Wetland inundation is crucial to the survival and prosperity of fauna and flora communities in wetland ecosystems. Even small changes in surface inundation may result in a substantial impact on the wetland ecosystem characteristics and function. This study presented a novel method for wetland inundation mapping at a subpixel scale in a typical wetland region on the Zoige Plateau, northeast Tibetan Plateau, China, by combining use of an unmanned aerial vehicle (UAV) and Landsat-8 Operational Land Imager (OLI) data. A reference subpixel inundation percentage (SIP) map at a Landsat-8 OLI 30 m pixel scale was first generated using high resolution UAV data (0.16 m). The reference SIP map and Landsat-8 OLI imagery were then used to develop SIP estimation models using three different retrieval methods (Linear spectral unmixing (LSU), Artificial neural networks (ANN), and Regression tree (RT)). Based on observations from 2014, the estimation results indicated that the estimation model developed with RT method could provide the best fitting results for the mapping wetland SIP (R2 = 0.933, RMSE = 8.73%) compared to the other two methods. The proposed model with RT method was validated with observations from 2013, and the estimated SIP was highly correlated with the reference SIP, with an R2 of 0.986 and an RMSE of 9.84%. This study highlighted the value of high resolution UAV data and globally and freely available Landsat data in combination with the developed approach for monitoring finely gradual inundation change patterns in wetland ecosystems. Full article

Increasing attention is being paid on leaf area index (LAI) retrieval in mountainous areas. Mountainous areas present extreme topographic variability, and are characterized by more spatial heterogeneity and inaccessibility compared with flat terrain. It is difficult to collect representative ground-truth measurements, and the validation of LAI in mountainous areas is still problematic. A cost-constrained sampling strategy (CSS) in support of LAI validation was presented in this study. To account for the influence of rugged terrain on implementation cost, a cost-objective function was incorporated to traditional conditioned Latin hypercube (CLH) sampling strategy. A case study in Hailuogou, Sichuan province, China was used to assess the efficiency of CSS. Normalized difference vegetation index (NDVI), land cover type, and slope were selected as auxiliary variables to present the variability of LAI in the study area. Results show that CSS can satisfactorily capture the variability across the site extent, while minimizing field efforts. One appealing feature of CSS is that the compromise between representativeness and implementation cost can be regulated according to actual surface heterogeneity and budget constraints, and this makes CSS flexible. Although the proposed method was only validated for the auxiliary variables rather than the LAI measurements, it serves as a starting point for establishing the locations of field plots and facilitates the preparation of field campaigns in mountainous areas. Full article

Land cover mapping in mountainous areas is a notoriously challenging task due to the rugged terrain and high spatial heterogeneity of land surfaces as well as the frequent cloud contamination of satellite imagery. Taking Southwestern China (a typical mountainous region) as an example, this paper established a new HC-MMK approach (Hierarchical Classification based on Multi-source and Multi-temporal data and geo-Knowledge), which was especially designed for land cover mapping in mountainous areas. This approach was taken in order to generate a 30 m-resolution land cover product in Southwestern China in 2010 (hereinafter referred to as CLC-SW2010). The multi-temporal native HJ (HuanJing, small satellite constellation for disaster and environmental monitoring) CCD (Charge-Coupled Device) images, Landsat TM (Thematic Mapper) images and topographical data (including elevation, aspect, slope, etc.) were taken as the main input data sources. Hierarchical classification tree construction and a five-step knowledge-based interactive quality control were the major components of this proposed approach. The CLC-SW2010 product contained six primary categories and 38 secondary categories, which covered about 2.33 million km² (accounting for about a quarter of the land area of China). The accuracies of primary and secondary categories for CLC-SW2010 reached 95.09% and 87.14%, respectively, which were assessed independently by a third-party group. This product has so far been used to estimate the terrestrial carbon stocks and assess the quality of the ecological environments. The proposed HC-MMK approach could be used not only in mountainous areas, but also for plains, hills and other regions. Meanwhile, this study could also be used as a reference for other land cover mapping projects over large areas or even the entire globe. Full article

Measuring the impact of livestock grazing on grassland above-ground net primary production (ANPP) is essential for grass yield estimation and pasture management. However, since there is a lack of accurate and repeatable techniques to obtain the details of grazing locations and stocking rates at the regional scale, it is an extremely challenging task to study the influence of regional grazing on the grassland ANPP. Taking Zoige County as a case, this paper proposes an approach to quantify the spatial and temporal variation of grazing intensity and grazing period through time-series remote sensing data, simulated grassland ANPP through the denitrification and decomposition (DNDC) model, and then explores the impact of grazing on grassland ANPP. The result showed that the model-estimated ANPP while considering grazing had a significant relationship with the field-observed ANPP, with the coefficient of determination (R²) of 0.75, root mean square error (RMSE) of 122.86 kgC/ha, and average relative error (RE) of 8.77%. On the contrary, if grazing activity was not considered in simulation, a large uncertainty was found when the model-estimated ANPP was compared with the field observation, showing R² of 0.4, RMSE of 211.51 kgC/ha, and average RE of 32.5%. For the whole area of Zoige County in 2012, the statistics of the estimation showed that the total regional ANPP was up to 3.815 × 10⁵ tC, while the total regional ANPP, without considering grazing, would be overestimated by 44.4%, up to 5.51 × 10⁵ tC. This indicates that the grazing parameters derived in this study could effectively improve the accuracy of ANPP simulation results. Therefore, it is feasible to combine time-series remote sensing data with the process model to simulate the grazing effects on grassland ANPP. However, some issues, such as selecting proper remote sensing data, improving the quality of model input parameters, collecting more field data, and exploring the data assimilation approaches, still should be considered in the future work. Full article

It is highly desirable to accurately detect the clouds in satellite images before any kind of applications. However, clouds and snow discrimination in remote sensing images is a challenging task because of their similar spectral signature. The shortwave infrared (SWIR, e.g., Landsat TM 1.55–1.75 µm band) band is widely used for the separation of cloud and snow. However, for some sensors such as the CBERS-2 (China-Brazil Earth Resources Satellite), CBERS-4 and HJ-1A/B (HuanJing (HJ), which means environment in Chinese) that are designed without SWIR band, such methods are no longer practical. In this paper, a new practical method was proposed to discriminate clouds from snow through combining the spectral reflectance with the spatio-temporal contextual information. Taking the Mt. Gongga region, where there is frequent clouds and snow cover, in China as a case area, the detailed methodology was introduced on how to use the 181 scenes of HJ-1A/B CCD images in the year 2011 to discriminate clouds and snow in these images. Visual inspection revealed that clouds and snow pixels can be accurately separated by the proposed method. The pixel-level quantitative accuracy validation was conducted by comparing the detection results with the reference cloud masks generated by a random-tile validation scheme. The pixel-level validation results showed that the coefficient of determination (R²) between the reference cloud masks and the detection results was 0.95, and the average overall accuracy, precision and recall for clouds were 91.32%, 85.33% and 81.82%, respectively. The experimental results confirmed that the proposed method was effective at providing reasonable cloud mask for the SWIR-lacking HJ-1A/B CCD images. Since HJ-1A/B have been in orbit for over seven years and these satellites still run well, the proposed method is helpful for the cloud mask generation of the historical archive HJ-1A/B images and even similar sensors. Full article

Time series remote sensing products with both fine spatial and dense temporal resolutions are urgently needed for many earth system studies. The development of small satellite constellations with identical sensors affords novel opportunities to provide such kind of earth observations. In this paper, a new dense time series 30-m image product was proposed respectively based on an 8-day, 16-day and monthly composition. The products were composited by the Charge Coupled Device (CCD) images from the 2-day revisit small satellite constellation for environmental monitoring and disaster mitigation of China (HJ-1A/B). Taking the Zoige plateau in China as a case area where it is covered by highly heterogeneous vegetation landscapes, a detailed methodology was introduced on how to use 183 scenes of CCD images in 2010 to create composite products. The quality of the HJ CCD composites was evaluated by inter-comparison with the monthly 30-m global Web-Enabled Landsat Data (WELD), 16-day 500-m MODIS NDVI, and 8-day 500-m MODIS surface reflectance products. Results showed that the radiometric consistency between HJ and WELD composited Top Of Atmosphere (TOA) reflectance was in good agreement except for May, June, July and August when more clouds and invalid data gaps appeared in WELD. Visual assessment and temporal profile analysis also revealed that HJ possessed better visual effects and temporal coherence than that of WELD. The comparison between HJ and MODIS products indicated that HJ composites were radiometrically consistent with MODIS products over areas consisting of large patches of homogeneous surface types, but can better reflect the detailed spatial differences in regions with heterogeneous landscapes. This paper highlights the potential of compositing HJ-1A/B CCD images, allowing for providing a cloud free, time-space consistent, 30-m spatial resolution, and dense in time series image product. Meanwhile, the proposed products could also be treated as a reference to generate regional or even global composited products for the on-going satellite constellations and even for the forthcoming satellite missions such as Sentinel-2A/B. Full article

The NDVI dataset with high temporal and spatial resolution (HTSN) is significant for extracting information about the phenological change of vegetation in regions with a complex earth surface. The Spatial and Temporal Adaptive Reflectance Fusion Model (STARFM) has been successfully applied to synthesize the HTSN by fusing the data with different characteristics. Based on the model, there are two different schemes for synthesizing the HTSN. One scheme is that red reflectance and near-infrared (NIR) reflectance are synthesized, respectively, and the HTSN is then obtained through algebraic operation (Scheme 1); the other scheme is that the red and NIR reflectance are used to calculate NDVI, which is directly taken as input data to synthesize the HTSN (Scheme 2). In this paper, taking the hill areas in eastern Sichuan China as a case, the two schemes were compared with each other. Seven Landsat images and time-series MOD13Q1 datasets spanning from October 2001 to February 2003 were used as the test data. The results showed the prediction accuracies of both derived HTSNs by the two different schemes were generally in good agreement, and Scheme 2 was slightly superior to Scheme 1 (R²: 0.14 < Scheme 1 < 0.53; 0.15 < Scheme 2 < 0.53). Although the two HTSNs showed high temporal and spatial consistence, the small spatiotemporal difference between them had a different influence on different applications. The coincidence rate of cropping intensity extracted from two derived HTSNs was fairly high, reaching up to 93.86%, while the coincidence rate of crop peak dates (i.e., the emerging dates of peaks in an annual time-series NDVI curve) was only 70.95%. Therefore, it is deemed that Scheme 2 can replace Scheme 1 in the application of extracting cropping intensity, so that more calculation time and memory space can be saved. For extracting more quantitative crop phenological information like crop peak dates, more tests are still needed in order to compare the absolute accuracy for both schemes. Full article

Optical remotely sensed images in mountainous areas are subject to radiometric distortions induced by topographic effects, which need to be corrected before quantitative applications. Based on Li model and Sandmeier model, this paper proposed an improved physics-based model for the topographic correction of Landsat Thematic Mapper (TM) images. The model employed Normalized Difference Vegetation Index (NDVI) thresholds to approximately divide land targets into eleven groups, due to NDVI’s lower sensitivity to topography and its significant role in indicating land cover type. Within each group of terrestrial targets, corresponding MODIS BRDF (Bidirectional Reflectance Distribution Function) products were used to account for land surface’s BRDF effect, and topographic effects are corrected without Lambertian assumption. The methodology was tested with two TM scenes of severely rugged mountain areas acquired under different sun elevation angles. Results demonstrated that reflectance of sun-averted slopes was evidently enhanced, and the overall quality of images was improved with topographic effect being effectively suppressed. Correlation coefficients between Near Infra-Red band reflectance and illumination condition reduced almost to zero, and coefficients of variance also showed some reduction. By comparison with the other two physics-based models (Sandmeier model and Li model), the proposed model showed favorable results on two tested Landsat scenes. With the almost half-century accumulation of Landsat data and the successive launch and operation of Landsat 8, the improved model in this paper can be potentially helpful for the topographic correction of Landsat and Landsat-like data. Full article

Evapotranspiration (ET) plays a key role in water resource management. It is important to understand the ET spatio-temporal pattern of South Asia for understanding and anticipating serious water resource shortages. In this study, daily ET in 2008 was estimated over South Asia by using MODerate Resolution Imaging Spectroradiometer (MODIS) products combined with field observations and Global Land Data Assimilation System (GLDAS) product through Surface Energy Balance System (SEBS) model. Monthly ET data were calculated based on daily ET and evaluated by the GLDAS ET data. Good agreements were found between two datasets for winter months (October to February) with R² from 0.5 to 0.7. Spatio-temporal analysis of ET was conducted. Ten specific sites with different land cover types at typical climate regions were selected to analyze the ET temporal change pattern, and the result indicated that the semi-arid or arid areas in the northwest had the lowest average daily ET (around 0.3 mm) with a big fluctuation in the monsoon season, while the sites in the Indo-Gangetic Plain and in southern India has bigger daily ET (more than 3 mm) due to a large water supplement. It is suggested that the monsoon climate has a large impact on ET spatio-temporal variation in the whole region. Full article

Land surface is normally considered as a mixture of soil and vegetation. Many applications, such as drought monitoring and crop-yield estimation, benefit from accurate retrieval of both soil and vegetation temperatures through satellite observation. A preliminary study has been conducted in this study on the estimation of land surface soil and vegetation component temperature using the geostationary satellite data acquired by Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard the Meteosat Second Generation (MSG) and TERRA-MODIS data. A synergetic algorithm is proposed to derive soil and vegetation temperatures by using the temporal and spatial information in SEVIRI and MODIS products. The approach is applied to both simulation data and satellite data. For simulation data, the component temperatures are well estimated with root mean squared error (RMSE) close to 0 K. For satellite data application, reasonable spatial distributions of the soil and vegetation temperatures are derived for eight cloud-free days in the Iberian Peninsula from June to August 2009. An evaluation is performed for the estimated vegetation temperature against the near surface air temperature. The correlation analysis between two datasets is found that the R-squareds are from 0.074 to 0.423 and RMSEs are within 4 K. Considering the impact of fraction of vegetation cover (FVC) on the validation, the pixels with FVC less than 30% are excluded in the total data comparison, and an obvious improvement is achieved with R-squared from 0.231 to 0.417 and RMSE from 2.9 K to 2.58 K. The validation indicates that the proposed algorithm is able to provide reasonable estimations of soil and vegetation temperatures. It is a potential way to map soil and vegetation temperature for large areas. Full article

Soil moisture is a vital parameter in various land surface processes, and microwave remote sensing is widely used to estimate regional soil moisture. However, the application of the retrieved soil moisture data is restricted by its coarse spatial resolution. To overcome this weakness, many methods were proposed to downscale microwave soil moisture data. The traditional method is the microwave-optical/IR synergistic approach, in which land surface temperature (LST), vegetation index and surface albedo are key parameters. However, due to the uncertainty in absolute LST estimation, this approach is partly dependent on the accuracy of LST estimation. To eliminate the impacts of LST estimation, an improved downscaling method is proposed in this study to downscale Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) Land Parameter Retrieval Model (LPRM) soil moisture product with visible and thermal data of Meteosat Second Generation (MSG)—Spinning Enhanced Visible and Infrared Imager (SEVIRI). Two temperature temporal variation parameters related to soil moisture, including mid-morning rising rate and daily maximum temperature time, are introduced in the proposed method to replace LST. The proposed method and the traditional method are both applied to the Iberian Peninsula area for July and August 2007. Comparison of the two results shows that the coefficient of determination (R-squared) has an average improvement of 0.08 and the root mean square error has a systematic decrease. The downscaled soil moisture by the proposed method was validated by REMEDHUS soil moisture network in the study area, and site specific validation gets poor correlation between the two datasets because of the low spatial representativeness of site measurement for one MSG-SEVIRI pixel. Although the comparisons at 15 km and network scale show an improvement over the site specific comparison, it is found that the downscaling method systematically degrades the accuracy in soil moisture data, with a R-squared of 0–0.4 and 0.218 for the downscaled data set against 0.7–0.8 and 0.571 for AMSR-E data at 15 km scale and the network scale respectively. Full article

Remotely sensed data, with high spatial and temporal resolutions, can hardly be provided by only one sensor due to the tradeoff in sensor designs that balance spatial resolutions and temporal coverage. However, they are urgently needed for improving the ability of monitoring rapid landscape changes at fine scales (e.g., 30 m). One approach to acquire them is by fusing observations from sensors with different characteristics (e.g., Enhanced Thematic Mapper Plus (ETM+) and Moderate Resolution Imaging Spectroradiometer (MODIS)). The existing data fusion algorithms, such as the Spatial and Temporal Data Fusion Model (STDFM), have achieved some significant progress in this field. This paper puts forward an Enhanced Spatial and Temporal Data Fusion Model (ESTDFM) based on the STDFM algorithm, by introducing a patch-based ISODATA classification method, the sliding window technology, and the temporal-weight concept. Time-series ETM+ and MODIS surface reflectance are used as test data for comparing the two algorithms. Results show that the prediction ability of the ESTDFM algorithm has been significantly improved, and is even more satisfactory in the near-infrared band (the contrasting average absolute difference [AAD]: 0.0167 vs. 0.0265). The enhanced algorithm will support subsequent research on monitoring land surface dynamic changes at finer scales. Full article

Maximal light use efficiency (LUE) is an important ecological index of a vegetation essential attribute, and a key parameter of the LUE-based model for estimating large-scale vegetation productivity by remote sensing technology. However, although currently used in different models there still exists extensive controversy. This paper takes the Zoige Plateau in China as a case area to develop a new approach for estimating the maximal LUEs for different vegetation. Based on an existing land cover map and MODIS NDVI product, the linear unmixing method with a moving window was adopted to estimate the time-series NDVI for different end members in a MODIS NDVI pixel; then Particle Swarm Optimizer (PSO) was applied to search for the optimization of LUE retrievals through the CASA (Carnegie-Ames-Stanford Approach) model combined with time-series NDVI and ground measurements. The derived maximal LUEs present significant differences among various vegetation types. These are 0.669 gC·MJ⁻¹, 0.450 gC·MJ⁻¹ and 0.126 gC·MJ⁻¹ for the xerophilous grasslands with high, moderate and low vegetation fraction respectively, 0.192 gC·MJ⁻¹ for the hygrophilous grasslands, and 0.125 gC·MJ⁻¹ for the helobious grasslands. The field validation shows that the estimated net primary productivity (NPP) by the derived maximal LUE is closely related to the ground references, with R² of 0.8698 and root-mean-square error (RMSE) of 59.37 gC·m⁻²·a⁻¹. This indicates that the default set in the CASA model is not suitable for NPP estimation for the regional mountain area. The derived maximal LUEs can significantly improve the capability of NPP mapping, and open up the perspective for long-term monitoring of vegetation ecological health and ecosystem productivity by combining the LUE-based model with remote sensing observations. Full article

The pattern of vegetation change in response to global change still remains a controversial issue. A Normalized Difference Vegetation Index (NDVI) dataset compiled by the Global Inventory Modeling and Mapping Studies (GIMMS) was used for analysis. For the period 1982–2006, GIMMS-NDVI analysis indicated that monthly NDVI changes show homogenous trends in middle and high latitude areas in the northern hemisphere and within, or near, the Tropic of Cancer and Capricorn; with obvious spatio-temporal heterogeneity on a global scale over the past two decades. The former areas featured increasing vegetation activity during growth seasons, and the latter areas experienced an even greater amplitude in places where precipitation is adequate. The discussion suggests that one should be cautious of using the NDVI time-series to analyze local vegetation dynamics because of its coarse resolution and uncertainties. Full article