The Lake Victoria Basin (LVB) is a significant resource for five states within East Africa, which faces major land use land cover changes that threaten ecosystem integrity and ecosystem services derived from the basin’s resources. To assess land use land cover changes between 1985 and 2014, and subsequently determine the trends and drivers of these changes, we used a series of Landsat images and field data obtained from the LVB. Landsat image pre-processing and band combinations were done in ENVI 5.1. A supervised classification was applied on 118 Landsat scenes using the maximum likelihood classifier in ENVI 5.1. The overall accuracy of classified images was computed for the 2014 images using 124 reference data points collected through stratified random sampling. Computations of area under various land cover classes were calculated between the 1985 and 2014 images. We also correlated the area from natural vegetation classes to farmlands and settlements (urban areas) to explore relationships between land use land cover conversions among these classes. Based on our land cover classifications, we obtained overall accuracy of 71% and a moderate Kappa statistic of 0.56. Our results indicate that the LVB has undergone drastic changes in land use land cover, mainly driven by human activities that led to the conversion of forests, woodlands, grasslands, and wetlands to either farmlands or settlements. We conclude that information from this work is useful not only for basin-scale assessments and monitoring of land cover changes but also for targeting, prioritizing, and monitoring of small scale, community led efforts to restore degraded and fragmented areas in the basin. Such efforts could mitigate the loss of ecosystem services previously derived from large contiguous land covers which are no longer tenable to restore. We recommend adoption of a basin scale, operational, Earth observation-based, land use change monitoring framework. Such a framework can facilitate rapid and frequent assessments of gains and losses in specific land cover classes and thus focus strategic interventions in areas experiencing major losses, through mitigation and compensatory approaches.

In this study we evaluated the applicability of a space-borne hyperspectral sensor, Hyperion, to resolve for chlorophyll a (Chl a) concentration in Lake Atitlan, a tropical mountain lake in Guatemala. In situ water quality samples of Chl a concentration were collected and correlated with water surface reflectance derived from Hyperion images, to develop a semi-empirical algorithm. Existing operational algorithms were tested and the continuous bands of Hyperion were evaluated in an iterative manner. A third order polynomial regression provided a good fit to model Chl a. The final algorithm uses a blue (467 nm) to green (559 nm) band ratio to successfully model Chl a concentrations in Lake Atitlán during the dry season, with a relative error of 33%. This analysis confirmed the suitability of hyperspetral-imagers like Hyperion, to model Chl a concentrations in Lake Atitlán. This study also highlights the need to test and update this algorithm with operational multispectral sensors such as Landsat and Sentinel-2.

State-of-the-art methodologies to monitor deforestation rely mostly on optical satellite observations. High-density optical time series can enable the detection of deforestation almost as soon as it occurs. However, deforestation monitoring in the tropics can be hindered by high cloud coverage, and thus the responsiveness of managers, enforcement agencies, and scientists. To understand the implications of cloud contamination in freely available optical data we analyzed combined time series from Landsat 7, 8, and Sentinel-2 over the tropics from 2017–2021. Datasets derived for each 30 m × 30 m of the 59.4 M km2 domain include a) number of cloud-free observations per year, b) maximum consecutive days without clear imagery within a year, and c) final date of the longest waiting period. The datasets reflect where and when data gaps in optical time series exist due to cloud contamination. Scripts to access and extend the datasets are shared and documented. The datasets can be used to prioritize areas where complementary observations, such as radar imagery, are needed for implementing effective deforestation alert systems.

Operational applications for Synthetic Aperture Radar (SAR) are under development around the world, driven by the free-and-open access of SAR C-band observations that Sentinel-1 of Copernicus has provided since 2014. Radiometric Terrain Correction (RTC) data are key entry-level products for multiple applications ranging from ecosystem to hazard monitoring. Various open-source software packages exist to create RTC products from Single Look Complex (SLC) or Ground Range Detected (GRD) level SAR data, including the Interferometric SAR Computing Environment (ISCE), and the Sentinel-1 Toolbox from the European Space Agency (SNAP 8). Despite the growing availability of RTC software solutions, little work has been performed to identify differences between RTC products generated using different software packages. This work evaluates several Sentinel-1 RTC products and two other Sentinel-1 Analysis Ready Data (ARD) to address the following questions: (1) Which software provides the most accurate RTC product? and (2) how appropriate for analysis are other non-RTC products that are readily available? The RTCs are produced with GAMMA, ISCE-2, and SNAP 8. The other two ARD products evaluated consisted of an angular-based radiometric slope correction produced in Google Earth Engine (GEE) following Vollrath et al., and the Sentinel-1 GRD product. Products are evaluated across 10 sites in a single image approach for (1) radiometric calibration, (2) geometric corrections, and for (3) geolocation quality. In addition, time-series stacks over two sites representing varied terrain and ecosystems are evaluated. The GAMMA-derived RTC product implemented by the Alaska Satellite Facility (ASF) is used as a reference for some of the time-series metrics. The results provide direct guidance and recommendations about the quality of the RTC and ARD products obtained from open source methods. The results indicate that it is not recommended to use the GRD product with no radiometric or geometric corrections for any applications given low performance in multiple metrics. The radiometric calibration and geometric corrections have overall good performance for all open-source solutions, only the non-RTC products (Vollrath et al. and GRD) portray some significant variances in steep terrain. The geolocation assessment indicated that the GRD product has the most significant displacement errors, followed by SNAP 8 with Digital Elevation Model (DEM) matching, and ISCE-2. RTCs created without DEM-matching performed better for both GAMMA and SNAP 8. The time-series results indicate that SNAP 8 products align more closely to GAMMA products than other open-source software in terms of radiometric and geometric quality. This understanding of software performance for SAR image processing is key to designing the affordable and scalable solutions needed for the operational application of SAR Sentinel-1 data.

Forest stand height (FSH), or average canopy height, serves as an important indicator for forest monitoring. The information provided about above-ground biomass for greenhouse gas emissions reporting and estimating carbon storage is relevant for reporting for Reducing Emissions from Deforestation and Forest Degradation (REDD+). A novel forest height estimation method utilizing a fusion of backscatter and Interferometric Synthetic Aperture Radar (InSAR) data from JAXA’s Advanced Land Observing Satellite Phased Array type L-band Synthetic Aperture Radar (ALOS PALSAR) is applied to a use case in Savannakhet, Lao. Compared with LiDAR, the estimated height from the fusion method had an RMSE of 4.90 m and an R² of 0.26. These results are comparable to previous studies using SAR estimation techniques. Despite limitations of data quality and quantity, the Savannakhet, Lao use case demonstrates the applicability of these techniques utilizing L-band SAR data for estimating FSH in tropical forests and can be used as a springboard for use of L-band data from the future NASA-ISRO SAR (NISAR) mission.

Since the launch of the European Space Agency’s (ESA’s) Sentinel-1 mission, the synthetic aperture radar (SAR) user community has grown exponentially, adding a large number of users to the field who have only limited experience with the peculiarities of SAR and interferometric SAR (InSAR) data. This growth has been fueled by the increasing availability of globally acquired, free and open SAR data and by a growing number of freely accessible, open source processing tools, such as the Sentinel Application Platform, InSAR Scientific Computing Environment, GMTSAR, and the Alaska Satellite Facility’s cloud-based Hybrid Pluggable Processing Pipeline and OpenScienceLab platforms. Anticipated to launch in early 2025, the NASA–Indian Space Research Organisation SAR (NISAR) mission will amplify this trend by bringing an unprecedented amount of new, free and open SAR data to the international SAR science community. As the first mission to provide L-band data freely, on a global scale, and in a regularly sampled manner, NISAR will further diversify the SAR application portfolio, likely accelerating community growth. Regional access to simultaneous L- and S-band data facilitated by NISAR will furthermore enable new SAR technology development that may lead to exciting new scientific discoveries. Focusing on efforts made by NASA and the broader U.S. SAR community, this article summarizes innovative approaches to making the massive datasets of Sentinel-1 and NISAR more accessible to and usable by the SAR community. We summarize novel concepts for data discovery and distribution and highlight tools and services that enable working with SAR data directly at the archive. We also discuss recent education and training efforts developed across the U.S. SAR community to help new users of SAR build capacity to use this exciting dataset. Here, we introduce a set of independent, yet interrelated, projects aimed at developing educational materials and open source science workflows for SAR users with different experience levels.