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UID:pretalx-soil-health-now-2026-LPYY8Q@pretalx.earthmonitor.org
DTSTART;TZID=Europe/Athens:20261207T150000
DTEND;TZID=Europe/Athens:20261207T151500
DESCRIPTION:Mediterranean agricultural landscapes in south-eastern Spain ar
 e increasingly affected by gradual\, spatially heterogeneous soil degradat
 ion processes\, including vegetation decline\, water stress\, bare soil ex
 posure and surface warming. Early detection is challenging when monitoring
  relies on occasional field observations alone\, yet timely identification
  of at-risk areas is critical for guiding soil health management and comba
 ting desertification. Developed within the NEMESIS project\, a Mediterrane
 an Soil Health Living Lab network\, this study supports efforts to combat 
 desertification\, reduce land degradation and promote sustainable land man
 agement through soil health descriptors\, Earth Observation (EO) data and 
 place-based monitoring.\nThis work presents a satellite-based framework fo
 r assessing and predicting desertification risk in agricultural plots loca
 ted in the Region of Murcia (37.930981 N\, −2.203217 E)\, using Sentinel
 -2 imagery acquired at five-day intervals between 2015 and 2026. Seven spe
 ctral and biophysical indicators were derived from the multitemporal archi
 ve: NDVI and MSAVI to describe vegetation vigour and canopy development\; 
 NDMI to represent moisture-related vegetation response\; BSI and BSF for m
 onitoring bare soil exposure\; a land surface temperature proxy (LST) to c
 apture thermal stress\; and FCover to estimate fractional vegetation cover
 .\nThese indicators were integrated into a Desertification Degree Index (D
 DI)\, a spatial composite combining all seven components using weighted ag
 gregation and global normalisation across the observation period to ensure
  temporal comparability. Pixel-wise temporal trend analysis was performed 
 using linear regression\, producing maps of change and statistical signifi
 cance masks to identify areas with consistent degradation or recovery sign
 als.\nTo explore short-term risk evolution\, predictive models were applie
 d to the multitemporal DDI series. These models used the historical behavi
 our of the indicators to forecast future desertification risk and generate
  maps of expected surface condition\, allowing both plot-level interpretat
 ion and pixel-level identification of vulnerable areas.\nThe results ident
 ified zones of elevated desertification risk and captured intra-annual veg
 etation and soil dynamics that would be overlooked by annual composites. A
 reas with lower vegetation indices\, persistent bare soil exposure\, reduc
 ed moisture response and higher surface temperature were associated with g
 reater vulnerability\, while zones with stable vegetation cover and lower 
 thermal stress showed lower apparent risk. The short-term forecasts provid
 ed information on likely surface condition\, supporting agronomic interven
 tion. By translating a dense decade-long satellite archive into actionable
  soil health indicators\, this work demonstrates the operational potential
  of EO for early warning and targeted monitoring in Mediterranean semi-ari
 d agricultural systems.
DTSTAMP:20260825T193325Z
LOCATION:Amphitheater II
SUMMARY:Satellite-Based Assessment and Neural Network Prediction of Deserti
 fication Risk in Mediterranean Agricultural Landscapes - Adrian Canovas-Ro
 driguez\, María Fernanda García-Cruz\, Andres Julian Colunje Diaz
URL:https://pretalx.earthmonitor.org/soil-health-now-2026/talk/LPYY8Q/
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