High-resolution optical satellite sensors, such as Multispectral Instrument (MSI) and the Operational Land Imager (OLI), show excellent performance in capturing fine-scale sea surface wave motion and spatial patterns. Their inter-band time lag offers the potential to resolve wave directional ambiguity and parameter estimation. Here, we investigate the spatial resolution and channel time lag of optical sensor, based on cross-spectral analysis of multi-channel imagery, to clarify their requirement in ocean wave monitoring. The analysis, validated by matched buoy data and optical imagery, demonstrates that the minimum detectable time lags for 180° directional ambiguity removal are 0.47s, 0.52s, and 0.74s for MSI 10 m, 20 m, and OLI 30 m resolution data, respectively. All three resolutions effectively detect wave system wavelengths ranging from 60 m to 300 m, with minimum detection limits of 20 m, 40 m, and 60 m, respectively. Additionally, the optimal statistics window size for consistent wave detection is about 8 km. These findings not only highlight the strengths of current satellite sensors but also provide references for future high-resolution optical sensor design in ocean wave monitoring.

Precisely delineating the distribution of moso bamboo forests is critical for forestry management and regional carbon cycle research. The unique phonological characteristics (i.e., on- and off-year phenomenon) of bamboo impose difficulties in bamboo identification. This study aims to develop a new algorithm for mapping bamboo distribution using remote sensing data with the consideration of bamboo phenological characteristics. Three optical indices were proposed based on canopy reflectance retrieved from Sentinel-2 and field inventory data, including modified bamboo index (MBI), bamboo phenological characteristic index (BPCI), and BPCI 2 (BPCI-2). The collaboration of these three indices with the recursive feature elimination (RFE) and extreme gradient boosting (XGBoost) methods can precisely map bamboo distribution and its phenological status. The model based on MBI, BPCI, and BPCI-2 outperformed the model driven by the existing bamboo extracting indices, i.e., bamboo index (BI), yearly change bamboo index (YCBI), and monthly change bamboo index (MCBI), increasing in overall accuracy (OA) by about 1.5%. Additionally, the proposed indices were calculated using the data synthesized from Sentinel-1 synthetic aperture radar (SAR) imageries by the cycle-consistent adversarial network (CycleGAN) method under the condition without cloudy-free Sentinel-2 data available to fill the time series data gaps. The performance of the model based on augmented data improved notably in comparison with the model driven only by indices from original optical images, with the identification accuracy for on- and off-year bamboo samples over 96%. The generated moso bamboo distribution map aligns well with forestry inventory data in terms of both area and spatial distribution. The proposed indices are less sensitive to terrain than the existing bamboo extracting indices. This merit is valuable for better mapping bamboo forests, which are mostly distributed in mountainous areas.

The probability of viewing sunlit leaves (PT) is a crucial variable influencing observed canopy spectra. Proper determination of PT is necessary for the quantitative retrieval of vegetation parameters using remote sensing. This article describes a spectral index for estimating PT from satellite-observed canopy spectra. For this purpose, we propose a normalized spectral angle index (NSAI) at near-infrared (NIR) wavelengths, based on the spectral shapes of leaf and soil background. The performance of NSAI in estimating PT was evaluated using one ground-based high-resolution imaging dataset, one synthetic satellite dataset, and one satellite-ground synchronous observation dataset. The results demonstrate that NSAI is more suitable for estimating PT from satellite data than five commonly used spectral indices, including enhanced vegetation index (EVI), normalized difference spectral index (NDSI), normalized difference vegetation index (NDVI), simple ratio (SR) index, and photochemical reflectance index (PRI). NSAI exhibits a significant linear correlation with PT. The empirical model for estimating PT based on NSAI has the best transferability from simulated to in situ satellite data. For the fine spectral–spatial resolution (Hyperion) data, the normalized root-mean-square error (nRMSE) and adjusted$R^{2}$of estimated PT were 14.9% and 0.744, respectively. For MODIS images, PT was estimated with satisfactory accuracy, with an nRMSE of 18.71% and an adjusted$R^{2}$of 0.670. NSAI is potentially applicable to satellite images for direct estimation of PT to improve the inversion accuracy of vegetation parameters.

Leaf chlorophyll content (LCC) is an important plant physiological trait and is critical for accurate modeling of vegetation photosynthesis over time and space. To date, there is still a lack of a global long time-series dataset of LCC. In this study, we developed an algorithm to retrieve global LCC from MODIS surface reflectance data from 2000–2020. An essential requirement for generating LCC time series is to capture its seasonal dynamics. This issue was addressed by using a matrix system with two pairs of vegetation indices to minimize the impacts of leaf area index and canopy non-photosynthetic material on LCC estimation in different seasons. The matrix system algorithm was applied to Landsat data and MODIS data, respectively. The validation based on Landsat data and ground measurements reveals the algorithm has the ability to catch the seasonal variations of LCC in different plant functional types, and the MODIS-derived LCC shows good agreement with Landsat-upscaled LCC (R2=0.77, RMSE=6.9 μg/cm2). The global 8-day LCC data at 500-m resolution in 2000–2020 was generated using the matrix system from MODIS and presented distinct temporal and spatial variations, which provides a new opportunity for analyzing vegetation physiological dynamics in climate change studies.

Optical remote sensing is applied in the identification, classification, and quantification of weathered oil spills. The automatic detection of oil spills through optical imaging is yet a challenge, because various oils under different sunglint reflections have complex optical image characteristics. Generally, there are two types of weathered oil spills, namely, non-emulsified oil slicks (NEOS) and oil emulsions (OE), which show different image characteristics under various sunglint reflections. The Coastal Zone Imager (CZI) onboard China’s HaiYang-1C/D (HY-1C/D) satellites can provide multispectral images with high spatial resolution and wide coverage for operational monitoring of oil spills. In this study, we applied an adaptive dynamic detector incorporating a built in oil–water mixture distribution classifier, specifically for different sunglint reflections, to automatically extract oil spills from CZI images. The spatial heterogeneity distribution of various oil spills could be quantified using a novel separability index, and then, the optimal oil–water segmentation proportion and scale could be obtained. Oil spills are discriminated and extracted under different sunglint reflections, with the variable scale detector implemented by tiling sliding windows of classifiers on detection images, from which respective volumes are derived with lower uncertainties. This approach also uses spatial and spectral ancillary information to improve weathered oils extraction confidence. The results show stable variable-scale extraction accuracies of approximately 90% and 80% for NEOS and EO, respectively. Therefore, the spatio–spectral–distribution comprehensive feature provides a new approach for the automatic extraction of oil spills from optical remote sensing images.

Recently, drought is recognized as a crucial factor on controlling inter-annual variations of the terrestrial carbon cycle. Remote sensing of hydrological and ecological land surface variables offers a very important aid for identifying the impact of droughts on terrestrial ecosystems. GLEAM soil moisture (SM), reconstructed GRACE terrestrial total terrestrial water storage change (TWS), GIMMS normalized difference vegetation index (NDVI), reconstructed continuous solar-induced fluorescence (SIF) data from OCO-2, and SIF data from SCIACHAMY and GOME-2, in conjunction with multiple carbon flux data and CRUNCEP meteorological data, were employed to study the drought impacts on the terrestrial ecosystems in Europe during 2001-2015. We found GLEAM SM and GRACE TWS can effectively detect hydrological anomalies for four reported strongest drought years on the continental scale, in line with evident reductions of terrestrial carbon uptake indicated by models. Most vegetation products agree well on indicating drought impacts, but with some unexpected inconsistencies. Also, these land products are able to reasonably indicate the spatial patterns of drought impacts on ecosystems. We highlight the importance of ensemble satellite land products in deepening the interpretation of drought impacts on terrestrial ecosystems, and also on examining carbon cycle models and identifying potential gaps in satellite data products within the context of drought-carbon cycle studies.

The PROSPECT model has been widely used to estimate leaf biochemical constituents, but retrieval of leaf mass per area (LMA) in fresh leaves has proved to be difficult due to the predominant water absorption in the infrared spectral region. At wavelengths where water absorption is low, both LMA absorption and light scattering are relatively high. Therefore, the uncertainty in scattering simulation at these wavelengths will lead to a relatively large error in LMA estimation. In this paper, we introduce a wavelength-independent factor to represent the first-order effect of anisotropic scattering in the elementary layer in the modified model PROSPECT-g, aiming at appropriately simulating leaf optical properties in spectral regions with high scattering and thus reducing the uncertainty in LMA estimation. In order to avoid introducing a new variable to be retrieved in model inversion, this factor is an intermediate variable derived from measured near infrared region spectral data and other existing model parameters. Results show that about 30%-40% of the tested samples are well simulated using PROSPECT-5, while for the rest of the samples simulation is greatly improved with PROSPECT-g. Leaf reflectance and transmittance reconstructions using PROSPECT-g are improved, especially at wavelengths with high scattering such as 750-1400 and 1500-1850 nm. LMA retrieval is significantly improved, with the average root-mean-square error decreasing from 38.7 (PROSPECT-5) to 16.6 g/m2(PROSPECT-g) for 628 leaves after considering anisotropic scattering in the elementary layer. Improvements are particularly noticeable for leaves with extremely high LMA contents.

The past decade has witnessed the disaggregation of remotely sensed land surface temperature (DLST), which aims for the generation of high temporal and spatial resolution land surface temperature (LST) and which has steadily evolved into a relatively independent subfield of thermal remote sensing. Limited by Tobler's first law of geography, DLST methods require a regression between LSTs and scaling factors using image pixels within a globalized or a localized regression window. Recommendations regarding the selection of the regression window have been provided, but they are mainly subjective and based on highly specific examples. In this context, 100 DLST samples with diversified land cover types and climates were employed to assess the global window strategy (GWS) and the local window strategy (LWS). To optimize disaggregation accuracy and computational complexity, the assessments show that the optimal moving-window size (MWS) for the LWS can be estimated by the resolution ratio between pre- and postdisaggregated LSTs. To identify the better strategy between the GWS and the LWS, an indirect criterion based on aggregation-disaggregation (ICAD) was formulated, which determines the better strategy from medium to high resolution according to the associated performances from low to medium resolution. Validations demonstrate that the accuracy predicted by the ICAD achieves 72%, and in cases in which predictions are incorrect, the performances of the GWS and the LWS are similar. Further evidences indicate that the use of historical high-resolution LSTs improves the LWS by using a locally varying MWS. These findings are able to guide researchers in choosing the most suitable regression window for any particular DLST.

Light use efficiency (LUE) models offer an effective way for regional gross primary productivity (GPP) estimation. However, LUE is not easily determined at the landscape level due to its complexity and dependence on various environmental factors. One possible strategy to avoid the requirement for assessing environmental stressors is using the photochemical reflectance index (PRI) to determine LUE via the epoxidation state of the xanthophyll cycle. Integration of such measurements into GPP models could lead to more realistic GPP estimates of landscape level. Conventional, “one-leaf” LUE models, however, seem less suitable for integration of such remote sensing observations, as optically derived estimates are dependent on the shadow fraction viewed at a given time. Here, we utilize the two-leaf LUE (TL-LUE) model to parameterize LUE from multiangle PRI observations and compare it with MOD17 approach. Significant relationships were found between LUE (LUE, LUEsun, and LUEshaded) and PRI (PRI, PRIsιn, and PRIshaded) over 8and 16-day time steps. Similarly, R values for the relationships between modeled GPP and observed GPP (EC derived measurements of GPP) were 0.87 (TL-LUE) and 0.81 (MOD17) at deciduous forest and 0.54 (TL-LUE) and 0.46 (MOD17) at evergreen forest for eight-day periods, as well as 0.84 (TL-LUE) and 0.74 (MOD17) at deciduous forest and 0.49 (TL-LUE) and 0.46 (MOD17) at evergreen forest for 16-day periods. Our results are relevant when planning potential future satellite missions to help constrain existing GPP models using remotely sensed data, as such observations will likely be affected by canopy shading effects at the time of observation.

Proper determination of light use efficiency (LUE) is a prerequisite for LUE models to simulate gross primary productivity (GPP). This study was devoted to apply the photochemical reflectance index (PRI) to accurately track LUE variations for a sub-tropical coniferous forest using tower-based PRI and GPP measurements. To improve the ability of PRI to track LUE, a simple two-leaf approach is used to process the remote sensing and flux data. The results showed: both PRI and LUE decreased with increases of bioclimatic factors. PRI is able to capture diurnal and seasonal changes in LUE. And the two-leaf approach significantly enhanced the correlation between PRI and LUE at either half-hourly or daily time steps.

A new geometric optical (GO)-radiative transfer (RT) model with a multiple scattering scheme suitable for sloping forest canopies is developed in this study. It is based on a Geometrical-Optical model for Sloping Terrains and an RT method. This new model overcomes the difficulty to prescribe bidirectional reflectance factors (BRFs) of shaded components (shaded foliage and background) in GO modeling through simulating radiation multiple scattering within a sloping forest. A case study shows that multiply scattered radiation depends on topographic factors and leaf area index. The contributions of the shaded components to stand-level BRF are less than 3% in the red band and can reach up to 40% in the near-infrared (NIR) band. The “multiangle” Moderate Resolution Imaging Spectroradiometer (MODIS) data over sloping pixels are selected to validate the modeled forest BRF. Considering the multiple scattering schemes and topographic factors, the modeled BRF is closer to the MODIS surface reflectance (BRF product) (red band: R2= 0.8614, rRMSE = 0.1339; NIR band: R2= 0.7573, rRMSE = 0.0850) than the modeled BRF (red band: R2= 0.7771, rRMSE=0.1839; NIR band: R2=0.5176, rRMSE = 0.1155) without topographic consideration. It is also shown that the MODIS surface reflectance of sloping forests at multiple angles can be simulated well using the newly developed model.

GOST is a geometric-optical (GO) model for sloping terrains developed in this study based on the four-scale GO model, which simulates the bidirectional reflectance distribution function (BRDF) of forest canopies on flat surfaces. The four-scale GO model considers four scales of canopy architecture: tree groups, tree crowns, branches, and shoots. In order to make this model suitable for sloping terrains, the mathematical description for the projection of tree crowns on the ground has been modified to consider the fact that trees grow vertically rather than perpendicularly to sloping grounds. The simulated canopy gap fraction and the area ratios of the four scene components (sunlit foliage, sunlit background, shaded foliage, and shaded background) by GOST compare well with those simulated by 3-D virtual canopy computer modeling techniques for a hypothetical forest. GOST simulations show that the differences in area ratios of the four scene components between flat and sloping terrains can reach up to 50%-60% in the principal plane and about 30% in the perpendicular plane. Two case studies are conducted to compare modeled canopy reflectance with observations. One comparison is made against Landsat-5 Thematic Mapper (TM) reflectance, demonstrating the ability of GOST to model canopy reflectance variations with slope and aspect of the terrain. Another comparison is made against MODIS surface reflectance, showing that GOST with topographic consideration outperforms that without topographic consideration. These comparisons confirm the ability of GOST to model canopy reflectance on sloping terrains over a large range of view angles.

The Multi-Angle Imaging Spectroradiometer (MISR) is a powerful sensor for leaf area index (LAI) mapping with its simultaneous multi-angle observations. However, the LAI product derived from MISR observations has low temporal resolution, which is unsatisfactory for many applications. This paper presents an algorithm that expands the MISR LAI product to high temporal resolution with the aid of Moderate Resolution Imaging Spectroradiometer (MODIS) data. The algorithm establishes relationships between the MISR LAI and the MODIS red/near-infrared band ratio (simple ratio (SR)) pixel by pixel using coincident data of these two sensors for the past nine years. Using these pixel-based SR-LAI relationships, a new LAI product with the merits of the original MISR product and high temporal resolution is obtained from MODIS surface reflectance. The expanded LAI series was compared with the original MISR and MODIS LAI products, as well as field LAI measurements made at the Baohe and Maoershan forest sites and the Hulunbeier grassland site, to assess the algorithm's performance. The results show that the temporal coverage of the MISR LAI improved from 15.5% to 65.2% in an 8-day composite, and the mean root-mean-square error is 0.74 for the vegetated pixels. This LAI product has similar temporal consistency and seasonal dynamics to the existing MODIS LAI product generated from the main algorithm, but is more robust against the low quality of reflectance inputs. The expanded LAI product differs with field measurements by about 11.5%, with agreement to field observations at all three sites within an accuracy of 0.8 LAI.

The three-dimentional plant canopy architecture is often characterized using the foliage clumping index useful for ecological and land surface modeling. In this paper, an algorithm is developed to retrieve the foliage clumping index with the Moderate Resolution Imaging Spectroradiometer bidirectional reflectance distribution function (BRDF) parameter product (MCD43A1), which is generated using the RossThick-LiSparse Reciprocal (Ross-Li) model. First, the Ross-Li model is modified to improve the simulation of the reflectance at hotspot using the Polarization and Directionality of Earth Reflectance measurements as benchmarks to determine BRDF parameters. Then, the modified model (Ross-Li-H) is used to simulate the reflectance at hotspot and darkspot, which is used to calculate the normalized difference between hotspot and darkspot (NDHD). With the relationship between clumping index and NDHD simulated by the 4-Scale geometrical model, the clumping index over China's landmass at 500-m resolution is retrieved every 8 days during the period from 2003 to 2008. Finally, The effect of topography on the retrieved clumping index is corrected using a topographic compensation function calculated from the digital elevation model at 90-m resolution. The topographically corrected clumping index values correlate well with field measurements at five sites over China, indicating the feasibility of the algorithm for retrieving the clumping index from the MCD43A1 product.

Both surface topography and vegetation heterogeneity are important factors introducing biases in regional ecological modeling, especially when the modeling is made at large grids. Several studies have demonstrated that gridding the land surface into coarse homogeneous pixels may cause important biases on ecosystem model estimations of carbon budget components at local, regional and global scales. These biases result from overlooking sub-pixel variability of land surface characteristics. This study suggests a simple algorithm that uses sub-pixel information on the spatial variability of vegetation and surface topography to correct net primary productivity (NPP) estimates, made at coarse spatial resolutions where the land surface is considered as homogeneous within each pixel. A spatial scaling algorithm is developed to correct biases in coarse-resolution NPP estimation. This algorithm considers the effect of sub-pixel heterogeneities of land cover, leaf area index (LAI), slope and elevation. Its application to a carbon-hydrology coupled model estimates made at a 1-km resolution over a watershed (named Baohe River Basin) located in the southwestern part of Qinling Mountains, in China, Shaanxi Province, China, improved estimates of average NPP as well as its temporal and spatial variability.

This paper aims to simulate China's terrestrial NPP using a carbon-water coupled process model based on remote sensing, and explore tempo-spatial patterns of China's terrestrial NPP and the mechanisms of its responses to various environmental factors. For these purposes, a national wide input database and a validation database have been set up on a 1 day-1 km tempo-spatial resolution, and the BEPS model has been improved and applied. Using these databases and BEPS model Maps of NPP for the entire China's landmass in 2001 have been produced: China's terrestrial total NPP and mean NPP were 2.235GtC and 235.2C.m(2).yr; and gross primary productivity (GPP), total GPP and mean GPP were 4.418GtC and 465gC.m(2).yr; autotrophic respiration (RA) have been estimated; total RA and mean RA were 2.227GtC and 234gC.m(2).yr; On average, NPP was 50.6% of GPP. In addition, the responses of NPP to changes in some key factors in 2001 have been analyzed.