Cropland Net Ecosystem Exchange Estimation for the Inland Pampas (Argentina) Using EVI, Land Cover Maps, and Eddy Covariance Fluxes

Estimations of Net Ecosystem Exchange (NEE) are crucial to assess the carbon sequestration/carbon source capacity of agricultural systems. Although several global models have been built to describe carbon flux patterns based on flux tower data, South American ecosystems (and croplands in particular) are underrepresented in the databases used to calibrate these models, leading to large uncertainties in regional and global NEE estimation. Despite the fact that almost half of the land surface is used worldwide for agricultural activities, these models still do not include variables related to cropland management. Using enhanced vegetation index (EVI) derived from MODIS imagery (250m) and monthly CO2 exchange from a 9-year record of an eddy covariance (EC) flux tower in a crop field in the Inland Pampas region, we developed regression models to predict monthly NEE. We tested whether including a term for crop identity/land cover as a categorical variable (maize, soybean, wheat, and fallow) could improve model capability in capturing monthly NEE dynamics. NEE measured at the flux tower site was scaled to croplands across the Inland Pampa using crop-type maps, from which annual NEE maps were generated for the 2018–2019, 2019–2020, and 2020–2021 agricultural campaigns. The model based solely on EVI showed to be a good predictor of monthly NEE for the study region (r2 = 0.78), but model adjustment was improved by including a term for crop identity (r2 = 0.83). A second set of maps was generated taking into account carbon exports during harvest to estimate Net Biome Productivity (NBP) at the county level. Crops across the region as a whole acted as a carbon sink during the three studied campaigns, although with highly heterogeneous spatial and temporal patterns. Between 60% and 80% of the carbon sequestered was exported during harvest, a large decrease from the carbon sequestration capacity estimated using just NEE, which further decreased if fossil carbon emissions from agricultural supplies are taken into account. Estimates presented in this study are a first step towards upscaling carbon fluxes at the regional scale in a South American cropland area, and could help to improve regional to global estimations of carbon fluxes and refine national greenhouse gas (GHG) inventories.Fil: Marconato, Ulises. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentina. Universidad de Buenos Aires. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); ArgentinaFil: Fernandez, Roberto J. Universidad de Buenos Aires. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA),; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Instituto de Investigaciones Fisiológicas y Ecológicas Vinculadas a la Agricultura (IFEVA); Argentina. Universidad de Buenos Aires. Facultad de Agronomía. Cátedra de Ecología; ArgentinaFil: Posse Beaulieu, Gabriela. Instituto Nacional de Tecnología Agropecuaria (INTA). Instituto de Clima y Agua; Argentin

Evaluation of a hybrid remote sensing evapotranspiration model for variable rate irrigation management

Accurate generation of spatial irrigation prescriptions is essential for implementation and evaluation of variable rate irrigation (VRI) technology. A hybrid remote sensing evapotranspiration (ET) model was evaluated for use in developing irrigation prescriptions for a VRI center pivot. The model is a combination of a two-source energy balance model and a reflectance based crop coefficient water balance model. Spatial ET and soil water depletion were modeled for a 10 km2 area consisting of rainfed and irrigated maize fields in eastern Nebraska for 2013. Multispectral images from Landsat 8 Operational Land Imager and Thermal Infrared Sensor were used as model input. Modeled net radiation and soil heat fluxes compared well with measurements from eddy covariance systems located within three fields in the study area. Modeled sensible heat flux did not compare well. Latent heat flux compared well for the only mid-summer image, but poorly for the one spring and two fall images. The water balance ET compared well with the two-source energy balance ET for irrigated maize, but not for dryland maize. Image frequency is thought to be a contributing factor in the poor performance of the water balance. In 2015 the hybrid model will be used to generate irrigation prescription maps for a VRI system located in the study area based on modeled soil moisture depletion. Future research will focus on model parameterization and utilize aerial imagery and satellite imagery from other sensors for improved image frequency. Note: this is a revision of the original paper correcting erroneous data where one of the flux sites was mistakenly analyzed as soybeans, when it was actually maize. Mean biased error signs have also been corrected

On Interpreting Eddy Covariance In Small Area Agricultural Situations With Contrasting Site Management.

This dissertation examined the carbon sequestration potential of a low C:N soil amendment and its incorporation into the soil over a rolling agricultural field. A segmented planar fit was developed to assess and correct the systematic errors the topography introduces on the carbon dioxide fluxes. The carbon dioxide fluxes were then be partitioned into gross primary productivity and soil respiration to understand the influence of the contrasting management practices, using flux variance partitioning. Concomitant with the partitioning, high resolution temporal and spatial scale remote sensing images were interpolated and standardized to conduct hypothesis testing for treatment effects

Using Micrometeorology to Gauge Agriculture\u27s Potential to Sequester Soil Carbon

In addition to reducing carbon dioxide (CO2) emissions from fossil fuel combustion, removing atmospheric CO2 may be critical to limit global warming to less than two degrees Celsius above pre-industrial levels recommended by leading experts. Since cropland occupies 11% of the earth’s land and is intensively managed, cropland agriculture provides one approach for removing CO2 from the atmosphere to mitigate climate change. However, current assessments indicate agriculture is a net emitter of CO2 and other greenhouse gases, and it is unclear how soil management can effect carbon sequestration.In this work micrometeorological methods are used to measure the exchange (flux) of CO2 between the surface and atmosphere and can assess whether an agricultural ecosystem is a source or sink for carbon. Three studies were performed using micrometeorology to understand agriculture’s potential to sequester carbon.Using Bowen Ratio Energy Balance (BREB) micrometeorological methods, the first study measured CO2 flux from a maize crop grown on no-till and tilled soils to determine tillage effects on CO2 emissions during 104 days of the 2015 maize growing season in north central Ohio. During this period, the no-till plot sequestered CO2, while the tilled plot was a net emitter.A second study determined if industrial biotechnology waste reutilization in agriculture could reduce CO2 emissions and generate environmental benefits, while meeting farmer yield expectations. Using both BREB and eddy covariance (EC) micrometeorological methods, CO2 flux was measured over maize where heat-inactivated, spent microbial biomass (SMB) amendment was land applied and compared with typical farmer practices from October 2016 to October 2017 in Loudon, Tennessee. While treatments with SMB emitted more CO2 than farmer practices, the SMB applications produced yields similar to farmer practices.Using BREB micrometeorology methods, the third study measured CO2 emissions over conservation agriculture (CA) practices as compared to conventional tillage from June 2013 to May 2016 in central Zimbabwe. The CA practices of no-till and cover crops produced significantly fewer CO2 emissions than conventional tillage.These studies demonstrate that micrometeorology can detect short- and long-term differences in CO2 flux between practices, providing data supporting agriculture’s potential to reduce CO2 emissions and sequester carbon

Evaluation of a Hybrid Reflectance-Based Crop Coefficient and Energy Balance Evapotranspiration Model for Irrigation Management

Accurate generation of spatial soil water maps is useful for many types of irrigation management. A hybrid remote sensing evapotranspiration (ET) model combining reflectance-based basal crop coefficients (Kcbrf) and a two-source energy balance (TSEB) model was modified and validated for use in real-time irrigation management. We modeled spatial ET for maize and soybean fields in eastern Nebraska for the 2011-2013 growing seasons. We used Landsat 5, 7, and 8 imagery as remote sensing inputs. In the TSEB, we used the Priestly-Taylor (PT) approximation for canopy latent heat flux, as in the original model formulations. We also used the Penman-Monteith (PM) approximation for comparison. We compared energy balance fluxes and computed ET with measurements from three eddy covariance systems within the study area. Net radiation was underestimated by the model when data from a local weather station were used as input, with mean bias error (MBE) of -33.8 to -40.9 W m-2. The measured incident solar radiation appeared to be biased low. The net radiation model performed more satisfactorily when data from the eddy covariance flux towers were input into the model, with MBE of 5.3 to 11.2 W m-2. We removed bias in the daily energy balance ET using a dimensionless multiplier that ranged from 0.89 to 0.99. The bias-corrected TSEB ET, using weather data from a local weather station and with local ground data in thermal infrared imagery corrections, had MBE = 0.09 mm d-1 (RMSE = 1.49 mm d-1) for PM and MBE = 0.04 mm d-1 (RMSE = 1.18 mm d-1) for PT. The hybrid model used statistical interpolation to combine the two ET estimates. We computed weighting factors for statistical interpolation to be 0.37 to 0.50 for the PM method and 0.56 to 0.64 for the PT method. Provisions were added to the model, including a real-time crop coefficient methodology, which allowed seasonal crop coefficients to be computed with relatively few remote sensing images. This methodology performed well when compared to basal crop coefficients computed using a full season of input imagery. Water balance ET compared favorably with the eddy covariance data after incorporating the TSEB ET. For a validation dataset, the magnitude of MBE decreased from -0.86 mm d-1 (RMSE = 1.37 mm d-1) for the Kcbrfalone to -0.45 mm d-1 (RMSE = 0.98 mm d-1) and -0.39 mm d-1 (RMSE = 0.95 mm d-1) with incorporation of the TSEB ET using the PM and PT methods, respectively. However, the magnitudes of MBE and RMSE were increased for a running average of daily computations in the full May-October periods. The hybrid model did not necessarily result in improved model performance. However, the water balance model is adaptable for real-time irrigation scheduling and may be combined with forecasted reference ET, although the low temporal frequency of satellite imagery is expected to be a challenge in real-time irrigation management

Carbon balance of a rain-fed maize field

This thesis analyses micrometeorological measurements carried out from June 2004 until October 2006 in the framework of the INTERREG IIIa Project Nr. 3c.10 ”Impacts of climate change on vegetation in the Upper Rhine Valley”. The study addresses the exchange processes of carbon, water and energy of a rain-fed field under maize-fallow rotation. Measurements with an ultrasonic anemometer-thermometer, an open-path CO2/H2O infra-red gas analyser and of the meteorological drivers such as photosynthetic photon flux density (PPFD), temperature and precipitation give insight in the interaction between atmosphere, soil and vegetation. Energy balance considerations show similar patterns of the energy flux densities for vegetation periods and bare field conditions. Energy balance closure is 80 % and 52 %, respectively. A closer look shows a clear diurnal pattern with bad closure during nighttime and an increasing closure fraction during daytime, in fact resulting in an overshooting in late afternoon. Evapotranspiration shows a clear seasonal pattern with maximum values of ~3.5 mm d-1 reached in mid-July. The total water need for the three subsequent years is 321, 397, and 422 mm per kg kernels (yield). The water use efficiency shows a strong relationship with PPFD and the amount of biomass. The focus of the study is on carbon balance. During the three subsequent vegetation periods 930, 785, and 841 g C m-2 are sequestered, respectively. The yield is 455, 417, and 340 g C m-2. About 40 % of the biomass remaining on the field at harvest are decomposed during the dormant season. The resulting numbers for the carbon balance show a ”yearly” sink of this agroecosystem of ~250 g C m-2. Besides unlimited photosynthetic active radiation the combination of the optimal temperature range with the needed precipitation amount corresponding to the need of the actual growth stage are essential for optimal maize growth

Monitoring Grassland Seasonal Carbon Dynamics, by Integrating MODIS NDVI, Proximal Optical Sampling, and Eddy Covariance Measurements

This study evaluated the seasonal productivity of a prairie grassland (Mattheis Ranch, in Alberta, Canada) using a combination of remote sensing, eddy covariance, and field sampling collected in 2012–2013. A primary objective was to evaluate different ways of parameterizing the light-use efficiency (LUE) model for assessing net ecosystem fluxes at two sites with contrasting productivity. Three variations on the NDVI (Normalized Difference Vegetation Index), differing by formula and footprint, were derived: (1) a narrow-band NDVI (NDVI680,800, derived from mobile field spectrometer readings); (2) a broad-band proxy NDVI (derived from an automated optical phenology station consisting of broad-band radiometers); and (3) a satellite NDVI (derived from MODIS AQUA and TERRA sensors). Harvested biomass, net CO2 flux, and NDVI values were compared to provide a basis for assessing seasonal ecosystem productivity and gap filling of tower flux data. All three NDVIs provided good estimates of dry green biomass and were able to clearly show seasonal changes in vegetation growth and senescence, confirming their utility as metrics of productivity. When relating fluxes and optical measurements, temporal aggregation periods were considered to determine the impact of aggregation on model accuracy. NDVI values from the different methods were also calibrated against fAPARgreen (the fraction of photosynthetically active radiation absorbed by green vegetation) values to parameterize the APARgreen (absorbed PAR) term of the LUE (light use efficiency) model for comparison with measured fluxes. While efficiency was assumed to be constant in the model, this analysis revealed hysteresis in the seasonal relationships between fluxes and optical measurements, suggesting a slight change in efficiency between the first and second half of the growing season. Consequently, the best results were obtained by splitting the data into two stages, a greening phase and a senescence phase, and applying separate fits to these two periods. By incorporating the dynamic irradiance regime, the model based on APARgreen rather than NDVI best captured the high variability of the fluxes and provided a more realistic depiction of missing fluxes. The strong correlations between these optical measurements and independently measured fluxes demonstrate the utility of integrating optical with flux measurements for gap filling, and provide a foundation for using remote sensing to extrapolate from the flux tower to larger regions (upscaling) for regional analysis of net carbon uptake by grassland ecosystems