Multiscale Variability and the Comparison of Ground and Satellite Radar Based Measures of Peatland Surface Motion for Peatland Monitoring

Peatland surface motion is highly diagnostic of peatland condition. Interferometric Synthetic Aperture Radar (InSAR) can measure this at the landscape scale but requires ground validation. This necessitates upscaling from point to areal measures (80 × 90 m) but is hampered by a lack of data regarding the spatial variability of peat surface motion characteristics. Using a nested precise leveling approach within two areas of upland and low-lying blanket peatland within the Flow Country, Scotland, we examine the multiscale variability of peat surface motion. We then compare this with InSAR timeseries data. We find that peat surface motion varies at multiple scales within blanket peatland with decreasing dynamism with height above the water table e.g., hummocks < lawn < hollows. This trend is dependent upon a number of factors including ecohydrology, pool size/density, peat density, and slope. At the site scale motion can be grouped into central, marginal, and upland peatlands with each showing characteristic amplitude, peak timing, and response to climate events. Ground measurements which incorporate local variability show good comparability with satellite radar derived timeseries. However, current limitations of phase unwrapping in interferometry means that during an extreme drought/event InSAR readings can only qualitatively replicate peat movement in the most dynamic parts of the peatland e.g., pool systems, quaking bog.

Ground validation of GPM IMERG-F precipitation products with the point rain gauge records on the extreme rainfall over a mountainous area of Sumatra Island

Accurate satellite precipitation estimates over areas of complex topography are still challenging, while such accuracy is of importance to the adoption of satellite data for hydrological applications. This study evaluated the ability of Integrated Multi-satellitE Retrievals for GPM -Final (IMERG) V06 product to observe the extreme rainfall over a mountainous area of Sumatra Island. Fifteen years of optical rain gauge (ORG) observation at Kototabang, West Sumatra, Indonesia (100.32°E, 0.20°S, 865 m above sea level), were used as reference surface measurement. The performance of IMERG-F was evaluated using 13 extreme rain indexes formulated by the Expert Team on Climate Change Detection and Indices (ETCCDI). The IMERG-F overestimated the values of all precipitation amount-based indices (PRCPTOT, R85P, R95P, and R99P), three precipitation frequency-based indices (R1mm, R10mm, R20mm), one precipitation duration-based indices (CWD), and one precipitation intensity-based indices (RX5day). Furthermore, the IMERG-F underestimated the values of precipitation frequency-based indices (R50mm), one precipitation duration-based indices (CDD), one precipitation intensity-based indices (SDII). In terms of correlation, only five indexes have a correlation coefficient (R) > 0.5, consistent with Kling–Gupta Efficiency (KGE) value. These results confirm the need to improve the accuracy of the IMERG-F data in mountainous areas.

A simple method of UV stray light correction for field spectrometers in Ground Validation Sites

Field spectrometers are widely applied in ground validation sites for remote sensing. However, the control of stray light is one of the most important factors to ensure accurate measurements, since the spectral distribution of target source differs significantly from the laboratory calibration source. Here, a simple UV stray light correction method for continuously distributed wide-spectrum light sources was established by using a set of bandpass filters. This analysis enabled a simple, low-cost method for correcting for the observed straylight.

Ground Validation Experiment and Spectral Detection Capability Evaluation of Mars Mineralogical Spectrometer (MMS) Aboard HX-1 Orbiter

As a hyperspectral imager aboard the orbiter “HX-1” of China’s first Mars mission, the Mars Mineralogical Spectrometer (MMS) is designed with hyperspectral and multispectral operation modes to survey the mineral types and their distribution on the surface of Mars, and to study the overall chemical composition and evolution history of Mars. The multispectral modes of MMS are different from hyperspectral modes on the bands selection, spatial and spectral resolution, Signal-to-Noise Ratio (SNR) etc. So the spectral detection capability of each mode of MMS is also different. The ground validation experiment of MMS is conducted to evaluate the hyperspectral and multispectral data quality and detection capabilities. The main conclusions include: (1) The measured hyperspectra of typical mineral samples obtained by MMS agree well with the data acquired by the Standard Comparison Spectrometers (SCS) under the same measurement conditions, and the spectral uncertainty between MMS and SCS is less than 7% in the key spectral ranges ($0.7\sim2.2~\upmu \text{m}$ 0.7 ∼ 2.2 μm ). For some typical minerals, the absorption band positions deviation between MMS and SCS are within $0.69\sim14.86~\text{nm}$ 0.69 ∼ 14.86 nm , which are within the spectral resolution limits of MMS. (2) The six sets of band combinations designed for MMS multispectral modes are slightly superior to CRISM’s multispectral mode in terms of spectral resolutions and bands selection, the water-containing minerals will be more accurately distinguished and identified, such as montmorillonite and kaolinite. Besides, the SNR of each multispectral mode is greater than 400 in the 500–2600 nm spectral range, which meets the requirements for the subtle spectral characteristics of water-containing minerals. (3) Benefiting from the MMS ground validation experiment and the experience of the OMEGA and CRISM, it is recommended that MMS first adopt the spatial continuous 52-sample or 104-sample (spatial resolution is about $0.53\sim1.06~\text{km}$ 0.53 ∼ 1.06 km ) multispectral operation mode for typical minerals global mapping and finding target areas of interest. Then the 208-sample multispectral mode (spatial resolution is about $\sim265~\text{m}$ ∼ 265 m ) or 26-sample hyperspectral mode can be used to survey target areas of interest for the subtle mineral types characteristics and distribution. At last, 26-sample hyperspectral mode could be used to monitor the atmospheric composition of Mars by limb observations.

A global evaluation of IMERG precipitation occurrence using SMAP detected soil moisture change

A globally consistent ground validation method for remotely sensed precipitation products is crucial for building confidence in these products. This study develops a new methodology to validate the IMERG precipitation products through the use of SMAP soil moisture changes as a proxy for precipitation occurrence. Using a standard 2x2 contingency table method, preliminary results provide confidence in SMAP’s ability to be utilized as a validation tool for IMERG as results are comparable to previous validation studies. However, the method allows for an overestimate of false alarm frequency due to light precipitation events that can evaporate before the subsequent SMAP overpass and changes in overpass-to-overpass SMAP soil moisture that are within the range of SMAP uncertainty. To counter these issues, a 3x3 contingency table is used to reduce noise and extract more signal from the detection method. Through the use of this novel approach, the validation method produces a global mean POD of 0.64 and global mean FAR of 0.40, the first global-scale ground validation skill scores for the IMERG products. Advancing the method to validate precipitation quantity and the development of a real-time validation for the IMERG Early product are the crucial next developments.

Understanding Stakeholder Synergies Through System Dynamics: Integrating Multi-Sectoral Stakeholder Narratives Into Quantitative Environmental Models

To reach the global aspiration of 17 ambitious SDGs, local realities must be integrated. Often, models are developed based on quantitative statistical data sources from databases on environmental indicators or economics to assess how a given SDG can be achieved. This process however removes the local realities from the equation. How can you best include stakeholders in this mathematical modelling processes distanced from their local realities, though, and ensure higher probability of future compliance with top-down global decisions that may have local consequences once implemented? When researching stakeholder involvement and their ability to form public policy, their opinions often get reported as a single assessment, like counting the fish in the ocean once and stating that as a permanent result. Too seldom do stakeholders get invited back and given the opportunity to validate results and allow researchers to adjust their models based on on-the-ground validation or change requests. We tested the full integration of stakeholders in the modelling process of environmental topics in six different case areas across Europe, with each area holding six sectoral and one inter-sectoral workshops. In these workshops, the scope of the issues relevant to the stakeholders was driven by first the sectoral priorities of the given sector, followed by a merging of issues. In this process, we were able to identify what the commonalities between different sectors were and where synergies lay in terms of governance paths. These results were then returned to the stakeholders in a mixed session where they were able to come with feedback and advice on the results researchers presented, so that the models reflected more closely the perceptions of the regional actors. We present these methods and reflect on the challenges and opportunities of using this deep-integration method to integrate qualitative data from stakeholder inclusion in a quantitative model.

Evaluation of IMERG satellite precipitation over the land-coast-ocean continuum – Part I: Detection

As a fundamental water flux, quantitative understanding of precipitation is important to understand and manage water systems under a changing climate, especially in transition regions such as the coastal interface between land and ocean. This work aims to assess the uncertainty in precipitation detection over the land-coast-ocean continuum in the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (IMERG) V06B. It is examined over three coastal regions of the U.S., i.e. the West Coast, the Gulf of Mexico, and the East Coast, each of which are characterized by different topographies and precipitation climatologies. Detection capabilities are contrasted over different surfaces (land, coast, ocean). A novel and integrated approach traces the IMERG detection performance back to its components (passive microwave, infrared, and morphing-based estimates). The analysis is performed by using high-resolution, high-quality Ground Validation Multi-Radar/Multi-Sensor (GV-MRMS) rainfall estimates as ground reference. The best detection performances are reported with PMW estimates (hit rates in the range of [25-39]%), followed by morphing ([20-34]%), morphing+IR ([17-27]%) and IR ([11-16]%) estimates. Precipitation formation mechanisms play an important role, especially in the West Coast where orographic processes challenge detection. Further, precipitation typology is shown to be a strong driver of IMERG detection. Over the ocean, IMERG detection is generally better but suffers from false alarms ([10-53]%). Overall, IMERG displays nonhomogeneous precipitation detection capabilities tracing back to its components. Results point toward a similar behavior across various land-coast-ocean continuum regions of the CONUS, which suggests that results can be potentially transferred to other coastal regions of the world.