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Remote Sensing | Free Full-Text | A Review of Quantifying pCO2 in Inland Waters with a Global Perspective: Challenges and Prospects of Implementing Remote Sensing Technology | HTML
1. Introduction . Inland waters are an important component of the global carbon cycle. They function as active pipes to transport and transform a large quantity of naturally and anthropogenically derived carbon [ 1 , 2 , 3 , 4 ]. They serve as passive conduits from soil to sea and also divert carbon to the atmosphere and sediment sink. Carbon exchange occurs through the vertical interactions between inland waters and the atmosphere, often in the form of greenhouse gases (GHGs). The globally averaged surface temperature (combining land and ocean) has increased by approximately 1.0 °C (0.8–1.2 °C) above the pre-industrial levels [ 5 ]. Rising emission of natural and anthropogenic GHGs is highly likely to be the dominant cause of the observed warming since the mid-20th century [ 6 ]. Carbon dioxide (CO 2 ) in the atmosphere is the most important GHG because it can enhance the greenhouse effect, with a contribution rate of 60%. A global CO 2 emission survey on inland waters indicated that 95% of the 6708 streams and rivers have a median partial pressure of carbon dioxide ( p CO 2 ) greater than the atmospheric value, and 7939 lakes and reservoirs are supersaturated [ 3 ]. The CO 2 flux released by inland waters is of the same order of magnitude as land–atmosphere and land–ocean net carbon exchanges. Hence, long-term monitoring of p CO 2 and CO 2 emissions from inland waters is essential for quantifying and understanding how inland waters contribute to the global carbon cycle [ 7 , 8 , 9 ]. The response of regional inland waters to global change has attracted the attention of the international research community [ 6 ]. Over the past decade, most of the research efforts have been on refining CO 2 flux estimation at the regional and global scales [ 3 , 10 , 11 , 12 , 13 ]. Nevertheless, the quantification of the p CO 2 in inland waters is also important for accurately estimating CO 2 flux in the water–atmosphere interface and understanding the role of CO 2 in inland waters in the Earth’s carbon budget. Some studies reported about the significant spatial and temporal variations of the p CO 2 in lakes and rivers [ 13 , 14 , 15 , 16 , 17 ] and the strong influence of ambient environment and river discharge on the p CO 2 of inland waters [ 18 , 19 , 20 ]. However, the current p CO 2 data of inland waters remain uncertain due to the large discrepancy of p CO 2 in the global inland waters. Moreover, the variation in CO 2 flux estimation to the atmosphere stems not only from the limited spatiotemporal data availability, but also from various methods in an un-unified p CO 2 estimation approach [ 12 , 21 , 22 ]. The common methods include the direct measurement of in situ p CO 2 using an air-flushing equilibrator connected to an infrared photoacoustic gas analyzer [ 23 , 24 ]; the underway p CO 2 system [ 25 ]; the underwater sensors, e.g., C-Sense TM , HydroC TM -CO 2, and Franatech CO 2 -sensor [ 25 , 26 ]; calculation of p CO 2 based on in situ pH, total alkalinity, water temperature, and salinity values of inland waters [ 27 ]; and estimation of p CO 2 based on the dissolved CO 2 concentration in the water [ 28 ]. There is a lack of an effective and generalized method to characterize the spatial and temporal dynamics of p CO 2 in detail, particularly in some regions with a large freshwater surface area and regions sensitive to climate change [ 28 , 29 ]. According to climate model projections, extreme climatic events (e.g., rainfall and flood) would increase in some regions [ 30 , 31 ]. Some studies showed that intense rainfall events and floods could modify the water–atmosphere exchange of CO 2 [ 32 , 33 , 34 ]. It is necessary to develop a common method to estimate p CO 2, which covers long-term records and large spatial coverage, so that we could better illustrate the potential impact of such events on p CO 2 and accurately quantify CO 2 flux and the role of inland waters in the global carbon cycle. Over the past two decades, remote sensing of p CO 2 in the water environment has received much attention due to its unique advantages against the traditional field-based technologies [ 35 ]. In addition, this method has the ability to achieve the simultaneous observation and comparison of p CO 2 values in different waters and different times over the same location. The assessment of p CO 2 variations based on multi-source remote sensing data has contributed greatly to the accurate quantification of CO 2 flux in the atmosphere–water interface at high-spatiotemporal resolution in the ocean and coastal waters [ 36 , 37 , 38 , 39 ], while a similar attempt has also been conducted in the inland waters [ 11 , 13 , 40 , 41 ]. The statement is strengthened by the fact that inland waters function as important elements in the global carbon balance despite the smaller overall size relative to the terrestrial ecosystem [ 42 , 43 , 44 ]. In this paper, we aim to summarize and discuss the temporal and spatial variability of p CO 2 in inland waters, especially in different water types based on data gathered by Aufdenkampe et al. (2011). We summarize the current state of CO 2 fluxes in inland waters and compare them in different water types and climatic zones. A key open question is the low accuracy of long-term monitoring of p CO 2 in inland waters, and the fact that p CO 2 in inland waters can vary with climate conditions and water types. It also varies seasonally and interannually. Therefore, we analyzed the current p CO 2 remote sensing method in marine and coastal waters at the global scale and put forward the challenges and prospects of using remote sensing to estimate p CO 2 in inland waters. 2. General Background and Motivation of p CO 2 Remote Sensing . 2.1. Spatio-Temporal Variability of pCO 2 in Inland Waters . The process of CO 2 exchange in the atmosphere–water system is regulated by the climate and watershed characteristics; meanwhile, the estimation of CO 2 evasion should consider the daily variability of p CO 2 . At present, there are limited data that characterize the connection between CO 2 flux and the daily course and variation of p CO 2 in inland waters [ 16 ]. Improving the understanding of the daily variation of p CO 2 is a critical step to reduce uncertainties in CO 2 flux estimations for inland waters. Significant daily variation in p CO 2 has been measured in University Lake, a shallow, subtropical, eutrophic lake located in Louisiana, USA, with a consistently declining trend of p CO 2 from early mornings to late afternoons [ 15 , 16 ] ( Figure 1 ). The daily variation in p CO 2 was also observed in stratified water bodies, with a strong relation to the diurnal cycles of metabolic activity [ 45 ], while p CO 2 in an unproductive lake in Northern Sweden was found to have low daily variation during summer [ 46 ]. In the daytime, p CO 2 dynamics are primarily driven by aquatic metabolism in a eutrophic lake and are associated with the lake’s primary and secondary production [ 16 ]. Elevated primary production during algal’s growing season in a eutrophic lake can draw down CO 2 levels in water. Previous studies showed that algal blooms can reduce carbon emissions to the atmosphere, but algal decomposition could release a large amount of CO 2 [ 47 , 48 , 49 ]. High algae productivity can turn a lake from a net CO 2 source to a net CO 2 sink to the atmosphere [ 50 ]. Furthermore, previous studies confirmed a close correlation between daily changes of p CO 2 and solar radiation, water temperature, and the lake trophic status [ 15 , 16 , 45 , 46 , 51 , 52 ]. The p CO 2 in inland waters often shows significant variability at the seasonal scale [ 45 , 46 , 53 ]. Relative to other seasons, the surface p CO 2 in summer is generally low due to the strong photosynthesis of phytoplankton in lakes and reservoirs, which absorb CO 2 in the water column for primary production [ 54 , 55 , 56 , 57 ]. In addition, the ice-melt period is a critical time window for CO 2 emissions from boreal lakes [ 9 , 58 , 59 ], because the accumulated CO 2 sealed in ice and sub-ice water can be quickly released to the atmosphere during ice melt. The growing interest in seasonal p CO 2 estimation indicates the need to consider the influence mechanism of p CO 2 in different inland waters. In stratified reservoirs, seasonal variability of p CO 2 is related to the water temperature dynamics and thermal stratification of the water column [ 45 ]. In an oligotrophic unproductive lake, seasonal p CO 2 variation could be driven by changing dissolved inorganic carbon and allochthonous organic matter [ 29 , 46 ]. In rivers, p CO 2 always shows a higher value during the rainy season compared with the dry season [ 53 ], and the seasonal p CO 2 variations are generally controlled by flows and dissolved oxygen enrichment [ 53 , 60 ]. Studies across the global inland waters demonstrated that nearly all freshwater bodies are CO 2 supersaturated compared to the atmosphere [ 62 , 63 ]. Measured or calculated p CO 2 values typically vary widely in the global inland waters. In general, according to the statistical analysis of Aufdenkampe et al. (2011), the p CO 2 in rivers and streams is higher than those in lakes and reservoirs in the same climatic zone, and the p CO 2 in tropical waters is higher than those in temperate, boreal, and arctic waters ( Figure 2 ). From published literature, the p CO 2 values of global lakes ranges from 17–65,250 μatm, with a mean value of 1287 ± 41 μatm, and the p CO 2 in Arctic lakes is significantly lower than those in lakes of other climatic zones [ 20 ]. The p CO 2 values in reservoirs ranges from 5–10,000 μatm [ 27 , 64 , 65 ], and CO 2 emissions in reservoirs are correlated to the built age and latitude, with CO 2 emission rates from the tropical Amazon region significantly higher than other climatic zones [ 65 , 66 ]. In addition, reservoirs often exhibit higher mean p CO 2 than lakes in the same region [ 27 , 42 , 63 ]. The p CO 2 in rivers and streams ranges from 582 μatm to more than 12,000 μatm [ 44 , 49 ]. The riverine p CO 2 at the global scale demonstrates a decreasing trend from low to high latitudes [ 3 , 44 , 65 ], and a similar trend is also well established with rivers’ and streams’ order and length in riverine networks [ 67 ]. Riverine p CO 2 interacts with aqueous carbon and nutrients and can reach significantly high levels when the level of nutrients in the water is high [ 61 ]. 2.2. The Current State of CO 2 Fluxes in Inland Waters . Inland waters are widely considered as significant sources of CO 2 to the atmosphere [ 7 , 42 , 63 , 70 , 71 , 72 ]. Most studies up-scaled the local or regional CO 2 fluxes’ measurements in inland waters to the globe by multiplying an average emission rate by the global area. However, these calculations contained large uncertainties due to the change and inaccurate estimation of global inland waters’ surface area and gas transfer rate. For example, the global CO 2 flux from inland waters estimated by Cole et al. (2007) was only 750 Tg y ?1 , because the data sets used in that estimation merely covered about 5000 individual lakes spanning across the globe, the largest reservoirs in the world (excluding the very small reservoirs), more than 80 of the world’s largest rivers, and only the main channels of the rivers. However, the global CO 2 flux from inland waters estimated by Raymond et al. (2013) reached 2100 Tg y ?1 . That estimation provided a total global surface area of inland waters of 3,620,000 km 2 . They combined lakes and reservoirs with streams and rivers, including lakes and reservoirs <3.16 km 2 and the first-order streams. To date, the global CO 2 evasion from inland waters to the atmosphere ranges from 1.40–3.28 Pg C y ?1 [ 3 , 42 ]. The contributions of inland water CO 2 to atmosphere also vary with regions and water types ( Table 1 ). For example, the inland waters in India and China yielded average CO 2 emissions of 22.0 Tg yr ?1 [ 73 ] and 98 ± 19 Tg yr ?1 [ 11 ], respectively. The total CO 2 emitted by global saline lakes ranges from 110–150 Tg yr ?1 [ 72 ], while that emitted by all German drinking water reservoirs is about 0.44 Tg y ?1 [ 74 ] and that emitted by the lakes and ponds of Florida is roughly 2.0 Tg y ?1 [ 70 ]. Fluxes of greenhouse gases in boreal reservoirs are usually 3–10 times higher than those in natural lakes at their maximum [ 42 ]. In addition, the global stream and rivers are also the hotspots of CO 2 efflux [ 3 ] and they make a nonnegligible contribution to CO 2 flux from inland waters to the atmosphere that does not correspond to their area proportion in the whole inland waters area. Globally, conservative estimates imply that 26.7–64.4% of total CO 2 emissions from inland waters originate from rivers and streams ( Figure 3 ). In the Amazon basin, CO 2 evasion from streams, rivers, and wetlands of the region could reach as high as 500 Tg y ?1 , and this value was later revised upward due to CO 2 supersaturation in some small headwater streams [ 75 , 76 ]. In the 2010s, the amounts of CO 2 evasion from streams and rivers in the United States, China, and Africa were 97 ± 32 Tg C y ?1 [ 77 ], 85.8 ± 19.4 Tg C y ?1 [ 11 ], and 270–370 Tg C yr ?1 [ 78 ], respectively. In addition, some studies suggested that the contribution of very small ponds (<0.001 km 2 ) to inland water CO 2 emissions could not be ignored despite their small total surface area of the inland water [ 79 ], and some researchers indicated the need of paying attention to the CO 2 emissions from exposed river sediments during drought period [ 80 , 81 ]. Furthermore, previous studies on long-term monitoring of the CO 2 flux in inland waters revealed that some lakes switched between acting as a CO 2 source and sink [ 7 , 8 , 9 ]. This highlights that it is important to fully understand the mechanisms and influence factors controlling CO 2 evasion. The increase of CO 2 flux in the atmosphere–lake system is generally considered synchronous to the decrease in photosynthetic activity of plankton [ 51 ]. CO 2 supersaturation often exists in lakes when the respiration exceeds photosynthesis in lakes [ 56 , 84 ]. Beyond that, the inputs of dissolved carbon from carbonate weathering in lake and watershed should also be considered for the CO 2 supersaturation [ 20 , 63 ]. The lake’s size, trophic status, ice presence/absence, algal blooms, and salinity all have important implications on CO 2 emissions [ 21 , 71 , 72 , 85 , 86 , 87 , 88 , 89 , 90 , 91 ]. Algal blooms in some lakes could reduce carbon emissions, while the algal-derived organic carbon during the algae degradation process could increase the subsequent CO 2 production [ 47 , 48 , 50 , 92 ]. Saline lakes could raise the total CO 2 emissions to the atmosphere more than freshwater lakes [ 72 ]. Eutrophication with the enhanced organic matter decay and biological activity could increase lacustrine CO 2 emissions [ 27 , 49 , 85 ]. Understanding the source of inland water CO 2 , the influence of diel and seasonal p CO 2 changes on CO 2 outgassing estimation, and the exchange mechanism of carbon between different ecosystems is important for the accurate estimation of CO 2 evasion in inland waters globally, which has a major impact on the global carbon biogeochemical cycles. 3. Studies on Remote Sensing of p CO 2 . According to existing theoretical analysis and research results, p CO 2 in water surface cannot be directly derived from satellite radiance. It is mostly an indirect measurement that requires the estimation of other variables first. The remote sensing of p CO 2 in water surface requires some environmental variables related to the p CO 2 controlling processes as indicators (e.g., water surface temperature (T), water salinity (S), plankton concentration (Chla), colored dissolved organic matter (CDOM), mixed layer depth). There is also some directly remote sensing research of the dissolved CO 2 concentration or p CO 2 by developing the estimation model based on satellite imagery-derived products. At present, while remote sensing technology has been successfully applied for the estimation of p CO 2 in water surface, most of these studies focused on ocean and coastal waters. 3.1. Remote Sensing Estimating pCO 2 in Marine and Coastal Waters . Research on remote sensing of p CO 2 in sea and coastal waters has received much attention in recent years. It is useful for the accurate description of the spatial-temporal heterogeneity of sea-surface CO 2 flux and for quantifying the ocean’s role in the global carbon cycle [ 39 , 93 , 94 ]. Moderate-Resolution Imaging Spectroradiometer (MODIS) imagery and MODIS-derived products are more commonly used in these p CO 2 remote sensing inversion processes [ 38 , 94 , 95 , 96 ]. Related studies using statistical approaches and machine learning techniques have been conducted in many seas and coastal sites ( Figure 4 ), e.g., the Gulf of Mexico [ 36 , 97 , 98 ], East China Sea [ 99 , 100 ], Caribbean Sea [ 94 ], Bering Sea [ 39 ], and West Florida Shelf [ 93 ]. In general, the empirical algorithms (e.g., linear or multiple regression relationships) and machine learning approaches can work reasonably well with good p CO 2 inversion results in the specified?areas [ 36 , 38 , 98 ]. However, p CO 2 in the open ocean and coastal regions often exhibits a profound spatiotemporal heterogeneity and is controlled by multiple factors. Due to incomprehension of p CO 2 variability mechanisms, these empirical algorithms can only function reliably for areas with available in situ p CO 2 data. Thus, more complex semi-analysis algorithms, combined with the analysis of the main mechanisms causing p CO 2 variability, have been developed in different coastal waters and seas, such as the first implementation of a mechanistic semi-analytic algorithm (MeSAA) in the East China Sea [ 39 , 97 , 100 ]. A satellite-based semi-mechanistic model was developed for the river-dominated Louisiana Continental Shelf [ 101 ], while a nonlinear semi-empirical model with the self-organizing map (SOM) was implemented in the Pacific coast of central North America [ 102 ]. Nevertheless, the existing semi-analytical algorithms also have limited applicability in different regions, primarily because of the difficulty in parameterizing and standardizing the physicochemical and biological influence on p CO 2 in sea and coastal waters. In the process of constructing the p CO 2 remote sensing algorithm/model, it is important to choose and develop accurate quantitative expressions relating satellite-derived parameters based on controlling mechanistic analysis, which can assist to better implement remote sensing of p CO 2 in the similar oceanic conditions. According to a survey of literature, the net sea–air CO 2 flux of the global ocean is approximately 1.4 Pg y ?1 [ 103 ], and this value is subjected to large uncertainty. The air–sea CO 2 fluxes are different depending on the latitudinal and ecosystem diversity of the coastal ocean (particularly near-shore systems). The physical-biogeochemical distinction (including ocean-dominated margin and river-dominated ocean margin) has significant influence on the sources’/sinks’ role of coastal waters [ 104 ]. In addition, the marginal seas at high and temperate latitudes often act as sinks of atmospheric CO 2 ; at subtropical and tropical regions, the marginal seas in these two climatic zones act as sources of atmospheric CO 2 [ 105 ]. When integrating CO 2 fluxes in the coastal ocean at the global scale, the diversity, latitudes, and seasonal biological effect on ecosystems should be fully considered. 3.2. Remote Sensing of pCO 2 and CO 2 Fluxes for Inland Waters . Typically, inland waters are characterized by the supersaturated, dissolved CO 2 concentrations. However, there are huge differences in optical properties, physicochemical environments, trophic status, and circulation of materials between inland waters and ocean/coastal waters [ 11 , 13 , 40 , 41 ]. Some effective remote sensing algorithms and models for p CO 2 in ocean/coastal waters cannot be used directly for that in inland waters. Considering the influencing factors and mechanisms of surface p CO 2 in inland waters, some remote sensing algorithms for p CO 2 in inland waters have been developed based on the relationship between p CO 2 and the retrieved water biogeochemical and optical parameters, e.g., chromophoric dissolved organic matter (CDOM) optical property, algal productivity, and water surface temperature [ 41 ]. Earlier studies demonstrated that the temporal and spatial distributions of p CO 2 in inland waters often exhibited high heterogeneity, which resulted in a large uncertainty in lake CO 2 flux calculations. Satellite observations of p CO 2 in inland waters could achieve a relatively high frequency and continuous, large-scale, and long-term data compared to field surveys. There are growing studies in this area in recent years despite a small number of published works. Combining with a high-resolution (25-m resolution), stream network map based on remote sensing, a Random Forest model was applied to predict the stream p CO 2 with an average of 1134 μatm (range: 154–8174 μatm) in Denmark, Sweden, and Finland [ 106 ]. Estimations of inland waters’ CO 2 emissions have been realized in relation to terrestrial net primary production, which can be obtained from a global data set based on remote sensing, such as in a temperate stream network [ 107 ] and in boreal lakes [ 13 ]. More recently, optical indicators generated from satellite-derived variables have been utilized to estimate p CO 2 indirectly in some rivers and lakes based on the strong relationship between them, such as CDOM optical properties used in the Lower Amazon River [ 31 ] and a turbidity index used in the Swedish lakes M?laren and T?mnaren [ 30 ]. Nevertheless, the direct application of the long-term satellite products to estimate p CO 2 or dissolve CO 2 in inland waters is still in its infancy. The long-term series mapping of dissolved CO 2 pattern based on the remote sensing technology was conducted in Lake Taihu, China, which developed a dissolved CO 2 estimation model based on MODIS-derived products. It was applied to perform the spatiotemporal distribution analysis of dissolved CO 2 concentrations from 2003 to 2018 [ 22 ]. MERIS products have also been used to estimate lake p CO 2 [ 40 ]. When using long-term remote sensing imagery to directly estimate the CO 2 concentration or p CO 2 in waters or retrieving p CO 2 in water from some relevant environmental remote sensing indicators based on stable relationship [ 38 , 41 , 101 ], it should be noted that the retrieved CO 2 concentration or p CO 2 values are the instantaneous value at the satellite transit time. The previous studies showed some pronounced changes in the CO 2 concentration over a day and seasons [ 15 , 22 , 52 ]. To achieve the transformation of retrieved p CO 2 values from an instant to hours/days, some researchers have established the relationship between instantaneous lake CO 2 concentration/pCO 2 at the regular satellite flyover and the daily/weekly mean value [ 15 , 22 , 45 ] by using the satellite estimation results to extrapolate the daily/weekly CO 2 mean values. In addition, combined with the in situ measured values of the diurnal p CO 2 variation and seasonal p CO 2 variation in a lake, we could realize the conversion of the daily value to the seasonal mean value of the lake’s CO 2 through cross verification between different sensors with different time resolutions. More observations and additional efforts would be needed to achieve them in the further studies. In fact, researchers have a full understanding of biogeochemical mechanism of CO 2 generation and consumption in inland waters. Most of the determining and influence factors of p CO 2 or dissolved CO 2 in different inland waters have been elucidated. Some of these factors can be derived from satellite data, e.g., lake surface temperature, chlorophyll-a concentration, latitude, dissolved organic carbon (DOC), and solar radiation absorption. Therefore, in principle, it is possible to identify the spatiotemporal distribution of p CO 2 in a specific lake or river using the satellite-derived variables and realize the long-term estimations. However, the accuracy and universality of the prediction models should be developed and evaluated as a priority in the large-scale estimation. Nevertheless, it is known that the relationships in the prediction models can vary among different lakes and lake regions, which is the current challenge of the p CO 2 remote sensing in inland waters [ 22 , 40 , 41 , 45 , 47 , 100 , 101 , 108 , 109 ]. Due to the great influence of outside source input, the geochemical processes of inland lakes can show strong spatial heterogeneity, and the influence factors of the p CO 2 in surface water are often coupled together. This leads to the unstable, non-universal relationship between p CO 2 and its indicators among different lakes and lake regions and the large uncertainties from such extrapolations. Consequently, the development of the inverse models based on dissolved biogeochemical processes and the machine learning algorithm based on lots of measurement data may have better applicability over longer periods and across larger spatial scales. 4. Challenges and Limitations of p CO 2 Remote Sensing Algorithms . As presented in this review, there are still many uncertainties about the p CO 2 dynamics of inland waters affected by human activities and climatic change. Due to the variations of p CO 2 in surface water, a significant challenge exists in the quantification of regional air–water CO 2 flux. Satellite remote sensing has been successfully implemented in the synoptic estimation of oceanic surface p CO 2 , with its unique advantages of spatiotemporal resolution and coverage. Moreover, recent studies have revealed the presence of four interrelated processes closely related to water surface p CO 2 , i.e., biological activities, physical mixing, a thermodynamic process, and the air–water gas exchange. In principle, understanding these control processes of p CO 2 in the inland waters and unearthing the environmental variables linking to these processes, which can be derived from satellite data, are the key to successfully achieving remote sensing of p CO 2 in inland waters. In addition, a longstanding challenge to upscaling based on environmental variables to remote sensing p CO 2 at the larger scale is the limited availability of spatially explicit data sets on inland water characteristics, such as the seasonal fluctuations of area and the ephemeral and intermittent water occurrence. Some tentative studies have used remote sensing data to estimate p CO 2 or CO 2 flux in inland waters [ 22 , 40 ]. These studies enabled high-resolution mapping of the whole-lake p CO 2 compared to field surveys. The sensors used in the current studies (Landsat, Sentinel-2, MODIS, and MERIS) have provided either high spatiotemporal coverage or sufficient radiometric sensitivity, which can assist reliable estimations of p CO 2 or CO 2 flux in single specific water [ 80 , 110 , 111 ]. For inland waters (except the optical indicators of surface water used indirectly to estimate p CO 2 ), direct satellite estimation of p CO 2 or dissolved CO 2 concentrations are required to construct a spatiotemporal map of p CO 2 . Additional works will be needed to develop more comprehensive p CO 2 remote algorithms/models in inland waters to improve the long-term and large-scale reliability and universality of models, particularly for inaccessible and remote sites. Considering the working conditions and the validity of remote sensing models, further model evaluation will be needed in other types of lakes or rivers to make it more general than for the particular water bodies for which it was developed. The remote sensing model sensitivity evaluation and model deviation caused by the input variables should be evaluated before model utilization. Furthermore, some typical challenges caused by clouds or algal blooms in satellite images can also reduce model accuracy and increase the uncertainty of p CO 2 estimations. 5. Conclusions . This paper reviewed the temporal and spatial variability of p CO 2 in inland waters (including lakes, reservoirs, rivers, and streams). Existing analyses indicated significant daily variation in p CO 2 in lakes, with a consistently declining trend of p CO 2 from early morning to late afternoon. Meanwhile, p CO 2 values in inland waters also exhibit seasonal variation at a global scale, and the ice-melt period is a critical time window for CO 2 emission from boreal lakes. Overall, tropical waters typically experience higher p CO 2 than temperate, boreal, and arctic waters, while rivers and streams demonstrate higher p CO 2 than in lakes and reservoirs. While rivers and streams occupy a smaller proportion in global inland waters’ area, their CO 2 flux contributions to atmosphere are not less than those from the lakes and reservoirs. This review also summarized previous investigations on remote sensing of p CO 2 in sea and coastal waters, which is essential to the accurate description of the spatial-temporal heterogeneity of sea-surface CO 2 flux. Given that the p CO 2 in sea surface cannot be directly derived from satellite radiance, the remote sensing models of sea surface p CO 2 often employ the environmental variables related to the p CO 2 controlling processes as the indicators. The p CO 2 in inland waters is driven by multiple complex factors and mechanisms (e.g., watershed environment, human activities interference, and water quality factors), which are completely different from those in oceans. Despite the studies on the satellite observations of p CO 2 in inland waters increasing rapidly in recent years, only a handful of them have been published. The optical indicators of water (e.g., CDOM optical properties and turbidity index) have been adopted to estimate p CO 2 indirectly in some inland waters. Future research on direct application of long-term satellite products to estimate p CO 2 in inland waters will be needed for mapping the long-term and large-scale p CO 2 distribution patterns. Reliable and generalized p CO 2 remote sensing models/algorithms in inland waters will need to be developed in future studies. In addition, how to achieve the transformation of retrieved instantaneous p CO 2 values to days/months remains a major technical challenge, which is crucial to the accurate estimation of global CO 2 flux from inland waters based on remote sensing technology. .
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