Investigating brazilian agriculture with Sentinel-5P: TROPOMI’s potential for characterizing the plant-animal-atmosphere system

Abstract

This study tackles a dual challenge for large-scale tropical agriculture under climate change: maintaining the competitiveness of highly mechanized systems while measuring and mitigating emissions at decision-relevant spatiotemporal scales (from field to national levels). It integrates Sentinel-5P/TROPOMI observations along two complementary axes. Axis 1 processes solar-induced fluorescence (SIF) time series to infer soybean phenology and operational metrics (planting/harvest intensity and synchrony), including daily mechanized-area peaks >100,000 ha day¹ in Central- West hubs. These phenological landmarks are validated across scales (official databases, higher-resolution imagery, agrometeorological indices) and tested for sensitivity to interannual climate variability, revealing stress signals and phenological delays during La Niña in southern Brazil. Axis 2 quantifies agricultural emissions using top-down CH inversions and leverages tropospheric NO anomalies to infer NO patterns over pastures, with implications for indirect NO via deposition. The analyses distinguish pasture conditions (degraded vs. intensified), identify hotspots aligned with production structure (e.g., Center–South livestock corridors), and quantify uncertainties from transport, retrieval errors, and management heterogeneity. Together, the axes form an observational framework linking agricultural logistics (phenology, operational windows, potential productivity) to atmospheric trace-gas accounting, supporting more transparent inventories, targeted mitigation, and adaptive management. Methodological contributions include: (i) operationalizing SIF- based crop phenology at continental scale; (ii) integrating TROPOMI hyperspectral products for sectoral CH and NO diagnostics; and (iii) proposing comparable metrics across regions and seasons for continuous monitoring of climate risk and environmental performance. Keywords: SIF; TROPOMI; soybean phenology; agricultural mechanization; CH; NO; indirect NO; top-down inversions; degraded pastures; climate variability; Brazil.Este estudo enfrenta dois desafios da agricultura tropical em larga escala sob mudança do clima: manter a competitividade de sistemas mecanizados e medir/mitigar emissões em escala útil à decisão (do talhão ao nacional). Para isso, integra dados orbitais do Sentinel-5P/TROPOMI em dois eixos. No Eixo 1, séries de SIF são usadas para inferir a fenologia da soja e métricas operacionais da safra (intensidade e sincronia de plantio/colheita), validadas com bases oficiais, imagens de maior resolução e índices agrometeorológicos, além de avaliar a sensibilidade à variabilidade interanual (ex.: atrasos e sinais de estresse em anos de La Niña no Sul do Brasil). No Eixo 2, o estudo estima emissões por inversões top-down de CH e utiliza anomalias de NO troposférico para inferir padrões de NO em pastagens, discutindo implicações para NO indireto por deposição. As análises distinguem pastagens degradadas e intensificadas, mapeiam hotspots coerentes com a estrutura produtiva e explicitam incertezas (transporte atmosférico, erros de retrieval e heterogeneidade de manejo). Em conjunto, os eixos conectam logística agrícola (fenologia e janelas operacionais) com contabilidade atmosférica de gases-traço, apoiando inventários mais transparentes, mitigação direcionada e manejo adaptativo. Palavras-chave: SIF; TROPOMI; fenologia da soja; mecanização agrícola; CH; NO; NO indireto; inversões “top-down”; pastagens degradadas; variabilidade climática; Brasil.