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

Deadline for manuscript submissions: 31 October 2025 | Viewed by 18550

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Special Issue Editors

Dr. Sassan Saatchi

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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

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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

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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.

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Publisher’s Notice

As stated above, the central purpose of this Special Issue is to present research from “NISAR Global Observations for Ecosystem Science and Applications”. Given this purpose, the Guest Editors’ contribution to this Special Issue may be greater than standard Special Issues published by MDPI. Further details on MDPI's Special Issue guidelines can be found here: https://www.mdpi.com/special_issues_guidelines. The Editorial Office and Editor-in-Chief of Remote Sensing has approved this and MDPI’s standard manuscript editorial processing procedure (https://www.mdpi.com/editorial_process) will be applied to all submissions. As per our standard procedure, Guest Editors are excluded from participating in the editorial process for their submission and/or for submissions from persons with whom a potential conflict of interest may exist. More details on MDPI’s Conflict of Interest policy for reviewers and editors can be found here: https://www.mdpi.com/ethics#_bookmark22.

Keywords

forest and vegetation
agriculture
ecosystem
synthetic aperture radar
NISAR
passive microwave measurements
LiDAR

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Published Papers (7 papers)

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Research

23 pages, 5159 KiB

Open AccessArticle

Modifying NISAR’s Cropland Area Algorithm to Map Cropland Extent Globally

by Kaylee G. Sharp, Jordan R. Bell, Hannah G. Pankratz, Lori A. Schultz, Ronan Lucey, Franz J. Meyer and Andrew L. Molthan

Remote Sens. 2025, 17(6), 1094; https://doi.org/10.3390/rs17061094 - 20 Mar 2025

Viewed by 397

Abstract

Synthetic aperture radar (SAR) is emerging as a valuable dataset for monitoring crops globally. Unlike optical remote sensing, SAR can provide earth observations regardless of solar illumination or atmospheric conditions. Several methods that utilize SAR to identify agriculture rely on computationally expensive algorithms, such as machine learning, that require extensive training datasets, complex data pre-processing, or specialized software. The coefficient of variation (CV) method has been successful in identifying agricultural activity using several SAR sensors and is the basis of the Cropland Area algorithm for the upcoming NASA-Indian Space Research Organization (ISRO) SAR mission. The CV method derives a unique threshold for an AOI by optimizing Youden’s J-Statistic, where pixels above the threshold are classified as crop and pixels below are classified as non-crop, producing a binary crop/non-crop classification. Training this optimization process requires at least some existing cropland classification as an external reference dataset. In this paper, general CV thresholds are derived that can discriminate active agriculture (i.e., fields in use) from other land cover types without requiring a cropland reference dataset. We demonstrate the validity of our approach for three crop types: corn/soybean, wheat, and rice. Using data from the European Space Agency’s (ESA) Sentinel-1, a C-band SAR instrument, nine global AOIs, three for each crop type, were evaluated. Optimal thresholds were calculated and averaged for two AOIs per crop type for 2018–2022, resulting in 0.53, 0.31, and 0.26 thresholds for corn/soybean, wheat, and rice regions, respectively. The crop type average thresholds were then applied to an additional AOI of the same crop type, where they achieved 92%, 84%, and 83% accuracy for corn/soybean, wheat, and rice, respectively, when compared to ESA’s 2021 land cover product, WorldCover. The results of this study indicate that the use of the CV, along with the average crop type thresholds presented, is a fast, simple, and reliable technique to detect active agriculture in areas where either corn/soybean, wheat, or rice is the dominant crop type and where outdated or no reference datasets exist. Full article

(This article belongs to the Special Issue NISAR Global Observations for Ecosystem Science and Applications)

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30 pages, 30620 KiB

Open AccessArticle

Characterizing Tidal Marsh Inundation with Synthetic Aperture Radar, Radiometric Modeling, and In Situ Water Level Observations

by Brian T. Lamb, Kyle C. McDonald, Maria A. Tzortziou and Derek S. Tesser

Remote Sens. 2025, 17(2), 263; https://doi.org/10.3390/rs17020263 - 13 Jan 2025

Viewed by 947

Abstract

Tidal marshes play a globally critical role in carbon and hydrologic cycles by sequestering carbon dioxide from the atmosphere and exporting dissolved organic carbon to connected estuaries. These ecosystems provide critical habitat to a variety of fauna and also reduce coastal flood impacts. Accurate characterization of tidal marsh inundation dynamics is crucial for understanding these processes and ecosystem services. In this study, we developed remote sensing-based inundation classifications over a range of tidal stages for marshes of the Mid-Atlantic and Gulf of Mexico regions of the United States. Inundation products were derived from C-band and L-band synthetic aperture radar (SAR) imagery using backscatter thresholding and temporal change detection approaches. Inundation products were validated with in situ water level observations and radiometric modeling. The Michigan Microwave Canopy Scattering (MIMICS) radiometric model was used to simulate radar backscatter response for tidal marshes across a range of vegetation parameterizations and simulated hydrologic states. Our findings demonstrate that inundation classifications based on L-band SAR—developed using backscatter thresholding applied to single-date imagery—were comparable in accuracy to the best performing C-band SAR inundation classifications that required change detection approaches applied to time-series imagery (90.0% vs. 88.8% accuracy, respectively). L-band SAR backscatter threshold inundation products were also compared to polarimetric decompositions from quad-polarimetric Phased Array L-band Synthetic Aperture Radar 2 (PALSAR-2) and L-band Uninhabited Aerial Vehicle Synthetic Aperture Radar (UAVSAR) imagery. Polarimetric decomposition analysis showed a relative shift from volume and single-bounce scattering to double-bounce scattering in response to increasing tidal stage and associated increases in classified inundated area. MIMICS modeling similarly showed a relative shift to double-bounce scattering and a decrease in total backscatter in response to inundation. These findings have relevance to the upcoming NASA-ISRO Synthetic Aperture Radar (NISAR) mission, as threshold-based classifications of wetland inundation dynamics will be employed to verify that NISAR datasets satisfy associated mission science requirements to map wetland inundation with classification accuracies better than 80% at 1 hectare spatial scales. Full article

(This article belongs to the Special Issue NISAR Global Observations for Ecosystem Science and Applications)

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22 pages, 14974 KiB

Open AccessArticle

Adapting CuSUM Algorithm for Site-Specific Forest Conditions to Detect Tropical Deforestation

by Anam Sabir, Unmesh Khati, Marco Lavalle and Hari Shanker Srivastava

Remote Sens. 2024, 16(20), 3871; https://doi.org/10.3390/rs16203871 - 18 Oct 2024

Cited by 3 | Viewed by 1410

Abstract

Forest degradation is a major issue in ecosystem monitoring, and to take reformative measures, it is important to detect, map, and quantify the losses of forests. Synthetic Aperture Radar (SAR) time-series data have the potential to detect forest loss. However, its sensitivity is influenced by the ecoregion, forest type, and site conditions. In this work, we assessed the accuracy of open-source C-band time-series data from Sentinel-1 SAR for detecting deforestation across forests in Africa, South Asia, and Southeast Asia. The statistical Cumulative Sums of Change (CuSUM) algorithm was applied to determine the point of change in the time-series data. The algorithm’s robustness was assessed for different forest site conditions, SAR polarizations, resolutions, and under varying moisture conditions. We observed that the change detection algorithm was affected by the site- and forest-management activities, and also by the precipitation. The forest type and eco-region affected the detection performance, which varied for the co- and cross-pol backscattering components. The cross-pol channel showed better deforested region delineation with less spurious detection. The results for Kalimantan showed a better accuracy at a 100 m spatial resolution, with a 25.1% increase in the average Kappa coefficient for the VH polarization channel in comparison with a 25 m spatial resolution. To avoid false detection due to the high impact of soil moisture in the case of Haldwani, a seasonal analysis was carried out based on dry and wet seasons. For the seasonal analysis, the cross-pol channel showed good accuracy, with an average Kappa coefficient of 0.85 at the 25 m spatial resolution. This work was carried out in support of the upcoming NISAR mission. The datasets were repackaged to the NISAR-like HDF5 format and processing was carried out with methods similar to NISAR ATBDs. Full article

(This article belongs to the Special Issue NISAR Global Observations for Ecosystem Science and Applications)

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16 pages, 92082 KiB

Open AccessArticle

LUCA: A Sentinel-1 SAR-Based Global Forest Land Use Change Alert

by Adugna Mullissa, Sassan Saatchi, Ricardo Dalagnol, Tyler Erickson, Naomi Provost, Fiona Osborn, Aleena Ashary, Violet Moon and Daniel Melling

Remote Sens. 2024, 16(12), 2151; https://doi.org/10.3390/rs16122151 - 13 Jun 2024

Cited by 3 | Viewed by 5282

Abstract

The Land Use Change Alert (LUCA) dataset was developed for effective and timely monitoring of global forest changes that are mostly associated with human activities. Near- real-time changes of forest land use are mapped at 0.05 ha minimum mapping unit for all forest types across the Earth’s ecoregions, every two weeks. LUCA is based on Sentinel-1 cloud penetrating synthetic aperture radar (SAR) observations to circumvent limitations of optical imagery from pervasive cloud cover over forested areas globally, and especially in the tropics. The methodology is based on a combination of time-series change detection and machine learning analytics to achieve high accuracy of alerts across all ecoregions and landscapes globally with an average area-adjusted users and producers accuracy of 83% and 63%, respectively. The bi-weekly global alert maps capture forest clearing associated with deforestation and industrial timber harvesting, along with forest degradation associated with selective logging, fragmentation, fire, and roads. The product was developed and released publicly through Google Earth Engine to allow for the rapid assessment of land use change activities, quantifying patterns and processes driving forest change and dynamics across forest ecoregions. LUCA is designed to help monitor a variety of emission reduction programs at the local to regional scales and play a key role in implementing regulations on deforestation-free products. Full article

(This article belongs to the Special Issue NISAR Global Observations for Ecosystem Science and Applications)

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18 pages, 35476 KiB

Open AccessArticle

Classification of Crop Area Using PALSAR, Sentinel-1, and Planet Data for the NISAR Mission

by Giovanni Anconitano, Seung-Bum Kim, Bruce Chapman, Jessica Martinez, Paul Siqueira and Nazzareno Pierdicca

Remote Sens. 2024, 16(11), 1975; https://doi.org/10.3390/rs16111975 - 30 May 2024

Cited by 1 | Viewed by 2113

Abstract

An algorithm for classifying crop areas using multi-frequency Synthetic Aperture Radar (SAR) and optical data is evaluated for the upcoming NASA ISRO SAR (NISAR) mission and its active crop area products. Two time-series of L-band ALOS-2 and C-band Sentinel-1A images over an agricultural region in the Southern United States are used as the input, as well as high-resolution Planet optical data. To overcome the delay by at least one year of existing landcover maps, training and validation sets of crop/non-crop polygons are derived with the contemporary Planet images. The classification results show that the 80% requirement on the NISAR science accuracy is achievable only with L-band HV input and with a resolution of 100 m. In comparison, HH polarized images do not meet this target. The spatial resolution is a key factor: 100 m is necessary to accomplish the 80% goal, while 10 m do not produce the desired accuracy. Unlike the previous study reporting that C-band performs better than L-band, we found otherwise in this study. This suggests that the performance likely depends on the site of interest and crop types. Alternative to the SAR images, the Normalized Difference Vegetation Index (NDVI) from the Planet data is not effective either as an input to the classification algorithm or as ground truth for training the algorithm. The reason is that NDVI becomes saturated and temporally static, thus rendering crop pixels to be misclassified as non-crop. Full article

(This article belongs to the Special Issue NISAR Global Observations for Ecosystem Science and Applications)

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19 pages, 10340 KiB

Open AccessArticle

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://doi.org/10.3390/rs16061056 - 16 Mar 2024

Cited by 3 | Viewed by 3913

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 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

Open AccessArticle

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://doi.org/10.3390/rs15143509 - 12 Jul 2023

Cited by 6 | Viewed by 2789

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 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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