion. These findings serve as a valuable reference for improving satellite/reanalysis SM products and conducting global-scale SM studies.

Study region: The natural river basins of Chile. Study focus: Drought effects on terrestrial ecosystems produce hydroclimatic stress with variable extensions. Particularly, hydrological drought duration can provide a better understanding of recovery together with catchment characteristics and climatology. This study focuses on the impacts of the multi-year drought experienced in Chile for more than a decade. The recovery of relevant catchment variables to quantify the drought termination (DT) and drought termination duration (DTD) after the hydrological drought is presented. A composite analysis of natural catchments using the CAMELS-CL data set discharge (1988–2020), k-NDVI (2000–2020), and soil moisture (1991–2020) provides the average response of the recovery after severe droughts. New hydrological insights for the region: This study demonstrates that local catchment properties can explain the recovery of studied variables after a hydrological drought. Explanatory variables from CAMELS-CL to derive the DT using random forest regression (RFR) were used with a strong correlation of 0.92, 0.84, and 0.89 for discharge, vegetation productivity, and soil moisture, respectively. The discharge patterns show longer recovery over environments dominated by shrublands with less precipitation and higher temperatures, in central Chile, while higher latitudes with higher vegetation cover, increasing precipitation, and lower temperatures present shorter recovery times. The vegetation productivity shows longer recovery over highly vegetated mountains in central Chile. The soil moisture recovery spatial distribution presented patterns that connect them with the discharge recovery. This work enables the identification of drought vulnerability, which is valuable for managing water resources and ecosystems and is helping to predict drought recovery periods in regions with a lack of observations.

Aim: Widespread greening and an increasing global terrestrial carbon sink over recent decades have been reported. However, the spatio-temporal relationships between vegetation greenness and productivity and the factors influencing this relationship remain unclear. We define a new metric of ecosystem-scale photosynthetic efficiency (EPE) to analyse its spatio-temporal pattern and investigate how potential drivers regulate the greenness–productivity relationship. Location: Global. Time period: From 2001 to 2016. Major taxa studied: Global terrestrial ecosystems. Methods: This study used global datasets of leaf area index (LAI) and solar-induced fluorescence (SIF) as proxies of vegetation greenness and ecosystem productivity, respectively, to propose a new metric of SIF/LAI, representing ecosystem-scale photosynthetic efficiency (EPE). We identified the spatial pattern and dynamics of EPE and examined factors influencing EPE. Results: The results showed a weaker increase in productivity compared with the global greening rate from 2001 to 2016, suggesting a decline in EPE at the global scale. This decline in EPE indicates a disproportionate increase in terrestrial productivity against the widespread greening. When stratified into areas following an aridity gradient, we found that EPE overall showed upward trends in arid and semi-arid areas, and downward trends in dry sub-humid and humid regions. The EPE was controlled primarily by soil moisture, which promoted or constrained the EPE in xeric and mesic ecosystems, respectively. Moreover, the increase in short vegetation cover and atmospheric water demand contributed positively or negatively to EPE changes in xeric and mesic ecosystems, respectively. Main conclusions: Our study shows that greening of the Earth is associated with decreasing EPE, revealing that current rates of carbon sequestration do not increase proportionally to greening of the Earth and highlighting that soil moisture is a key controller of EPE. These results help to reduce the uncertainties in future climate change impacts on vegetation dynamics, thus having implications for sustainable ecosystem management and climate change mitigation.

We analyzed changes in the long-term vegetation cover and in fire activity over the past 5000 years in the Ecuadorian páramo using a sediment core from Papallacta (Ecuador). The chronology is constrained by three tephra layers and 32 AMS 14C ages, and 168 samples yielded a high-resolution record of environmental changes. We estimated the upslope wind convectivity as the ratio between pollen transported from the Andean cloud forest and Poaceae pollen to distinguish changes in atmospheric moisture from changes in soil moisture. The record showed that the two sources of moisture, either from year-round adiabatic cloud dripping linked to SASM activity or to ENSO variability at decadal-scale, influenced vegetation-cover changes. Between 5000 and 2450 cal yr BP, both soil moisture and biomass burning were higher than after 2450 cal yr BP. The shift between the two states matches the zonal increase in summer insolation that drove the ITCZ to its southernmost position. Our results underline resilience to volcanic activity, the importance of the upslope convective dripping with the lowest convective index observed at ~4500 cal yr BP, the anomalous last century with the highest convective activity and the driest soil conditions recorded in the last 5000 years, the recent increase in fire activity and the link between soil moisture and the position of the ITCZ.

In-depth knowledge about soil moisture dynamics is crucial for irrigation management in precision agriculture. This study evaluates the feasibility of high spatial resolution near-infrared remote sensing with unmanned aerial systems for soil moisture estimation to provide decision support for precision irrigation management. A new trapezoid model based on near-infrared transformed reflectance (NTR) and the normalized difference vegetation index (NDVI) is introduced and used for estimation and mapping of root zone soil moisture and plant extractable water. The performance of the proposed approach was evaluated via comparison with ground soil moisture measurements with advanced time domain reflectometry sensors. We found the estimates based on the NTR−NDVI trapezoid model to be highly correlated with the ground soil moisture measurements. We believe that the presented approach shows great potential for farm-scale precision irrigation management but acknowledge that more research for different cropping systems, soil textures, and climatic conditions is needed to make the presented approach viable for the application by crop producers.

Ilustraciones La respuesta hidrológica (HR) de los cauces a eventos de precipitación, es reportada como un proceso de importante variabilidad debido a los múltiples y complejos factores controladores que lo influencian. Este trabajo explora dicha variabilidad en las sub-cuencas del valle de Aburrá (AV), enfocándose en los dos factores con mayor dominio en la HR a escala de evento: la precipitación detonante y las condiciones de humedad antecedentes. Para ello se comparó información hidrológica de nivel y precipitación de radar, con una resolución temporal fina, en 58 sub-cuencas dentro del área de estudio. A su vez, esta información fue contrastada con las principales características físicas de las subcuencas. Se realizaron también análisis de los registros de la primera red de humedad del suelo (SM) in-situ de la zona, que incluyó 7 sub-cuencas del AV, en comparación con la información de eventos de precipitación y crecientes. El análisis de los eventos de tormentas en el AV, exhibió una relación creciente entre las magnitudes de la precipitación acumulada y el nivel máximo alcanzado por las crecientes. El estudio sugiere que la manifestación de dicha relación es susceptible al tamaño de la muestra de eventos y a la metodología usada para identificar los mismos; al analizar con menos cantidad de eventos, las magnitudes de precipitaciones y crecientes no presentaron relaciones observables. Por otro lado, se encontró que al final de las temporadas de lluvia en el AV se necesitan menores magnitudes de lluvia para detonar crecientes, mientras que al final de las temporadas secas son necesarios eventos de precipitación de mayor acumulado. En contraste, los patrones encontrados respecto al rol de la precipitación en la HR, no presentaron relaciones observables con las principales características físicas de las sub-cuencas de interés. Respecto a la humedad del suelo, los análisis manifestaron relaciones crecientes entre los picos de SM y de las crecientes durante eventos de precipitación. Adicionalmente, se encontraron relaciones de umbral entre la precipitación acumulada y humedad del suelo máxima durante eventos de lluvia, así como entre las magnitudes pico de humedad del suelo y de las crecientes; ambos comportamientos de umbral asociadas a valores de similares SM. Estos hallazgos sugieren que al superar cierto umbral de humedad del suelo, los eventos de creciente más relevantes pueden presentar mayor probabilidad de ocurrencia. En general, los resultados de este estudio pusieron de manifiesto el rol de ambos factores controladores de la HR, la precipitación y las condiciones de humedad antecedente, en las subcuencas del Valle de Aburrá. Estos sugieren que el monitoreo de ambos factores es potencialmente útil para el entendimiento y la alerta temprana de eventos de creciente en el área de estudio. (Texto tomado de la fuente) The hydrological response (HR) of watersheds to storm events, is recognized as a quite variable process due to the multiple and complex controlling factors that can influence it. This work aims to explore this variability in the sub-basins within the Aburr´a Valley (AV) watershed, focused on the two most dominant factors regarding the HR: the triggering rainfall and the antecedent moisture conditions. To achieve this, we compared the hydrological information of water level and radar-derived precipitation, at a fine temporal scale, within 58 sub-basins in the study area. At the same time, these records were contrasted with the main physical features of the sub-basins. We also fulfilled an analysis of the first soil moisture network records in the zone, which include 7 of the AV sub-basins, in comparison to the precipitation and flood events. The analysis of the storm events within the AV exhibited an increasing relationship between the magnitudes of accumulated precipitation and the peak level reached by the floods. The study suggests that the display of that relationship is susceptible to the sizes of the events samples, as well as to the methodology used to identify them; in analysis with much fewer events, the magnitudes of rainfall and floods did not show perceptible relationships. On the other hand, it was found that at the end of the AV rainfall seasons, less rainfall is needed to trigger floods, while at the end of dry seasons, greater accumulated precipitation events are needed to trigger floods of similar magnitudes. In contrast, the patterns regarding the role of precipitation on the HR did not exhibit an observable relationship with the main physical features of the studied sub-basins. In regard to soil moisture, the analysis showed increasing relationships between the peaks of SM and floods during rainfall events. Additionally, threshold relationships were found between the accumulated precipitation and the peak soil moisture during events, as well as between the peak magnitudes of soil moisture and flood events; both threshold behaviors were found associated with similar SM values. These findings suggest that the most relevant flood events can be more probable if the soil moisture exceeds a certain threshold. In general, the results of this study exhibited the role of both HR controlling factors, rainfall and antecedent moisture conditions. They suggest the monitoring of both factors to be potentially useful for the understanding and the early warning of flood events within the study area. Maestría Magister en Ingeniería - Recursos Hidráulicos Área Curricular de Medio Ambiente

Influenced by the topography, the spatial variation of soil thickness on karst slopes is very large, and accordingly the spatial variation of soil moisture is also large. Therefore, analyzing the spatial heterogeneity of soil moisture on hillslopes is important for maintaining ecosystem stability. Combining geostatistical methods and GIS technology, the spatial variability and distribution pattern of soil moisture and the influencing factors of spatial variation and surface soil moisture (0–7 cm) on a typical karst shrub–grass hillslope were analyzed. The results showed that the mean soil moisture and coefficient of variation (CV) ranged between 25.7–42.6% and 10.3–20.9%, respectively, showing a moderate variation. The soil moisture presented a moderate or strong spatial autocorrelation in the sampling scale. The occurrence of rainfall events can exert a great influence on reducing the spatial heterogeneity of soil moisture. The spatial distribution pattern of soil moisture showed roughly plaque or stripe distribution. When soil moisture was much lower, the patch space fragmentation of soil moisture was higher. The soil moisture was higher in the low and middle parts of the plot. We can conclude that factors such as topography, vegetation, and weather conditions will exert a significant effect on soil moisture spatial variability. Areas with lower slope and higher vegetation coverage were more conducive to the retention of soil moisture.

Soil moisture is a key factor controlling vegetation construction and ecological restoration in arid and semiarid areas. Understanding its spatiotemporal patterns and influencing factors is essential for effective vegetation water management. In this study, we analyzed the spatiotemporal characteristics of black locust plants using field investigations and statistical analyses and determined the effects of the rainfall and plant characteristics on the soil moisture content (SMC) in a typical watershed in the Loess Plateau, China. The results show that the SMC increases with increasing distance from the tree trunk in the horizontal direction. The vertical profile of the SMC includes layers characterized by rapid decrease, decreased fluctuation, and slow increase. Temporal SMC changes exhibit higher variabilities in the surface layer than in deeper soil layers. Rainfall characteristics notably affect soil moisture. The influence of the rainfall amount is stronger than that of the rainfall duration and intensity. The diameter at breast height, tree height, and canopy width positively affects the soil moisture, whereas the leaf area index and canopy openness negatively affect it. The results of this study provide insights into soil moisture change mechanisms and theoretical references for sustainable plant water use management in arid and semiarid areas.

Automated soil moisture systems are commonly used in precision agriculture and environmental monitoring. Using low-cost sensors, the spatial extension can be maximized, but the accuracy might be reduced. In this paper, we address the trade-off between cost and accuracy comparing low-cost and commercial soil moisture sensors. The analysis is based on the capacitive sensor SKU:SEN0193 under lab and field conditions. The laboratory tests aimed at evaluating the response speed, best supply voltage, temperature dependency, calibration, and applicability for controlled infiltration column tests (one meter high). Laboratory tests indicated that the sensor is temperature and voltage-sensitive. The use of 5.5 V as supply voltage for the sensors drastically reduced the correlation between output and degree of soil saturation, thus we suggest the use of 3.3V. Soil temperature had a negligible impact on the sensor output: 0.27% of soil saturation degree per degree Celsius. For field implementation, a low-cost monitoring station was built using Arduino as a microcontroller and tested during three months. The sensors could represent daily and seasonal oscillation in soil moisture resulting from solar heating and precipitation. In addition to individual calibration, two simplified calibration techniques are proposed: universal calibration, based on all 63 sensors, and a single-point calibration using the sensor's response in dry soil. The monitored wetting front was compared to the one estimated by Hydrus model and had a high correlation. The low-cost sensor performance was later compared to commercial sensors based on five variables: (1) cost, (2) accuracy, (3) qualified labor demand, (4) sample volume, and (5) life expectancy. Commercial sensors promote soil moisture measurements with high accuracy at a high acquisition cost. On the other hand, low-cost sensors, such as the SKU:SEN0193, provide data with medium accuracy at a very low acquisition cost, enabling spatial monitoring through multiple-point measurements. Thus, the use of the SKU:SEN0193 sensor is suggested in projects with budget limitations with short duration where there is a medium requirement accuracy or when the spatial variability of soil water content is considerable. Despite the physical fragility of the hardware used (sensors and monitoring station) and the lower accuracy when compared to other commercial sensors, this work demonstrates through the case study of the SKU:SEN0193 sensor the possibility of using low-cost technologies for monitoring environmental variables.

Nighttime irrigation scheduling is a longstanding water conservation approach to reduce evaporative losses in Utah's semi-arid, urban landscapes. However, residential demand now exceeds system capacity, and the efficiency of the practice has come under question. Therefore, the objectives of this study include: 1) quantify whether a water savings exists between day and night irrigation with a water balance and energy balance approach, and 2) analyze microclimate and crop quality effects to determine any changes to evaporative demand or plant stress by irrigation timing. In 2019, we established 12 field plots in North Logan, UT (41.77° N, -111.81° W, 1380 m elevation) to test two irrigation timings (2:00 - 4:00 am and 2:00 - 4:00 pm) with two urban crops (turfgrass and zinnia cut flowers) on a sprinkler system in triplicate. Field instrumentation included a central weather station, flow meters on sprinkler sets, soil moisture sensors at 0.08 m and 0.18 m depths, canopy air temperature and vapor pressure, surface temperature, and catch cups for wind drift and canopy interception calculations. Daytime irrigation resulted in greater wind drift and evaporative losses (WDEL) by 7% compared to nighttime in both crops. A decrease in canopy and surface temperature, as well as the vapor pressure deficit was greater with daytime irrigation than nighttime, decreasing the evaporative demand at maximum air temperature (Tmax). Turf and zinnia yield and quality were not affected by irrigation timing, though the percentage cover of turf was significantly greater with daytime irrigation compared to nighttime. Thus, landscape irrigation scheduling should avoid hours with peak wind speed to reduce the WDEL and irrigate the field at or after Tmax to reduce the evaporative or heat stress to the crop.

Soil moisture dynamics are known to vary highly in space and time, especially given soil-plant-water and human interactions in agricultural environments. In this thesis, soil moisture fluctuations under two agricultural soil conditions – cover cropping and soil compaction – were examined over both seasonal (study 1) and event (study 2) timescales. Study 1 revealed that the activation of lateral soil water flow pathways varied seasonally and across soil conditions. Vertical flow pathway activation varied with soil condition and depth but showed minimal seasonality. In Study 2, soil moisture responses to dry spell events were similar between soil conditions, while soil moisture responses to rainfall events were different between cover-cropped and compacted soils. Given the linkages between soil water and nutrient export, this thesis provides valuable information about short- and medium-term soil water dynamics, which can help guide policy makers and crop advisors in developing region-specific agricultural water and nutrient management strategies. Ontario Agri-Food Innovation Alliance, Submission Number: UG-T1-2020-100260, Program: Alliance - Tier I

Panax notoginseng is an important medicinal crop in China. The high incidence of root rot in P. notoginseng during the rainy season has restricted the development of the industry. It is believed that frequent rainfall and a warm soil environment are important factors that promote root rot incidence. However, there is still a significant knowledge gap in the relationship between rainfall and root rot incidence. To understand the effects of rainfall and fertilizer on root exudates, the soil bacterial structure, and root rot in P. notoginseng, four treatments were chosen for both field and pot experiments. These treatments included DW (rain shelter and no fertilizer), RW (no rain shelter and no fertilizer), DWF (rain shelter and fertilizer), and RWF (no rain shelter and fertilizer). The results showed that both factors (rain shelter and fertilizer) significantly affected root rot incidence and several other parameters. Among them, the effect of a rain shelter is more significant than that of fertilizer, and the combination of the two further improves the effect. DW and DWF treatments significantly reduced the soil moisture, phenolic acid, and root rot incidence, while significantly increasing the soil temperature and enzyme activities compared to RW or RWF. Seven phenolic acids secreted by P. notoginseng roots were all positively correlated with root rot incidence. Root rot was also positively correlated with Planctomycetota, Acidobacteriota, and Gemmatimonadota and negatively correlated with Firmicutes, Proteobacteria, Patescibacteria, and Nitrospirota. DWF treatment decreased the soil moisture and the concentration of p-hydroxybenzoic acid, syringic acid, phthalic acid, and vanillic acid and promoted the growth of Firmicutes and Proteobacteria, leading to the lowest incidence of root rot.

The land surface is an important component of numerical weather and climate forecast models due to their effect on energy–water balances and fluxes, and it is essential for forecasts on a seasonal scale. The present study aimed to understand the effects of land surface processes on initialization of seasonal forecasts in the austral spring, in particular soil moisture. We built forecasts with the Brazilian global Atmospheric Model hindcast from 2000 to 2010, with a configuration similar to those used in the operational environment. To improve it, we developed a new initial condition of the land surface using the Land Information System over South America and the Global Land Data Assimilation System for the rest of the globe and used it as the input in the forecast model. The results demonstrated that the model is sensitive to changes in soil moisture and that the new high–resolution soil moisture dataset can be used in model initialization, which resulted in an increase in the correlation of precipitation over part of South America. We also noticed an improvement in the representation of surface fluxes and an increase in soil moisture content and specific humidity at medium and low levels of the atmosphere. The analysis of the coupling between the land surface and the atmosphere showed that, for Central Brazil, the states of the continental surface define the surface fluxes. For the Amazon and La Plata Basins, the model did not correctly represent the coupling because it underestimated the soil moisture content.

Irrigated turfgrass is a major crop in urban areas of the drought-stricken Western United States. A considerable proportion of irrigation water is wasted through the use of conventional sprinkler systems. While smart sprinkler systems have made progress in reducing temporal mis-applications, more research is needed to determine the most appropriate variables for accurately and cost-effectively determining spatial zones for irrigation application. This research uses data from ground and drone surveys of two large sports fields. Surveys were conducted pre-, within and towards the end of the irrigation season to determine spatial irrigation zones. Principal components analysis and k-means classification were used to develop zones using several variables individually and combined. The errors associated with uniform irrigation and different configurations of spatial zones are assessed to determine comparative improvements in irrigation efficiency afforded by spatial irrigation zones. A determination is also made as to whether the spatial zones can be temporally static or need to be re-determined periodically. Results suggest that zones based on spatial soil moisture surveys and simple observations of whether the grass felt wet or dry are better than those based on NDVI, other variables and several variables in combination. In addition, due to the temporal variations observed in spatial patterns, ideally zones should be re-evaluated periodically. However, a less labor-intensive solution is to determine temporally static zones based on patterns in soil moisture averaged from several surveys. Of particular importance are the spatial patterns observed prior to the start of the irrigation season as they reflect more temporally stable variation that relates to soil texture and topography rather than irrigation management.

The retrieval of soil moisture information with spatially and temporally high resolution from Synthetic Aperture Radar (SAR) observations is still a challenge. By using multi-orbit Sentinel-1 C-band time series, we present a novel approach for estimating volumetric soil moisture content for agricultural areas with a temporal resolution of one to two days, based on a short-term change detection method. By applying an incidence angle normalization and a Fourier Series transformation, the effect of varying incidence angles on the backscattering signal could be reduced. As the C-band co-polarized backscattering signal is prone to vegetational changes, it is used in this study for the vegetational correction of its related backscatter ratios. The retrieving algorithm was implemented in a cloud-processing environment, enabling a potential global and scalable application. Validated against eight in-situ cosmic ray neutron probe stations across the Rur catchment (Germany) as well as six capacitance stations at the Apulian Tavoliere (Italy) site for the years 2018 to 2020, the method achieves a correlation coefficient of R of 0.63 with an unbiased Root Mean Square Error of 0.063 m3/m3.

Soil moisture (SM) and vegetation optical depth (VOD) are essential variables in the terrestrial ecosystem. The multi-frequency radiometers AMSR-E and AMSR2 provide >20 years of data records, enabling the development of long-term SM and VOD products. Most of the current retrieval algorithms either only focus on SM or VOD, and generally ignore the polarization or simplify the frequency dependence of vegetation effects for reducing the unknowns and facilitating the retrieval process, limiting the synergic applicability of VOD and SM products in the soil-plant-atmosphere continuum. In this study, a new global SM and frequency- and polarization-dependent VOD product from 2002 to 2021 was developed using the multi-channel collaborative algorithm (MCCA), based on the inter-calibrated AMSR-E/2 multi-frequency passive microwave measurements. The MCCA algorithm comprehensively considers the physical relationship between multiple microwave channels and could retrieve frequency- and polarization-dependent VOD while considering the accuracy of the SM retrievals. In the overall comparison with other SM products (AMSR-ANN, CCI-passive v07.1, LPRM-C/X, JAXA) over 25 dense SM networks, MCCA achieved the best scores in terms of root mean square error (RMSE = 0.074 m3/m3), unbiased root mean square error (ubRMSE = 0.073 m3/m3) and bias (0.007 m3/m3), and presented slightly lower value of Pearson's correlation coefficient (R = 0.709) than LPRM-X (R = 0.735). For the indirect evaluation of VOD with aboveground biomass (AGB) and MODIS NDVI, the MCCA product showed the performance comparable to other products (LPRM-C/X, VODCA-C/X/Ku). MCCA-derived VODs, especially for the H-polarized VODs, exhibited smooth non-linear density distribution with AGB and high temporal correlations with MODIS NDVI over most regions of the globe. In particular, MCCA-derived VODs can physically present reasonable variations across the microwave spectrum (values of VOD increase with microwave frequency), which is superior to the LPRM and VODCA products. It is expected that the MCCA algorithm can be extended to the observations of the ongoing AMSR2 or other similar satellite missions with multi-frequency capability, such as FY-3B/C/D/F/G or the upcoming AMSR3 and CMIR missions.

Phenology and productivity are important functional indicators of grassland ecosystems. However, our understanding of how intra-annual precipitation patterns affect plant phenology and productivity in grasslands is still limited. Here, we conducted a two-year precipitation manipulation experiment to explore the responses of plant phenology and productivity to intra-annual precipitation patterns at the community and dominant species levels in a temperate grassland. We found that increased early growing season precipitation enhanced the above-ground biomass of the dominant rhizome grass, Leymus chinensis, by advancing its flowering date, while increased late growing season precipitation increased the above-ground biomass of the dominant bunchgrass, Stipa grandis, by delaying senescence. The complementary effects in phenology and biomass of the dominant species, L. chinensis and S. grandis, maintained stable dynamics of the community above-ground biomass under intra-annual precipitation pattern variations. Our results highlight the critical role that intra-annual precipitation and soil moisture patterns play in the phenology of temperate grasslands. By understanding the response of phenology to intra-annual precipitation patterns, we can more accurately predict the productivity of temperate grasslands under future climate change.

The water cycle around the globe is significantly impacted by the moisture in the soil. However, finding a quick and practical model to cope with the enormous amount of data is a difficult issue for remote sensing practitioners. The traditional methods of measuring soil moisture are inefficient at large sizes, which can be replaced by remote sensing techniques for obtaining soil moisture. While determining the soil moisture, the low return frequency of satellites and the lack of images pose a severe challenge to the current remote sensing techniques. Therefore, this paper suggested a novel technique for Soil Moisture Retrieval. In the initial phase, image acquisition is made. Then, VI indexes (NDVI, GLAI, Green NDVI (GNDVI), and WDRVI features) are derived. Further, an improved Water Cloud Model (WCM) is deployed as a vegetation impact rectification scheme. Finally, soil moisture retrieval is determined by the hybrid model combining Deep Max Out Network (DMN) and Bidirectional Gated Recurrent Unit (Bi-GRU) schemes, whose outputs are then passed on to enhanced score level fusion that offers final results. According to the results, the RMSE of the Hybrid Classifier (Bi-GRU and DMN) method was lower (0.9565) than the RMSE of the Hybrid Classifier methods. The ME values of the HC (Bi-GRU and DMN) were also lower (0.728697) than those of the HC methods without the vegetation index, the HC methods without the presence of water clouds, and the HC methods with traditional water clouds. In comparison to HC (Bi-GRU and DMN), the HC method without vegetation index has a lower error of 0.8219 than the HC method with standard water cloud and the HC method without water cloud.

The rapid and efficient acquisition of field-scale farmland soil profile moisture-distribution information is very important for achieving precise irrigation and the adjustment and deployment of irrigation strategies in farmland. EM38-MK2 is a portable, non-invasive device that induces electric currents in soil to generate secondary magnetic fields for the rapid measurement of apparent electrical conductivity in the field. In this study, cotton fields were used as experimental objects to obtain soil apparent conductivity data for three periods, which were combined with soil-moisture content data collected simultaneously from soil samples and measured in the laboratory to construct an apparent soil-profile moisture regression model. A simple kriging interpolation method was used to map the distribution of the irrigation volume in the field, considering only the highest irrigation volume in the field as the maximum water-holding capacity in the field. The results showed that EM38 could accurately detect the spatial variation of soil moisture in the field. The R2 of the linear fit between measured and predicted soil-water content ranged from 0.51 to 0.89; the RMSE ranged from 0.66 to 1.87; and the R2 and RPD of each soil-layer water content model of the single-period model were higher than those of the full-period model. By plotting the distribution of field irrigation, it could be seen that by comparing the predicted field irrigation with the actual irrigation, at least 160 m3 ha−1 of irrigation could be saved in all three periods at an irrigation depth of 40 cm, which is about 30% of the actual irrigation; at an irrigation depth of 60 cm, about 30% and 15% of irrigation could be reduced in July and August, respectively. There are three areas in the study area with high fixed-irrigation volumes located in the northwest corner, near 500 m in the northern half of the study area and 750 m east of the southern half of the study area. The results of this study proved that the use of EM38-MK2 to monitor and evaluate the soil-moisture content of the farmland at different periods can, to a certain extent, guide the irrigation amount needed to achieve efficient and precise irrigation in the field.

The ability to obtain an accurate measure of irrigation water use is urgently needed in order to provide further scientific guidance for irrigation practices. This investigation took soil moisture and precipitation as the study objects and quantitatively analyzed their relationship by establishing four models: a linear model, a logarithmic model, a soil water balance model, and a similarity model. The results from building models on every site clearly revealed the relationship between soil moisture and precipitation and confirmed the feasibility of estimating irrigation water use when soil moisture data are known. Four models combined with soil moisture data were used to estimate irrigation water use. First, the 16 sites which monitor soil moisture conditions in Hebi City were identified as study objects, from which everyday meteorological data (temperature, precipitation, atmospheric pressure, wind speed, sunshine duration) and soil moisture data from 2015 to 2020 (totaling six years) were collected. Second, the eligible data from the first four years in the date range were used to create four kinds of models (linear model, logarithmic model, soil water balance model, and similarity model) to estimate the amount of water input to the soil surface based on soil moisture. Third, the eligible data from the last two years in the established date range were used to verify the established models on every site and then judge the accuracy of the models. For example, for site 53990, the RMSE of the linear model, logarithmic model, soil water balance model, and similarity model was 10,547, 10,302, 8619, and 7524, respectively. The results demonstrate that the similarity model proposed in this study can express the quantitative relationship between soil moisture and precipitation more accurately than the other three models. Based on this conclusion, the eligible soil moisture data known in the specific site were ultimately used to estimate the irrigation water use in the field by the relationship expressed in the similarity model. Compared with the amount of irrigation water data recorded, the estimated irrigation water use yielded by the similarity model in this study was 18.11% smaller. In a future study, microwave satellite remote sensing of soil moisture data, such as SMAP and SMOS soil moisture data, will be used to evaluate the performance of estimated regional irrigation water use.

Passive microwave remote sensing of soil moisture (SM) requires a physically based dielectric model that quantitatively converts the volumetric SM into the soil bulk dielectric constant. Mironov 2009 is the dielectric model used in the operational SM retrieval algorithms of the NASA Soil Moisture Active Passive (SMAP) and the ESA Soil Moisture and Ocean Salinity (SMOS) missions. However, Mironov 2009 suffers a challenge in deriving SM over organic soils, as it does not account for the impact of soil organic matter (SOM) on the soil bulk dielectric constant. To this end, we presented a comparative performance analysis of nine advanced soil dielectric models over organic soil in Alaska, four of which incorporate SOM. In the framework of the SMAP single-channel algorithm at vertical polarization (SCA-V), SM retrievals from different dielectric models were derived using an iterative optimization scheme. The skills of the different dielectric models over organic soils were reflected by the performance of their respective SM retrievals, which was measured by four conventional statistical metrics, calculated by comparing satellite-based SM time series with in-situ benchmarks. Overall, SM retrievals of organic-soil-based dielectric models tended to overestimate, while those from mineral-soil-based models displayed dry biases. All the models showed comparable values of unbiased root-mean-square error (ubRMSE) and Pearson Correlation (R), but Mironov 2019 exhibited a slight but consistent edge over the others. An integrated consideration of the model inputs, the physical basis, and the validated accuracy indicated that the separate use of Mironov 2009 and Mironov 2019 in the SMAP SCA-V for mineral soils (SOM

The transfer of water and energy fluxes between the ground and the atmosphere is influenced by soil moisture (SM), which is an important factor in land surface dynamics. Accurate representation of SM over permafrost-affected regions remains challenging. Leveraging blended SM from microwave satellites, this study examines the potential for satellite SM assimilation to enhance LSM (Land Surface Model) seasonal dynamics. The Ensemble Kalman Filter (EnKF) is used to integrate SM data across the Iya River Basin, Russia. Considering the permafrost, only the summer months (June to August) are utilized for assimilation. Field data from two sites are used to validate the study’s findings. Results show that assimilation lowers the dry bias in Noah LSM by up to 6%, which is especially noticeable in the northern regions of the Iya Basin. Comparison with in situ station data demonstrates a considerable improvement in correlation between SM after assimilation (0.94) and before assimilation (0.84). The findings also reveal a significant relationship between SM and surface energy balance. publishedVersion

Spaceborne Global Navigation Satellite System-Reflectometry (GNSS-R) has been proven to be a cost-effective and efficient tool for monitoring the Earth’s surface soil moisture (SSM) with unparalleled spatial and temporal resolution. However, the accuracy and reliability of GNSS-R SSM estimation are affected by surface vegetation and roughness. In this study, the sensitivity of delay Doppler map (DDM)-derived effective reflectivity to SSM is analyzed and validated. The individual effective reflectivity is projected onto the 36 km × 36 km Equal-Area Scalable Earth-Grid 2.0 (EASE-Grid2) to form the observation image, which is used to construct a global GNSS-R SSM retrieval model with the SMAP SSM serving as the reference value. In order to improve the accuracy of retrieved SSM from CYGNSS, the effective reflectivity is corrected using vegetation opacity and roughness coefficient parameters from SMAP products. Additionally, the impacts of vegetation and roughness on the estimated SSM were comprehensively evaluated. The results demonstrate that the accuracy of SSM retrieved by GNSS-R is improved with correcting vegetation over different types of vegetation-covered areas. The retrieval algorithm achieves an accuracy of 0.046 cm3cm−3, resulting in a mean improvement of 4.4%. Validation of the retrieval algorithm through in situ measurements confirms its stability.

An on-farm field experiment on a locally adapted conservation tillage method was undertaken to evaluate its effect on soil erosion, surface runoff, and agronomic parameters. It was conducted on five farmer fields with 3–14% slopes in the Rift Valley and the Eastern escarpment of Ethiopia’s central highlands region for two cropping seasons. The treatments were conventional tillage (CT), repeated ploughing performed with a traditional ox-drawn plough named ‘Maresha’, and minimized contour ploughing (MT) at most twice with a locally adapted sweep-like attachment assembled to Maresha. Surface runoff and soil loss in the MT system were 30 to 60% and 49 to 76% lower than those in the CT system on 3 to 14% slopes, respectively. Despite the wide variation in surface runoff, limited differences in soil water content for the depth from 0 to 20 cm were observed between the treatments. Significant differences (p < 0.05) in grain yields (kg ha−1) of 246 and 323 in the 1st and 2nd growing seasons, respectively, were recorded between the MT and CT treatments. The results of this study demonstrated that the MT system can significantly reduce surface runoff and soil loss while improving crop yields in rainfed smallholder farming systems of Ethiopia.

Dissertation submitted in partial fulfilment of the requirements for the Degree of Master of Science in Geospatial Technologies Soil moisture is a critical ecological parameter because it is a primary input for all processes that involve the complex interaction between land surface and the atmosphere. Remote sensing, especially using microwaves, has shown great promise in measuring soil moisturewith several operating satellites focused on its continuous estimation and monitoring on a global scale. Portugal is predominantly characterized by Mediterranean and semi-arid climates that feature low and sporadic precipitation. Over 10% of Portugal’s land area has been planted with Eucalyptus globulus- a non-native, fast-growing tree primarily planted for industrial use. Some studies have demonstrated that eucalyptus plantations adversely affect water availability, but overall results have been inconclusive as there are numerous other confounding variables. The goals of this study were to determine, using fully polarimetric L-band SAR and machine learning, if soil moisture could be accurately predicted in eucalyptus forests, and if there is a significant difference in soil moisture inside eucalyptus forests relative to other forests. Vegetated surfaces complicate the estimation of soil moisture because their structure and water content contribute significantly to backscatter of the radar signal. Thus, four polarimetric decompositions were compared to separate vegetative versus surface backscatter. The inputs from those decompositions, as well as several additional radar indices and polarizations from the microwave images, were used as feature inputs into two different machine learning models. After a feature selection process, the soil moisture estimations were retrieved and compared using cross-validation. The best overall soil moisture retrieval for Eucalyptus forests came from Random Forest with a RMSE of 0.021, a MAE of 0.017, and a MBE of 0.001. Through a statistical t-test, predicted soil moisture values in eucalyptus forests did not differ significantly as compared to other forest types in the study area.