Journal Paper Digests

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Journal Paper Digests 2022 #24

  • Fusion of Gamma-rays and portable X-ray fluorescence spectral data to measure extractable potassium in soils
  • Tipping points of marine phytoplankton to multiple environmental stressors
  • Warming reduces global agricultural production by decreasing cropping frequency and yields
  • Benchmarking downscaled satellite-based soil moisture products using sparse, point-scale ground observations
  • A web-based system for satellite-based high-resolution global soil moisture maps

A web-based system for satellite-based high-resolution global soil moisture maps

Nowadays, a wide range of applications require near-real-time Surface Soil Moisture (SSM) data at high spatial resolution. However, operational passive microwave systems like SMOS and SMAP can only acquire such information at a relatively coarser resolution. Therefore, several downscaling algorithms have been developed to address this issue and provide SSM maps at a finer spatial scale. Users may, however, find it difficult to implement the downscaling algorithm due to the complexity of integrating various data sources. Disaggregation based on Physical and Theoretical scale Change (DisPATCh) is one of the algorithms that is widely accepted to downscale passive microwave SSM observations. But the complexity of modeling, the variety of data sources and formats of input data make it very difficult for users to implement the algorithm. Thus, we developed a Satellitebased Hydrological Monitoring System (SHMS), which facilitates this gap through the implementation of the DisPATCh algorithm to generate large-scale SSM maps with high resolution, which is achieved by combining SMAP and MODIS products. The System Usability Scale (SUS) method was used to evaluate the system’s strengths and weaknesses. The SUS evaluation results show that 74.75% of SHMS users are satisfied with the system’s performance.

Benchmarking downscaled satellite-based soil moisture products using sparse, point-scale ground observations

While strides have been made in their accuracy and availability, the overall utility of satellite-derived surface soil moisture (SM) datasets derived from passive microwave radiometry is still reduced by their relatively coarse spatial resolution (typically >30 km). In response to this shortcoming, many independent satellite-based SM downscaling approaches have been introduced recently. However, owing to limitations in the spatial sampling characteristics of existing SM ground-monitoring networks, it has proven difficult to obtain reliable reference SM observations at the target downscaling resolution for these approaches (typically 1 to 10 km). As a result, the objective evaluation of SM downscaling approaches is often challenging and/or limited to very localized conditions. Here, we introduce and evaluate a point-scale downscaling (PSD) benchmarking strategy whereby spatially sparse, long-term, point-scale SM observations available from existing ground-based SM networks are utilized for the objective benchmarking of downscaled satellite-based SM products. First, we define criteria that must be met for a given SM downscaling strategy to add either temporal accuracy or spatial skill relative to its coarse-resolution SM baseline. Next, we illustrate, both analytically and numerically, that such criteria can be accurately evaluated using sparse, point-scale SM observations available from existing ground-based SM networks. Finally, we apply our new PSD benchmarking approach to evaluate existing fine-scale SM products. Results demonstrate that the PSD approach, in concert with existing ground-based network data, can be leveraged to robustly evaluate SM downscaling approaches.

Warming reduces global agricultural production by decreasing cropping frequency and yields

Annual food caloric production is the product of caloric yield, cropping frequency (CF, number of production seasons per year) and cropland area. Existing studies have largely focused on crop yield, whereas how CF responds to climate change remains poorly understood. Here, we evaluate the global climate sensitivity of caloric yields and CF at national scale. We find a robust negative association between warming and both caloric yield and CF. By the 2050s, projected CF increases in cold regions are offset by larger decreases in warm regions, resulting in a net global CF reduction (-4.2 +/- 2.5% in high emission scenario), suggesting that climate-driven decline in CF will exacerbate crop production loss and not provide climate adaptation alone. Although irrigation is effective in offsetting the projected production loss, irrigation areas have to be expanded by >5% in warm regions to fully offset climate-induced production losses by the 2050s.

Climate change will impact agriculture, and this study shows cropping frequency and caloric yield are negatively impacted on the global scale by warming. While cold regions will increase cropping frequency, warm regions will see greater decreases, resulting in an overall decline in production.

Tipping points of marine phytoplankton to multiple environmental stressors

The authors establish machine learning models to identify multifactor tipping points of global marine phytoplankton. They show that temperature and carbon dioxide dominate risks, and project crossing tipping points in tropical area production (50%) and resistance (41%) by 2100 under high emissions.

Globally, anthropogenic climate change is threatening marine species. However, whether and how global marine phytoplankton, which represent the base of marine food webs, will exceed their tipping points under multiple climate factors remain unclear. Here, by establishing machine learning models, we identified the tipping points of global marine phytoplankton production and resistance under eight environmental stressors. Phytoplankton production and resistance are affected by multiple factors and the temperature and partial pressure of carbon dioxide dominate the risks for reaching their tipping points. If the current emission scenario continues, 50% (40-61% at 90% confidence) and 41% (2-80% at 90% confidence) of tropical areas would reach the tipping points of ongoing phytoplankton production and resistance decline, respectively, in 2100. Compared with single- or few-factor studies, machine learning (for example, ensemble machine learning) provides a powerful and realistic solution for policy-makers facing large-scale ecological responses to global climate changes under multiple environmental stressors.

Fusion of Gamma-rays and portable X-ray fluorescence spectral data to measure extractable potassium in soils

The detection and mapping of plant-extractable potassium (K-a) using proximal soil sensing and data fusion (DF) techniques are essential to optimise K2O fertiliser application, improve crop yield, and minimise environmental and financial costs. This work evaluates the potential of combined use of portable gamma ray and x-ray fluorescence spectroscopy for in field detection and mapping of K-a. After subjected to various pre-processing methods, spectral data were split into calibration (75%) and validation (25%) sets, and single sensor and DF models were developed using partial least squares regression (PLSR). Maps of Ka were used to present spatial variability of potassium across an 8.4 ha Voor de Hoeves target field, in Flanders, Belgium. Results showed that the gamma-ray PLSR model using wet soils had greater predictive ability with coefficient of determination (R-2) = 0.71, ratio of performance deviation (RPD) = 1.89, root mean square error (RMSE) = 31.7 mg kg(-1), and ratio of performance to interquartile range (RPIQ) = 2.36 than the corresponding wet-XRF PLSR model (R-2 = 0.61, RPD = 1.64, RMSE = 48.8 mg kg(-1) and RPIQ = 1.84). The DF PLSR model on wet soils, resulted in a more accurate Ka predictive ability (R-2 = 0.75, RPD = 2.03, RMSE = 31.3 mg kg(-1) and RPIQ = 2.79), compared to the individual gamma ray or XRF sensors in wet soils. The best accuracy was obtained with XRF spectrometer using dry samples (R-2 = 0.77, RPD = 2.14, RMSE = 26.5 mg kg(-1) and RPIQ = 3.39). All Ka prediction maps showed spatial similarity to the corresponding measured maps in the target field. In conclusion, since DF increased the Ka prediction accuracy compared to the single sensor models using wet soils, it is recommended to be adopted in future studies as a potential solution for Ka measurement, mapping, and ultimately for site-specific K2O fertilisation management. The XRF analysis of dry spectra is recommended as the best method for accurate measurement of K-a.

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