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The rapid and accurate extraction of water information from satellite imagery has been a crucial topic in remote sensing applications and has important value in water resources management, water environment monitoring, and disaster emergency management. Although the OLI-2 sensor onboard Landsat-9 is similar to the well-known OLI onboard Landsat-8, there were significant differences in the average absolute percentage change in the bands for water detection. Additionally, the performance of Landsat-9 in water body extraction is yet to be fully understood. Therefore, it is crucial to conduct comparative studies to evaluate the water extraction performance of Landsat-9 with Landsat-8. In this study, we analyze the performance of simultaneous Landsat-8 and Landsat-9 data for water body extraction based on eight common water indices (Normalized Difference Water Index (NDWI) and Modified Normalized Difference Water Index (MNDWI), Augmented Normalized Difference Water Index (ANDWI), Water Index 2015 (WI2015), tasseled cap wetness index (TCW), Automated Water Extraction Index for scenes with shadows (AWEIsh) and without shadows (AWEInsh) and Multi-Band Water Index (MBWI)) to extract water bodies in seven study sites worldwide. The Otsu algorithm is utilized to automatically determine the optimal segmentation threshold for water body extraction. The results showed that (1) Landsat-9 satellite data can be used for water body extraction effectively, with results consistent with those from Landsat-8. The eight selected water indices in this study are applicable to both Landsat-8 and Landsat-9 satellites. (2) The NDWI index shows a larger variability in accuracy compared to other indices when used on Landsat-8 and Landsat-9 imagery. Therefore, additional caution should be exercised when using the NDWI for water body analysis with both Landsat-8 and Landsat-9 satellites simultaneously. (3) For Landsat-8 and Landsat-9 imagery, ratio-based water indices tend to have more omission errors, while difference-based indices are more prone to commission errors. Overall, ratio-based indices exhibit greater variability in overall accuracy, whereas difference-based indices demonstrate lower sensitivity to variations in the study area, showing smaller overall accuracy fluctuations and higher robustness. This study can provide necessary references for the selection of water indices based on the newest Landsat-9 data. The results are crucial for guiding the combined use of Landsat-8 and Landsat-9 for global surface water mapping and understanding its long-term changes.

The Landsat series has marked the history of Earth observation by performing the longest continuous imaging program from space. The recent Landsat-9 carrying Operational Land Imager 2 (OLI-2) captures a higher dynamic range than sensors aboard Landsat-8 or Sentinel-2 (14-bit vs. 12-bit) that can potentially push forward the frontiers of aquatic remote sensing. This potential stems from the enhanced radiometric resolution of OLI-2, providing higher sensitivity over water bodies that are usually low-reflective. This study performs an initial assessment on retrieving water quality parameters from Landsat-9 imagery based on both physics-based and machine learning modeling. The concentration of chlorophyll-a (Chl-a) and total suspended matter (TSM) are retrieved based on physics-based inversion in four Italian lakes encompassing oligo to eutrophic conditions. A neural network-based regression model is also employed to derive Chl-a concentration in San Francisco Bay. We perform a consistency analysis between the constituents derived from Landsat-9 and near-simultaneous Sentinel-2 imagery. The Chl-a and TSM retrievals are validated using in situ matchups. The results indicate relatively high consistency among the water quality products derived from Landsat-9 and Sentinel-2. However, the Landsat-9 constituent maps show less grainy noise, and the matchup validation indicates relatively higher accuracies obtained from Landsat-9 (e.g., TSM R2 of 0.89) compared to Sentinel-2 (R2 = 0.71). The improved constituent retrieval from Landsat-9 can be attributed to the higher signal-to-noise (SNR) enabled by the wider dynamic range of OLI-2. We performed an image-based SNR estimation that confirms this assumption.

The Landsat-9 satellite, launched in September 2021, carries the Operational Land Imager-2 (OLI-2) as one of its payloads. This instrument is a clone of the Landsat-8 OLI and its mission is to continue the operational land imaging of the Landsat program. The OLI-2 instrument is not significantly different from OLI though the instrument-level pre-launch spectral characterization process was much improved. The focal plane modules used on OLI-2 were manufactured as spares for OLI and much of the spectral characterization of the components was performed for OLI. However, while the spectral response of the fully assembled OLI was characterized by a double monochromator system, the OLI-2 spectral characterization made use of the Goddard Laser for Absolute Measurement of Radiance (GLAMR). GLAMR is a system of tunable lasers that cover 350–2500 nm which are fiber-coupled to a 30 in integrating sphere permanently monitored by NIST-traceable radiometers. GLAMR allowed the spectral characterization of every detector of the OLI-2 focal plane in nominal imaging conditions. The spectral performance of the OLI-2 was, in general, much better than requirements. The final relative spectral responses (RSRs) represent the best characterization any Landsat instrument spectral response. This paper will cover the results of the spectral characterization from the component-level to the instrument-level of the Landsat-9 OLI-2.

Canopy height is a crucial indicator for assessing the structure and function of the forest ecosystems. It plays a significant role in carbon sequestration, sink enhancement, and promoting green development. This study aimed to evaluate the accuracy of GEDI L2A version 2 data in estimating ground elevation and canopy height by comparing it with airborne laser scanning (ALS) data. Among the six algorithms provided by the GEDI L2A data, algorithm a2 demonstrated higher accuracy than the others in detecting ground elevation and canopy height. Additionally, a relatively strong correlation (R-squared = 0.35) was observed between rh95 for GEDI L2A and RH90 for ALS. To enhance the accuracy of canopy height estimation, this study proposed three backpropagation (BP) neural network inversion models based on GEDI, Landsat 8 OLI, and Landsat 9 OLI-2 data. Multiple sets of relative heights and vegetation indices were extracted from the GEDI and Landsat datasets. The random forest (RF) algorithm was employed to select feature variables with a cumulative importance score of 90% for training the BP neural network inversion models. Validation against RH90 of ALS revealed that the GEDI model outperformed the OLI or OLI-2 data models in terms of accuracy. Moreover, the quality improvement of OLI-2 data relative to OLI data contributed to enhanced inversion accuracy. Overall, the models based on a single dataset exhibited relatively low accuracy. Hence, this study proposed the GEDI and OLI and GEDI and OLI-2 models, which combine the two types of data. The results demonstrated that the combined model integrating GEDI and OLI-2 data exhibited the highest performance. Compared to the weakest OLI data model, the inversion accuracy R-squared improved from 0.38 to 0.74, and the MAE, RMSE, and rRMSE decreased by 1.21 m, 1.81 m, and 8.09%, respectively. These findings offer valuable insights for the remote sensing monitoring of forest sustainability.

Measurements of Sulak River plume parameters in the Caspian Sea conducted from June 2 to 7, 2023, concurrently with satellite survey allowed tracing changes in water turbidity (WT) and suspended particulate matter (SPM) concentration in the near-mouth zone after the arrival of mudflow masses into the sea. Heavy rains in the mountains of Dagestan on May 31, 2023, caused mudflows, which entered, in particular, the Sulak River. Two days later, on June 2, mud masses, together with river water, flowed into the Caspian Sea. In the near-mouth zone, WT values exceeded 1000 NTU, which was beyond the allowable threshold of a portable turbidimeter employed for turbidity measurements. On June 2, 4, 5 and 7, quasi-synchronously with satellite imaging, measurements were conducted from a small boat using various oceanographic equipment. They were accompanied with water sampling for further determination of SPM concentration and mineral composition. The linear dependence revealed between WT and SPM concentration made it possible to calculate WT that could not be measured in situ: it amounted to 1247 NTU at SPM concentration of 1097.4 g/m 3 . Satellite data were used to compile WT maps using the Dogliotti 2015 algorithm. The results of satellite observations and in situ measurements showed that, within 2 days, WT and SPM concentration at the nearest to the river mouth station dropped sixfold and continued to decrease to the average values for this area in early June. Determined by X-ray phase analysis, the mineral composition of suspended solids on the day of mudflow arrival into the sea was represented mainly by clay minerals, their content reaching 75%. Later, by June 7, the mineral composition returned to the average values and the content of clay minerals did not exceed 44%.