Recent advances in space-borne optical remote sensing systems for monitoring global terrestrial ecosystems

Abstract

Since the launch of the first Landsat satellite in the early 1970s, the field of space-borne optical remote sensing has made significant progress. Advances have been made in all aspects of optical remote sensing data, including improved spatial, temporal, spectral and radiometric resolutions, which have increased the uptake of these data by wider scientific communities. Flagship satellite missions such as NASA’s Terra and Aqua and ESA’s Envisat with their high temporal (<3days) and spectral (15–36 bands) resolutions opened new opportunities for routine monitoring of various aspects of terrestrial ecosystems at the global scale and have provided greater understanding of critical biophysical processes in the terrestrial ecosystem. The launch of new satellite sensors such as Landsat 8 and the European Space Agency’s Copernicus Sentinel missions (e.g. Sentinel 2 with improved spatial resolution (10–60 m) and potential revisit time of five days) is set to revolutionise the availability and use of remote sensing data in global terrestrial ecosystem monitoring. Furthermore, the recent move towards use of constellations of nanosatellites (e.g. the Flock missions by Planet Labs) to collect on-demand high spatial and temporal resolution optical remote sensing data would enable uptake of these data for operational monitoring. As a result of increase in data availability, optical remote sensing data are now increasingly used to support a number of operational services (e.g. land monitoring, atmosphere monitoring and climate change studies). However, many challenges still remain in exploiting the growing volume of optical remote sensing data to monitor global terrestrial ecosystems. These challenges include ensuring the highest data quality both in terms of the sensitivity of sensors and the derived biophysical products, affordability and availability of the data and continuity of data acquisition. This review provides an overview of the developments in space-borne optical remote sensing in the past decade and discusses a selection of aspects of global terrestrial ecosystems where the data are currently used. It concludes by highlighting some of the challenges and opportunities of using optical remote sensing data in monitoring global terrestrial ecosystems.

The future of Earth observation in hydrology

“…The coarse resolution of passive-based retrievals remains a challenge, but advances in antenna technology may provide improvements on this. Shallow retrieval depths also limit the determination of root-zone moisture profiles and dynamics, although modelling approaches seek to improve deeper-soil representation (Das and Mohanty, 2006;Li et al, 2010). Despite the failure of the SMAP radar after only 3 months of operation, the performance of SMAP's passive retrievals has recently been evaluated with encouraging results (Pan et al, 2016).…”

“…While the next generation of CubeSats has the potential to revolutionize Earth observation, data from such platforms should ideally complement, and not necessarily replace, the high-quality imagery that is currently acquired by conventional large satellite missions. To harness the potential and exploit these technological advances demands prepara- tion (Dash and Ogutu, 2016) and this will only be realized through synergistic exploration and leadership from government space agencies, the science community, and increasingly the private sector. An underlying assumption here is that space junk will not continue to accumulate to the point of becoming an intolerable risk to launching satellites to low Earth and geosynchronous orbits: though that dystopia would actually enhance the importance of the sub-orbital alternative technologies described throughout this section.…”

“…A singularly positive outcome of this is that there now exists a range of global VNIR near-daily to daily measurement platforms that are available (albeit at a cost) from the commercial sector, providing ultra-high resolution detail. These commercial sensors can provide data at a higher spatial and temporal resolution than comparable space agency systems (Dash and Ogutu, 2016), although the radiometric quality of the imagery may not always be as refined (Houborg and McCabe, 2016). As already noted, there is generally no underlying scientific purpose or social good directly driving these efforts; commercial launches are ultimately driven by an economic incentive.…”

Daily Monitoring of Shallow and Fine-Grained Water Patterns in Wet Grasslands Combining Aerial LiDAR Data and In Situ Piezometric Measurements

“…The results highlighted the spatiotemporal dynamics of the shallow flooded areas. We showed that the differences in the flooding durations among the years were mainly related to a narrow contrast in topography (40 cm), and occurred over a short period of time (two months).Sustainability 2018, 10, 708 2 of 16 address these challenges, data with both spatial fine-scale and intensive temporal resolutions for the flooding proxies are required.There have been recent advances in Earth observation technology, including improved spatial, temporal, spectral, and radiometric resolutions [7]; however, the monitoring of shallow and fine-grained water pattern dynamics is still limited by the trade-off between either high-resolution images or images with intensive repetition over time [8]. As an example, some studies have shown an interest in SAR (Synthetic Aperture Radar) time series for monitoring flooded areas in wetlands at a regional scale [9,10], while other studies have underlined an interest in single-date light detection and ranging (LiDAR) intensity [11] or multispectral data [12] for detecting fine-scale spatial patterns in the flooding of shallow waters.Earth observation data can be used to calibrate and develop flood models [13].…”

“…There have been recent advances in Earth observation technology, including improved spatial, temporal, spectral, and radiometric resolutions [7]; however, the monitoring of shallow and fine-grained water pattern dynamics is still limited by the trade-off between either high-resolution images or images with intensive repetition over time [8]. As an example, some studies have shown an interest in SAR (Synthetic Aperture Radar) time series for monitoring flooded areas in wetlands at a regional scale [9,10], while other studies have underlined an interest in single-date light detection and ranging (LiDAR) intensity [11] or multispectral data [12] for detecting fine-scale spatial patterns in the flooding of shallow waters.…”