Systematic Divergence Between NDVI-Based and Energy-Balance Water-Stress Classifications in Sparse Hyper-Arid Olive Orchards: A Two-Decade Landsat–METRIC Evaporative CWSI Assessment

Water stress in olive orchards is often mapped from the Normalized Difference Vegetation Index (NDVI), although in sparse orchards NDVI also reflects fractional cover and bare soil. We compared an NDVI-based classification with a METRIC-derived evaporative Crop Water Stress Index (CWSIET=1−ETa/ETo) over hyper-arid, groundwater-irrigated olive orchards of La Yarada–Los Palos (Peru), using Landsat 5/8/9 imagery from eighteen austral summers (2006–2026). Both classifications rely on operational thresholds that were not physiologically calibrated for olive: the NDVI ranges were defined from field-observed canopy condition, and the CWSIET classes are equal-width bins of the reference-normalized evaporative deficit. NDVI placed most of the cropland in severe-to-extreme classes and CWSIET mainly in mild-to-moderate classes (medians 0.26–0.41); NDVI was more severe over 89% of the cropland. The divergence grew as cover decreased, persisted at native thermal resolution and was reproduced by surface temperature, although both continuous indices correlated (r=−0.61). Without in situ physiological or flux measurements, these results establish a disagreement between indicators, not which of them matches plant water status. The cropland expanded fourfold (98 to 356 km2) with no trend in mean evapotranspiration or CWSIET. NDVI-based stress maps should be interpreted with caution in sparse orchards and combined with energy-balance, meteorological or field information.

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Journal
Remote Sensing
Published
2026-10-14
DOI
https://doi.org/10.3390/rs18203527
Primary Topic
Remote Sensing in Agriculture
Type
article
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article

Systematic Divergence Between NDVI-Based and Energy-Balance Water-Stress Classifications in Sparse Hyper-Arid Olive Orchards: A Two-Decade Landsat–METRIC Evaporative CWSI Assessment

David Ascencios, Edwin Pino-Vargas, Javier Quille-Mamani, Germán Huayna et al.
Remote Sensing
Remote Sensing in Agriculture
article

Systematic Divergence Between NDVI-Based and Energy-Balance Water-Stress Classifications in Sparse Hyper-Arid Olive Orchards: A Two-Decade Landsat–METRIC Evaporative CWSI Assessment

David Ascencios, Edwin Pino-Vargas, Javier Quille-Mamani, Germán Huayna, Jorge Muchica-Huamantuma
article en

Abstract

Water stress in olive orchards is often mapped from the Normalized Difference Vegetation Index (NDVI), although in sparse orchards NDVI also reflects fractional cover and bare soil. We compared an NDVI-based classification with a METRIC-derived evaporative Crop Water Stress Index (CWSIET=1−ETa/ETo) over hyper-arid, groundwater-irrigated olive orchards of La Yarada–Los Palos (Peru), using Landsat 5/8/9 imagery from eighteen austral summers (2006–2026). Both classifications rely on operational thresholds that were not physiologically calibrated for olive: the NDVI ranges were defined from field-observed canopy condition, and the CWSIET classes are equal-width bins of the reference-normalized evaporative deficit. NDVI placed most of the cropland in severe-to-extreme classes and CWSIET mainly in mild-to-moderate classes (medians 0.26–0.41); NDVI was more severe over 89% of the cropland. The divergence grew as cover decreased, persisted at native thermal resolution and was reproduced by surface temperature, although both continuous indices correlated (r=−0.61). Without in situ physiological or flux measurements, these results establish a disagreement between indicators, not which of them matches plant water status. The cropland expanded fourfold (98 to 356 km2) with no trend in mean evapotranspiration or CWSIET. NDVI-based stress maps should be interpreted with caution in sparse orchards and combined with energy-balance, meteorological or field information.

Remote SensingVol. 18(20)
Jorge Basadre Grohmann National University (PE), Universitat Politècnica de València (ES), Universidad Nacional Agraria La Molina (PE)
Zero hunger
Openalex Percentile: Top 16%
Remote Sensing in Agriculture
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