Mapping of arable land in Russia using multi-year time series of MODIS data and the LAGMA classification technique

The sustainable agriculture requires a regular country-wide update of information on the status and extension of arable land in Russia. The arable land mapping method is developed based on multi-year time series of Moderate Resolution Imaging Spectroradiometer (MODIS) data. The method exploits differences between the intra- and inter-annual changes in the spectral reflectance of arable land and the corresponding changes for other land cover types. It involves a set of satellite data-derived phenological metrics generated using a 6 years long time series of the perpendicular vegetation index (PVI). The approach utilizes the Locally Adaptive Global Mapping Algorithm (LAGMA), which is a supervised classification technique accounting for the spatial variability of intra-classes spectral properties. The method has been applied to produce a uniform time series of comparable annual arable land maps for Russia at 250 m spatial resolution for the years 2005–2013. Countrywide arable land area trends over the above time series were found to be consistent with official statistics (ROSSTAT).The mapping result has been evaluated using reference data providing F-score exceeding 80% for the most productive regions.     

Assessing the impact of land cover map resolution and geolocation accuracy on evapotranspiration simulations by a land surface model

The land surface models used in numerical weather forecasts and hydrological applications rely on the accuracy of land cover maps available from satellite remote sensing to simulate the energy and water balance at the surface of the Earth. While the impact of classification accuracy on land surface simulations has already been reported, little attention has been paid on the consequences of land cover map uncertainty driven by geolocation accuracy. This impact on the estimated evapotranspiration (ET) from the land surface model H-TESSEL at spatial resolutions ranging from 1 to 30 km is evaluated here, making use of land cover maps at two different initial spatial resolutions, 300 and 1200 m, derived from the GlobCover global product. Geolocation uncertainty affects the land cover maps aggregated at different resolution (from 1.2 to 30 km). However, the effect decreases towards the coarsest resolutions. In addition, aggregated land cover maps are less affected by geolocation errors when the finest original resolution (300 m) is used. The maximum possible effect on ET is quantified over a heterogeneous/transition area in Europe. The result shows an impact up to 10% at 1.2-km resolution to less than 1% at 10-km resolution, at daily timescale, stressing the importance of such issues for kilometre scale applications of land surface models. The use of the highest initial land cover resolution (300 m) reduces by a factor 3 the impact of geolocation on estimated ET at 1.2- to 3-km scale. This study, therefore, stresses on the importance of a careful choice of the land cover map before carrying on land surface model simulations at the kilometre scale.     

Adding value to INSAT-3D sea surface temperature fields using MODIS data over the tropical Indian Ocean

Accurate estimates of sea surface temperature (SST) are crucial for climate studies, numerical weather prediction and air–sea interactions. Following the launch of India’s advanced geostationary satellite – INSAT-3D with two thermal infrared split window channels in 2013, it is now possible to monitor land and ocean surfaces more reliably at higher spatiotemporal scale. In this article, an attempt has been made to develop a more accurate infrared-based cloud-free SST estimates over the tropical Indian Ocean by the synergistic use of geostationary INSAT-3D and polar-orbiting Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) satellite measurements through a successive correction method. This method is applied primarily for the month of May 2015 at coarser spatial resolution and at a daily scale. Results are compared independently with multi-satellite SST estimates and also against in situ observations such as Argo floats and buoys. The merged SST product shows noticeable improvement over INSAT-3D-based estimates alone. Comparison of the merged SST product with Argo observations shows that the root mean square difference (RMSD) has been improved from 1.23 to 0.79 K, and bias and correlation are also significantly improved. Overall results indicate that the synergistic use of INSAT-3D and MODIS satellite observations has potential for more accurate SST estimation over the tropical Indian Ocean at finer temporal resolution and larger spatial coverage for several near-real time meteorological and oceanographic applications.     

Linking ground-based to satellite-derived phenological metrics in support of habitat assessment

Changes in the timing of plant phenology are important indicators of inter-annual climatic variations and are a critical driver of food availability and habitat use for a range of species. A number of remote sensing techniques have recently been developed to observe vegetation cycles throughout the year, including the use of inexpensive visible spectrum digital cameras at the stand level and the use of high temporal frequency Advanced Very High Resolution Radiometer National Oceanic and Atmospheric Administration (AVHRR NOAA) and MODerate resolution Imaging Spectroradiometer (MODIS) imagery at a satellite scale. A fundamental challenge with using satellite data to track plant phenology, however, is the trade-off between the level of spatial detail and the revisit time provided by the sensor, and the ability to verify the interpretation of phenological activity. One way to address this challenge is to integrate remotely sensed observations obtained at different spatial and temporal scales to provide information that contains both high temporal density and fine spatial resolution observations. In this article, we compare measures of vegetation phenology observed from a network of ground-based cameras with satellite-derived measures of greenness derived from a fused broad (MODIS) and fine spatial (Landsat) scale satellite data set. We derive and compare three key indicators of phenological activity including the start date of green-up, start date of senescence and length of growing season from both a ground-based camera network and 30 m spatial resolution synthetic Landsat scenes. Results indicate that although field-based estimates, generally, predicted an earlier start and end of the vegetation season than the fused satellite observations, highly significant relationships were found for the prediction of the start (R ²?=?0.65), end (R ²?=?0.72) and length (R ²?=?0.70) of the growing season across all sites. We conclude that some predictable bias exists however unlike visual field measures of the collected data represent both a spectral and a visual archive for later use.     

Latent heat flux estimation using trapezoidal relationship between MODIS land surface temperature and fraction of vegetation – application and validation in a humid tropical region

The present study was taken up with the objective of developing a methodology for estimation of actual evapotranspiration (AET) using only satellite data. Accordingly, an algorithm based on the popular Priestley–Taylor method was developed. While previous studies have assumed a triangular relationship between land surface temperature (LST) and fraction of vegetation (FV) to calculate the Priestley–Taylor parameter (φ), a trapezoidal relationship was adopted in the present study to enable applications in forested regions in the humid tropics. The developed algorithm was applied to the humid tropical Mae Klong region, Thailand, and latent heat flux (λ_ET) estimates were validated with measurements made at a flux tower located at the centre of the region. Moderate Resolution Imaging Spectroradiometer (MODIS) remote sensing satellite data products corresponding to the study area were used to derive various inputs required by the algorithm. Comparison of estimated and measured fluxes on five cloud-free days in 2003 yielded root mean square error (RMSE) of 64.73 W m^?2 which reduced to 18.65 W m^?2 when one day was treated as an outlier. The methodology developed in this study derived inputs only from satellite imagery and provided reasonably accurate estimates of latent heat flux at a humid tropical location.     

Evapotranspiration in Korea estimated by application of a neural network to satellite images

Previous biophysical and empirical models of evapotranspiration retrieval are difficult to parameterize because of the effects of the nonlinear biophysics of plants, terrestrial and solar radiation and soils, despite attempts made using various satellite products. In this study, the multilayer feed-forward neural network approach with Levenberg–Marquardt back propagation (LM-BP) was used to successfully estimate evapotranspiration using the input of various satellite-based products. When applying neural network training, value-added satellite-based products such as normalized difference vegetation index (NDVI), normalized difference water index (NDWI), land surface temperature (LST), air temperature and insolation are used instead of only spectral information from satellite sensors to reflect the spatial representativeness of the neural network. The evapotranspiration estimated from the neural network with input parameters showed better statistical accuracy than the MODIS products (MOD16) and Priestley–Taylor methods when compared with ground station eddy flux measurements, which were considered as reference data. Additionally, the temporal variation in neural network evapotranspiration well reflected seasonal patterns of eddy flux evapotranspiration, especially for the high cloudiness in the summer season.     

Results of the Global WaterPack: a novel product to assess inland water body dynamics on a daily basis

The understanding and assessment of surface water variability of inland water bodies, for example, due to climate variability and human impact, requires steady and continuous information about its inter- and intra-annual dynamics. In this letter, we present an approach using dynamic threshold techniques and utilizing time series to generate a data set containing detected surface water bodies on a global scale with daily temporal resolution. Exemplary results for the year 2013 that were based on moderate resolution imaging spectroradiometer products are presented in this letter. The main input data sets for the presented product were MOD09GQ/MYD09GQ and MOD10A1/MYD10A1 with a spatial resolution of 250 m and 500 m, respectively. Using the static water mask MOD44W, we extracted training pixels to generate dynamic thresholds for individual data sets on daily basis. In a second processing step, the generated sequences of water masks were utilized to interpolate the results for any missing observations, either due to cloud coverage or missing data, as well as to reduce misclassification due to cloud shadow. The product provides an opportunity for further research and for assessing the drivers of changes of inland water bodies at a global scale.     

Blending of satellite SST products using ensemble Bayesian model averaging (EBMA)

Sea surface temperature (SST) is an important parameter in understanding atmosphere–ocean circulation processes and monitoring global climate change. In addition to in situ observations of SST, a series of satellite-borne instruments provide global coverage of SST through infrared and microwave remote sensing. This study was the first application of the ensemble Bayesian model averaging (EBMA) method to the blending of satellite SST products to minimize inherent uncertainties and improve the validation statistics. Monthly SST products from moderate resolution imaging spectroadiometer, Advanced Very High Resolution Radiometer and Advanced Microwave Scanning Radiometer-EOS were used as ensemble members. The mean bias and root-mean-square error (RMSE) of the EBMA method were better than those of the individual members or generic methods such as ensemble mean and median. This is because the weighting scheme adjusted by the expectation–maximization algorithm was based on the suitability of each member derived from training procedures. The errors of EBMA in our experiment had almost no spatial and temporal autocorrelation with regard to the latitude and month, which implies that the EBMA method can serve as a viable option for blending of satellite SST, although more experiments are necessary to determine its feasibility in more detail.     

Sensitivity to spatio-temporal resolution of satellite-derived daily surface solar irradiation

Solar radiation data are essential for many applications, and in particular for solar energy systems. Because ground-based measurements of solar radiation are usually scarce, several methods have been proposed to estimate the solar radiation incoming on a horizontal surface at ground level from satellite imagery. These satellite-based estimations can be used as such, or combined with ground-based measurements. Because the satellite data sets differ in spatial and temporal resolution, this study evaluates the sensitivity of the satellite-derived daily surface solar irradiation to the underlying space and time resolution. More precisely, three surface solar radiation data sets retrieved from the Meteosat Second Generation (MSG) satellites are compared against ground-based measurements. Additionally, the benefit of merging information from the ground-based measurements with satellite data is explored. The study finds that the accuracy of daily surface solar irradiation estimates increases by up to 10% by doubling the temporal resolution of the MSG data, while it is largely insensitive to spatial resolution. This suggests that future geostationary satellite missions might primarily improve temporal rather than spatial resolution.     

High-resolution landcover classification using Random Forest

Potential data sets for landcover classification, such as Landsat (or pre-processed data such as the National Land Cover Dataset (NLCD)), are often too coarse for fine-scale research needs or are cost-prohibitive (Quickbird, Ikonos and Geoeye). Repeated attempts at classifying high spatial resolution data, National Agricultural Imagery Program (NAIP) imagery, based on traditional techniques, such as a maximum likelihood supervised classification, have failed to produce a product with sufficient accuracy. We used the ensemble Random Forests (RFs) classifier to classify landcover at 1 m resolution using 2009 NAIP imagery in south-eastern Wyoming. We classified riparian areas within a 225 km² study area, at 1 m spatial resolution, using RFs with emphasis on riparian corridors that yielded a land cover map with overall accuracy of 81% and a kappa coefficient of 79%. Users’ accuracy of important riparian vegetation species, aspen, riparian grasses and willow were 79%, 81% and 83%, respectively. Techniques presented in this paper, which exploit free NAIP imagery for landcover classification, represent an inexpensive and reliable alternative to purchasing commercial imagery when high spatial resolution landcover data is needed.     

Identifying patterns and hotspots of global land cover transitions using the ESA CCI Land Cover dataset

Land use/land cover change is a continuing research focus, not only because of its ecological and environmental effects but also because of the difficulties with accurate change detection and analysis uncertainty. The principal difficulty is the lack of a long time series of annual global land cover maps at a fine resolution. A new global long-term time series of annual datasets called the European Space Agency (ESA) Climate Change Initiative Land Cover (CCI-LC) has been published, making it possible to detect the global land cover changes. Using this ESA CCI-LC product from 1992–2015, we quantified the annual transitions of land cover change globally with the trajectory analysis method, analyzed the changes patterns and identified the land cover change hotspots. The total land cover change area for the world was 5.99 million km², amounting to only 3.36% of the total continental area. Most changes happened in forest and cropland, accounting 32% of all the land cover changes. Most land cover changes happened in tropical ecoregions. Grassland changes were mainly distributed in the temperate ecoregions, while cropland expansion occurred mainly in the tropical or subtropical ecoregions. The hotspots identified in this paper could provide target areas for further research.     

Multi-dimensional histogram-based information capacity analysis of urban heat island effect using Landsat 8 data

The vegetation and impervious surface area (ISA) are the most key indicators for the urban heat islands (UHI). This study used the normalized difference vegetation index (NDVI) as the indicator of vegetation abundance, the modified normalized difference impervious surface index (MNDISI) as the indicator of impervious surface fraction to estimate the land surface temperature (LST)–vegetation relationship and LST–ISA relationship. The land surface cover types were obtained by classification and regression tree (CART). Results demonstrate and verify that LST possessed strong negative correlations with NDVI and positive correlations with MNDISI at various spatial resolutions (30–960 m). Both correlation coefficients reached their strongest points at 30 m resolution, which is believed to be the operational scale of LST, NDVI and MNDISI. Further, information capacity (IC), as a spatial index, is used to characterize the spatial pattern of UHI. Results show that the IC of LST (LST_IC) possessed strong positive correlations with the IC of NDVI (NDVI_IC) and the IC of MNDISI (MNDISI_IC). It is suggested that the spatial pattern of UHI has a direct correspondence with the vegetation and ISA.     

Agreement between monthly land rainfall estimates from TRMM-PR and gauge-based observations over South Asia

There is a demand for reliable rainfall data-set over the South Asia region covering both land and ocean for model validation/development and various applications. For satellite rainfall estimates (SREs), the algorithm development groups also need validation information on SRE. The Tropical Rainfall Measuring Mission (TRMM) project has produced recently improved version 7 (V7) rainfall data-sets. Version 6 (V6) and V7 of 3A25, the surface rainfall products derived from TRMM precipitation radar (PR), are compared with gauge-based observations at 0.5° latitude/longitude resolution for the period of 1998–2007 over the South Asian land region. Both 3A25V7 and 3A25V6 represent the mean rainfall distribution patterns reasonably well. However, 3A25 products overestimate rainfall over the Indonesian region compared to gauge-based data. For some parts of South Asia, SREs show considerable difference in the magnitude of coefficient of variation compared to gauge-based information. At seasonal scale, a contrasting feature in bias over India during the pre-monsoon and monsoon seasons is noticed from both the versions of 3A25 data-set. In general, 3A25 rainfall data-sets are able to capture the interannual variability of rainfall over South Asia. The frequency distribution of monthly rainfall rate reveals that 3A25 products marginally underestimate rainfall below 10 mm day^?1 and overestimate higher rainfall rate compared to gauge-based data. Overall, 3A25V7 product is marginally better than its previous version (3A25V6) over the South Asian land region.     

Modelling aboveground biomass using MODIS FPAR/LAI data in alpine grasslands of the Northern Tibetan Plateau

Aboveground biomass (AGB) is a vital variable in global carbon cycling and plays important roles in ecosystem structure and function in grasslands. Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 leaf area index (LAI) and fractional photosynthetically active radiation absorbed by vegetation (FPAR) data were used to model AGB in alpine grasslands in the Northern Tibetan Plateau. MYD15A2H (derived from the Aqua MODIS with a temporal resolution of eight days) FPAR/LAI with lower RMSE values (<18 g m^?2) may have higher estimated accuracies than MOD15A2H (derived from the Terra MODIS with a temporal resolution of eight days), MCD15A2H (derived from the combination of Terra MODIS and Aqua MODIS with a temporal resolution of eight days) and MCD15A3H (derived from the combination of Terra MODIS and Aqua MODIS with a temporal resolution of four days) FPAR/LAI with higher RMSE values (>20 g m^?2). The RMSE values (11.42–13.27 g m^?2) in the open grazed areas were lower than those (17.46–17.56 g m^?2) in fenced areas for MYD15A2H. Therefore, the estimated accuracies of AGB may vary among the four MODIS FPAR/LAI products and land use types in alpine grasslands of the Northern Tibetan Plateau.     

Development and validation of the global surface type data product from S-NPP VIIRS

Accurate representation of actual terrestrial surface types at regional to global scales is an important element for many applications. Based on National Aeronautics and Space Administration Moderate Resolution Imaging Spectroradiometer land cover algorithms, a global surface-type product from observations of the Visible Infrared Imaging Radiometer Suite (VIIRS) on board the Suomi National Polar-orbiting Partnership, provides consistent global land cover classification map for various studies, such as land surface modelling for numerical weather predictions, land management, biodiversity and hydrological modelling, and carbon and ecosystem studies. This letter introduces the development and validation of the VIIRS global surface-type product using the land cover classification scheme of the International Geosphere-Biosphere Programme. Surface reflectance data from VIIRS were composited into monthly data and then into annual metrics. The C5.0 decision tree classifier was used to determine the surface type for each pixel in a 1 km grid. To quantitatively evaluate accuracies of the new surface type product, a visual interpretation-based validation was performed in which high-resolution satellite images and other ancillary data were used as the reference. The validation result based on the large validation data set indicated that (78.64 ± 0.57)% overall classification accuracy was achieved.     

Estimation of volumetric surface soil moisture content using microwave-calibrated soil evaporative efficiency information

ABSTRACT

Soil moisture (SM) is a critical variable in energy and water partitioning at the interface between the land surface and atmosphere. In this study, we provided a robust method to retrieve soil moisture using optimal remotely sensed soil evaporative efficiency (SEE) information. Specifically, SEE was deduced from the triangle space constituted by remotely sensed land surface temperature (LST) and fractional vegetation cover (Fc). Theoretical solutions of the dry and wet boundaries were derived by annual-scale optimization and microwave SM calibration. The two limits of SM were obtained by linear fit function between SEE and microwave-based SM. The proposed method was validated at the Liaoning Province of China in the year 2011 by using MODerate Resolution Imaging Spectroradiometer (MODIS) and Soil Moisture and Ocean Salinity (SMOS) satellite images as input. Results indicated that the new method has not only bypassed the complex parametric scheme in the calculation of boundaries within the LST-Fc feature space but also performed superior in the estimation of soil moisture status at all-sky days. Besides, the optimal method has reproduced the spatial and temporal patterns of soil moisture reasonably well, with a root mean square error of 0.07 m³ m^?3. Therefore, the proposed method can be regarded as a suitable tool to provide accurate and continuous monitoring of soil moisture.     

Generation and validation of analysed wind vectors over the global oceans

Daily and 12 hourly gridded analysed wind vectors (AWVs) over global ocean were generated using the simple spatial interpolation scheme of ‘box averaging’, with a horizontal resolution of 0.5°?×?0.5°. For daily analysed winds, observations only from Oceansat-2 Scatterometer (OSCAT) were used. The 12 hourly AWVs were generated by combining the data from both OSCAT and Advanced Scatterometer (ASCAT). Apart from ocean wind vectors, an effort was made also to produce analysed wind stress, divergence and curl of wind stress. The daily and 12 hourly analysed winds were validated using in situ observations from 97 global moored buoys and data from European Centre for Medium Range Weather Forecasting analyses for a period of 9 months. The validation result shows a good agreement between AWV products and the buoy and model analysis data, yielding a standard deviation of around 2 m s^?1 in wind speed and around 20° in wind direction.     

A global corrected SRTM DEM product for vegetated areas

It has been found that the Shuttle Radar Topography Mission (SRTM) digital elevation model (DEM) is systematically higher than the actual land surface in vegetated areas. This study developed a new globally corrected SRTM DEM through reducing its systematic bias in vegetated areas. Over 150, 000 km² airborne light detection and ranging (LiDAR) data along with spaceborne LiDAR, global canopy height data, global canopy cover data and global land cover data were collected to correct the SRTM DEM. A linear regression based method was used to estimate the original SRTM DEM error and therefore correct the SRTM DEM data. The results show that the original SRTM DEM data is around 6 m higher than the actual land surfaces on average across all vegetation types. Our corrected SRTM DEM data can significantly reduce the significant bias to near zero, and can also reduce the root-mean-square error by 1 m.     

Feasibility of using consumer-grade unmanned aerial vehicles to estimate leaf area index in Mangrove forest

As unmanned aerial vehicles (UAVs) become more popular, many studies investigate vegetation based on commercial UAV data. Although compared to satellite data, commercial UAV data can have flexible revisit frequencies, the possibility of using an even cheaper data source, consumer UAVs (red, green, and blue (RGB) only), to study vegetation remains unknown. The purpose of most frequent uses of consumer UAVs is recreation. This paper tests the feasibility of using consumer UAVs for mangrove research and proposed a method for mapping leaf area index (LAI) of mangrove. A commercial UAV image is also used for comparison. RGB-based vegetation indices like Excess Green Vegetation Index (ExG), Negative Excess Red Vegetation Index (NegExR), Green Leaf Index (GLI) and Normalized Green-red Difference Index (NGRDI) were used to build regression models against field measured LAI. The results showed that it was feasible to use consumer UAV data for mapping mangrove forest LAI, and the NegExR achieved the highest coefficient of determination (R²) in predicting LAI among all the indices. This paper showed that researchers who are neither familiar with aerial photogrammetry nor have access to commercial UAV data could perform high spatial resolution vegetation studies at a low cost.     

A note on the height variation of foliage clumping: comparison with remote sensing retrievals

Clumping index (CI), quantifying the level of foliage grouping within distinct canopy structures relative to a random distribution, is a key structural parameter of plant canopies and is very useful in ecological and meteorological models. In this letter, we report on validating the global foliage clumping map derived from the Moderate Resolution Imaging Spectroradiometer (MODIS) data at 500 m resolution using new information about vertical profiles of foliage clumping in a wide range of forest type stands. We report that in moderate to dense forest stands with developed undergrowth layer, in situ measurements near the ground surface may considerably underestimate the overall canopy-level clumping effect. This is because the large gaps between tree crowns at upper levels of the canopy may not be all measured near the ground due to obscurity by lower vegetation of branches. This information about height variation of CI is shown to be important for correct estimating and validating the foliage clumping from airborne/satellite remote sensing.