Satellite Remote Sensing PhD: Comparing Soil Organic Carbon and Moisture under Regenerative and Conventional Farming

Satellite Remote Sensing PhD: Comparing Soil Organic Carbon and Moisture under Regenerative and Conventional Farming

Quick Decision

Assessment: Strong but verify fit. This University of Leeds Geography PhD will test whether satellite observations and machine learning can distinguish regenerative from conventional agricultural management, and whether the groups differ in soil organic carbon, bare-soil characteristics or soil-moisture response. The official work plan is concrete: combine field and regional management records with Sentinel-1 radar, Sentinel-2 optical imagery, crop and land-cover products, and Copernicus services; build code and interpretable models; assess the resulting landscape patterns. It sits close to Guy Ziv's remote-sensing/GIS and land-management work and to current Leeds research using Sentinel-2 for agricultural soil carbon. The main decision point is feasibility: the project does not specify farms, management labels, in-situ validation, sample size or model transfer plan, and satellite signals are affected by bare-soil timing, moisture and crop residue. YES-DTN advertises a 3.5-year award with stipend and fee coverage. The deadline is 13:00 GMT on 12 January 2027, but official pages disagree on whether applications open on 2 or 12 November 2026. Confirm that date, field-data access, international award availability and the 2027/28 stipend before investing heavily in an application.

Research Problem and Doctoral Work

Agricultural soils support food production, carbon storage, biodiversity and water regulation. Regenerative practices such as reduced tillage, cover cropping, diversified rotations and improved organic-matter management are receiving policy and farming attention, but adoption and effects are difficult to assess at regional or national scale. The proposed PhD asks whether remote-sensing observations can identify management systems and measure differences in soil condition and landscape function. Its three stated questions are whether satellite data and machine learning can distinguish regenerative and conventional fields; whether soil-carbon dynamics and bare-soil characteristics differ; and whether soil-moisture response reveals hydrological differences. These are hypotheses to test, not promised findings.

The project builds on Leeds work using Sentinel-2 to identify bare-soil exposures and reconstruct soil spectra for soil-organic-carbon estimation. It proposes adding Sentinel-1 radar, Sentinel-2 optical imagery, crop-type data, land-cover products and emerging Copernicus agricultural monitoring services. The student will combine field-scale and regional records of agricultural management with those observations, use a cloud platform such as Google Earth Engine, write code and apply machine learning and explainable AI. The project promises interdisciplinary training in Earth observation, remote sensing, machine learning, geospatial analytics and environmental data science. The page does not specify the algorithms, soil-moisture product, project sites, record providers or whether new field sampling will occur.

This is an inference from the planned methods and external evidence: success will depend as much on reference data and labeling as on model choice. Soil-organic-carbon estimates from optical imagery can be affected by whether soil is exposed, dry, smooth and free of residue. Moisture is both a project outcome and a factor that changes radar/optical signals, so the model must separate management-related response from surface-condition and seasonal effects. Independent soil-carbon and moisture measurements, reliable farm-management histories and clear validation across fields/seasons will be important. A European study of Sentinel-1/2 soil-carbon mapping identifies sampling density/timing, moisture, roughness, crop residues and limited bare-soil windows as constraints; another Sentinel-2 study evaluates surface-condition uncertainty. Their results illustrate methodological issues, not expected accuracy for UK farms.

The environmental context is active. UK policy aims to bring 60% of agricultural soil in England into sustainable management by 2030 and establish a national soil-monitoring baseline by 2029. Government evidence also says hydrological effects of soil-management actions vary with starting wetness, location and managed area, while soil-carbon responses vary by practice. These targets create demand for better monitoring evidence; they do not establish that satellite proxies can already provide it or that a particular regenerative practice will improve carbon or water outcomes.

School, Programme and Field Context

The official 2027 listing identifies YES-27-G-16 as a PhD project hosted by the University of Leeds School of Geography. The primary supervisor is Professor Guy Ziv and the academic supervisor is Professor Richard Grayson. The page says this is not a CASE project, marks it as non-collaborative and lists no project partners. It does not advertise a joint degree. Leeds is therefore the named host; confirm the formal registration wording with admissions before accepting an offer.

Leeds describes its Geography research-degree setting as an active environment with Doctoral College support, professional-skills courses and external research links. Its full-time Geography PhDs typically take three to three-and-a-half years; this YES-DTN studentship is advertised for 3.5 years. Leeds reports that 93% of its Geography REF 2021 submission was world-leading or internationally excellent and that 100% of its research environment was conducive to work at that level. Its School page lists Geography and Environmental Science 14th in the UK in the Complete University Guide 2027, Geography in the global top 75 in ShanghaiRanking 2027, and Leeds 77th overall in QS 2027. These are useful school/research signals, not measures of this PhD's supervision or outcomes.

Supervisor and Lab Assessment

Guy Ziv is Professor in Socio-Environmental Systems. His official profile covers remote sensing, modelling, GIS, agriculture, land use and ecosystem services, with research connecting policy and land-management decisions to environmental outcomes. The project page identifies Richard Grayson as academic supervisor. A current Leeds PhD under Ziv and Pippa Chapman maps European agricultural soil carbon with Sentinel-2, machine learning and Google Earth Engine and works on bare-soil spectral reflectance. This is a close methodological predecessor, although it does not establish that this new project has the same sites or access to its data.

Three recent publications provide context for the group’s soil-carbon and evidence work:

• The 2025 paper “Consistent soil organic carbon accumulation under hedges driven by increase in light particulate organic matter” reports field evidence about carbon under hedges in agricultural landscapes. It shows nearby group experience with land management and soil-carbon change; it is not a satellite-validation study and does not predict the project's regenerative/conventional comparison.
• The 2025 manuscript “The role of information in shaping the emerging agricultural soil carbon market” examines the role of information in an emerging market. It is relevant to how credible soil-carbon evidence can inform decisions, but it is not an EO or hydrological-effects study; its journal publication details were not independently verified.
• The 2022 global comparison “What makes an operational farm soil carbon code?” discusses measurement and evidence requirements across soil-carbon codes. It reinforces the value of field-scale validation and transparent uncertainty, not a particular Sentinel model result.

The School also has an adjacent current PhD using Sentinel-2 and machine learning for soil-carbon mapping. This combination is a meaningful research environment advantage. Ask how Ziv and Grayson divide supervision, whether the new student will share datasets or field links with existing projects, and what additional independent validation is planned.

Funding, Application, and Living Cost

YES-DTN advertises a 3.5-year studentship with a stipend at UKRI rates, university fees and support for research, fieldwork, training and conference travel. Its current FAQ says international students can receive up to 30% of full scholarships and that awarded students receive fee coverage regardless of the home/international difference. The FAQ also says visa, immigration health surcharge and relocation costs are not covered. An older programme information page describes about seven international awards per year; because that page is for a 2026 cohort, treat the exact 2027 allocation and this project's international eligibility as unknown until confirmed.

UKRI's current 2026/27 minimum stipend is £21,805 and its minimum fee benchmark is £5,238. The stipend for an October 2027 start was not available in the material reviewed. Leeds estimates 2026/27 living costs at £320 a week, or £16,640 for a 52-week year, excluding tuition. Its provisional 2026/27 postgraduate residence examples include 51-week self-catered options around £10,163–£11,247, with utilities and internet included. At current-year reference rates the average living-cost estimate is about 76% of the stipend benchmark, leaving roughly £5,165 for variation; this is not a 2027/28 disposable-income promise. The University notes that costs vary by housing choice and circumstance.

Official detailed application guidance and the 2027 project archive say applications open at 10:00 GMT on 12 November 2026 and close at 13:00 GMT on 12 January 2027. The general YES-DTN Join Us page instead gives an opening date of 2 November 2026. The closing date agrees. Applications require both a Leeds Online Application and a YES-DTN form. Confirm the opening-date conflict and document checklist with the programme.

Applicant Fit and Preparation

The YES-DTN general eligibility rule is a related 2:1 honours degree or equivalent. A first-class degree is usually more competitive for funding; a Master's can help but is not required. The project page lists no relevant degree courses and does not state a mandatory coding language, GIS package or ML tool. It does promise interdisciplinary training, so treat remote sensing, GIS and programming as helpful preparation rather than formal prerequisites unless admissions confirms otherwise. If English is not the applicant's first language, the programme requires evidence of English-language qualification; the exact score was not specified on this project page.

Useful application evidence could include geospatial or satellite-data analysis, coding, machine learning/statistics, soil or water/environmental data, independent research, and examples of communicating uncertainty. A candidate who wants predominantly laboratory soil chemistry or field hydrology should check the work balance: the advertised core is satellite-data and model analysis, but the amount of field validation is unknown.

Next Actions and Questions to Verify

Before Applying

• Ask YES-DTN whether applications open on 2 or 12 November 2026 and confirm the two-form sequence and complete document list. Plan around the stated 13:00 GMT deadline on 12 January 2027.
• Confirm whether the specific studentship can be held by an international candidate in 2027 and what fee, stipend, visa, health-surcharge and relocation costs are covered.
• Contact both supervisors to ask which farms and management records are in scope, what counts as regenerative management, and whether ground-truth soil-carbon and soil-moisture measurements are already available.

At Interview

• Ask how many fields/seasons and what spatial resolution are planned; how cloud, vegetation, residue, soil wetness and roughness will be handled; and how independent model validation will be done.
• Clarify how the work is divided among field/record collection, management classification, soil-carbon analysis, hydrological response, explainable AI and stakeholder communication.
• Ask what coding/GIS tools are expected at entry, what training is provided, and what compute/data access the student will have.

Before Accepting an Offer

• Get the 2027/28 stipend, tuition-fee coverage, international award eligibility and project research-cost allowance in writing.
• Confirm Leeds registration and degree-awarding arrangements, supervision cadence, field/data access and planned deliverables.
• Recalculate living costs using the accommodation rates and stipend for the actual entry year; budget visa and relocation costs separately where applicable.

Evidence and Sources

Project, programme and Leeds research environment

• Official YES-DTN project: YES-27-G-16
• 2027 YES-DTN project archive and cohort dates
• YES-DTN application steps
• YES-DTN eligibility and funding FAQ
• YES-DTN Join Us: award overview
• YES-DTN application information
• Professor Guy Ziv - University of Leeds
• Leeds PhD research on agricultural soil carbon and Sentinel-2
• Leeds Geography research degrees
• Leeds Geography reputation, REF and rankings
• University of Leeds contact details and country

Soil carbon, remote sensing and policy evidence

• Consistent soil carbon accumulation under hedges (2025)
• Information and the agricultural soil-carbon market (2025)
• Global comparison of operational farm soil-carbon codes (2022)
• Unlocking private investment in soil carbon in England (2023)
• Farming Roadmap 2050: soil policy and monitoring
• Government evidence on run-off and soil management
• Environmental Improvement Plan annual progress, 2025–26
• Sentinel mission capabilities
• Copernicus Sentinel-1 soil-moisture product update
• Google Earth Engine official documentation
• Sentinel-1/2 bare-soil mosaics for SOC mapping (2023)
• Sentinel-2 soil-composite conditions and uncertainty (2023)

Funding, costs and career context

• UKRI stipend and fee rates
• University of Leeds living-cost guide
• Leeds 2026/27 accommodation comparisons
• UK Geospatial Sector Market Report 2024
• Royal Geographical Society careers in mapping, GI and data science