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Remote Sensing
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NISAR Global Observations for Ecosystem Science and Applications
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NISAR Global Observations for Ecosystem Science and Applications

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Ecological Remote Sensing".

Deadline for manuscript submissions: 20 September 2024 | Viewed by 3919

Special Issue Editors

Dr. Sassan Saatchi

E-Mail Website
Guest Editor
NASA Jet Propulsion Laboratory, Pasadena, CA 91109, USA
Interests: remote sensing tropical ecology; carbon and water cycling; climate change; machine learning; SAR; LiDAR
Prof. Dr. Paul Siqueira

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Massachusetts Amherst, Amherst, MA 01003, USA
Interests: microwave sensor development and implementation; numerical modeling of electromagnetic fields within natural media; signal and image processing applied to environmental remote sensing
Special Issues, Collections and Topics in MDPI journals
Dr. Anup Das

E-Mail Website
Guest Editor
Indian Space Research Organization—Space Applications Centre (SAC), Ahmedabad, India
Interests: data analysis; environmental monitoring; remote sensing; geographic information systems (GIS)

Special Issue Information

Dear Colleagues,

The world’s growing population is experiencing unprecedented climate change through intensifying events, such as floods, droughts, wildfires, hurricanes, tornadoes, insect infestations and related health effects. These impacts are putting pressure on our landscapes and ecosystems, which we use to generate food, fiber and energy globally. It is, therefore, imperative to systematically monitor global ecosystems to understand the connections between ecosystem responses and their management to create a sustainable future.

The NASA–ISRO Synthetic Aperture Radar (NISAR) mission, a collaboration between the National Aeronautics and Space Administration (NASA) and the Indian Space Research Organization (ISRO), was designed to provide observations of global ecosystems and land surfaces to systematically quantify their state and changes thereof. NISAR’s unprecedented coverage in space and time could reveal biomass variability far more comprehensively than any other measurement method. The detailed observations are predicted to reveal information allowing us to better manage our resources, as well as to prepare for and cope with global change.

NISAR mission observations include global SAR imagery at L- and S-band frequencies, with multiple polarizations and repeat-pass interferometric measurements at very high spatial and temporal resolutions. The mission is planned to launch in 2023, starting with the provision of data for use in a variety of ecosystem sciences and applications, including mapping vegetation above ground biomass, wetland inundation, cropland extent and classification, freeze/thaw monitoring and soil moisture monitoring. In recent years, extensive studies have been conducted using airborne and satellite data to simulate and quantify the NISAR performance, developing algorithms for science and applications of data products, as well as calibration and validation experiments.

The proposed Special Issue calls for submissions presenting the results of NISAR-related research and the development of science algorithms for the ecosystem biophysical parameter retrieval, calibration and validation of science products, as well as applications of management and monitoring in different ecosystems. The aim of the Special Issue is to focus on the use of L- and S-band SAR time-series of observations, but it also welcomes synergistic studies and comparisons with other observations from SAR in different frequencies, passive microwave measurements, lidar and optical imagery.  

Topics To Be Covered

The broad topics of this Special Issue include, but are not limited to:

  • Forest and vegetation: management applications and modeling;
  • Forest and vegetation: structure, biomass and carbon cycle;
  • Forest and vegetation: disturbance and recovery;
  • Agriculture: crop area and classification, crop biomass and water content;
  • Wetlands and inundation: forest and nonforest wetlands and coastal ecosystems;
  • Soil moisture: forests and nonforest ecosystems and croplands;
  • SAR theoretical algorithms for ecosystem sciences and applications;
  • SAR calibration and validation experiment results;
  • NISAR and GEDI synergism for forest structure and biomass;
  • NISAR synergism with passive microwave and optical observations;
  • NISAR ecosystem product error analysis and uncertainty modeling.

Dr. Sassan Saatchi
Prof. Dr. Paul Siqueira
Dr. Anup Das
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2700 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (2 papers)

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Research

19 pages, 10340 KiB  
Article
The Amazon’s 2023 Drought: Sentinel-1 Reveals Extreme Rio Negro River Contraction
by Fabien H. Wagner, Samuel Favrichon, Ricardo Dalagnol, Mayumi C. M. Hirye, Adugna Mullissa and Sassan Saatchi
Remote Sens. 2024, 16(6), 1056; https://0-doi-org.brum.beds.ac.uk/10.3390/rs16061056 - 16 Mar 2024
Viewed by 1565
Abstract
The Amazon, the world’s largest rainforest, faces a severe historic drought. The Rio Negro River, one of the major Amazon River tributaries, reached its lowest level in a century in October 2023. Here, we used a U-net deep learning model to map water [...] Read more.
The Amazon, the world’s largest rainforest, faces a severe historic drought. The Rio Negro River, one of the major Amazon River tributaries, reached its lowest level in a century in October 2023. Here, we used a U-net deep learning model to map water surfaces in the Rio Negro River basin every 12 days in 2022 and 2023 using 10 m spatial resolution Sentinel-1 satellite radar images. The accuracy of the water surface model was high, with an F1-score of 0.93. A 12-day mosaic time series of the water surface was generated from the Sentinel-1 prediction. The water surface mask demonstrated relatively consistent agreement with the global surface water (GSW) product from the Joint Research Centre (F1-score: 0.708) and with the Brazilian MapBiomas Water initiative (F1-score: 0.686). The main errors of the map were omission errors in flooded woodland, in flooded shrub, and because of clouds. Rio Negro water surfaces reached their lowest level around the 25th of November 2023 and were reduced to 68.1% (9559.9 km2) of the maximum water surfaces observed in the period 2022–2023 (14,036.3 km2). Synthetic aperture radar (SAR) data, in conjunction with deep learning techniques, can significantly improve near-real-time mapping of water surfaces in tropical regions. Full article
(This article belongs to the Special Issue NISAR Global Observations for Ecosystem Science and Applications)
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17 pages, 3038 KiB  
Article
Small Field Plots Can Cause Substantial Uncertainty in Gridded Aboveground Biomass Products from Airborne Lidar Data
by K. C. Cushman, Sassan Saatchi, Ronald E. McRoberts, Kristina J. Anderson-Teixeira, Norman A. Bourg, Bruce Chapman, Sean M. McMahon and Christopher Mulverhill
Remote Sens. 2023, 15(14), 3509; https://0-doi-org.brum.beds.ac.uk/10.3390/rs15143509 - 12 Jul 2023
Viewed by 1633
Abstract
Emerging satellite radar and lidar platforms are being developed to produce gridded aboveground biomass (AGB) predictions that are poised to expand our understanding of global carbon stocks and changes. However, the spatial resolution of AGB map products from these platforms is often larger [...] Read more.
Emerging satellite radar and lidar platforms are being developed to produce gridded aboveground biomass (AGB) predictions that are poised to expand our understanding of global carbon stocks and changes. However, the spatial resolution of AGB map products from these platforms is often larger than the available field plot data underpinning model calibration and validation efforts. Intermediate-resolution/extent remotely sensed data, like airborne lidar, can serve as a bridge between small plots and map resolution, but methods are needed to estimate and propagate uncertainties with multiple layers of data. Here, we introduce a workflow to estimate the pixel-level mean and variance in AGB maps by propagating uncertainty from a lidar-based model using small plots, taking into account prediction uncertainty, residual uncertainty, and residual spatial autocorrelation. We apply this workflow to estimate AGB uncertainty at a 100 m map resolution (1 ha pixels) using 0.04 ha field plots from 11 sites across four ecoregions. We compare uncertainty estimates using site-specific models, ecoregion-specific models, and a general model using all sites. The estimated AGB uncertainty for 1 ha pixels increased with mean AGB, reaching 7.8–33.3 Mg ha−1 for site-specific models (one standard deviation), 11.1–28.2 Mg ha−1 for ecoregion-specific models, and 21.1–22.1 Mg ha−1 for the general model for pixels in the AGB range of 80–100 Mg ha−1. Only 3 of 11 site-specific models had a total uncertainty of <15 Mg ha−1 in this biomass range, suitable for the calibration or validation of AGB map products. Using two additional sites with larger field plots, we show that lidar-based models calibrated with larger field plots can substantially reduce 1 ha pixel AGB uncertainty for the same range from 18.2 Mg ha−1 using 0.04 ha plots to 10.9 Mg ha−1 using 0.25 ha plots and 10.1 Mg ha−1 using 1 ha plots. We conclude that the estimated AGB uncertainty from models estimated from small field plots may be unacceptably large, and we recommend coordinated efforts to measure larger field plots as reference data for the calibration or validation of satellite-based map products at landscape scales (≥0.25 ha). Full article
(This article belongs to the Special Issue NISAR Global Observations for Ecosystem Science and Applications)
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