Field Scale Research Articles

Thermo-hydro-mechanical (THM) coupled simulation of the land subsidence due to aquifer thermal energy storage (ATES) system in soft soils

Aquifer thermal energy storage (ATES) system has received attention for heating or cooling buildings. However, it is well known that land subsidence becomes a major environmental concern for ATES projects. Yet, the effect of temperature on land subsidence has received practically no attention in the past. This paper presents a thermo-hydro-mechanical (THM) coupled numerical study on an ATES system in Shanghai, China. Four water wells were installed for seasonal heating and cooling of an agriculture greenhouse. The target aquifer at a depth of 74–104.5 m consisted of alternating layers of sand and silty sand and was covered with clay. Groundwater level, temperature, and land subsidence data from 2015 to 2017 were collected using field monitoring instruments. Constrained by data, we constructed a field scale three-dimensional (3D) model using TOUGH (Transport of Unsaturated Groundwater and Heat) and FLAC3D (Fast Lagrangian Analysis of Continua) equipped with a thermo-elastoplastic constitutive model. The effectiveness of the numerical model was validated by field data. The model was used to reproduce groundwater flow, heat transfer, and mechanical responses in porous media over three years and capture the thermo- and pressure-induced land subsidence. The results show that the maximum thermo-induced land subsidence accounts for about 60% of the total subsidence. The thermo-induced subsidence is slightly greater in winter than that in summer, and more pronounced near the cold well area than the hot well area. This study provides some valuable guidelines for controlling land subsidence caused by ATES systems installed in soft soils.

A spatially explicit model of landscape pesticide exposure to bees: Development, exploration, and evaluation

Pesticides represent one of the greatest threats to bees and other beneficial insects in agricultural landscapes. Potential exposure is generated through compound- and crop-specific patterns of pesticide use over space and time and unique degradation behavior among compounds. Realized exposure develops through bees foraging from their nests across the spatiotemporal mosaic of floral resources and associated pesticides throughout the landscape. Despite the recognized importance of a landscape-wide approach to assessing exposure, we lack a sufficiently-evaluated predictive framework to inform mitigation decisions and environmental risk assessment for bees. We address this gap by developing a bee pesticide exposure model that incorporates spatiotemporal pesticide use patterns, estimated rates of pesticide degradation, floral resource dynamics across habitats, and bee foraging movements. We parameterized the model with pesticide use data from a public database containing crop-field- and date-specific records of uses throughout our study region over an entire year. We evaluate the model performance in predicting bee pesticide exposure using a dataset of pesticide residues in pollens gathered by bumble bees (Bombus vosnesenskii) returning to colonies across 14 spatially independent landscapes in Northern California. We applied alternative model formulations of pesticide accumulation and degradation, floral resource seasonality, and bee foraging behavior to evaluate different levels of detail for predicting observed pesticide exposure. Our best model explained 73 % of observed variation in pesticide exposure of bumble bee colonies, with generally positive correlations for the dominant compounds. Timing and location of pesticide use were integral, but more detailed parameterizations of pesticide degradation, floral resources, and bee foraging improved the predictions little if at all. Our results suggest that this approach to predict bees' pesticide exposure has value in extending from the local field scale to the landscape in environmental risk assessment and for exploring mitigation options to support bees in agricultural landscapes.

Landscape-based nutrient application in wheat and teff mixed farming systems of Ethiopia: farmer and extension agent demand driven approach

IntroductionAdapting fertilizer use is crucial if smallholder agroecosystems are to attain the sustainable development goals of zero hunger and agroecosystem resilience. Poor soil health and nutrient variability characterize the smallholder farming systems. However, the current research at the field scale does not account for nutrient variability across landscape positions, posing significant challenges for targeted nutrient management interventions. The purpose of this research was to create a demand-driven and co-development approach for diagnosing farmer nutrient management practices and determining landscape-specific (hillslope, mid-slope, and foot slope) fertilizer applications for teff and wheat.MethodA landscape segmentation approach was aimed to address gaps in farm-scale nutrient management research as well as the limitations of blanket recommendations to meet local nutrient requirements. This approach incorporates the concept of interconnected socio-technical systems as well as the concepts and procedures of co-development. A smart mobile app was used by extension agents to generate crop-specific decision rules at the landscape scale and forward the specific fertilizer applications to target farmers through SMS messages or print formats.Results and discussionThe findings reveal that farmers apply more fertilizer to hillslopes and less to mid- and foot slopes. However, landscape-specific fertilizer application guided by crop-specific decision rules via mobile applications resulted in much higher yield improvements, 23% and 56% at foot slopes and 21% and 6.5% at mid slopes for wheat and teff, respectively. The optimized net benefit per hectare increase over the current extension recommendation was $176 and $333 at foot slopes and $159 and $64 at mid slopes for wheat and teff (average of $90 and $107 for wheat and teff), respectively. The results of the net benefit-to-cost ratio (BCR) demonstrated that applying landscape-targeted fertilizer resulted in an optimum return on investment ($10.0 net profit per $1.0 investment) while also enhancing nutrient use efficiency across the three landscape positions. Farmers are now cognizant of the need to reduce fertilizer rates on hillslopes while increasing them on parcels at mid- and foot-slope landscapes, which have higher responses and profits. As a result, applying digital advisory to optimize landscape-targeted fertilizer management gives agronomic, economic, and environmental benefits. The outcomes results of the innovation also contribute to overcoming site-specific yield gaps and low nutrient use efficiency, they have the potential to be scaled if complementing innovations and scaling factors are integrated.

Detection of magnetospheric ion drift patterns at Mars

Mars lacks a global magnetic field, and instead possesses small-scale crustal magnetic fields, making its magnetic environment fundamentally different from intrinsic magnetospheres like those of Earth or Saturn. Here we report the discovery of magnetospheric ion drift patterns, typical of intrinsic magnetospheres, at Mars using measurements from Mars Atmosphere and Volatile EvolutioN mission. Specifically, we observe wedge-like dispersion structures of hydrogen ions exhibiting butterfly-shaped distributions (pitch angle peaks at 22.5°−45° and 135°−157.5°) within the Martian crustal fields, a feature previously observed only in planetary-scale intrinsic magnetospheres. These dispersed structures are the results of drift motions that fundamentally resemble those observed in intrinsic magnetospheres. Our findings indicate that the Martian magnetosphere embodies an intermediate case where both the unmagnetized and magnetized ion behaviors could be observed because of the wide range of strengths and spatial scales of the crustal magnetic fields around Mars.

Crop diversity and within field multi-species interactions mediate herbivore abundances in cotton fields

Insect herbivore abundances in agricultural fields partly depend on surrounding landscape compositional heterogeneity (e.g., landscape complexity). Landscape complexity can directly (e.g., dilution of host crops) and indirectly (promoting herbivore biocontrol) regulate herbivores in agricultural fields. While much is known about direct (e.g., resource concentration) and indirect effects (i.e., promoting biocontrol) of landscape complexity on herbivore populations, more work is needed to study whether landscape complexity can regulate herbivore populations by mediating within field multi-species interactions among herbivores and their shared natural enemies. During 2019 and 2020, we estimated Bemisia tabaci and Aphis gossypii abundances, their dominant predators (coccinellids, spiders, Orius spp., and Geocoris spp.), and their interaction (using molecular analysis) in 38 cotton fields along a gradient of landscape diversity across Georgia, USA. Within cotton fields, we assessed the effect of predator abundances, their frequency of feeding on herbivores, and the correlation between herbivore abundances (B. tabaci and A. gossypii) on the B. tabaci and A. gossypii abundance. At the landscape scale, crop diversity and different cover types influenced the abundance of B. tabaci and A. gossypii within cotton fields. We found a complex interaction among pests at the field scale, with higher aphid abundance correlated with decreased whitefly abundance. Our results support crop diversification for improving suppression of generalist pests in cotton landscapes through promoting biocontrol and diluting host crop area. Our result further suggests that the landscape complexity effect on whiteflies can indirectly mediate aphid abundance in cotton fields, indicating the importance of within field species interactions.

Machine Learning Downscaling of SoilMERGE in the United States Southern Great Plains

SoilMERGE (SMERGE) is a root-zone soil moisture (RZSM) product that covers the entire continental United States and spans 1978 to 2019. Machine learning techniques, Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Gradient Boost (GBoost) downscaled SMERGE to spatial resolutions straddling the field scale domain (100 to 3000 m). Study area was northern Oklahoma and southern Kansas. The coarse resolution of SMERGE (0.125 degree) limits this product’s utility. To validate downscaled results in situ data from four sources were used that included: United States Department of Energy Atmospheric Radiation Measurement (ARM) observatory, United States Climate Reference Network (USCRN), Soil Climate Analysis Network (SCAN), and Soil moisture Sensing Controller and oPtimal Estimator (SoilSCAPE). In addition, RZSM retrievals from NASA’s Airborne Microwave Observatory of Subcanopy and Surface (AirMOSS) campaign provided a nearly spatially continuous comparison. Three periods were examined: era 1 (2016 to 2019), era 2 (2012 to 2015), and era 3 (2003 to 2007). During eras 1 and 2, RF outperformed XGBoost and GBoost, whereas during era 3 no model dominated. Performance was better during eras 1 and 2 as opposed to the pre-L band era 3. Improvements across all eras, regions, and models realized from downscaling included an increase in correlation from 0.03 to 0.42 and a decrease in ubRMSE from −0.0005 to −0.0118 m3/m3. This study demonstrates the feasibility of SMERGE downscaling opening the prospect for the development of a long-term RZSM dataset at a more desirable field-scale resolution with the potential to support diverse hydrometeorological and agricultural applications.

Ultra-thin 2D Fe3GaTe2 rare-earth free permanent magnet at finite temperatures

Despite extensive studies on permanent magnets (PMs) in bulk-type materials, the possibility of PMs in two-dimensional (2D) materials is barely explored so far. In this work, we systematically investigate temperature dependent magnetic properties of 2D trilayer and four-layer Fe3GaTe2 systems based on the first principle calculations. The calculated Curie temperature (T C) in both trilayer and four-layer structures are 340 K–352 K. Both systems have perpendicular magnetic anisotropy, and the uniaxial anisotropy constant is monotonically decreased with increasing temperature. At 300 K, the 2D Fe3GaTe2 has a coercive field of 0.34 T in the trilayer and it becomes 0.44 T in the four-layer. Besides, both systems have a magnetic hardness parameter κ larger than 1 even at 300 K. We also obtain a maximum energy product (BH)max of 24 kJ m−3 in the trilayer, and it is further increased to 26 kJ m−3 in the four-layer at 300 K. Nonetheless, these (BH)max are decreased by more than two times with including the demagnetization factor. Overall, we obtain that 2D Fe3GaTe2 at 2–3 nm (trilayer and four-layer) thickness possesses the same scale of coercive field and maximum energy product of well-known bulk ferrite PM. Our findings may indicate that the atomically thin 2D system can be a potential rare-earth-free PM for small-scale device applications.

Numerical simulation of different pollutant control measures around an old landfill contaminated site: A field scale study

The remediation of contaminated soils is a great challenge for global environmental sciences and engineering. The landfill was a kind of infrastructure to deal with waste from different sources while it would also cause the threat to groundwater. Cut-off walls and pumping wells were usually applied in the landfill to prevent the spread of pollutants to wider areas. However, the combination of using both of methods was rarely analyzed, especially using field data for calibrating and fitting groundwater flow and pollutant transport. 7 monitoring wells were arranged in the study area to survey the subsurface seepage. The pollution monitoring was carried out for a period of 50 days, covering 31 types of inorganic and organic pollutants. The concentration of 2,4,6-trichlorophenol (TCP) was 556.7 times greater than the standard concentration. A coupled numerical model of groundwater flow and pollutant transport was developed to assess the effectiveness of various control methods. Three options were tested, including the implementation of a single cut-off wall as well as a combination of a cut-off wall and a pumping well, for preventing the discharge of pollutants from landfills. The combination of a cut-off wall and a pumping well is the best strategy for removal of TCP. The combination approaches lead to a reduction of pollution plumes by a factor of 11 compared to the case without pollution control measures. The research findings may provide a basis and reference for the application of cutoff walls and pumping well in landfill sites or contaminated groundwater.

Compaction-induced variability in the 80/20 sand-bentonite mixture: Spatial variation in dry density at different scales in the laboratory and large-scale gas seal test

Sand-bentonite (SB) mixtures are considered as backfill materials for sealing designated sections of nuclear waste repositories. The dry density of the SB mixture is the crucial parameter for the quality control during emplacement because it strongly controls the hydraulic and mechanical properties. Backfilling of an SB mixture is often performed layer by layer by keeping track of the average dry density of each layer. The compaction is typically done in a dynamic manner leading to more compaction near the compaction surface. In this study, an SB mixture with 80% sand and 20% bentonite was compacted in the laboratory by static and dynamic procedures to a height of 10 cm and the dry density profile was characterized by destructive slicing and microfocus X-ray CT imaging. When dynamically compacted, a distinct dry density gradient was identified. The variation in the dry density identified by the slicing was as much as 0.4 g/cm3 which leads to hydraulic conductivity variation by a factor of 4.8. The SB seal emplaced in the large-scale gas seal test (GAST) for demonstrating the effective functioning of the gas permeable seals was also analyzed to identify if any variability was present at the field scale. The samples collected from the top 2 cm of each 10 cm-thick horizontal layers yielded a higher overall average by 0.06 g/cm3 than the average of the mass balance method. In conjunction with the laboratory studies, it was implied that sampling results, if only collected from the very top few centimeters of the layers, could lead to an overestimation in the global dry density. Overall, the results with different methods at different scales indicated that the dynamic compaction-induced variability within a single layer was more pronounced than the variability in the larger scales detected in the mass balance and sampling methods.

Connectivity elements and mitigation measures in policy-relevant soil erosion models: A survey across Europe

The current use of soil erosion models in Europe was investigated through an exploratory survey of 46 model applications covering 18 European countries. This revealed novel information on erosion model applications, their parameterisation, incorporation of landscape elements and mitigation measures with implications for connectivity and their use in decision-making in Europe. The model application predictions were applied at national, regional, catchment or field scale. The majority of model applications used the USLE or versions thereof, but a range of semi-empirical, decision-tree and process-based models were also used. The majority of model applications were used for policy relevant purposes such as erosion risk assessment or mitigation measure implementation at a range of spatial scales. The analysis identified an evident prevalence towards the use of national or regional data sets and a highly varying parameterisation of model applications. Landscape elements and mitigation measures with effects on connectivity were implemented in most model applications, but not with a focus on modelling connectivity within the landscape. Altogether, the results demonstrate a need for improving connectivity modelling in diverse agricultural landscapes across multiple scales. Models should be chosen dependent on their ability to reflect erosion risk at different spatial scales. Albeit, harmonisation of data sets, parameterisation procedures and validation approaches is needed for certain modelling scenarios to ensure comparability of soil erosion risk assessment and suitable mitigation practices. Furthermore, we recommend that policy-relevant erosion risk maps should be verified by empirical data and thresholds derived from erosion risk maps should be adapted to regional conditions when used for policy guidelines. Hence, comparability, comprehensibility and regional adaptation are essential qualities of policy-relevant erosion maps.

Evaluating topsoil salinity via geophysical methods in rice production systems in the Vietnam Mekong Delta

AbstractThe Vietnam Mekong Delta (VMD) is threatened by increasing saltwater intrusion due to diminishing freshwater availability, land subsidence, and climate change induced sea level rise. Through irrigation, saltwater can accumulate in the rice fields and decrease rice production. The study aims at evaluating topsoil salinity and examining a potential link between topsoil salinity and rice production systems in a case study in the Tra Vinh province of the VMD. For this, we applied two geophysical methods, namely, 3D electrical resistivity tomography (ARES II) and electromagnetic induction (EM38‐MK2). 3D ARES II measurements with different electrode spacings were compared with EM38‐MK2 topsoil measurements to evaluate their respective potential for monitoring topsoil salinity on an agricultural scale and the relationship between land‐use types and topsoil salinity. Results show that EM38‐MK2 is a rapid and powerful tool for obtaining high‐resolution topsoil salinity maps for rice fields. With ARES II data, 3D maps up to 40 m depth can be created, but compared with EM38‐MK2 topsoil maps, topsoil salinity was underestimated due to limitations in resolution. Salt contamination of above 300 mS m−1 was found in some double‐cropped rice fields, whereas in triple‐cropped rice fields salinity was below 200 mS m−1. Results clearly show a relation between topsoil salinity and proximity to the saline water sources; however, a clear link between rice production and topsoil salinity could not be established. The study proved that geophysical methods are useful tools for assessing and monitoring topsoil salinity at agricultural fields scale in the VMD.

Cracking process in expansive soil with and without vegetation covers in dry and rainy seasons at field scale

ABSTRACT The presence of desiccation cracks in the soil alters its hydromechanical behavior, increasing the soil's water infiltration capacity, mobilizing the potential for expansion. This may affect the performance of the structural elements of the construction. This study aimed to evaluate the mechanics of expansion, contraction and cracking of the expansive soil of Paulista - Pernambuco, Brazil, through field trials, subject to wetting cycles and drying. The studied soil is a sandy silty clay of high compressibility with medium to very high expansion potential. The process of formation and propagation of cracks was analyzed using digital images and the monitoring of samples subjected to drying and wetting cycles. The indices of crack geometry increased with the advancement of desiccation but did not stabilize. During the wetting period, they tend to close. The pattern of cracks in the tests varied according to the presence or absence of vegetation. It was concluded that the vegetation cover has a significant influence on the standardization and the crack formation and propagation process.

Evaluation of total infiltration and storage capacities for different soil types in Sarawak using SWMM

Climate change and irresponsible urbanization practices are anticipated to further exacerbate flood risk. The different soil types’ physical, chemical, and biological characteristics significantly impact surface water movement, porosity, permeability, and infiltration potential. Therefore, soil infiltration is perhaps the most challenging and crucial process to characterize on a field scale. Various methods have been developed to measure the infiltration rate empirically and theoretically. However, the relationship between different soil types and infiltration rates in Sarawak remains unknown as no previous study has been conducted. This study uses the Storm Water Management Model (SWMM) to evaluate the infiltration rates for five different soil types: clay, clay loam, loam, sandy loam, and sand. 30 samples of various types and soil depths were examined at intervals of 0.5 m, 1.0 m, 1.5 m, 2.0 m, 2.5 m, and 3 m. The study was carried out using a standardized slope of 0.7% and an impervious land of 25% with a catchment size of 2 acres. Extreme rainfall data on the 5th and 6th of December 2021 was input into the infiltration model. Results showed that the difference between initial and final water storage of all the investigated soil depths for clay, clay loam, loam, sandy loam, and sand was found to be 48.42 mm, 51.20 mm, 58.01 mm, 66.96 mm, and 115.54 mm, respectively. The findings demonstrated that clay has the lowest water storage capability, followed by clay loam, loam, and sandy loam. Sand could store a comparatively large amount of rainwater. In contrast, sand has the highest infiltration rate with 2.541 mm/h, followed by sandy loam with 1.835 mm/h, loam with 1.432 mm/h, clay loam with 1.039 mm/h. Clay has the lowest infiltration rate, with 0.852 mm/h. This research concluded that sandy soil could significantly reduce surface runoff and help reduce flood risk in urban regions.

Carbon dynamics as a function of soil moisture following repeated wet-dry cycles in irrigated soils

Drying-rewetting cycles are ubiquitous across natural and managed ecosystems. These cycles are known to mobilize carbon (C) in soils producing dramatic pulses in microbial respiration. While many factors contribute to these pulses, the drying-rewetting history of soils affecting carbon emissions remains unclear, especially in irrigated soils where soil moisture fluctuations are more repetitive and/or frequent than natural, seasonally influenced soils. To understand the controls of repeated wet-up and dry down effects on agricultural soils, we used a systems approach to examine the cross section of a furrow irrigated orchard to delineate soil C dynamics. Specifically, we compared two contrasting water regimes, (1) soils temporarily but repeatedly inundated during water delivery (i.e., furrows) and (2) soils at the base of trees (i.e., berms) that only receive water during precipitation events in a semi-arid Mediterranean climate. Overall, our findings show that the heterogeneous landscape of a furrow irrigated field results in two separate systems within the field scale in gaseous release of C as CO2, microbial selectivity of substrates, and mechanisms for C stabilization. By monitoring soil moisture as a function of depth for over two years, our results reveal that furrow soils undergo dramatic wet-dry cycles, while moisture within the berm is relatively constant. We were able to capture the distinct heterogeneity of soil moisture changes within the furrow and berm soils by continuously monitoring CO2 flux throughout water input events in both the wet and dry season. Soil CO2 efflux is suppressed upon irrigation within furrows, while carbon oxidation in berm soils exhibits pore-connectivity limitations that result in lower fluxes when dry. Solid phase soil C speciation determined by C 1s NEXAFS demonstrated C of higher aromaticity remained in furrow soil compared to berm soils. Microbial community analysis shows significantly different communities reside within berm and furrow soils, where furrow soils support more anaerobic metabolisms and spore-formers while berm soils have relatively higher abundance of aerobic microbes capable of degrading larger, more complex C compounds. Our findings show that water regime (periodic inundation vs episodic rainfall) controlling rewetting history can greatly differentiate C respiration within managed soils.

Adapting crop production to climate change and air pollution at different scales.

Air pollution and climate change are tightly interconnected and jointly affect field crop production and agroecosystem health. Although our understanding of the individual and combined impacts of air pollution and climate change factors is improving, the adaptation of crop production to concurrent air pollution and climate change remains challenging to resolve. Here we evaluate recent advances in the adaptation of crop production to climate change and air pollution at the plant, field and ecosystem scales. The main approaches at the plant level include the integration of genetic variation, molecular breeding and phenotyping. Field-level techniques include optimizing cultivation practices, promoting mixed cropping and diversification, and applying technologies such as antiozonants, nanotechnology and robot-assisted farming. Plant- and field-level techniques would be further facilitated by enhancing soil resilience, incorporating precision agriculture and modifying the hydrology and microclimate of agricultural landscapes at the ecosystem level. Strategies and opportunities for crop production under climate change and air pollution are discussed.

Potential Yield of Glutinous Rice Varieties (Oryza sativa L. Var. glutinosa) Under Different Water Supply

Rice is a Gramineae plant. Each rice plant type has different morphology, physiological characteristics, and yields. Thus, genetic and environmental factors such as water availability influence those differences. West Nusa Tenggara (NTB) is one of Indonesia’s provinces with local glutinous rice strains cultivated by farmers. Local glutinous rice is grown on a field scale which depends on rainfall in this region. Therefore, plant growth rate and yield might decrease caused by inadequate amount of water during plant growth and development. Hence it is essential to find out the response of several strains of glutinous rice due to different water supplies, in addition to obtain adaptive glutinous rice strains under water deficit. This research was a factorial experiment with a randomized group design that contained two factors. The first factor was glutinous rice varieties, consisting of three local glutinous rice types: Me’e, Kala, Samada, and one National variety, Paketih. The second factor was the field capacity condition implemented by the amount of water supply: 2,250 ml (50 percent FC), 3,375 ml (75 percent FC), 4,500 ml (100 percent FC), and 5,625 ml (125 percent FC). Data were analyzed using a 5 percent analysis of variance, then proceeded with DMRT. The results showed that glutinous rice strains and water supply interacted with weight grains per clump. Meanwhile, there was no interaction on plant length, number of leaves, and weight of 100 grains. Samada variety showed adaptive behavior under limited water, presented on yield variable compared to the other varieties.

Mapping and monitoring peatland conditions from global to field scale

Peatlands cover only 3–4% of the Earth’s surface, but they store nearly 30% of global soil carbon stock. This significant carbon store is under threat as peatlands continue to be degraded at alarming rates around the world. It has prompted countries worldwide to establish regulations to conserve and reduce emissions from this carbon rich ecosystem. For example, the EU has implemented new rules that mandate sustainable management of peatlands, critical to reaching the goal of carbon neutrality by 2050. However, a lack of information on the extent and condition of peatlands has hindered the development of national policies and restoration efforts. This paper reviews the current state of knowledge on mapping and monitoring peatlands from field sites to the globe and identifies areas where further research is needed. It presents an overview of the different methodologies used to map peatlands in nine countries, which vary in definition of peat soil and peatland, mapping coverage, and mapping detail. Whereas mapping peatlands across the world with only one approach is hardly possible, the paper highlights the need for more consistent approaches within regions having comparable peatland types and climates to inform their protection and urgent restoration. The review further summarises various approaches used for monitoring peatland conditions and functions. These include monitoring at the plot scale for degree of humification and stoichiometric ratio, and proximal sensing such as gamma radiometrics and electromagnetic induction at the field to landscape scale for mapping peat thickness and identifying hotspots for greenhouse gas (GHG) emissions. Remote sensing techniques with passive and active sensors at regional to national scale can help in monitoring subsidence rate, water table, peat moisture, landslides, and GHG emissions. Although the use of water table depth as a proxy for interannual GHG emissions from peatlands has been well established, there is no single remote sensing method or data product yet that has been verified beyond local or regional scales. Broader land-use change and fire monitoring at a global scale may further assist national GHG inventory reporting. Monitoring of peatland conditions to evaluate the success of individual restoration schemes still requires field work to assess local proxies combined with remote sensing and modeling. Long-term monitoring is necessary to draw valid conclusions on revegetation outcomes and associated GHG emissions in rewetted peatlands, as their dynamics are not fully understood at the site level. Monitoring vegetation development and hydrology of restored peatlands is needed as a proxy to assess the return of water and changes in nutrient cycling and biodiversity.

Simulation of Spatiotemporal Distribution and Variation of 30 m Resolution Permafrost in Northeast China from 2003 to 2021

The high-resolution permafrost distribution maps have a closer relationship with engineering applications in cold regions because they are more relative to the real situation compared with the traditional permafrost zoning mapping. A particle swarm optimization algorithm was used to obtain the index η with 30 m resolution and to characterize the distribution probability of permafrost at the field scale. The index consists of five environmental variables: slope position, slope, deviation from mean elevation, topographic diversity, and soil bulk density. The downscaling process of the surface frost number from a resolution of 1000 m to 30 m is achieved by using the spatial weight decomposition method and index η. We established the regression statistical relationship between the surface frost number after downscaling and the temperature at the freezing layer that is below the permafrost active layer base. We simulated permafrost temperature distribution maps with 30 m resolution in the four periods of 2003–2007, 2008–2012, 2013–2017, and 2018–2021, and the permafrost area is, respectively, 28.35 × 104 km2, 35.14 × 104 km2, 28.96 × 104 km2, and 25.21 × 104 km2. The proportion of extremely stable permafrost (<−5.0 °C), stable permafrost (−3.0~−5.0 °C), sub-stable permafrost (−1.5~−3.0 °C), transitional permafrost (−0.5~−1.5 °C), and unstable permafrost (0~−0.5 °C) is 0.50–1.27%, 6.77–12.45%, 29.08–33.94%, 34.52–39.50%, and 19.87–26.79%, respectively, with sub-stable, transitional, and unstable permafrost mainly distributed. Direct and indirect verification shows that the permafrost temperature distribution maps after downscaling still have high reliability, with 83.2% of the residual controlled within the range of ±1 °C and the consistency ranges from 83.17% to 96.47%, with the identification of permafrost sections in the highway engineering geological investigation reports of six highway projects. The maps are of fundamental importance for engineering planning and design, ecosystem management, and evaluation of the permafrost change in the future in Northeast China.

Advancing Skyborne Technologies and High-Resolution Satellites for Pasture Monitoring and Improved Management: A Review

The timely and accurate quantification of grassland biomass is a prerequisite for sustainable grazing management. With advances in artificial intelligence, the launch of new satellites, and perceived efficiency gains in the time and cost of the quantification of remote methods, there has been growing interest in using satellite imagery and machine learning to quantify pastures at the field scale. Here, we systematically reviewed 214 journal articles published between 1991 to 2021 to determine how vegetation indices derived from satellite imagery impacted the type and quantification of pasture indicators. We reveal that previous studies have been limited by highly spatiotemporal satellite imagery and prognostic analytics. While the number of studies on pasture classification, degradation, productivity, and management has increased exponentially over the last five years, the majority of vegetation parameters have been derived from satellite imagery using simple linear regression approaches, which, as a corollary, often result in site-specific parameterization that become spurious when extrapolated to new sites or production systems. Few studies have successfully invoked machine learning as retrievals to understand the relationship between image patterns and accurately quantify the biophysical variables, although many studies have purported to do so. Satellite imagery has contributed to the ability to quantify pasture indicators but has faced the barrier of monitoring at the paddock/field scale (20 hectares or less) due to (1) low sensor (coarse pixel) resolution, (2) infrequent satellite passes, with visibility in many locations often constrained by cloud cover, and (3) the prohibitive cost of accessing fine-resolution imagery. These issues are perhaps a reflection of historical efforts, which have been directed at the continental or global scales, rather than at the field level. Indeed, we found less than 20 studies that quantified pasture biomass at pixel resolutions of less than 50 hectares. As such, the use of remote sensing technologies by agricultural practitioners has been relatively low compared with the adoption of physical agronomic interventions (such as ‘no-till’ practices). We contend that (1) considerable opportunity for advancement may lie in fusing optical and radar imagery or hybrid imagery through the combination of optical sensors, (2) there is a greater accessibility of satellite imagery for research, teaching, and education, and (3) developers who understand the value proposition of satellite imagery to end users will collectively fast track the advancement and uptake of remote sensing applications in agriculture.

Sensitivity of surface soil moisture retrieval to satellite-derived vegetation descriptors over wheat fields in the Kairouan plain

ABSTRACT Soil moisture estimation is a key component in hydrological processes and irrigation amounts' estimation. The synergetic use of optical and radar data has been proven to retrieve the surface soil moisture at a field scale using the Water Cloud Model (WCM). In this work, we evaluate the impact of staellite-derived vegetation descriptors to estimate the surface soil moisture. Therefore, we used the Sentinel-1 data to test the polarization ratio ( σ V H 0 / σ V V 0 ) and the normalized polarization ratio (IN) and the frequently used optical Normalized Difference vegetation Index (NDVI) as vegetation descriptors. Synchronous with Sentinel-1 acquisitions, in situ soil moisture were collected over wheat fields in the Kairouan plain in the center of Tunisia. To avoid the bare soil roughness effect and the radar signal saturation in dense vegetation context, we considered the data where the NDVI values vary between 0.25 and 0.7. The soil moisture inversion using the WCM and NDVI as a vegetation descriptor was characterized by an RMSE value of 5.6 vol.%. A relatively close performance was obtained using IN and ( σ V H 0 / σ V V 0 ) with RMSE under 7. 5 vol.%. The results revealed the consistency of the radar-derived data in describing the vegetation for the retrieval of soil moisture.