About

Santosh S. Palmate, Ph.D., is an Assistant Professor in the Department of Biological and Agricultural Engineering at Texas A&M University. His research and extension interests focus on Arid Hydrology and Water Systems, with a particular emphasis on sustainable water management in Far-West Texas. Dr. Palmate plays a key role in integrating AgriLife Research and AgriLife Extension Service programs to address water resource challenges in arid and semi-arid environments. He leverages advanced aerial remote sensing technologies, including multispectral, thermal, and hyperspectral sensors, as one of the leading FAA-Certified drone pilots at AgriLife, to investigate the impacts of climate change, land use, and management practices on hydrologic components. His work involves developing hydrological models, resilient water management strategies, and decision-making tools to meet current and future water demands. Dr. Palmate collaborates with binational stakeholders to improve the management of shared water resources along the Texas-Mexico border and emphasizes arid water conservation and agricultural water use efficiency through partnerships with local farmers and water managers. His research addresses the challenges posed by declining freshwater supplies and increasing water demands in Southwestern United States arid regions.

Research topics

• Artificial Intelligence
• Computer Science
• Environmental science
• Physical geography
• Geography
• Algorithm
• Ecology
• Mathematics
• Statistics

Selected publications

• Treated wastewater enhances soil aggregate stability by increasing soil organic carbon: Evidence from δ13C and δ15N signatures

  Journal of Environmental Management · 2026-04-01

  articleOpen accessSenior author

  Treated wastewater (TWW) irrigation is increasingly used in arid regions, and its effects on soil salinity are well documented. However, its influence on soil carbon fractions, aggregation, and stable carbon (δ 13 C) and nitrogen (δ 15 N) isotopes remains poorly understood. This study evaluated the impacts of TWW irrigation on soil total carbon (TC), soil organic carbon (SOC), active carbon (AC), inorganic carbon (IOC), water-stable aggregates (WSA), δ 13 C, δ 15 N, and aggregate-associated carbon and nitrogen isotopes across multiple soil depths. Compared to freshwater (FW), TWW significantly increased AC at 0–15, 15–30, and 30–45 cm by 16.8%, 23.0%, and 32.4%, respectively, and enhanced SOC by 31.7% at 45–60 cm depth. The >2 mm WSA increased by 16.7% and 48.9% at 0–15 and 30–45 cm, respectively, under TWW irrigation. TWW also enhanced aggregate-associated carbon at depths of 0–15 and 15–30 cm and enriched δ 15 N at depths of 30–45 and 45–60 cm. In contrast, aggregate-associated δ 13 C decreased at 30–60 cm, and δ 13 C values across aggregate fractions were consistently lower than bulk soil at depths of 15–60 cm. Variations in SOC and AC were over 30% greater in subsoil than topsoil, whereas δ 13 C and δ 15 N showed no comparable depth trend, indicating a decoupling between soil carbon accumulation and organic matter quality. Structural equation modeling further revealed that soil total carbon, δ 13 C, and δ 15 N jointly act as dominant drivers of WSA, highlighting that aggregate stabilization under TWW irrigation is governed by integrated carbon quantity and transformation processes. Overall, TWW irrigation enhances subsoil carbon accumulation and aggregate stability while influencing soil isotopic composition, revealing new insights into soil carbon dynamics in arid agroecosystems. • Wastewater increased active and organic carbon, especially in subsoil layers. • Wastewater irrigation enhanced macroaggregate and aggregate-associated carbon. • Stable δ 13 C and δ 15 N shifts indicate carbon sources and stabilization mechanisms. • Wastewater irrigation promotes subsoil carbon storage in arid agroecosystems.

  PublisherDOI

• Soil carbon fractions and aggregate stability across management zones under flood-irrigated pecan orchards in an arid region

  Journal of Cleaner Production · 2026-02-27 · 1 citations

  articleOpen accessSenior author

  Soil organic carbon (SOC) dynamics in arid regions are influenced by climatic and edaphic factors; however, limited research has assessed how flood irrigation affects carbon fractions and their role in stabilizing soil aggregates in pecan orchards. This study evaluated the impact of three management zones, such as tree root zone (RT), inter-row (IR), and bare land (BL), on soil physical and chemical properties in a pecan orchard in El Paso, Texas. At the 0–15 cm depth, soil organic carbon (OC) and permanganate oxidizable carbon (POXC) were significantly higher in RT by 90.2% and 50.1%, than in IR. These differences were even more impressive with depth: at 15–30 cm, OC and POXC in RT were 124.6% and 97.2% higher than IR. Water-stable aggregates across all size classes were significantly higher under RT and BL compared to IR at all depths (0–75 cm). At 0–15 and 15–30 cm, the RT zone had significantly greater >2 mm by 48.6% and 70.2%, respectively, than the IR zone. At 0–15 cm, the 0.5 and 0.25 mm aggregate fractions contained significantly higher total carbon (TC) than the 1 mm and >2 mm fractions. At 15–30 cm, OC in the RT zone was higher than in the IR by 49.2% and 34.5% in the >2 mm and 1 mm aggregate fractions, respectively. In contrast, IR showed greater soil inorganic carbon (SIC) at 15–30, 30–45, and 60–75 cm. In all management zones, Olsen-P in the 0–15 cm layer was higher than in the 30–70 cm layer. Positive significant correlations between >2 mm aggregates and TC, OC, POXC, clay, silt, CEC, Olsen-P, and exchangeable cations suggest that organic matter inputs from pecan roots and leaf litter enhance soil structural stability. These findings indicate that increased OC and POXC in the RT zone contribute to improved soil resilience under arid conditions. • Tree root zones had higher > 2 mm water-stable aggregates than inter-row zones. • >2 mm aggregates positively correlated with TC, OC, POXC, and available nutrients. • Flood irrigation led to deeper SIC and salt accumulation in inter-row zones. • SOC increased in 0.5 and 0.25 mm aggregates, enhancing carbon stabilization.

  PublisherDOI

• A Global Review of Rainfall Erosivity Estimation: Methods, Challenges, and Way Forward

  Earth Systems and Environment · 2026-01-08 · 1 citations

  article
  PublisherDOI

• Treated wastewater enhances soil aggregate stability by increasing soil organic carbon: Evidence from δ13C and δ15N signatures

  SSRN Electronic Journal · 2026-01-01

  preprintOpen accessSenior author
  PublisherDOI

• Effects of no-tillage, mulching, drip irrigation, and nitrogen fertilization on greenhouse gas emissions, soil carbon sequestration, and crop yields in dryland agroecosystems: A meta-analysis

  Agriculture Ecosystems & Environment · 2026-01-14 · 7 citations

  articleOpen access
  PublisherDOI

• Flow Regime Shifts in Eastern India Under Changing Climate: A Causality and Trend Perspective

  Earth Systems and Environment · 2026-01-06

  article
  PublisherDOI

• Machine learning-based estimation of blue water footprints for transboundary water management in the Rio Grande River watershed

  Elsevier eBooks · 2025-01-01 · 1 citations

  book-chapter1st authorCorresponding
  PublisherDOI

• List of contributors

  Elsevier eBooks · 2025-01-01

  book-chapter
  PublisherDOI

• Harnessing hyperspectral imaging and machine learning to enhance salinity stress detection in canola

  Computers and Electronics in Agriculture · 2025-12-08

  article
  PublisherDOI

• 30 Years of simultaneous crop & land cover land use maps for Middle Rio Grande from 1994 to 2024

  Scientific Data · 2025-08-22 · 1 citations

  articleOpen access

  This study introduces the crop and land cover land use (CLCLU) dataset, a 30 m resolution product providing annual maps of CLCLU across the transnational Middle Rio Grande (MRG) region, spanning both the U.S. and Mexico from 1994 to 2024. The model was trained using the Cropland Data Layer (CDL) on the US side. Dual-month (July and December) Landsat composites and a semantic segmentation model, MANet with ResNeXt-101 encoder, under four strategies were used to address sensor and temporal variability. This model architecture was chosen for its intrinsic ability to capture detailed spatial patterns and contextual dependencies through its attention-based design and ResNeXt-101 encoder, which demonstrated strong performance, particularly in generalizing across data-scarce regions in Mexico. The dataset achieved 97.10% overall accuracy and 78.85% mean Intersection over Union (mIoU), over validation process using a held-out CDL subset. Validation against NLCD and MCD12Q1-UMD confirmed high agreement. Data availability differences, minimal ground truth on the Mexican side, and cloud-related artifacts in early years led to some misclassification.

  PublisherOA PDFDOI

Frequent coauthors

• Ashish Pandey

  Indian Institute of Technology Roorkee

  20 shared

• Christina Orieschnig

  Gestion de l'Eau, Acteurs, Usages

  8 shared

• Sushil Kumar Himanshu

  6 shared

• Deen Dayal

  Indian Institute of Technology Roorkee

  5 shared

• Deepak Khare

  Indian Institute of Technology Roorkee

  5 shared

• Saurav Kumar

  Indian Institute of Technology Guwahati

  5 shared

• Rajendra Pandey

  National Institute of Hydrology

  5 shared

• Sivarama Krishna Reddy Chidepudi

  Université de Caen Normandie

  4 shared

Labs

• Arid Hydrology and Water SystemsPI

Education

• Other, Agricultural Engineering

  Marathwada Agricultural University Parbhani

• Other, Irrigation Water Management

  Indian Institute of Technology Roorkee

• Ph.D., Water Resources Development and Management

  Indian Institute of Technology Roorkee