Monitoring cropland daily carbon dioxide exchange at field scales with Sentinel-2 satellite imagery DOI Creative Commons
Pia Gottschalk, Aram Kalhori, Zhan Li

и другие.

Biogeosciences, Год журнала: 2024, Номер 21(16), С. 3593 - 3616

Опубликована: Авг. 16, 2024

Abstract. Improving the accuracy of monitoring cropland CO2 exchange at heterogeneous spatial scales is great importance for reducing and temporal uncertainty in estimating terrestrial carbon (C) dynamics. In this study, an approach to estimate daily C fluxes developed tested by combining time series field-scale eddy covariance (EC) flux data Sentinel-2 satellite-based vegetation indices (VIs) after appropriately accounting alignment between two datasets. The study was carried out agricultural field (118 ha) lowlands northeastern Germany. ability different VIs net ecosystem (NEE) gross primary productivity (GPP) based on linear regression models assessed. Most showed high (>0.9) statistically significant (p<0.001) correlations with GPP NEE, although some deviated from seasonal pattern exchange. By contrast, respiration (Reco) were weak not significant, no attempt made Reco VIs. Linear explained generally more than 80 % 70 variability NEE GPP, respectively, among individual performance varied depending component (NEE or GPP) observation period. Root mean square error (RMSE) values ranged 1.35 g m−2 d−1 using green normalized difference index (GNDVI) 5 simple ratio (SR) GPP. This equated underestimated uptake only 41 (18 %) overestimation 854 (73 %). Differences measured estimated mainly diversion VI signal during winter when remained low, while indicated increased due relatively crop leaf area. Overall, results exhibited similar margins mechanistic models. Thus, they suitability expandability proposed satellite-derived

Язык: Английский

The food-water-climate nexus of green infrastructure: Examining ecosystem services trade-offs of peri-urban agriculture DOI Creative Commons
Ricard Segura, Johannes Langemeyer, Alba Badía

и другие.

The Science of The Total Environment, Год журнала: 2024, Номер 951, С. 175799 - 175799

Опубликована: Авг. 25, 2024

Emission reduction, heat mitigation, and improved access to water food provision are increasingly critical challenges for urban areas in the context of global climate change adaptation mitigation. The revival local agricultural production is often lauded as a potential nature-based solution. However, an expansion peri-urban agriculture (peri-UA) may entail significant ecosystem trade-offs. This study explores impacts on food-water-climate nexus different scenarios semi-arid, Mediterranean climate, addressing provision, freshwater use, temperature regulation, trade-offs thereof. We estimate irrigation requirements based georeferenced metabolism approach along with atmospheric biosphere models examine four land-use Metropolitan Area Barcelona. Our reveals that 31 % (+17.27 km

Язык: Английский

Процитировано

3

Demands and possibilities for field-scale estimation of agricultural greenhouse gas balances DOI Creative Commons
Taru Palosuo, Jaakko Heikkinen,

Emmi Hilasvuori

и другие.

CATENA, Год журнала: 2024, Номер 249, С. 108649 - 108649

Опубликована: Дек. 21, 2024

Язык: Английский

Процитировано

3

Evaluation and improvement of Copernicus HR-VPP product for crop phenology monitoring DOI Creative Commons
Egor Prikaziuk, Cláudio Silva Figueira, Gerbrand Koren

и другие.

Computers and Electronics in Agriculture, Год журнала: 2025, Номер 233, С. 110136 - 110136

Опубликована: Фев. 28, 2025

Язык: Английский

Процитировано

0

Advancing winter wheat yield anomaly prediction with high-resolution satellite-based gross primary production DOI
Hassan Bazzi, Philippe Ciais, David Makowski

и другие.

One Earth, Год журнала: 2024, Номер unknown

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

2

Winter Wheat Yield Anomaly Prediction Using Sentinel-2 Derived Gross Primary Production DOI
Hassan Bazzi, Philippe Ciais, David Makowski

и другие.

Опубликована: Янв. 1, 2024

Predicting winter wheat yield anomalies remains challenging due to the long growing season of influenced by compounding climate and management factors. Here, we investigate use a new Sentinel-2 Gross Primary Production (S2-GPP) product, predict yields over France using multilinear regression (MLR). The performance MLR S2-GPP was compared naïve model considering Enhanced Vegetation Index (S2-EVI) as predictor. Main results showed strong correlation patterns between spatially temporally. Yield anomaly predictions revealed that performed better than S2-EVI with satisfactory obtained more one month before harvest. Including rainfall further predictors anomalies, enhanced estimation anomalies. Extreme losses in extreme weather conditions were significantly predicted S2-GPP. While considerably minimizing required input features, proposed prediction seems promising for forecasting.

Язык: Английский

Процитировано

0

Monitoring cropland daily carbon dioxide exchange at field scales with Sentinel-2 satellite imagery DOI Creative Commons
Pia Gottschalk, Aram Kalhori, Zhan Li

и другие.

Biogeosciences, Год журнала: 2024, Номер 21(16), С. 3593 - 3616

Опубликована: Авг. 16, 2024

Abstract. Improving the accuracy of monitoring cropland CO2 exchange at heterogeneous spatial scales is great importance for reducing and temporal uncertainty in estimating terrestrial carbon (C) dynamics. In this study, an approach to estimate daily C fluxes developed tested by combining time series field-scale eddy covariance (EC) flux data Sentinel-2 satellite-based vegetation indices (VIs) after appropriately accounting alignment between two datasets. The study was carried out agricultural field (118 ha) lowlands northeastern Germany. ability different VIs net ecosystem (NEE) gross primary productivity (GPP) based on linear regression models assessed. Most showed high (>0.9) statistically significant (p<0.001) correlations with GPP NEE, although some deviated from seasonal pattern exchange. By contrast, respiration (Reco) were weak not significant, no attempt made Reco VIs. Linear explained generally more than 80 % 70 variability NEE GPP, respectively, among individual performance varied depending component (NEE or GPP) observation period. Root mean square error (RMSE) values ranged 1.35 g m−2 d−1 using green normalized difference index (GNDVI) 5 simple ratio (SR) GPP. This equated underestimated uptake only 41 (18 %) overestimation 854 (73 %). Differences measured estimated mainly diversion VI signal during winter when remained low, while indicated increased due relatively crop leaf area. Overall, results exhibited similar margins mechanistic models. Thus, they suitability expandability proposed satellite-derived

Язык: Английский

Процитировано

0