Various empirical algorithms have been developed to retrieve chlorophyll-a (Chl-a) from multispectral and hyperspectral images as a proxy variable for algal blooms in inland waters. In most previous studies, a single empirical model (global model) was calibrated for the entire water body under study. Our analysis shows that the performance of a global model is limited for optically complex inland waters. We discovered that the global model tends to overestimate in some regions and underestimate in other regions, and that the model residuals (errors) display an apparent spatial autocorrelation pattern. To address the inadequacy of the global empirical model, this paper presents regionally or locally adaptive models to better estimate Chl-a concentrations for the first time. We collected two dense sets of Chl-a measurements over Harsha Lake in Ohio during a Sentinel-2A satellite overpass and a dedicated airborne hyperspectral flight. Based on the atmospherically corrected multispectral and hyperspectral images and concurrent in situ measurements, we implemented and evaluated the performance of regionally and locally adaptive models in comparison with the single global model. Among a number of candidate empirical algorithms, the two-band algorithm produces the best global model for Chl-a retrievals for both the multispectral and hyperspectral image sources. By subdividing the water body under investigation into several regions or a set of local areas, we demonstrate that regionally and locally adaptive models can improve Chl-a estimate accuracy by 13%-28% for the multispectral image and by 33%-47% for the hyperspectral image, in comparison with the best global Chl-a model.

A total of 13 satellite missions have been launched since 1985, with different types of radar altimeters on board. This study intends to make a comprehensive evaluation of historic and currently operational satellite radar altimetry missions for lake water level retrieval over the same set of lakes and to develop a strategy for constructing consistent long-term water level records for inland lakes at global scale. The lake water level estimates produced by different retracking algorithms (retrackers) of the satellite missions were compared with the gauge measurements over 12 lakes in four countries. The performance of each retracker was assessed in terms of the data missing rate, the correlation coefficient r, the bias, and the root mean square error (RMSE) between the altimetry-derived lake water level estimates and the concurrent gauge measurements. The results show that the model-free retrackers (e.g., OCOG/Ice-1/Ice) outperform the model-based retrackers for most of the missions, particularly over small lakes. Among the satellite altimetry missions, Sentinel-3 gave the best results, followed by SARAL. ENVISAT has slightly better lake water level estimates than Jason-1 and Jason-2, but its data missing rate is higher. For small lakes, ERS-1 and ERS-2 missions provided more accurate lake water level estimates than the TOPEX/Poseidon mission. In contrast, for large lakes, TOPEX/Poseidon is a better option due to its lower data missing rate and shorter repeat cycle. GeoSat and GeoSat Follow-On (GFO) both have an extremely high data missing rate of lake water level estimates. Although several contemporary radar altimetry missions provide more accurate lake level estimates than GFO, GeoSat was the sole radar altimetry mission, between 1985 and 1990, that provided the lake water level estimates. With a full consideration of the performance and the operational duration, the best strategy for constructing long-term lake water level records should be a two-step bias correction and normalization procedure. In the first step, use Jason-2 as the initial reference to estimate the systematic biases with TOPEX/Poseidon, Jason-1, and Jason-3 and then normalize them to form a consistent TOPEX/Poseidon–Jason series. Then, use the TOPEX/Poseidon–Jason series as the reference to estimate and remove systematic biases with other radar altimetry missions to construct consistent long-term lake water level series for ungauged lakes.

Monitoring harmful algal blooms (HABs) in freshwater over regional scales has been implemented through mapping chlorophyll-a (Chl-a) concentrations using multi-sensor satellite remote sensing data. Cloud-free satellite measurements and a sufficient number of matched-up ground samples are critical for constructing a predictive model for Chl-a concentration. This paper presents a methodological framework for automatically pairing surface reflectance values from multi-sensor satellite observations with ground water quality samples in time and space to form match-up points, using the Google Earth Engine cloud computing platform. A support vector machine model was then trained using the match-up points, and the prediction accuracy of the model was evaluated and compared with traditional image processing results. This research demonstrates that the integration of multi-sensor satellite observations through Google Earth Engine enables accurate and fast Chl-a prediction at a large regional scale over multiple years. The challenges and limitations of using and calibrating multi-sensor satellite image data and current and potential solutions are discussed.

Downscaling microwave soil moisture (SM) with optical/thermal remote sensing data has considerable application potential. Spatial correlations between SM and land surface temperature (LST) or LST-derived SM indexes (SMIs) are vital to the current optical/thermal and microwave fusion downscaling methods. In this study, the spatial correlations were evaluated at the same spatial scale using SMAPVEX12 SM data and MODIS day/night LST products. LST-derived SMIs was calculated using NLDAS-2 gridded meteorological data with conventional trapezoid and two-stage trapezoid models. Results indicated that (1) SM agrees better with daytime LST than the nighttime or the day-night differential LST; (2) the daytime LSTs on Aqua and Terra present very similar spatial agreement with SM and they have very similar performances as downscaling factors in simulating SM; (3) decoupling effect among SM, LST, and LST-derived SMIs occurs not only in very wet but also in very dry condition; and (4) the decoupling effect degrades the performance of LST as a downscaling factor. The future downscaling algorithms should consider net surface radiation and soil type to tackle the decoupling effect.

Downscaling of microwave remotely sensed that soil moisture content (SMC) is an efficient way to obtain spatial continuous SMC at a finer resolution. However, the classical optical/thermal and microwave fusion, and the active and passive microwave fusion cannot work under all-weather conditions because of contamination of clouds or the lack of suitable radar data source. In this study, a microwave and meteorological fusion (MMF) is provided. The MMF method is based on a complementary relationship hypothesis assuming SMC is reflected in the adjacent surface atmospheric moisture under midday conditions. By this method, daily passive SMC products from Soil Moisture Active Passive (SMAP) mission with 36-km resolution were disaggregated using a daily gridded meteorological data with nominal 4-km resolution. The original and downscaled SMCs were evaluated by comparing with in situ SMC obtained from three core validation sites and three sparse networks. The experiment was conducted in the central part of the U.S. from April 2015 to June 2018. Results demonstrated that the downscaled SMC maintained the dynamic range of original SMC product and energy was conserved. Furthermore, the downscaled SMC showed good agreement with and slightly outperformed the original SMC as compared with in situ SMC. The downscaling method is shown to capture higher resolution SMC spatial variability while preserving the quality of original SMC. However, because of the complexity of soil moisture–atmosphere interactions, the actual contributing domain of downscaled SMC may be greater than 4 km. The MMF method is suggested as a supplementary for all-weather downscaling coarse-resolution SMC.

The paper discusses various improvements to the authors’ Antarctic Digital Elevation Model (DEM) published in 1999. Those improvements include error corrections based on anomalies discovered through applications of the DEM, incorporation of new topographic data, and the use of grounding lines and a coastline newly extracted from a SAR image mosaic to constrain the surface interpolation. The authors also illustrate how radar remote sensing data and GIS tools can be effectively and efficiently used to refine the DEM.