Generating high spatiotemporal resolution LAI based on MODIS/GF-1 data and combined Kriging-Cressman interpolation

A Spatio-Temporal Enhancement Method for medium resolution LAI (STEM-LAI)

High-resolution Leaf Area Index estimation from synthetic Landsat data generated by a spatial and temporal data fusion model

The normalized difference vegetation index (NDVI) is a simple but powerful indicator, that can be used to observe green live vegetation efficiently. Since its introduction in the 1970s, NDVI has been used widely for land management, food security, and physical models. For these applications, acquiring NDVI in both high spatial resolution and high temporal resolution is preferable. However, there is generally a trade-off between temporal and spatial resolution when using satellite images. To relieve this problem, a convolutional neural network (CNN) based downscaling model was proposed in this research. This model is capable of estimating 10-m high resolution NDVI from MODIS (Moderate Resolution Imaging Spectroradiometer) 250-m resolution NDVI by using Sentinel-1 10-m resolution synthetic aperture radar (SAR) data. First, this downscaling model was trained to estimate Sentinel-2 10-m resolution NDVI from a combination of upscaled 250-m resolution Sentinel-2 NDVI and 10-m resolution Sentinel-1 SAR data, by using data acquired in 2019 in the target area. Then, the generality of this model was validated by applying it to test data acquired in 2020, with the result that the model predicted the NDVI with reasonable accuracy (MAE = 0.090, ρ = 0.734 on average). Next, 250-m NDVI from MODIS data was used as input to confirm this model under conditions replicating an actual application case. Although there were mismatch in the original MODIS and Sentinel-2 NDVI data, the model predicted NDVI with acceptable accuracy (MAE = 0.108, ρ = 0.650 on average). Finally, this model was applied to predict high spatial resolution NDVI using MODIS and Sentinel-1 data acquired in target area from 1 January 2020~31 December 2020. In this experiment, double cropping of cabbage, which was not observable at the original MODIS resolution, was observed by enhanced temporal resolution of high spatial resolution NDVI images (approximately ×2.5). The proposed method enables the production of 10-m resolution NDVI data with acceptable accuracy when cloudless MODIS NDVI and Sentinel-1 SAR data is available, and can enhance the temporal resolution of high resolution 10-m NDVI data.

The leaf area index (LAI) is a key biophysical parameter that determines the state of plant growth. A global LAI has been routinely produced by the Moderate Resolution Imaging Spectro-radiometer (MODIS) and Advanced Very High Resolution Radiometer (AVHRR). However, the MODIS and AVHRR LAI products cannot be synchronized with the same spatial and temporal resolution. The LAI features are not discernible when a global LAI product is implemented at the regional scale because it has low resolution and different land cover types. To obtain high spatial and temporal resolution of LAI products, an empirical model based on the pixel scale was developed. The approach to generate a long (multi-decade) time series of a 1-km spatial resolution LAI normally integrates both AVHRR and MODIS datasets for different land cover types. In this paper, a regression-based model for generating a vegetation LAI was developed using the AVHRR Global Inventory Modelling and Mapping Studies Normalized Difference Vegetation Index (NDVI), MODIS LAI and land cover as input data; the model was evaluated by using relevant data from the same period data from 2000 to 2006. The results of this method show a good consistency in LAI values retrieved from the AVHRR NDVI and MODIS LAI. This simple method has no specific-limited data requirements and can provide improved spatial and temporal resolution in a region without ground data.

Development of the GLASS 250-m leaf area index product (version 6) from MODIS data using the bidirectional LSTM deep learning model

Satellite data holds considerable potential as a source of information on rice crop growth which can be used to inform agronomy. However, given the typical field sizes in many rice-growing countries such as China, data from coarse spatial resolution satellite systems such as the Moderate Resolution Imaging Spectroradiometer (MODIS) are inadequate for resolving crop growth variability at the field scale. Nevertheless, systems such as MODIS do provide images with sufficient frequency to be able to capture the detail of rice crop growth trajectories throughout a growing season. In order to generate high spatial and temporal resolution data suitable for mapping rice crop phenology, this study fused MODIS data with lower frequency, higher spatial resolution Landsat data. An overall workflow was developed which began with image preprocessing, calculation of multi-temporal normalized difference vegetation index (NDVI) images, and spatiotemporal fusion of data from the two sensors. The Spatial and Temporal Adaptive Reflectance Fusion Model was used to effectively downscale the MODIS data to deliver a time-series of 30 m spatial resolution NDVI data at 8-day intervals throughout the rice-growing season. Zonal statistical analysis was used to extract NDVI time-series for individual fields and signal filtering was applied to the time-series to generate rice phenology curves. The downscaled MODIS NDVI products were able to characterize the development of paddy rice at fine spatial and temporal resolutions, across wide spatial extents over multiple growing seasons. These data permitted the extraction of key crop seasonality parameters that quantified inter-annual growth variability for a whole agricultural region and enabled mapping of the variability in crop performance between and within fields. Hence, this approach can provide rice crop growth data that is suitable for informing agronomic policy and practice across a wide range of scales.

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.

Soil moisture, especially surface soil moisture (SSM), plays an important role in the development of various natural hazards that result from extreme weather events such as drought, flooding, and landslides. There have been many remote sensing methods for soil moisture retrieval based on microwave or optical thermal infrared (TIR) measurements. TIR remote sensing has been popular for SSM retrieval due to its fine spatial and temporal resolutions. However, because of limitations in the penetration of optical TIR radiation and cloud cover, TIR methods can only be used under clear sky conditions. Microwave SSM retrieval is based on solid physical principles, and has advantages in cases of cloud cover, but it has low spatial resolution. For applications at the local scale, SSM data at high spatial and temporal resolutions are important, especially for agricultural management and decision support systems. Current remote sensing measurements usually have either a high spatial resolution or a high temporal resolution, but not both. This study aims to retrieve SSM at both high spatial and temporal resolutions through the fusion of Moderate Resolution Imaging Spectroradiometer (MODIS) and Land Remote Sensing Satellite (Landsat) data. Based on the universal triangle trapezoid, this study investigated the relationship between land surface temperature (LST) and the normalized difference vegetation index (NDVI) under different soil moisture conditions to construct an improved nonlinear model for SSM retrieval with LST and NDVI. A case study was conducted in Iowa, in the United States (USA) (Lat: 42.2°~42.7°, Lon: −93.6°~−93.2°), from 1 May 2016 to 31 August 2016. Daily SSM in an agricultural area during the crop-growing season was downscaled to 120-m spatial resolution by fusing Landsat 8 with MODIS, with an R2 of 0.5766, and RMSE from 0.0302 to 0.1124 m3/m3.

Leaf area index (LAI) is a critical vegetation structural parameter in biogeochemical and biophysical ecosystems. High-resolution LAI products play an essential role in regional studies. Empirical methods, which normally use field measurements as their training samples and have been identified as the most commonly used approaches to retrieve structural parameters of vegetation from high-resolution remote-sensing data, are limited by the quality of training samples. Few efforts have been made to generate training samples from existing global LAI products. In this study, two methods (a homogeneous and pure pixel filter method (method A) and a pixel unmixing method (method B)) were developed to extract training samples from moderate-resolution imaging spectroradiometer (MODIS) surface reflectance and LAI products, and a support vector regression (SVR) algorithm trained by the samples was used to retrieve the high-resolution LAI from Landsat data at Baoding, situated in the Hebei Province in China, and Des Moines, situated in Iowa, United States. For the homogeneous and pure pixel filter method, two different sets of training samples were designed. One was composed of upscaled Landsat reflectance at the 500-m resolution and MODIS LAI products (dataset A1); the other was composed of MODIS reflectance and LAI products (dataset A2). With them, two inversion models were developed using SVR. For the pixel unmixing method, the training samples (dataset B) were extracted from unmixed MODIS surface reflectance and LAI products at 30-m resolution, and the third inversion model was obtained with them. LAI inversion results showed that good agreement with field measurements was achieved using these three inversion models. The R2 (coefficient of determination) value and the root mean square error (RMSE) value were computed to assess the results. For all tests, the R2 values are higher than 0.74 and RMSE values are less than 0.73. These tests showed that three models for the two methods combined with MODIS products can retrieve 30-m resolution LAI from Landsat data. The results of the pixel unmixing method was slightly better than that of the homogeneous and pure pixel filter method.

Application of a new leaf area index algorithm to China's landmass using MODIS data for carbon cycle research

Leaf area index (LAI) products is widely applied for vegetation monitoring, yield estimation, ecological monitoring and global change studies. The demand for high-quality LAI products with different spatial and temporal resolutions is gradually increasing. Multiple LAI products have been generated from satellite remote sensing data. However, most of these LAI products have low spatial resolution. In recent years, deep learning algorithms have played a prominent role in scientific computing. Its powerful feature extraction and nonlinear fitting capabilities are very suitable for parameter estimation. In this paper, a new method based on convolutional neural network is proposed to estimate LAI values at 250m spatial resolution from Moderate-Resolution Imaging Spectroradiometer (MODIS) surface reflectance data by combining the existing Global Land Surface Satellite (GLASS) LAI product. The convolutional neural network consists of three convolutional layers, and its input is two bands (red and NIR) of MODIS surface reflectance at 250m spatial resolution and GLASS LAI at 500m spatial resolution. The results show that the method proposed in this paper can effectively estimate LAI values at 250m from MODIS surface reflectance data by combining the GLASS LAI product. The retrieved LAI values have better temporal continuity and agree well with ground measured LAI values when compared with the MODIS LAI product.

The Global Land Surface Satellite (GLASS) leaf area index (LAI) product is one of the most widely used global LAI products in the scientific community. The latest (version 5) GLASS LAI product has been generated from Moderate Resolution Imaging Spectroradiometer (MODIS) surface reflectance data. The purpose of this paper is to evaluate the quality of the version 5 GLASS LAI product. The GLASS LAI product was compared with the latest MODIS LAI product (MCD15A2 H, Collection 6) and the second version of Geoland2 (GEOV2) LAI product to evaluate their temporal and spatial discrepancies. A direct validation was conducted to compare these LAI products to the LAI values derived from the high-resolution reference maps from the Validation of Land European Remote Sensing Instruments (VALERI) and Implementing Multi-Scale Agricultural Indicators Exploiting Sentinels (IMAGINES) sites. The results show that the GLASS and GEOV2 LAI products have great spatial integrity. However, the MODIS LAI product contains many missing pixels in tropical areas. These LAI products follow fairly consistent seasonal characteristics. The spatial discrepancies of these LAI products mainly exist in forest areas, especially evergreen broadleaf forests where the GLASS LAI values are generally lower than the GEOV2 LAI values by approximately 1.0 LAI units and lower than the MODIS LAI values by 0.5 to 1.0 LAI units. The spatial distribution of these LAI products has slight discrepancies in savannahs, broadleaf crops, grasses/cereal crops and shrubs. The GLASS and GEOV2 LAI products capture a complete and reasonable temporal profile, contrasting with the MODIS LAI product, which shows dramatic fluctuations, particularly during the growing seasons. These LAI products show similar temporal trajectories and interannual variations for all biome types except evergreen broadleaf forests. The direct validation shows that the accuracy of the GLASS LAI product is better than the accuracy of the MODIS and GEOV2 LAI products. The coefficient of determination (R2) of the GLASS, MODIS and GEOV2 LAI products versus the LAI values derived from the high-resolution reference maps are 0.68, 0.47 and 0.55, respectively, and the root mean square error (RMSE) of these products are 0.86, 1.22 and 1.21, respectively.

HighlightsThe potential of fusing GF-1 WFV and MODIS data by the ESTARFM algorithm was demonstrated.A better time window selection method for estimating yields was provided.A better vegetation index suitable for yield estimation based on spatiotemporally fused data was identified.The effect of the spatial resolution of remote sensing data on yield estimations was visualized.Abstract. The accurate estimation of crop yields is very important for crop management and food security. Although many methods have been developed based on single remote sensing data sources, advances are still needed to exploit multisource remote sensing data with higher spatial and temporal resolution. More suitable time window selection methods and vegetation indexes, both of which are critical for yield estimations, have not been fully considered. In this article, the Chinese GaoFen-1 Wide Field View (GF-1 WFV) and Terra Moderate Resolution Imaging Spectroradiometer (MODIS) data were fused by the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to generate time-series data with a high spatial resolution. Then, two time window selection methods involving distinguishing or not distinguishing the growth stages during the monitoring period, and three vegetation indexes, the normalized difference vegetation index (NDVI), two-band enhanced vegetation index (EVI2) and wide dynamic range vegetation index (WDRVI), were intercompared. Furthermore, the yield estimations obtained from two different spatial resolutions of fused data and MODIS data were analyzed. The results indicate that taking the growth stage as the time window unit division basis can allow a better estimation of winter wheat yield; and that WDRVI is more suitable for yield estimations than NDVI or EVI2. This study demonstrates that the spatial resolution has a great influence on yield estimations; further, this study identifies a better time window selection method and vegetation index for improving the accuracy of yield estimations based on a multisource remote sensing data fusion. Keywords: Remote sensing, Spatiotemporal data fusion, Winter wheat, Yield estimation.

Leaf area index (LAI) is one of the key parameters in crop growth monitoring and global change studies. Multiple LAI products have been generated from satellite observations, many of which suffer from data discontinuities due to persistent cloud contamination and retrieval algorithm inaccuracies. This study proposes an extended data-based mechanistic method (EDBM) for estimating LAI time series from Moderate Resolution Imaging Spectroradiometer (MODIS) data. The data-based mechanistic model is universalized to supply the LAI background information, and then the vegetation canopy radiative-transfer model (PROSAIL) is coupled to calculate reflectances with the same observation geometry as MODIS reflectance data. The ensemble Kalman filter (ENKF) is introduced to improve LAI estimation based on the difference between simulated and observed reflectances. Field measurements from seven Benchmark Land Multisite Analysis and Intercomparison of Products (BELMANIP) sites and reference maps from the Imagine-S project La Albufera, Spain site were used to validate the model. The results demonstrate that when compared with field measurements, the LAI time-series estimates obtained using this approach were superior to those obtained with the MODIS 500 m resolution LAI product. The root mean square errors (RMSE) of the MODIS LAI product and of the LAI estimated with the proposed method were 1.26 and 0.5, respectively. When compared with reference LAI maps, the results indicate that the estimated LAI is spatially and temporally consistent with LAI reference maps. The average differences between EDBM and the LAI reference map on the selected four days was 0.32.

While data like HJ-1 CCD images have advantageous spatial characteristics for describing crop properties, the temporal resolution of the data is rather low, which can be easily made worse by cloud contamination. In contrast, although Moderate Resolution Imaging Spectroradiometer (MODIS) can only achieve a spatial resolution of 250 m in its normalised difference vegetation index (NDVI) product, it has a high temporal resolution, covering the Earth up to multiple times per day. To combine the high spatial resolution and high temporal resolution of different data sources, a new method (Spatial and Temporal Adaptive Vegetation index Fusion Model [STAVFM]) for blending NDVI of different spatial and temporal resolutions to produce high spatial–temporal resolution NDVI datasets was developed based on Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM). STAVFM defines a time window according to the temporal variation of crops, takes crop phenophase into consideration and improves the temporal weighting algorithm. The result showed that the new method can combine the temporal information of MODIS NDVI and spatial difference information of HJ-1 CCD NDVI to generate an NDVI dataset with both high spatial and high temporal resolution. An application of the generated NDVI dataset in crop biomass estimation was provided. An average absolute error of 17.2% was achieved. The estimated winter wheat biomass correlated well with observed biomass (R 2 of 0.876). We conclude that the new dataset will improve the application of crop biomass estimation by describing the crop biomass accumulation in detail. There is potential to apply the approach in many other studies, including crop production estimation, crop growth monitoring and agricultural ecosystem carbon cycle research, which will contribute to the implementation of Digital Earth by describing land surface processes in detail.