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Published： 07 August 2023 ，
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张秀英，刘磊，秦佳遥，董佳琦，程苗苗，卢学鹤，丁佳.2023.大气无机氮沉降卫星遥感估算研究进展.遥感学报，27（8）： 1769-1781
Zhang X Y，Liu L，Qin J Y，Dong J Q，Cheng M M，Lu X H and Ding J. 2023. Research progress on estimating atmospheric inorganic nitrogen deposition based on satellite observations. National Remote Sensing Bulletin， 27（8）：1769-1781
大气无机氮沉降是全球氮循环的重要过程，对生态系统的可持续发展至关重要。监测大气氮沉降主要包括地面站点监测和大气化学传输模型模拟两种方法；但在区域尺度上两种方法获得的氮沉降通量具有较大差异。因此，急需发展新的方法以补充探讨区域尺度大气氮沉降时空分布格局。卫星遥感技术具有监测全球高时空分辨率大气含氮化合物浓度的能力，已应用于区域尺度氮沉降通量估算。本文系统回顾了监测大气NO
2
和NH
3
的主要卫星载荷及其数据特点、基于卫星监测的NO
2
和NH
3
柱浓度开展氮沉降估算的基本原理，大气硝态氮和氨态氮的气体、颗粒物和湿沉降遥感估算模型，中国大气无机氮沉降时空分布格局，并指出了当前基于卫星监测信息开展大气无机氮沉降通量估算研究中存在的不足。
As an important process in the nitrogen (N) cycle
inorganic N deposition from the atmosphere can be observed at ground sites or simulated by an atmospheric Chemical Transport Model (CTM). However
the atmospheric N deposition fluxes obtained by these two methods on a regional scale show huge gaps. The atmospheric NO
2
and NH
3
columns remotely sensed by satellites have the potential to estimate the N depositions with high spatial and temporal resolutions. This study reviewed those sensors that provide atmospheric NO
2
and NH
3
columns and those models that estimate atmospheric inorganic N depositions based on satellite observations.
Several sensors can provide the daily NO
2
and NH
3
columns in the tropospheric atmosphere since 1995
with spatial resolutions ranging from 7×3.5 km
2
to 320×40 km
2
. In general
the datasets of NO
2
columns have higher spatial resolutions
cover longer periods
and show better data precision compared with those of NH
3
columns.
An inferential model is often used to estimate dry nitrogen depositions using the N-related component concentrations at the ground level and the deposition velocity (often estimated by an atmospheric CTM). The N-related components in the atmosphere include the gas formats of NO
2
HNO
3
and NH
3
and the particulate formats of NO
3
-
and NH
4
+
which can be estimated from the satellite observations of NO
2
and NH
3
columns
respectively. Three methods were used to covert the atmospheric columns of the N-related components to the concentrations at the ground level
namely
by taking the columns as the ground-level concentrations
by constructing a statistical model that involved the columns and the related influencing factors to estimate the ground-level concentrations
and by considering the profiles of N-related components simulated from CTM to estimate the ground-level concentrations.
Three methods were also used to estimate wet inorganic nitrogen depositions
including an empirical formula
a statistical model
and a process model. The process model can reflect the combined effects of the N-related component concentrations in the atmosphere and the precipitation amounts. In this model
the key parameter of washing coefficient on N-related components from the atmosphere was estimated by using a mixed model that involved a large amount of N depositions observed at ground sites.
Each model for estimating inorganic N depositions had its respective advantages and disadvantages. Those methods that consider the atmospheric profiles of N-related components to estimate the dry N depositions and the process model to estimate the wet N depositions have been used to systematically estimate inorganic N depositions on a regional scale
including gas
particulate
and wet depositions. These methods have obtained reliable estimates of N depositions across China.
Although some methods have been developed to estimate inorganic N depositions based on satellite observations
these methods still show some limitations. To accurately estimate the N deposition on a regional scale
advanced techniques that use satellite sensors
show high spatial and temporal resolutions on N-related components in the atmosphere
and utilize new models for estimating the key parameters with high precision should be developed in future.
大气无机氮沉降气体沉降模型颗粒物沉降模型湿沉降模型卫星遥感
atmospheric inorganic nitrogen depositiongas deposition modelparticulate deposition modelwet deposition modelsatellite remote sensing
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