Understanding of Coupled Terrestrial Carbon, Nitrogen and Water Dynamics—An Overview

Coupled terrestrial carbon (C), nitrogen (N) and hydrological processes play a crucial role in the climate system, providing both positive and negative feedbacks to climate change. In this review we summarize published research results to gain an increased understanding of the dynamics between vegetation and atmosphere processes. A variety of methods, including monitoring (e.g., eddy covariance flux tower, remote sensing, etc.) and modeling (i.e., ecosystem, hydrology and atmospheric inversion modeling) the terrestrial carbon and water budgeting, are evaluated and compared. We highlight two major research areas where additional research could be focused: (i) Conceptually, the hydrological and biogeochemical processes are closely linked, however, the coupling processes between terrestrial C, N and hydrological processes are far from well understood; and (ii) there are significant uncertainties in estimates of the components of the C balance, especially at landscape and regional scales. To address these two questions, a synthetic research framework is needed which includes both bottom-up and top-down approaches integrating scalable (footprint and ecosystem) models and a spatially nested hierarchy of observations which include multispectral remote sensing, inventories, existing regional clusters of eddy-covariance flux towers and CO2 mixing ratio towers and chambers

A global dataset of spatiotemporally seamless daily mean land surface temperatures: generation, validation, and analysis

Daily mean land surface temperatures (LSTs) acquired from polar orbiters are crucial for various applications such as global and regional climate change analysis. However, thermal sensors from polar orbiters can only sample the surface effectively with very limited times per day under cloud-free conditions. These limitations have produced a systematic sampling bias (ΔT$_{sb}$) on the daily mean LST (T$_{dm}$) estimated with the traditional method, which uses the averages of clear-sky LST observations directly as the T$_{dm}$. Several methods have been proposed for the estimation of the T$_{dm}$, yet they are becoming less capable of generating spatiotemporally seamless T$_{dm}$ across the globe. Based on MODIS and reanalysis data, here we propose an improved annual and diurnal temperature cycle-based framework (termed the IADTC framework) to generate global spatiotemporally seamless T$_{dm}$ products ranging from 2003 to 2019 (named the GADTC products). The validations show that the IADTC framework reduces the systematic ΔT$_{sb}$ significantly. When validated only with in situ data, the assessments show that the mean absolute errors (MAEs) of the IADTC framework are 1.4 and 1.1 K for SURFRAD and FLUXNET data, respectively, and the mean biases are both close to zero. Direct comparisons between the GADTC products and in situ measurements indicate that the MAEs are 2.2 and 3.1 K for the SURFRAD and FLUXNET datasets, respectively, and the mean biases are −1.6 and −1.5 K for these two datasets, respectively. By taking the GADTC products as references, further analysis reveals that the T$_{dm}$ estimated with the traditional averaging method yields a positive systematic ΔT$_{sb}$ of greater than 2.0 K in low-latitude and midlatitude regions while of a relatively small value in high-latitude regions. Although the global-mean LST trend (2003 to 2019) calculated with the traditional method and the IADTC framework is relatively close (both between 0.025 to 0.029 K yr$^{–1}$), regional discrepancies in LST trend do occur – the pixel-based MAE in LST trend between these two methods reaches 0.012 K yr$^{–1}$. We consider the IADTC framework can guide the further optimization of T$_{dm}$ estimation across the globe, and the generated GADTC products should be valuable in various applications such as global and regional warming analysis

Remote Sensing Of Rice-Based Irrigated Agriculture: A Review

The ‘Green Revolution’ in rice farming of the late 1960’s denotes the beginning of the extensive breeding programs that have led to the many improved rice varieties that are now planted on more than 60% of the world’s riceland (Khush, 1987). This revolution led to increases in yield potential of 2 to 3 times that of traditional varieties (Khush, 1987). Similar trends have also been seen in the Irrigation Areas and Districts of southern New South Wales (NSW) as the local breeding program has produced many improved varieties of rice adapted to local growing conditions since the 1960’s (Brennan et al., 1994). Increases in area of rice planted, rice quality, and paddy yield resulted (Brennan et al., 1994). Increased rice area, however, has led to the development of high water tables and risk of large tracts of land becoming salt-affected in southern NSW (Humphreys et al., 1994b). These concerns have led to various environmental regulations on rice in the region, culminating in 1994 when restrictions on rice area, soil suitability, and water consumption were fully enacted (Humphreys et al., 1994b). Strict environmental restrictions in combination with large areas of land make the management of this region a difficult task. Land managers require, among other things, a way of regulating water use, assessing or predicting crop area and productivity, and making management decisions in support of environmentally and economically sustainable agriculture. In the search for more time and cost effective methods for attaining these goals, while monitoring complex management situations, many have turned to remote sensing and Geographic Information System (GIS) technologies for assistance. The spectral information and spatial density of remote sensing data lends itself well to the measurement of large areas. Since the launch of LANDSAT-1 in 1972, this technology has been used extensively in agricultural systems for crop identification and area estimation, crop yield estimation and prediction, and crop damage assessment. The incorporation of remote sensing and GIS can also help integrate management practices and develop effective management plans. However, in order to take advantage of these tools, users must have an understanding of both what remote sensing is and what sensors are now available, and how the technology is being used in applied agricultural research. Accordingly, a description of both follows: first a description of the technology, and then how it is currently being applied. The applications of remote sensing relevant to this discussion can be separated into crop type identification; crop area measurement; crop yield; crop damage; water use/ moisture availability (ma) mapping; and water use efficiency monitoring/mapping. This report focuses on satellite remote sensing for broad-scale rice-based irrigation agricultural applications. It also discusses related regional GIS analyses that may or may not include remote sensing data, and briefly addresses other sources of finer-scale remote sensing and geospatial data as they relate to agriculture. Since a complete review of the remote sensing research was not provided in the rice literature alone, some generic agricultural issues have been learned from applications not specifically dealing with rice. Remote sensing specialists may wish to skip to section 2

Remote Sensing Of Rice-Based Irrigated Agriculture: A Review

The ‘Green Revolution’ in rice farming of the late 1960’s denotes the beginning of the extensive breeding programs that have led to the many improved rice varieties that are now planted on more than 60% of the world’s riceland (Khush, 1987). This revolution led to increases in yield potential of 2 to 3 times that of traditional varieties (Khush, 1987). Similar trends have also been seen in the Irrigation Areas and Districts of southern New South Wales (NSW) as the local breeding program has produced many improved varieties of rice adapted to local growing conditions since the 1960’s (Brennan et al., 1994). Increases in area of rice planted, rice quality, and paddy yield resulted (Brennan et al., 1994). Increased rice area, however, has led to the development of high water tables and risk of large tracts of land becoming salt-affected in southern NSW (Humphreys et al., 1994b). These concerns have led to various environmental regulations on rice in the region, culminating in 1994 when restrictions on rice area, soil suitability, and water consumption were fully enacted (Humphreys et al., 1994b). Strict environmental restrictions in combination with large areas of land make the management of this region a difficult task. Land managers require, among other things, a way of regulating water use, assessing or predicting crop area and productivity, and making management decisions in support of environmentally and economically sustainable agriculture. In the search for more time and cost effective methods for attaining these goals, while monitoring complex management situations, many have turned to remote sensing and Geographic Information System (GIS) technologies for assistance. The spectral information and spatial density of remote sensing data lends itself well to the measurement of large areas. Since the launch of LANDSAT-1 in 1972, this technology has been used extensively in agricultural systems for crop identification and area estimation, crop yield estimation and prediction, and crop damage assessment. The incorporation of remote sensing and GIS can also help integrate management practices and develop effective management plans. However, in order to take advantage of these tools, users must have an understanding of both what remote sensing is and what sensors are now available, and how the technology is being used in applied agricultural research. Accordingly, a description of both follows: first a description of the technology, and then how it is currently being applied. The applications of remote sensing relevant to this discussion can be separated into crop type identification; crop area measurement; crop yield; crop damage; water use/ moisture availability (ma) mapping; and water use efficiency monitoring/mapping. This report focuses on satellite remote sensing for broad-scale rice-based irrigation agricultural applications. It also discusses related regional GIS analyses that may or may not include remote sensing data, and briefly addresses other sources of finer-scale remote sensing and geospatial data as they relate to agriculture. Since a complete review of the remote sensing research was not provided in the rice literature alone, some generic agricultural issues have been learned from applications not specifically dealing with rice. Remote sensing specialists may wish to skip to section 2

A physics-constrained machine learning method for mapping gapless land surface temperature

More accurate, spatio-temporally, and physically consistent LST estimation has been a main interest in Earth system research. Developing physics-driven mechanism models and data-driven machine learning (ML) models are two major paradigms for gapless LST estimation, which have their respective advantages and disadvantages. In this paper, a physics-constrained ML model, which combines the strengths in the mechanism model and ML model, is proposed to generate gapless LST with physical meanings and high accuracy. The hybrid model employs ML as the primary architecture, under which the input variable physical constraints are incorporated to enhance the interpretability and extrapolation ability of the model. Specifically, the light gradient-boosting machine (LGBM) model, which uses only remote sensing data as input, serves as the pure ML model. Physical constraints (PCs) are coupled by further incorporating key Community Land Model (CLM) forcing data (cause) and CLM simulation data (effect) as inputs into the LGBM model. This integration forms the PC-LGBM model, which incorporates surface energy balance (SEB) constraints underlying the data in CLM-LST modeling within a biophysical framework. Compared with a pure physical method and pure ML methods, the PC-LGBM model improves the prediction accuracy and physical interpretability of LST. It also demonstrates a good extrapolation ability for the responses to extreme weather cases, suggesting that the PC-LGBM model enables not only empirical learning from data but also rationally derived from theory. The proposed method represents an innovative way to map accurate and physically interpretable gapless LST, and could provide insights to accelerate knowledge discovery in land surface processes and data mining in geographical parameter estimation

Understanding and Predicting Vadose Zone Processes

Vadose zone hydrologic and biogeochemical processes play a significant role in the capture, storage and distribution of contaminants between the land surface and groundwater. One major issue facing geoscientists in dealing with investigations of the unsaturated zone flow and transport processes is the evaluation of heterogeneity of subsurface media. This chapter presents a summary of approaches for monitoring and modeling of vadose zone dynamics in the presence of heterogeneities and complex features, as well as incorporating transient conditions. Modeling results can then be used to provide early warning of soil and groundwater contamination before problems arise, provide scientific and regulatory credibility to environmental management decision-making process to enhance protection of human health and the environment. We recommend that future studies target the use of RTMs to identify and quantify critical interfaces that control large-scale biogeochemical reaction rates and ecosystem functioning. Improvements also need to be made in devising scaling approaches to reduce the disconnect between measured data and the scale at which processes occur

Satellite-based prediction of pCO2 in coastal waters of the eastern North Pacific

Continental margin carbon cycling is complex, highly variable over a range of space and time scales, and forced by multiple physical and biogeochemical drivers. Predictions of globally significant air–sea CO2 fluxes in these regions have been extrapolated based on very sparse data sets. We present here a method for predicting coastal surface-water pCO2 from remote-sensing data, based on self organizing maps (SOMs) and a nonlinear semi-empirical model of surface water carbonate chemistry. The model used simple empirical relationships between carbonate chemistry (total dissolved carbon dioxide (TCO2) and alkalinity (TAlk)) and satellite data (sea surface temperature (SST) and chlorophyll (Chl)). Surface-water CO2 partial pressure (pCO2) was calculated from the empirically-predicted TCO2 and TAlk. This directly incorporated the inherent nonlinearities of the carbonate system, in a completely mechanistic manner. The model’s empirical coefficients were determined for a target study area of the central North American Pacific continental margin (22–50°N, within 370 km of the coastline), by optimally reproducing a set of historical observations paired with satellite data. The model-predicted pCO2 agreed with the highly variable observations with a root mean squared (RMS) deviation of 0.8 (r = 0.81; r2 = 0.66). This level of accuracy is a significant improvement relative to that of simpler models that did not resolve the biogeochemical sub-regions or that relied on linear dependences on input parameters. Air–sea fluxes based on these pCO2 predictions and satellite-based wind speed measurements suggest that the region is a ∼14 Tg C yr−1 sink for atmospheric CO2 over the 1997–2005 period, with an approximately equivalent uncertainty, compared with a ∼0.5 Tg C yr−1 source predicted by a recent bin-averaging and interpolation-based estimate for the same area.Fil: Hales, Burke. State University of Oregon; Estados UnidosFil: Strutton, Peter G.. University Of Tasmania; AustraliaFil: Saraceno, Martin. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmosfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmosfera; ArgentinaFil: Letelier, Ricardo. State University of Oregon; Estados UnidosFil: Takahashi, Taro. Lamont-Doherty Earth Observatory; Estados UnidosFil: Feely, Richard. National Oceanic and Atmospheric Administration. Pacific Marine Environmental Laboratory; Estados UnidosFil: Sabine, Christopher. National Oceanic and Atmospheric Administration. Pacific Marine Environmental Laboratory; Estados UnidosFil: Chavez, Francisco. Monterey Bay Aquarium Research Institute; Estados Unido