Evaluating autumn phenology derived from field observations, satellite data, and carbon flux measurements in a northern mixed forest, USA

2020 ◽  
Vol 64 (5) ◽  
pp. 713-727 ◽  
Author(s):  
Bailu Zhao ◽  
Alison Donnelly ◽  
Mark D. Schwartz
2020 ◽  
Author(s):  
Pascal Wintjen ◽  
Frederik Schrader ◽  
Martijn Schaap ◽  
Burkhard Beudert ◽  
Christian Brümmer

<p>Reactive nitrogen (N<sub>r</sub>) compounds comprise essential nutrients for plants. However, a large supply of nitrogen by fertilization through atmospheric deposition may be harmful for ecosystems such as peatlands and may lead to a loss of biodiversity, soil acidification and eutrophication. In addition, nitrogen compounds may cause adverse human health impacts. Large parts of N<sub>r</sub> emissions originate from anthropogenic activities.  Emission hotspots of ΣN<sub>r</sub>, i.e. the sum of all N<sub>r</sub> compounds, are related to crop production and livestock farming (mainly through ammonia, NH<sub>3</sub>) and fossil fuel combustion by transport and industry (mainly through nitrogen oxides, NO<sub>2 </sub>and NO). Such additional amount of N<sub>r</sub> will enhance its biosphere-atmosphere exchange, affect plant health and can influence its photosynthetic capacity. Therefore, it is necessary to thoroughly estimate the nitrogen exchange between biosphere and atmosphere.</p><p>For measuring the nitrogen mixing ratios a converter for reactive nitrogen (TRANC: Total Reactive Atmospheric Nitrogen Converter) was used. The TRANC converts all reactive nitrogen compounds, except for nitrous oxide (N<sub>2</sub>O), to nitric oxide (NO) and is coupled to a fast-response chemiluminescence detector (CLD). Due to a low detection limit and a response time of about 0.3s the TRANC-CLD system can be used for flux calculation based on the eddy covariance (EC) technique. Flux losses, which are related to the experimental setup, different response characteristics and the general high reactivity of most N gases and aerosols, occur in the high frequency range. We estimated damping factors of approximately 20% with an empirical cospectral approach.</p><p>For getting a reliable prediction of ΣN<sub>r</sub> fluxes through deposition models, long-term flux measurements offer the possibility to verify the nitrogen uptake capacity and to investigate exchange characteristics of ΣN<sub>r </sub>in different ecosystems.</p><p>In this study, we compare modelled dry deposition fluxes using the deposition module DEPAC (DEPosition of Acidifying Compounds) within the chemical transport model LOTOS-EUROS (LOng Term Ozone Simulation – EURopean Operational Smog) against ΣN<sub>r</sub> flux measurements of the TRANC-CLD for a remote mixed forest site with hardly any local anthropogenic emission sources. This procedure allows to determine the background load and the natural exchange characteristics of nitrogen under low atmospheric concentrations. Therefore, the broad-scale dry deposition predicted directly by LOTOS-EUROS was compared to site-specific modelling results obtained using measured meteorological input data as well as the directly measured ΣN<sub>r</sub> fluxes. In addition, the influence of land-use weighting in LOTOS-EUROS was examined. We further compare our results to ΣN<sub>r</sub> deposition estimates obtained with canopy budget techniques. Measured ΣN<sub>r</sub> dry deposition at the site was 4.5 kg N ha<sup>-</sup><sup>1</sup> yr<sup>-</sup><sup>1</sup>, in close agreement with modelled estimates using DEPAC with measured drivers (5.2 kg N ha<sup>-</sup><sup>1</sup> yr<sup>-</sup><sup>1</sup>) and as integrated in the chemical transport model LOTOS-EUROS (5.2 kg N ha<sup>-</sup><sup>1</sup> yr<sup>-</sup><sup>1</sup> to 6.9 kg N ha<sup>-</sup><sup>1</sup> yr<sup>-</sup><sup>1</sup> depending on the weighting of land-use classes).</p><p>Our study is the first one presenting 2.5 years flux measurements of ΣN<sub>r</sub> above a remote mixed forest. Further verifications of long-term flux measurements against deposition models are useful to improve them and result in better understanding of exchange processes of ΣN<sub>r</sub>.</p>


2002 ◽  
Vol 47 (2) ◽  
pp. 571-575 ◽  
Author(s):  
Richard S. Lampitt ◽  
Paulo Y. G. Sumida ◽  
Fernando Pérez- Castillo

2010 ◽  
Vol 10 (10) ◽  
pp. 22469-22513
Author(s):  
M. Gordon ◽  
R. M. Staebler ◽  
J. Liggio ◽  
A. Vlasenko ◽  
S.-M. Li ◽  
...  

Abstract. Aerosol fluxes were measured above a mixed forest by Eddy Covariance (EC) with a Fast Mobility Particle Sizer (FMPS) at the Borden Forest Research Station in Ontario, Canada between 13 July and 12 August 2009. The FMPS, mounted at a height of 33 m (approximately 10 m above the canopy top) and housed in a temperature controlled enclosure, measured size-resolved particle concentrations for 3 to 410 nm at a rate of 1 Hz. For the size range 20


2021 ◽  
Vol 8 ◽  
Author(s):  
Frédéric Mélin

Uncertainty estimates are needed to assess ocean color products and qualify the agreement between missions. Comparison between field observations and satellite data, a process defined as validation, has been the traditional way to assess satellite products. However validation statistics can provide only an approximation for satellite data uncertainties as field measurements have their own uncertainties and as the validation process is imperfect, comparing data potentially differing in temporal, spatial or spectral characteristics. This study describes a method to interpret in terms of uncertainties the validation statistics obtained for ocean color remote sensing reflectance RRS knowing the uncertainties associated with field data. This approach is applied to observations collected at sites part of the Ocean Color component of the Aerosol Robotic Network (AERONET-OC) located in coastal regions of the European seas, and to RRS data from the VIIRS sensors on-board the SNPP and JPSS1 platforms. Similar estimates of uncertainties σVRS (term accounting for non-systematic contributions to the uncertainty budget) are obtained for both missions, decreasing with wavelength from the interval 0.8–1.4 10−3 sr−1 in the blue to a maximum of 0.24 10−3 sr−1 in the red, values that are at least twice (but up to 8 times) the uncertainties reported for the field data. These uncertainty estimates are then used to qualify the agreement between the VIIRS products, defining the extent to which they agree within their stated uncertainty. Despite significant biases between the two missions, their RRS products appear fairly compatible.


2020 ◽  
Vol 12 (3) ◽  
pp. 522 ◽  
Author(s):  
Abdul Qadir ◽  
Pinki Mondal

Monsoon crops play a critical role in Indian agriculture, hence, monitoring these crops is vital for supporting economic growth and food security for the country. However, monitoring these crops is challenging due to limited availability of optical satellite data due to cloud cover during crop growth stages, landscape heterogeneity, and small field sizes. In this paper, our objective is to develop a robust methodology for high-resolution (10 m) monsoon cropland mapping appropriate for different agro-ecological regions (AER) in India. We adapted a synergistic approach of combining Sentinel-1 Synthetic Aperture Radar (SAR) data with Normalized Difference Vegetation Index (NDVI) derived from Sentinel-2 optical data using the Google Earth Engine platform. We developed a new technique, Radar Optical cross Masking (ROM), for separating cropland from non-cropland by masking out forest, plantation, and other non-dynamic features. The methodology was tested for five different AERs in India, representing a wide diversity in agriculture, soil, and climatic variations. Our findings indicate that the overall accuracy obtained by using the SAR-only approach is 90%, whereas that of the combined approach is 93%. Our proposed methodology is particularly effective in regions with cropland mixed with tree plantation/mixed forest, typical of smallholder dominated tropical countries. The proposed agriculture mask, ROM, has high potential to support the global agriculture monitoring missions of Geo Global Agriculture Monitoring (GEOGLAM) and Sentinel-2 for Agriculture (S2Agri) project for constructing a dynamic monsoon cropland mask.


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