Journal Paper Digests 2019 #20
- Needle in a haystack: Mapping rare and infrequent crops using satellite imagery and data balancing methods
- Generation of spatially complete and daily continuous surface soil moisture Chock forof high spatial resolution
- The SMAP and Copernicus Sentinel 1A/B microwave active-passive high resolution surface soil moisture product
- Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years
- Interannual variation of terrestrial carbon cycle: Issues and perspectives
- External shocks, agent interactions, and endogenous feedbacks - Investigating system resilience with a stylized land use model
- Exploring resilience with agent-based models: State of the art, knowledge gaps and recommendations for coping with multidimensionality
- State factor network analysis of ecosystem response to climate change
- Soil organic matter is stabilized by organo-mineral associations through two key processes: The role of the carbon to nitrogen ratio
- Prediction of phosphorus sorption indices and isotherm parameters in agricultural soils using mid-infrared spectroscopy
Needle in a haystack: Mapping rare and infrequent crops using satellite imagery and data balancing methods
By:Waldner, F (Waldner, Francois)[ 1 ] ; Chen, Y (Chen, Yang)[ 2 ] ; Lawes, R (Lawes, Roger)[ 3 ] ; Hochman, Z (Hochman, Zvi)[ 1 ]
REMOTE SENSING OF ENVIRONMENT
Volume: 233 Pages: 11375-11375 Published: NOV 2019
Document Type: Article
Abstract Most cropping systems around the world are organised around few dominant crops and a larger number of less frequent crops. While rare and infrequent crops occupy a small share of the cropped area, they produce ecological benefits on farmland, contribute to sustainability and help provide food and nutritional security. However, data about their location and extent derived from satellite imagery generally lack accuracy, largely owing to the class imbalance problem. Class imbalance occurs when only few instances of some classes are available for training classifiers, and leads to large error rates of the infrequent classes. In this study, we assessed the magnitude of the class imbalance problem in crop classification and evaluated balancing methods to combat it by creating synthetic minority observations or by removing majority observations. To that aim, we generated 18 unbalanced data sets from Sentinel-2 time series and crop type observations in Victoria, Australia. These data sets covered a wide range of complexity, number of classes, number of samples per class and spectral separability which enabled us to gather evidence about the benefits and drawbacks of balancing methods in various settings. Classification accuracy was assessed with two metrics: the Overall Accuracy (OA), which gives more weight to majority classes, and the G-Mean accuracy (GM), which is more sensitive to minority classes. Results showed that class imbalance explained near 40% of the accuracy variability. We found that balancing methods boosted GM by 0.01-0.54 but no single best solution emerged. The price for increasing the accuracy of minority classes was a drop in OA of a magnitude that was problem- and method-specific. We thus applied an algorithm selection method called the F-race to identify optimal balancing methods in a computationally economic fashion. Optimal balancing methods lead to maximum gain in GM and minimum loss in OA. We demonstrated that this approach either successfully identified optimal balancing methods or ones that were not significantly sub-optimal, while reducing the computational cost by up to 60%. It can readily be incorporated to operational crop classification systems with little disruption to the existing processing chains. This contribution paves the way for achieving a more comprehensive and detailed view of crop distribution and cropping sequences.
Generation of spatially complete and daily continuous surface soil moisture Chock forof high spatial resolution
By:Long, D (Long, Di)[ 1 ] ; Bai, LL (Bai, Liangliang)[ 1 ] ; Yan, L (Yan, La)[ 1 ] ; Zhang, CJ (Zhang, Caijin)[ 1 ] ; Yang, WT (Yang, Wenting)[ 1 ] ; Lei, HM (Lei, Huimin)[ 1 ] ; Quan, JL (Quan, Jinling)[ 2 ] ; Meng, XY (Meng, Xianyong)[ 3 ] ; Shi, CX (Shi, Chunxiang)[ 4 ]
View Web of Science ResearcherID and ORCID REMOTE SENSING OF ENVIRONMENT
Volume: 233 Pages: 11364-11364 Published: NOV 2019
Document Type: Article
Abstract Surface soil moisture (SSM), as a vital variable for water and heat exchanges between the land surface and the atmosphere, is essential for agricultural production and drought monitoring, and serves as an important boundary condition for atmospheric models. The spatial resolution of soil moisture products from microwave remote sensing is relatively coarse (e.g., similar to 40 km x 40 km), whereas SSM of higher spatiotemporal resolutions (e.g., 1 km x 1 km and daily continuous) is more useful in water resources management. In this study, first, to improve the spatiotemporal completeness of SSM estimates, we downscaled land surface temperature (LST) output from the China Meteorological Administration Land Data Assimilation System (CLDAS, 0.0625 degrees x 0.0625 degrees) using a data fusion approach and MODIS LST acquired on clear-sky days to generate spatially complete and temporally continuous LST maps across the North China Plain. Second, spatially complete and daily continuous 1 km x 1 km SSM was generated based on random forest models combined with quality LST maps, normalized difference vegetation index (NDVI), surface albedo, precipitation, soil texture, SSM background fields from the European Space Agency Soil Moisture Climate Change Initiative (CCI, 0.25 degrees x 0.25 degrees) and CLDAS land surface model (LSM) SSM output (0.0625 degrees x 0.0625 degrees) to be downscaled, and in situ SSM measurements. Third, the importance of different input variables to the downscaled SSM was quantified. Compared with the original CCI and CLDAS SSM, both the accuracy and spatial resolution of the downscaled SSM were largely improved, in terms of a bias (root mean square error) of -0.001 cm(3)/cm(3) (0.041 cm(3)/cm(3)) and a correlation coefficient of 0.72. These results are generally comparable and even better than those in published studies, with our SSM maps featuring spatiotemporal completeness and relatively high spatial resolution. The downscaled SSM maps are valuable for monitoring agricultural drought and optimizing irrigation scheduling, bridging the gaps between microwave-based and LSM-based SSM estimates of coarse spatial resolution and thermal infrared-based LST at a 1 km x 1 km resolution.
The SMAP and Copernicus Sentinel 1A/B microwave active-passive high resolution surface soil moisture product
By:Das, NN (Das, Narendra N.)[ 1 ] ; Entekhabi, D (Entekhabi, Dara)[ 2 ] ; Dunbar, RS (Dunbar, R. Scott)[ 1 ] ; Chaubell, MJ (Chaubell, Mario J.)[ 1 ] ; Colliander, A (Colliander, Andreas)[ 1 ] ; Yueh, S (Yueh, Simon)[ 1 ] ; Jagdhuber, T (Jagdhuber, Thomas)[ 3 ] ; Chen, F (Chen, Fan)[ 4 ] ; Crow, W (Crow, Wade)[ 5 ] ; O’Neill, PE (O’Neill, Peggy E.)[ 6 ] …More
REMOTE SENSING OF ENVIRONMENT
Volume: 233 Pages: 11380-11380 Published: NOV 2019
Document Type: Article
Abstract Soil Moisture Active Passive (SMAP) mission of NASA was launched in January 2015. Currently, SMAP has an L-band radiometer and a defunct L-band radar with a rotating 6-m mesh reflector antenna. On July 7th, 2015, the SMAP radar malfunctioned and became inoperable. Consequently, the production of high-resolution active-passive soil moisture product got hampered, and only similar to 2.5 months (April 15th, 2015 to July 7th, 2015) of data remain available. Therefore, during the SMAP post-radar phase, many ways were examined to restart the high-resolution soil moisture product generation of the SMAP mission. One of the feasible approaches was to substitute the SMAP radar with other available SAR data. Sentinel-1A/Sentinel-1B SAR data was found most suitable for combining with the SMAP radiometer data because of its nearly similar orbit configuration that allows overlapping of their swaths with a minimal time difference, a key feature/requirement for the SMAP active-passive algorithm. The Sentinel interferometric wide swath (IW) mode acquisition also provides the co-polarized and cross-polarized observations required for the SMAP active-passive algorithm. However, some differences do exist between the SMAP and Sentinel SAR data. They are mainly: 1) Sentinel has a C-band SAR whereas SMAP operates at L-band; 2) Sentinel has multiple incidence angles within its swath, and SMAP has one single incidence angle; and 3) Sentinel 1A/B Interferometric Wide (IW) swath width is similar to 250 km as compared to SMAP with 1000 km swath width. On any given day, the narrow swath width of the Sentinel observations significantly reduces the overlap spatial coverage between SMAP and Sentinel as compared to the original SMAP radar and radiometer swath coverage. Hence, the temporal resolution (revisit interval) suffers due to narrow overlapped swath width and degrades from 3 days to 12 days. One advantage of using very high-resolution resolution Sentinel-1A/Sentinel-1B data in the SMAP active-passive algorithm is the potential of obtaining the disaggregated brightness temperature and thus soil moisture at a much finer spatial resolution of 3 km and 1 km at global extent. The assessment of high-resolution product at 3 km and 1 km using the soil moisture calibration and validations sites shows reasonable accuracy of similar to 0.05 m(3)/m(3). The SMAP-Sentinel1 active-passive high-resolution product is now available to the public (new version released in October 2018) through NSIDC (NASA DAAC). The duration of this product is from April 2015 to current date.
Remote sensing of the terrestrial carbon cycle: A review of advances over 50 years
By:Xiao, JF (Xiao, Jingfeng)[ 1 ] ; Chevallier, F (Chevallier, Frederic)[ 2 ] ; Gomez, C (Gomez, Cecile)[ 3 ] ; Guanter, L (Guanter, Luis)[ 4 ] ; Hicke, JA (Hicke, Jeffrey A.)[ 5 ] ; Huete, AR (Huete, Alfredo R.)[ 6 ] ; Ichii, K (Ichii, Kazuhito)[ 7 ] ; Ni, WJ (Ni, Wenjian)[ 8 ] ; Pang, Y (Pang, Yong)[ 9 ] ; Rahman, AF (Rahman, Abdullah F.)[ 10 ] …More
REMOTE SENSING OF ENVIRONMENT
Volume: 233 Pages: 11383-11383 Published: NOV 2019
Document Type: Review
Abstract Quantifying ecosystem carbon fluxes and stocks is essential for better understanding the global carbon cycle and improving projections of the carbon-climate feedbacks. Remote sensing has played a vital role in this endeavor during the last five decades by quantifying carbon fluxes and stocks. The availability of satellite observations of the land surface since the 1970s, particularly the early 1980s, has made it feasible to quantify ecosystem carbon fluxes and stocks at regional to global scales. Here we provide a review of the advances in remote sensing of the terrestrial carbon cycle from the early 1970s to present. First, we present an overview of the terrestrial carbon cycle and remote sensing of carbon fluxes and stocks. Remote sensing data acquired in a broad wavelength range (visible, infrared, and microwave) of the electromagnetic spectrum have been used to estimate carbon fluxes and/or stocks. Second, we provide a historical overview of the key milestones in remote sensing of the terrestrial carbon cycle. Third, we review the platforms/sensors, methods, findings, and challenges in remote sensing of carbon fluxes. The remote sensing data and techniques used to quantify carbon fluxes include vegetation indices, light use efficiency models, terrestrial biosphere models, data-driven (or machine learning) approaches, solar-induced chlorophyll fluorescence (SIF), land surface temperature, and atmospheric inversions. Fourth, we review the platforms/sensors, methods, findings, and challenges in passive optical, microwave, and lidar remote sensing of biomass carbon stocks as well as remote sensing of soil organic carbon. Fifth, we review the progresses in remote sensing of disturbance impacts on the carbon cycle. Sixth, we also discuss the uncertainty and validation of the resulting carbon flux and stock estimates. Finally, we offer a forward-looking perspective and insights for future research and directions in remote sensing of the terrestrial carbon cycle. Remote sensing is anticipated to play an increasingly important role in carbon cycling studies in the future. This comprehensive and insightful review on 50?years of remote sensing of the terrestrial carbon cycle is timely and valuable and can benefit scientists in various research communities (e.g., carbon cycle, remote sensing, climate change, ecology) and inform ecosystem and carbon management, carbon-climate projections, and climate policymaking.
Interannual variation of terrestrial carbon cycle: Issues and perspectives
By:Piao, SL (Piao, Shilong)[ 1,2,3 ] ; Wang, XH (Wang, Xuhui)[ 1 ] ; Wang, K (Wang, Kai)[ 1 ] ; Li, XY (Li, Xiangyi)[ 1 ] ; Bastos, A (Bastos, Ana)[ 4 ] ; Canadell, JG (Canadell, Josep G.)[ 5 ] ; Ciais, P (Ciais, Philippe)[ 1,6 ] ; Friedlingstein, P (Friedlingstein, Pierre)[ 7 ] ; Sitch, S (Sitch, Stephen)[ 8 ]
View Web of Science ResearcherID and ORCID GLOBAL CHANGE BIOLOGY
Pages: NIL_1-NIL_19 Published: NOV 29 2019
Document Type: Article
Abstract With accumulation of carbon cycle observations and model developments over the past decades, exploring interannual variation (IAV) of terrestrial carbon cycle offers the opportunity to better understand climate-carbon cycle relationships. However, despite growing research interest, uncertainties remain on some fundamental issues, such as the contributions of different regions, constituent fluxes and climatic factors to carbon cycle IAV. Here we overviewed the literature on carbon cycle IAV about current understanding of these issues. Observations and models of the carbon cycle unanimously show the dominance of tropical land ecosystems to the signal of global carbon cycle IAV, where tropical semiarid ecosystems contribute as much as the combination of all other tropical ecosystems. Vegetation photosynthesis contributes more than ecosystem respiration to IAV of the global net land carbon flux, but large uncertainties remain on the contribution of fires and other disturbance fluxes. Climatic variations are the major drivers to the IAV of net land carbon flux. Although debate remains on whether the dominant driver is temperature or moisture variability, their interaction,that is, the dependence of carbon cycle sensitivity to temperature on moisture conditions, is emerging as key regulators of the carbon cycle IAV. On timescales from the interannual to the centennial, global carbon cycle variability will be increasingly contributed by northern land ecosystems and oceans. Therefore, both improving Earth system models (ESMs) with the progressive understanding on the fast processes manifested at interannual timescale and expanding carbon cycle observations at broader spatial and longer temporal scales are critical to better prediction on evolution of the carbon-climate system.
External shocks, agent interactions, and endogenous feedbacks - Investigating system resilience with a stylized land use model
By:Chen, Y (Chen, Yang)[ 1,2 ] ; Bakker, MM (Bakker, Martha M.)[ 2 ] ; Ligtenberg, A (Ligtenberg, Arend)[ 1 ] ; Bregt, AK (Bregt, Arnold K.)[ 1 ]
ECOLOGICAL COMPLEXITY
Volume: 40 Pages: 765-765 Part: B Special Issue: SI Published: DEC 2019
Document Type: Article
Abstract Dynamics of coupled Social-Ecological Systems (SES) result from the interplay of society and ecology. To assess SES resilience, we constructed an Agent-Based Model (ABM) of a land use system as a stereotypical example of SES and investigated how resilience of the represented system is affected by both external disturbances and internal dynamics. The model explicitly considered different aspects of resilience in a framework derived from literature, which includes “resilience to”, “resilience of”, “resilience at”, “resilience due to”, and “indicators of resilience”. External disturbances were implemented as shocks in crop yields. Internal dynamics comprised of two types of social interaction between agents (learning and cooperation), an ecological feedback of soil depletion and an economic feedback of agglomeration benefits. We systematically varied these mechanisms and measured indicators that reflected spatial, social, and economic resilience. Results showed that (1) internal mechanisms increased the ability of the system to recover from external shocks, (2) feedbacks resulted in different regimes of crop cultivation, each with a distinctive set of functions, and (3) resilience is not a generic system property, but strongly depends on what system function is considered. We recommend future studies to include internal dynamics, especially feedbacks, and to systematically assess them across different aspects of resilience.
Exploring resilience with agent-based models: State of the art, knowledge gaps and recommendations for coping with multidimensionality
By:Egli, L (Egli, Lukas)[ 1 ] ; Weise, H (Weise, Hanna)[ 2 ] ; Radchuk, V (Radchuk, Viktoriia)[ 3 ] ; Seppelt, R (Seppelt, Ralf)[ 4 ] ; Grimm, V (Grimm, Volker)[ 1,5,6 ]
View Web of Science ResearcherID and ORCID ECOLOGICAL COMPLEXITY
Volume: 40 Pages: 718-718 Part: B Special Issue: SI Published: DEC 2019
Document Type: Article
Abstract Anthropogenic pressures increasingly alter natural systems. Therefore, understanding the resilience of agent-based complex systems such as ecosystems, i.e. their ability to absorb these pressures and sustain their functioning and services, is a major challenge. However, the mechanisms underlying resilience are still poorly understood. A main reason for this is the multidimensionality of both resilience, embracing the three fundamental stability properties recovery, resistance and persistence, and of the specific situations for which stability properties can be assessed. Agent-based models (ABM) complement empirical research which is, for logistic reasons, limited in coping with these multiple dimensions. Besides their ability to integrate multidimensionality through extensive manipulation in a fully controlled system, ABMs can capture the emergence of system resilience from individual interactions and feedbacks across different levels of organization. To assess the extent to which this potential of ABMs has already been exploited, we reviewed the state of the art in exploring resilience and its multidimensionality in ecological and socio-ecological systems with ABMs. We found that the potential of ABMs is not utilized in most models, as they typically focus on a single dimension of resilience by using variability as a proxy for persistence, and are limited to one reference state, disturbance type and scale. Moreover, only few studies explicitly test the ability of different mechanisms to support resilience. To overcome these limitations, we recommend to simultaneously assess multiple stability properties for different situations and under consideration of the mechanisms that are hypothesised to render a system resilient. This will help us to better exploit the potential of ABMs to understand and quantify resilience mechanisms, and hence support solving real-world problems related to the resilience of agent-based complex systems.
State factor network analysis of ecosystem response to climate change
By:Phillips, JD (Phillips, Jonathan D.)[ 1 ]
ECOLOGICAL COMPLEXITY
Volume: 40 Pages: 789-789 Part: A Published: DEC 2019
Document Type: Article
Abstract Case studies of ecosystem responses to changing climates are necessary in understanding and adapting to these changes. However, more general conceptual frameworks are also needed to contextualize and synthesize case studies, and to provide guidelines for assessment and prediction. This study analyzes a network model of ecological and soil state factor interrelationships to address issues such as sensitivity, resilience, and complexity of climate-driven terrestrial ecosystem changes. A factorial ecosystem model is analyzed using techniques from algebraic graph theory. Results show high values for spectral radius, graph energy, and algebraic connectivity. These indicate complexity, dynamical instability, active reverberating feedbacks, and high synchronization. Implications are that climate effects on ecosystems will be complicated, complex, and difficult to predict. We should be prepared for surprises in the form of unanticipated pathways and outcomes. The inherent nature of ecosystem interconnectivity indicated by the state factor model also suggests that when simulation models and change assessments do turn out to be wrong, it does not necessarily mean the underlying scientific understandings, data, or assumptions are wrong, as sensitivity to relatively minor variations and disturbances is high, and complexity and low predictability are inherent. Ecosystem reactions to climate (and other, often contemporaneous) changes will reverberate through the ecosystem. Both filtering and amplification may occur, but net amplification is indicated by the graph analysis. Ecosystem responses are integrated system responses-state factors will respond as a single integrated unit, not as a collection or sequence of individual responses.
Soil organic matter is stabilized by organo-mineral associations through two key processes: The role of the carbon to nitrogen ratio
By:Kopittke, PM (Kopittke, Peter M.)[ 1 ] ; Dalal, RC (Dalal, Ram C.)[ 1 ] ; Hoeschen, C (Hoeschen, Carmen)[ 2 ] ; Li, C (Li, Cui)[ 1,3 ] ; Menzies, NW (Menzies, Neal W.)[ 1 ] ; Mueller, CW (Mueller, Carsten W.)[ 1,2 ]
View Web of Science ResearcherID and ORCID GEODERMA
Volume: 357 Pages: 13974-13974 Published: JAN 1 2020
Document Type: Article
Abstract The loss of organic matter (OM) from soil during long-term agricultural cropping results in a decrease in the inherent fertility of the soil as well as releasing greenhouse gases. Despite the importance of organo-mineral associations in the stabilization of OM within soils, much remains unknown about these organo-mineral associations. We used nano-scale secondary ion mass spectrometry (NanoSIMS) to investigate the incorporation and stabilization of C-13 and N-15 labelled residues of lucerne (Medicago sativa) and buffel grass (Cenchrus ciliaris) when incubated in a Vertisol from temperate Australia for up to 365 d. We show that newly-added OM forms organo-mineral associations through two mechanisms. Firstly, it was observed that the newly-added OM forms associations with the existing mineral-bound OM. However, this apparent stabilization of newly-added OM by associating with existing mineral-bound OM was not influenced by the C:N ratio of the plant residues, with the lucerne residues (C:N ratio of 11) being incorporated to a similar extent as the buffel grass (C:N ratio of 35). Secondly, we observed that N-rich microbial metabolites attached directly to mineral particle surfaces that did not contain existing OM patches, thereby creating new organo-mineral associations through which additional stabilization of OM would be possible. The information obtained in this study is valuable in understanding the stabilization of OM through organo-mineral associations, and raises the possibility of using cover crops or catch crops with narrow C:N ratios to allow for formation of new organo-mineral associations for increased stabilization of OM in soil.
Prediction of phosphorus sorption indices and isotherm parameters in agricultural soils using mid-infrared spectroscopy
By:Dunne, KS (Dunne, K. S.)[ 1,2 ] ; Holden, NM (Holden, N. M.)[ 2 ] ; O’Rourk, SM (O’Rourk, S. M.)[ 2 ] ; Fenelon, A (Fenelon, A.)[ 1 ] ; Daly, K (Daly, K.)[ 1 ]
GEODERMA
Volume: 358 Pages: 13981-13981 Published: JAN 15 2020
Document Type: Article
Abstract Phosphorus is a macro nutrient essential for optimum crop growth and animal health. The soil’s ability to supply P in an available form is influenced by sorption capacity and P binding energies in soil. These properties are usually derived from sorption isotherms that are time consuming and difficult for routine analysis. Mid-infrared diffuse reflectance Fourier transform (MIR DRIFT) spectroscopy is a rapid analysis technique that can potentially replace extractive and digestive techniques traditionally used in soil analysis. This study explored the application of MIR DRIFT in combination with chemometrics, to predict indicators of soil fertility and quality, specifically, P sorption properties. Using an archive of 11 great soil groups a P sorption reference library was generated using five different sorption models; (1) single point sorption index; (2) Langmuir sorption isotherm in the 0-25 mg l(-1) P range; (3) Langmuir sorption isotherm in the 0-50 mg l(-1) P range; (4) Freundlich sorption isotherm in the 0-25 mg l(-1 )P range; and (5) Freundlich sorption isotherm in the 0-50 mg l(-1) P range. Thirteen reference values were generated for each sample for calibration and validation of P sorption models developed from MIR DRIFT spectra. Validation of the single point P sorption index and Langmuir parameters were satisfactory for rough screening with the exception of the Langmuir binding energy in the 0-50 mg l(-1) P range (k(50) [8]). The P sorption capacity remaining was the best predicted parameter and the Freundlich sorption isotherm predictions were poor. The results indicated that there is potential for benchtop MIR to describe P sorption properties in agricultural soil to improve management decisions and that soil specific models could be developed to further enhance prediction performance.