MODIS–Sentinel-2 Data Fusion for Cloud-Robust Crop Evapotranspiration Estimation in a Nitrate-Sensitive Irrigated Maize System: Evaluating Gap-Filling Strategies for Evidence-Based Irrigation Scheduling

Reliable quantification of crop evapotranspiration (ETc) at field resolution is a prerequisite for evidence-based irrigation scheduling in agricultural systems subject to nitrate leaching constraints. This study presents and evaluates a multi-sensor data fusion framework integrating MODIS Terra (500 m, daily) and Sentinel-2 (10–20 m, 5-day revisit) imagery to generate cloud-robust, daily ETc maps for an 87.5 ha irrigated maize field in Nyírbátor, Hungary, during the 2020 and 2021 growing seasons. Three gap-filling strategies for missing Sentinel-2 NDVI observations were systematically compared: (i) co-regionalisation with cokriging, (ii) local time series interpolation of MODIS pixel centres using ordinary kriging, and (iii) a median time series of cotemporal MODIS pixels—a novel approach developed to suppress sub-pixel spectral contamination from roads and irrigation infrastructure. For field-mean temporal reconstruction, the median approach consistently outperformed the alternatives (adjusted R2 = 0.81, NRMSE = 0.15–0.17; pixel-wise correlation 0.70–0.85), effectively filtering heterogeneous landscape artefacts. Daily crop coefficients (Kc) derived from fused NDVI time series via the FAO-56 framework yielded ETc ranging from 0.99 mm day−1 (initial stage) to 6.40 mm day−1 (peak crop development). Seasonal precipitation–ETc deficit analyses revealed contrasting patterns: near balance in 2020 versus an 85 mm mid-season deficit at critical nodes in 2021, demonstrating the potential utility of spatially explicit daily ETc monitoring for irrigation scheduling. These deficit estimates represent irrigation demand indicators; a complete water balance would additionally require measured irrigation volumes, soil water storage changes, deep percolation, and surface runoff data. The methodology provides a proof-of-concept framework for EU Nitrates Directive compliance monitoring, relying solely on freely available satellite data. Independent ETc validation is required before operational deployment, and transferability to other crops and regions requires validation across contrasting pedoclimatic conditions.

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Publication Details

Journal
Water
Published
2026-07-06
DOI
https://doi.org/10.3390/w18131644
Primary Topic
Remote Sensing in Agriculture
Type
article
Field-Weighted Citation Impact
0.00

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article

MODIS–Sentinel-2 Data Fusion for Cloud-Robust Crop Evapotranspiration Estimation in a Nitrate-Sensitive Irrigated Maize System: Evaluating Gap-Filling Strategies for Evidence-Based Irrigation Scheduling

János Tamás, Attila Nagy, Nxumalo Gift Siphiwe, Fehér Zsolt Zoltán
Water
Remote Sensing in Agriculture
article

MODIS–Sentinel-2 Data Fusion for Cloud-Robust Crop Evapotranspiration Estimation in a Nitrate-Sensitive Irrigated Maize System: Evaluating Gap-Filling Strategies for Evidence-Based Irrigation Scheduling

János Tamás, Attila Nagy, Nxumalo Gift Siphiwe, Fehér Zsolt Zoltán
article en

Abstract

Reliable quantification of crop evapotranspiration (ETc) at field resolution is a prerequisite for evidence-based irrigation scheduling in agricultural systems subject to nitrate leaching constraints. This study presents and evaluates a multi-sensor data fusion framework integrating MODIS Terra (500 m, daily) and Sentinel-2 (10–20 m, 5-day revisit) imagery to generate cloud-robust, daily ETc maps for an 87.5 ha irrigated maize field in Nyírbátor, Hungary, during the 2020 and 2021 growing seasons. Three gap-filling strategies for missing Sentinel-2 NDVI observations were systematically compared: (i) co-regionalisation with cokriging, (ii) local time series interpolation of MODIS pixel centres using ordinary kriging, and (iii) a median time series of cotemporal MODIS pixels—a novel approach developed to suppress sub-pixel spectral contamination from roads and irrigation infrastructure. For field-mean temporal reconstruction, the median approach consistently outperformed the alternatives (adjusted R2 = 0.81, NRMSE = 0.15–0.17; pixel-wise correlation 0.70–0.85), effectively filtering heterogeneous landscape artefacts. Daily crop coefficients (Kc) derived from fused NDVI time series via the FAO-56 framework yielded ETc ranging from 0.99 mm day−1 (initial stage) to 6.40 mm day−1 (peak crop development). Seasonal precipitation–ETc deficit analyses revealed contrasting patterns: near balance in 2020 versus an 85 mm mid-season deficit at critical nodes in 2021, demonstrating the potential utility of spatially explicit daily ETc monitoring for irrigation scheduling. These deficit estimates represent irrigation demand indicators; a complete water balance would additionally require measured irrigation volumes, soil water storage changes, deep percolation, and surface runoff data. The methodology provides a proof-of-concept framework for EU Nitrates Directive compliance monitoring, relying solely on freely available satellite data. Independent ETc validation is required before operational deployment, and transferability to other crops and regions requires validation across contrasting pedoclimatic conditions.

WaterVol. 18(13)
University of Debrecen (HU)
Debreceni Egyetem
Openalex Percentile: Top 8%
Remote Sensing in Agriculture
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