The leaf area index (LAI) is a critical parameter for monitoring vegetation health and studying climate change. The spatial resolutions of most LAI products range from 500–1,000 m. Only a few LAI products exhibit spatial resolutions ranging from 16–30 m, but notable missing data occur because of the revisit cycle of satellites and the effect of weather conditions. These methods cannot satisfy the requirements of LAI application communities. To address this issue, a workflow was proposed for high-resolution seamless mapping of the LAI on the basis of multisource data and the Transformer deep learning model. Jiangsu Province in China was chosen as the study area. In this area, numerous cloudy and rainy days occur annually. Harmonized Landsat and Sentinel-2 (HLS) and Moderate Resolution Imaging Spectroradiometer (MODIS) reflectance images were obtained. The MODIS and HLS images were first composited and spatially aligned. Then, on the basis of the MODIS images and the spatiotemporal Fusion Incorporating Spectral Autocorrection (FIRST) method, missing HLS image data were reconstructed, thus producing a reflectance product with a spatial resolution of 30 m and a temporal resolution of 12 days. On the basis of the reflectance product, a Transformer model was designed for LAI prediction and compared with models designed through backpropagation neural network (BPNN), convolutional neural network (CNN), long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM) methods. These models were compared with and without transfer learning on the basis of an independent dataset. The best model was selected and employed to produce a LAI product for the study area, which was subsequently compared with an existing MODIS product from spatial and temporal perspectives. The results showed that the procedure for HLS reconstruction is effective, with errors varying between 4.00% and 15.93% for different bands. Among the LAI prediction models, the Transformer model consistently performed the best across all scenarios. Notably, the Transformer model trained via transfer learning yielded the best results, with a testR2value of 0.62, a root mean square error (RMSE) of 0.79 and a mean relative error (MRE) of 14.80%. TheR2values of the other models ranged from 0.31 to 0.59, theRMSEvalues ranged from 0.82 to 1.06, and theMREvalues ranged from 15.41% to 22.28%. In addition, the HLS LAI established via the above best model provided greater spatiotemporal accuracy than did the MODIS LAI product. This study provides reference data for establishing seamless LAI products with high spatial and temporal resolutions, contributing to applications such as vegetation health monitoring and global change research.

Leaf inclination angle (LIA), the angle between leaf surface normal and zenith directions, is a vital parameter in radiative transfer, rainfall interception, evapotranspiration, photosynthesis, and hydrological processes. This study generated the global first 500 m mean LIA (MLA) product by gap-filling the LIA measurements. The results show that MLA increases with the rise of latitude. Cereal crops show the highest MLA (54.9°) while evergreen broadleaf forest has the lowest MLA (36.9°). The global vegetation MLA is 46.6° and the MLA of broadleaf forests is less than that of other plant functional types. The global MLA product has a medium consistency with the upscaled MLA samples (R2= 0.58, RMSE = 6.89°). The product enhances our understanding of global LIA and should greatly assist remote sensing retrieval and land surface modeling studies.

Time series leaf area index (LAI) is essential to studying vegetation dynamics and climate changes. The LAI at current status can be regarded as the accumulative consequence of the counterpart at prior times. Although the deep learning algorithm - Long short-term memory (LSTM) can capture long-time dependencies from sequential satellite data for time series LAI estimation, it only uses the information at prior statuses, and neglects the backward propagation of current vegetation change information. Thus, the LSTM-based LAI quality might be limited. In this letter, the bidirectional LSTM (Bi-LSTM) approach was proposed to integrate the information of multiple satellite products from both the past and future for temporal LAI retrieval. The fused values from GLASS, MODIS, and VIIRS LAI products, as well as MODIS reflectance in 2014-2015, serve as the output response and input for the Bi-LSTM training. Then, we compared the Bi-LSTM predictions with the counterparts from the LSTM, the fused LAI and three products using independent validation datasets in 2016. Results illustrated that our proposed Bi-LSTM method achieved better performance with higher accuracy (R2=0.84, RMSE=0.76) when compared to the LSTM estimation (R2=0.83, RMSE=0.82) and LAI products (R2<0.68, RMSE>1). Furthermore, our proposed method provided smoother and more continuous temporal profiles of LAI than other retrieval approaches.

Leaf area index (LAI) is an important vegetation parameter. The vertical profile of forest LAI is critical for understanding the forest carbon, nitrogen, and water cycles and remote sensing radiative transfer processes. This study developed a method to estimate the LAI profile from airborne laser scanning (ALS) data during the leaf-on season. The LAI profile was validated using field measurement data from the digital hemispherical photography (DHP). The results show that both DHP and ALS data can get the vertical distribution of LAI, and similar trends can be obtained. The cumulative LAI derived from ALS data shows a good relationship with that from the DHP (R2=0.84). The LAI profile derived from ALS can be applied to land surface models and remote sensing radiative transfer models.

We present and discuss some recent developments to reach the ultimate validation stage as defined by the Land Product Validation group of the Committee on Earth Observation Systems (CEOS LPV) for vegetation products. We specifically focus on the complementarity between the exhaustive spatial sampling provided by Unmanned Aerial Vehicles (UAVs) or high-resolution satellite data and the exhaustive temporal sampling available with IoTs.

Ground surface roughness is an important parameter for land surface models and remote sensing applications. For the first time, a vegetation roughness index (VRI) product was derived from the Second Generation Global Imager (SGLI) sensor onboard the Japanese Global Change Observation Mission-Climate (GCOM-C) satellite. Characterization of the VRI product is therefore necessary for better understanding of the ground surface roughness condition. This study analyzed the monthly VRI data for different land cover types and the relationship between VRI and terrain parameters derived as digital elevation and terrain roughness. The VRI data for forest types are obviously higher than those of the other land cover types. The forest VRI also shows clear seasonal variations, but those of the other land cover types remain stable over the year. The VRI shows a higher relationship with the terrain roughness than the digital elevation. The VRI provides supplementary information to precisely model the land surface structure. It's worthy to understand more characteristics of the VRI product and deeply explore its usefulness.

Global canopy clumping index (CI), fractional vegetation cover (FVC), and leaf area index (LAI) are important vegetation biophysical variables. Understanding their relationship is crucial for land surface radiative transfer and land surface modeling. This study analyzed the global CI (CAS-CI, 1.1), FVC (GLASS V2.0), and LAI (MODIS V6.0) relationship derived from the satellite data from 2003 to 2017. The results show that, over the past 15 years, the global FVC and LAI has increased whereas the vegetation CI has decreased during this period. In general, CI shows a negative convex relationship with the global FVC and a concave relationship with LAI. The relationship will be further explored for different biome types and at different regions. The statistical relationship provides a deep understanding of the global CI, FVC, and LAI products. The relationship is useful for estimating the global canopy CI from FVC and LAI.

This study investigates the long-term variation of global leaf area index (LAI) and their uncertainties based on an analysis of the most recent global GEOV2 and MODIS (V6.0) products from 2003 to 2018. The global LAI values, the product qualitative quality flags (QQFs) and quantitative quality indicators (QQIs) were analyzed to study their temporal trend. Results shows that the global GEOV2 and MODIS LAI increased from 2003 to 2018. Meanwhile, the GEOV2 product shows that the global LAI uncertainty (0.019/decade) and relative uncertainties (0.8%/decade) have also increased during the same period. Seasonally, both spring (MAM) and summer (JJA) have seen stronger increase in LAI, uncertainty and relative uncertainty from GEOV2. Among the biome types, the LAI increase mainly occur over the savanna regions, where the LAI uncertainty also increased swiftly (0.033/decade for GEOV2). The GEOV2 product shows that the LAI uncertainties for the evergreen broadleaf forest (EBF) increased abruptly since 2014, possibly because of the shift of satellite sensors. The global urban areas have observed a steady decrease in LAI. This study highlights the importance of global LAI uncertainty study and the information is critical for global environmental studies.

The objective of this study is to validate the recent moderate resolution leaf area index (LAI) products generated by MODIS collection 6 (C6) and Geoland2/BioPar project (GEOV2) over major agricultural croplands. Field campaigns were conducted and seasonal continuous LAI measurements were obtained over rice, maize, soybean, and sorghum fields in the Honghe farm (2012 and 2013) and Hailun city (2016) in northeastern China. High resolution 30 m LAI maps were first derived from HJ-1 and Landsat with a look-up table (LUT) method and evaluated with ground-based measurements (R2≥0.67 and RMSE≤0.90). Consequently, the high resolution LAI was upscaled and compared with the moderate resolution LAI products. The results indicate that both MODIS and GEOV2 capture a consistent seasonal pattern of the crop LAI variation. The moderate resolution LAI products are very closed related to the upscaled high resolution reference LAI.

Bidirectional Reflectance Distribution Function (BRDF) describes the anisotropic distribution of surface reflectance. Bidirectional Reflectance Factor (BRF) is an alternative parameter to BRDF and is measurable while BRDF is only a theoretical concept. Land surface albedo plays a crucial role in climate and biosphere modeling. Paddy rice is a special crop type which grows in the water flooded environment. Reflectance of paddy rice field is coupled by rice plants and water flooded soil background. Refraction and absorption coefficients of water differ from soil and green vegetation [1]. Anisotropic properties of paddy rice and effects from background water which covers the rice field in most of growing season must be considered and evaluated. BRF and albedo properties of agricultural regions in climate model were derived mainly from upland crops and paddy rice field features have not been considered adequately. In this study, we aim to examine the comprehensive seasonal characteristics of paddy rice and explore the reflectivity relationships between background water and the whole canopy.

Clumping index (CI) indicates the spatial distribution pattern of foliage. It is important for evapotranspiration (ET) and net primary productivity (NPP) estimation. Remote sensing methods offer an opportunity for CI estimation at the global scale. However, higher resolution and more stable temporal CI are critical for spatial and seasonal characteristic understanding of CI. In this study both 275 m MISR multi-angle reflectance product and 500 m MODIS BRDF parameter product were used for CI estimation. The CI map derived from MISR shows detailed information of spatial distribution and MODIS derived CI can effectively reflect the CI seasonal variation of rice and has good consistency with the variation tendency of field measurement CI. The combination of MISR and MODIS will help us better understanding the spatial and temporal characteristics of CI.

Three major global moderate resolution leaf area index (LAI) products: MODIS/TERRA+AQUA (MCD15 C5), SPOT/VEGETATION CYCLOPES V3.1, and the GLOBCARBON V2.0, were compared in this study. Results show that the three products agree very well for grasses/cereal crops and shrubs. The products differ considerably for EBF, where GLOBCARBON shows systematically lower LAIs than MODIS (~1.02) and CYCLOEPS (~0.50). The discrepancies for EBF are attributed to the different LAI definitions and clumping corrections. MODIS and CYCLOPES generally agree with each other for DBF, ENF and DNF during the peak growth period. The product theoretical uncertainties, indicated by the quantitative quality indicators (QQIs), show that MODIS has the lowest uncertainty (0.19) followed by CYCLOPES (0.54) and GLOBCARBON (0.65).

In this paper, a methodology for improving the Leaf Area Index (LAI) product of the vegetation canopy and the preliminary retrieval results by integrating Moderate Resolution Imaging Spectroradiometer (MODIS) and Multi-angle Imaging SpectroRadiometer (MISR) data is presented. We attempt to improve the estimation of LAI through a physical inversion algorithm with a canopy reflectance model. Taking Konza Prairie experiment as an example, the results suggest that this method can utilize effectively the MISR and MODIS observing information and the prior knowledge which can be obtained from the ground measuring and the sensor products.

Semiarid rangelands are very sensitive to global climatic change; studies of their biophysical attributes are crucial to understanding the dynamics of rangeland ecosystems under human disturbance. In the Santa Rita Experimental Range, AZ, the vegetation has changed considerably, and there have been many management activities applied. This study calculates seven surface variables: the enhanced vegetation index, the normalized difference vegetation index (NDVI), surface albedos (total shortwave, visible, and near-infrared), leaf area index (LAI), and the fraction of photosynthetically active radiation (FPAR) absorbed by green vegetation from the Enhanced Thematic Mapper (ETM+) data. Comparison with the Moderate Resolution Imaging Spectroradiometer vegetation index and albedo products indicates they agree well with our estimates from ETM+, while their LAI and FPAR are larger than from ETM+. Human disturbance has significantly changed the cover types and biophysical conditions. Statistical tests indicate that surface albedos increased and FPAR decreased following tree-cutting disturbances. The recovery will require more than 67 years and is about 50% complete within 40 years at the higher elevation. Grass cover, vegetation indexes, albedos, and LAI recovered from cutting faster at the higher elevation. Woody plants, vegetation indexes, and LAI have recovered to their original characteristics after 65 years at the lower elevation. More studies are needed to examine the spectral characteristics of different ground components.

We apply a hybrid inversion algorithm to estimate land surface biophysical variables (e.g., leaf area index) from the CHRIS (Compact High Resolution Imaging Spectrometer), and ETM+. Field campaigns were conducted over Tucson, Arizona to validate the algorithms and the products. The derived products were compared for different human management activities. These products are then available for input to a plant growth model for calculating the potential for carbon sequestration.

This study presents some preliminary results on estimating crop yield at the regional scale from MODIS (Medium resolution imaging spectroradiometer) data using the data assimilation method. MODIS data products include leaf area index (LAI) and enhanced vegetation index (EVI). The crop growth models of DSSAT were used in this study, which are driven by weather, soil and crop management data. Some of the variables of the models were adjusted through data assimilation algorithms for accurate prediction of crop yields.

There is an increased trend toward quantitative estimation of land surface variables from hyperspectral remote sensing. One challenging issue is retrieving surface reflectance spectra from observed radiance through atmospheric correction, most methods for which are intended to correct water vapor and other absorbing gases. In this letter, methods for correcting both aerosols and water vapor are explored. We first apply the cluster matching technique developed earlier for Landsat-7 ETM+ imagery to Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) data, then improve its aerosol estimation and incorporate a new method for estimating column water vapor content using the neural network technique. The improved algorithm is then used to correct Hyperion imagery. Case studies using AVIRIS and Hyperion images demonstrate that both the original and improved methods are very effective to remove heterogeneous atmospheric effects and recover surface reflectance spectra.

Leaf area index (LAI) is a crucial biophysical parameter that is indispensable for many biophysical and climatic models. A neural network algorithm in conjunction with extensive canopy and atmospheric radiative transfer simulations is presented in this paper to estimate LAI from Landsat-7 Enhanced Thematic Mapper Plus data. Two schemes were explored; the first was based on surface reflectance, and the second on top-of-atmosphere (TOA) radiance. The implication of the second scheme is that atmospheric corrections are not needed for estimating the surface LAI. A soil reflectance index (SRI) was proposed to account for variable soil background reflectances. Ground-measured LAI data acquired at Beltsville, Maryland were used to validate both schemes. The results indicate that both methods can be used to estimate LAI accurately. The experiments also showed that the use of SRI is very critical.

The Advanced Land Imager (ALI) is a multispectral sensor onboard the National Aeronautics and Space Administration Earth Observing 1 (EO-1) satellite. It has similar spatial resolution to Landsat-7 Enhanced Thematic Mapper Plus (ETM+), with three additional spectral bands. We developed new algorithms for estimating both land surface broadband albedo and leaf area index (LAI) from ALI data. A recently developed atmospheric correction algorithm for ETM+ imagery was extended to retrieve surface spectral reflectance from ALI top-of-atmosphere observations. A feature common to these algorithms is the use of new multispectral information from ALI. The additional blue band of ALI is very useful in our atmospheric correction algorithm, and two additional ALI near-infrared bands are valuable for estimating both broadband albedo and LAI. Ground measurements at Beltsville, MD, and Coleambally, Australia, were used to validate the products generated by these algorithms.

For pt.I see ibid., vol.39, no.11, p.2490-8 (2001). This is the second paper of the series on atmospheric correction of Enhanced Thematic Mapper-Plus (ETM+) land surface imagery. In the first paper, a new algorithm that corrects heterogeneous aerosol scattering and surface adjacency effects was presented. In this study, our objectives are to (1) evaluate the accuracy of this new atmospheric correction algorithm using ground radiometric measurements, (2) apply this algorithm to correct Moderate-Resolution Imaging Spectroradiometer (MODIS) and SeaWiFS imagery, and (3) demonstrate how much atmospheric correction of ETM+ imagery can improve land cover classification, change detection, and broadband albedo calculations. Validation results indicate that this new algorithm can retrieve surface reflectance from ETM+ imagery accurately. All experimental cases demonstrate that this algorithm can be used for correcting both MODIS and SeaWiFS imagery. Although more tests and validation exercises are needed, it has been proven promising to correct different multispectral imagery operationally. We have also demonstrated that atmospheric correction does matter.

To extract quantitative information from the Enhanced Thematic Mapper-Plus (ETM+) imagery accurately, atmospheric correction is a necessary step. After reviewing historical development of atmospheric correction of Landsat Thematic Mapper (TM) imagery, the authors present a new algorithm that can effectively estimate the spatial distribution of atmospheric aerosols and retrieve surface reflectance from ETM+ imagery under general atmospheric and surface conditions. This algorithm is therefore suitable for operational applications. A new formula that accounts for adjacency effects is also presented. Several examples are given to demonstrate that this new algorithm works very well under a variety of atmospheric and surface conditions.