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The use of satellite remote sensing has considerably improved scientific understanding of the heterogeneity of Amazonian rainforests. However, the persistent cloud cover and strong Bidirectional Reflectance Distribution Function (BRDF) effects make it difficult to produce up-to-date satellite image composites over the huge extent of Amazonia. Advanced pre-processing and pixel-based compositing over an extended time period are needed to fill the data gaps caused by clouds and to achieve consistency in pixel values across space. Recent studies have found that the multidimensional median, also known as medoid, algorithm is robust to outliers and noise, and thereby provides a useful approach for pixel-based compositing. Here we describe Landsat-7 and Landsat-8 composites covering all Amazonia that were produced using Landsat data from the years 2013–2021 and processed with Google Earth Engine (GEE). These products aggregate reflectance values over a relatively long time, and are, therefore, especially useful for identifying permanent characteristics of the landscape, such as vegetation heterogeneity that is driven by differences in geologically defined edaphic conditions. To make similar compositing possible over other areas and time periods (including shorter time periods for change detection), we make the workflow available in GEE. Visual inspection and comparison with other Landsat products confirmed that the pre-processing workflow was efficient and the composites are seamless and without data gaps, although some artifacts present in the source data remain. Basin-wide Landsat-7 and Landsat-8 composites are expected to facilitate both local and broad-scale ecological and biogeographical studies, species distribution modeling, and conservation planning in Amazonia.

The Fluorescence Explorer (FLEX) satellite will carry the high-resolution Fluorescence Imaging Spectrometer (FLORIS) that measures the complete fluorescence spectrum emitted by chlorophyll of terrestrial vegetation. This small signal must be validated. One validation approach is comparing the fluorescence signal retrieved from satellite-based measurements with ground based measurements. However, the difference in spatial resolution of the satellite and ground-based instruments and a geolocation mismatch will result in differences in the detected signal and thus, in uncertainties of the validation strategy. In a case study, we identify a representative ground site for validating the fluorescence signal by analyzing surface reflectance measurements from an aeroplane.We define requirements of representativeness for a validation ground site in vegetated areas. Based on those requirements, we identify a suitable position within a case study in central Italy using surface reflectance data from the airborne High-Performance Airborne Imaging Spectrometer (HyPlant) measured in summer 2018. The representativeness is quantified by the relative difference between the single HyPlant pixel representing a ground-based measurement and the averaged signal of several HyPlant pixels that mimics a FLORIS pixel. With this measure, we quantify the validation uncertainty due to spatial resolution and geolocation mismatch. The effect of the temporal evolution of the surface properties on the validation uncertainty due to spatial resolution is investigated.We select the ground site position by minimizing the validation uncertainty due to spatial resolution. Especially for wavelengths larger than 700 nm, this uncertainty is smaller than 2 % for all different reference areas. The largest differences between ground-based like measurement and satellite-like measurement of the surface reflectance is due to geolocation mismatch. The uncertainty due the geolocation mismatch is very large for wavelengths smaller than 720 nm and moderate for wavelengths larger than 720 nm. Thus, the surface reflectance at the chosen position for the validation site is not homogeneous enough for validation purpose. Considering a reference area of 13.5 × 13.5 m2, we quantify temporal stable and small uncertainties for the spectral range between 720 and 800 nm. For an all-embracing validation of the surface reflectance of vegetated areas, the chosen site is not appropriate.

The Landsat global consolidated data archive now exceeds 50 years. In recognition of the need for consistently processed data across the Landsat satellite series, the U.S. Geological Survey (USGS) initiated collection-based processing of the entire archive that was processed as Collection 1 in 2016. In preparation for the data from the now successfully launched Landsat 9, the USGS reprocessed the Landsat archive as Collection 2 in 2020. This paper describes the rationale for, and the contents and advancements provided by Collection 2, and highlights the differences between the Collection 1 and Collection 2 products. Notably, the Collection 2 products have improved geolocation and, for the first time, the USGS provides a global inventory of Level 2 surface reflectance and surface temperature products. Also for the first time, the USGS used a commercial cloud computing architecture to efficiently process the archive and enable direct cloud access of the Landsat products. The paper concludes with discussion of likely improvements expected in Collection 3 in preparation for the Landsat Next mission that is planned for launch in the early 2030s.

Advances in the capabilities of commercial CubeSat constellations have enabled the retrieval of multi-spectral surface reflectance data over the Earth’s terrestrial surfaces on an almost daily basis. For example, while the earliest versions of Planet’s CubeSats provided tri-band and then quad-band optical image data, the most recent iterations deliver imaging capabilities with eight unique spectral bands for Earth system monitoring. To determine the utility of these rich geospatial data collections for a range of applications, it is necessary to characterise their radiometric accuracy. Leveraging on-ground spectroradiometer measurements of radiometrically pseudo-invariant features, we assess the absolute accuracy of an annual sequence of Planet SuperDove surface reflectance data. Date-coincident and spectrally overlapping Sentinel-2 image data were also used to assess the relative radiometric accuracy of the CubeSat reflectance data. Additionally, confidence levels for acquiring consistent SuperDove surface reflectance data were calculated, and the multi-temporal patterns of surface reflectance between the SuperDove and Sentinel-2 surface reflectance products were evaluated by examining their rank correlation. Our findings demonstrate that (1) the average accuracy of the Coastal-Blue, Blue, Green I, and Green II SuperDove surface reflectance bands was 5% higher than for the Yellow, Red, Red-Edge, and Near-Infrared bands; (2) the SuperDove surface reflectance data was on average almost identical to the surface reflectance derived from the coincident Sentinel-2 data; and (3) the radiometric accuracies (i.e., mean errors) can be improved by using band combinations (e.g. normalised difference vegetation index). However, due to the different radiometric performance between the spectral bands, some vegetation indices (e.g. the Yellowness Index) did not provide a linear relationship between the SuperDove and reference data. The probability of acquiring surface reflectance data with less than 5% reflectance variation was approximately 90% for the annual multi-satellite dataset. On average, 68% of the data derived from a single satellite had at least 95% probability of acquiring surface reflectance with less than 5% variation. The time series patterns of the SuperDove reflectance data had an average rank correlation coefficient of 0.67 for all types of surface units and 0.77 for vegetated surfaces when compared with the spectrally overlapping bands of the Sentinel-2 surface reflectance data. In addition to providing confidence levels for users to directly assess the radiometric accuracies of SuperDove surface reflectance products, we also provide suggestions to improve the data quality from an end-user’s perspective.

Total and fine mode aerosol optical depth (AODT and AODF), as well as the fine mode fraction (FMF = AODF/AODT), are critical variables for climate change and atmospheric environment studies. The retrievals with high accuracy from satellite observations, particularly FMF and AODF over land, remain challenging. This study aims to improve the Moderate-resolution Imaging Spectro-radiometer (MODIS) land dark target (DT) algorithm for retrieving AODT, AODF, and FMF on a global scale. Based on the fact that the underestimated surface reflectance (SR) could overestimate the AODT and underestimate the aerosol size parameter in the DT algorithm, two robust schemes were developed to improve SR determination: the first (NEW1 DT) used the top of the atmosphere reflectance instead of SR at 2.12 µm; the second (NEW2 DT) used eleven-year MODIS data to establish a monthly spectral SR relationship model (2.12–0.47 and 2.12–0.65 µm) database at pixel-by-pixel scale. Then a novel lookup table approach based on the physical process was proposed to retrieve the AODF and FMF. The new MODIS AODT, FMF, and AODF were compared to AERosol RObotic NETwork (AERONET) retrievals. Results showed that the root mean square error (RMSE) was 0.096–0.103, 0.098–0.099, and 0.167–0.180 for the new AODTs, AODFs, and FMFs, respectively, which were better than that of the Collection 6.1 (C6.1) DT (0.117, 0.235, and 0.426) in the validation by global AERONET sites. From the validation results, NEW2 DT provided better AODT and coarse mode AOD retrievals, while NEW1 DT had better AODF and FMF performances. The spatial patterns of AODF, FMF, and AODC of the new DT algorithms were comparable to those of the Polarization and Directionality of the Earth's Reflectances aerosol product. Hence, the new algorithms have the potential to provide global AODT, FMF, and AODF products over land to the scientific community with high accuracy using long-term MODIS data.

Identifying shifting cultivation areas and assessing their spatio-temporal dynamics are essential in framing climate-adaptive policies for efficient forest management and agriculture practices for the benefit of people. The current study attempts to develop an alternative approach to classify the shifting cultivation areas using an ensemble technique, integrating multiple spectral indices in three states of northeast India (NEI), such as Assam, Manipur, and Meghalaya. The adopted approach integrates green cover and leaf water content changes during shifting cultivation land preparation in Landsat imagery. The deforested burned area patches were identified based on threshold values using Landsat data-derived indices on vegetation, burned area and leaf water, and digital elevation model (DEM). The ensemble approach provided shifting cultivation maps with good overall accuracy (> 83%). The maximum shifting cultivation area was observed in Assam (126.87 km2), followed by Meghalaya (51.53 km2) and Manipur (46.04 km2) in 2016. The normalized difference vegetation index (NDVI) and NDVI difference performed better than other vegetation indices. The ensemble approach can be applied in other regions with minor modifications in threshold values, thus having the potential for accounting to shifting cultivation dynamics on an operational basis. Future research may include blending local traditional knowledge and modern scientific solutions for improved forest and land resources planning for the benefit of inhabitants and the mountain environment under the climate change scenarios.

The fractional coverage of the illuminated vegetation (IV), illuminated soil (IS), shaded vegetation (SV), and shaded soil (SS) are four essential structural parameters (fIV, fIS, fSV, and fSS) describing the vegetation canopy. Their correct estimation can help improve the monitoring of vegetation growth. In this study, we propose and evaluate four coastal/aerosol-band-based vegetation-shadow indices (CVSIs) using the normalized difference of top-of-atmosphere (TOA) reflectance in the coastal/aerosol band and in the blue, green, red, and near-infrared (NIR) bands. We used the four CVSIs to evaluate the illumination conditions of the vegetation-soil canopy for two cases: shadows produced by vegetation and shadows produced by mountains. We also evaluated the combined use of linear spectral mixture analysis (LSMA), CVSIs, and the normalized difference vegetation index (NDVI) to estimate fIV, fIS, fSV, and fSS. The results led to several conclusions: (i) the effects of atmospheric path radiation mainly influence the optical bands, resulting in the coastal/aerosol-band TOA reflectance exceeding the TOA reflectance of the blue, green, red, and near-infrared bands; (ii) CVSIs can be used to evaluate the illumination conditions of the vegetation-soil canopy; (iii) the proposed strategy produces reasonably accurate estimates of fIV, fIS, fSV, and fSS (n = 191,268; R2 = 0.830–0.981; mean absolute error = 0.053–0.098; root mean square error = 0.063–0.126). Maps of fIV, fIS, fSV, and fSS are consistent with maps of the actual field illumination conditions.

The long-term data record (LTDR) from the Advanced Very High-Resolution Radiometer (AVHRR) provides daily surface reflectance with global coverage from the 1980s to present day, making it a unique source of information for the study of land surface properties and their long-term dynamics. Surface reflectance is a critical input for the generation of products such as vegetation indices, albedo, and land cover. Therefore, it is of utmost importance to quantify its uncertainties to better understand how they might propagate into downstream products. Due to the prolonged length of the surface reflectance LTDR and previous unavailability of a well calibrated reference, no comprehensive evaluation of the complete record has been reported so far. Recently, the United States Geological Survey (USGS) began production of surface reflectance datasets from the Landsat 4–8 satellites, which provide a suitable reference against which the LTDR can be compared to. In this study, we evaluate the LTDRV5 between 1984 and 2011 using surface reflectance data from the Landsat-5 Thematic Mapper (TM5) Collection-1 as a reference. Data from TM5 was obtained from over 740,000 globally distributed scenes which gave a representative set of land surface types and atmospheric conditions. Differences due to observation geometry were accounted for using the Vermote-Justice-Breon (VJB) Bidirectional Reflectance Distribution Function (BRDF) normalization method to adjust the AVHRR surface reflectance to TM5 observation conditions; the spectral response differences were minimized using spectral band adjustment factors (SBAFs) derived from the Earth Observing One (EO-1) Hyperion atmospherically corrected hyperspectral spectra. The performance of the AVHRR record is reported in terms of the accuracy, precision, and uncertainty (APU). Results show that the LTDR performance is close or within the combined uncertainty specification of 0.071ρ + 0.0071, where ρ is the estimated reflectance.

The Himawari-8 aerosol algorithm was updated to version 3 (V30). However, no study has evaluated its performance. The purpose of this study is to verify and to compare version 2.1 (V21) and V30 aerosol products, to explain which factor dominates the aerosol optical depth (AOD) error, and to provide recommendations for aerosol product usage. The AOD accuracy of V30 was better than that of V21, with a higher correlation coefficient (R) and a higher expected error (EE&#95;DT). The V30 AOD metrics (including R, EE&#95;DT, and the root mean square error) exceeded those of V21 on more than 69% of the AERONET sites and its bias from MODIS AOD was smaller than that of V21 AOD. However, the V30 AOD does not meet the metric of EE&#95;DT > 0.66. The analysis results suggest that aerosol type parameters (primarily the Ångström exponent (AE)) may be the dominant factor determining the AOD error. This reveals the direction of H8 algorithm improvement. More than 59% of the H8 AE value meets the expected error but they do not capture the variety (R < 0.3). The FMF and SSA retrieved by H8 performed poorly. The V30 AOD performs best in Japan and South Korea (83.3% of AERONET sites meet the EE&#95;DT > 0.66 requirement) and has better data accuracy in the morning. Therefore, we recommend V30 AOD morning data to users in Japan and South Korea regions., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 Elsevier B.V. All rights reserved.)

The management of suspended solids and associated contaminants in rivers requires knowledge of sediment sources. In-situ sampling can only describe the integrated impact of the upstream sources. Empirical models that use surface reflectance from satellite images to estimate total suspended solid (TSS) concentrations can be used to supplement measurements and provide spatially continuous maps. However, there are few examples, especially in narrow, shallow and hydrologically dynamic rivers found in mountainous areas. A case study of the Didipio catchment in Philippines was used to address these issues. Four 5-m resolution RapidEye images, from between the years 2014 and 2016, and near-simultaneous ground measurements of TSS concentrations were used to develop a power law model that approximates the relationship between TSS and reflectance for each of four spectral bands. A second dataset using two 2-m resolution Pleiades-1A and a third using a 6-m resolution SPOT-6 image along with ground-based measurements, were consistent with the model when using the red band data. Using that model, encompassing data from all three datasets, gave an R 2 value of 65% and a root mean square error of 519mgL -1 . A linear relationship between reflectance and TSS exists from 1mgL -1 to approximately 500mgL -1 . In contrast, for TSS measurements between 500mgL -1 and 3580mgL -1 reflectance increases at a generally lower and more variable rate. The results were not sensitive to changing the pixel location within the vicinity of the ground sampling location. The model was used to generate a continuous map of TSS concentration within the catchment. Further ground-based measurements including TSS concentrations that are higher than 3580mgL -1 would allow the model to be developed and applied more confidently over the full relevant range of TSS., (Copyright © 2018 Elsevier B.V. All rights reserved.)