Artificial forest (AF) is an effective means of human intervention in forest ecosystems, aiming at preventing issues, such as soil erosion and land desertification. However, owing to the characteristics of large-scale afforestation projects, which often involve vast spatial extents and extended temporal scales, AF usually exhibits complex distribution patterns. In such cases, traditional remote sensing methods usually fail to accurately monitor AF conditions. To address this issue, this study introduced deep learning (DL) algorithms to extract multilevel features from remote sensing images for AF mapping and employed image processing techniques to enhance AF boundary determination. Through integrating these two approaches, high-resolution mapping of AF parcels was generated for a typical region in the middle reach valley of the Yarlung Tsangpo River. In the validation phase, the extracted regions were compared with manually labeled datasets and three accuracy metrics were calculated to demonstrate the extraction performance of the model. The accuracy reached 90.12% with the intersection over union (IoU) of 88.42%, and the cross-entropy loss function is only 0.0218. Meanwhile, three sampling areas with different coverages were selected for comparison, and the extractions have better performance than the SAM model based on the comparison with the samples. The findings reveal that this method can segment each AF parcel into independent objects, and the results would be helpful for parcel-based researches.

As a key driving factor of land-atmosphere system, land surface temperature (LST) is widely applied in geoscience studies across various fields. Among numerous LST retrieval methods, the Split-Window (SW) algorithm has been widely used because of its advantage of free of atmospheric profile data. However, some satellites provide only one single available thermal infrared (TIR) channel, which limits the direct application of the SW algorithm. To overcome this shortcoming, this study takes Landsat-8 as an example, whose TIR channel-11 is affected by degraded calibration accuracy caused by stray light, and develops a method to construct a virtual channel using MODIS TIR data, enabling the application of the SW algorithm to Landsat-8 data for LST retrieval. During the construction, the angular normalization is adopted to the MODIS TIR data in advance. The validation results derived from the simulated dataset shows that the RMSE of LST retrieval based the virtual channel using the SW method is less than 1.2 K. Further validation with ground-based measurements from the FPK station results in an RMSE of 2.44 K, demonstrating better accuracy than the result from single channel algorithm. Moreover, the angular normalization applied to MODIS data leads to an improvement of 0.36 K in LST retrieval accuracy. The results demonstrate the advantages of LST retrieval from Landsat-8 data with virtual channel and extend the applicability of the SW algorithm.

Currently, the poor spatial resolution (10-50 km) and accuracy of satellite-based precipitation products limit their applications at regional scales. To overcome these issues, a hybrid downscaling framework, named soil moisture-based precipitation downscaling and merging methods (SMPD-MERG), that merging soil moisture-based precipitation downscaling results with European Space Agency (ESA) Climate Change Initiative (CCI) soil moisture product and multi-source data from rain gauge measurements and European Center for Medium-Range Weather Forecasts ERA5-Land precipitation data with random forest model was proposed to derive high-resolution and high-accuracy precipitation data at daily scale. The method was successfully applied to the Global Precipitation Measurement (GPM) daily precipitation product and improved its spatial resolution from 10 km to 1 km in the central part of the Iberia Peninsula during 2016-2018. The validation with field measurements revealed that the proposed method has good performance with correlation coefficient (CC), relative bias (BIAS), root mean square error (RMSE), and the modified Kling-gupta efficiency (KGE’) values of 0.94, 1.00%, 1.27 mm, and 0.88, respectively. Meanwhile, the intercomparison with other downscaling algorithms including geographically weighted regression and interpolation methods, highlights the significant advantages of the proposed method. It improves the CC from around 0.60 to over 0.90, reducing the RMSE to below 1.30 mm, and decreasing BIAS by nearly an order of magnitude. In general, different from previous empirical downscaling methods, the proposed method not only considers the physical dynamics of the precipitation process but also well integrates the advantage of multi-source data. According to the satisfactory downscaling accuracy, this method shows good potential for producing high-quality precipitation data with high spatiotemporal resolution.

Because of the primary role of land surface temperature (LST) in the physical processes of surface energy balance at local through global scales, dynamic, continuous, and seamless LST monitoring is constantly in urgent need. Thermal infrared (TIR) remote sensing serves as the most commonly used sources for LST retrieval owing to its relatively fine spatial-temporal resolution and presentable accuracy. However, limited by the inability to penetrate clouds, original TIR LST data suffers significantly from data missing problems. Furthermore, the view time of pixels along the scan line differs significantly for polar-orbiting satellites, exerting appreciable influence on the subsequent data applications. To cope with the above setbacks simultaneously, we proposed a practical reconstruction framework based on the inner physical connection between LST and solar radiation, which was accurately expressed by random forest regression model, with the consideration of various auxiliary environmental factors (i.e., elevation, slope, longitude, latitude, and surface reflectance). Taking the Tibetan Plateau (TP) as the study area, the proposed method was applied to generate spatially seamless and time-consistent LST products with the use of the Moderate Resolution Imaging Spectroradiometer (MODIS) Terra daytime LST product. From visual assessment, the reconstructed product exhibits ideal spatial-temporal continuity within the TP. Through the validation with in-situ observations from five different stations, the results show a higher consistency with ground measurements than the LST product from the Global Land Data Assimilation System (GLDAS) and other all-weather LST product, with an average improvement on RMSE of 1.06 K and 1.59 K under clear conditions, and 1.86 K and 2.72 K under cloudy conditions. The validation demonstrates that the proposed method is well applicable for all-weather LST reconstruction over a large-scale area with significant surface heterogeneity, which also shows good ability to remove the temporal inconsistency induced by satellite observations. Additionally, it can be reliably generalized to different areas with similar data requirements for its sufficient effectiveness and flexibility.

Remotely sensed (RS) vegetation indices (VIs) are increasingly being employed as a direct proxy for gross primary productivity (GPP). When estimating mountain vegetation GPP from VI, efforts often focus on the RS-related topographic effect (i.e., distort VIs), while the micrometeorology-related topographic effect is so far ignored. Here, a topographically adjusted VI (TAVI) scheme was developed based on removing the RS-related effect by path length correction (PLC) first and integrating the micrometeorology-related effect associated with the topography-induced redistributions of radiation and water subsequently. The proposed TAVI scheme was applied to three VIs, namely, normalized difference VI (NDVI), enhanced VI (EVI), and near-infrared reflectance of vegetation (NIRv), at 14 eddy covariance (EC) sites. The determination coefficient (${R}^{2}$) and root-mean-square-error (RMSE) between VI-estimated and EC GPP were used for evaluation. Results showed that both EVI and NIRv outperformed NDVI in GPP estimation before correction, with${R}^{2}$increased by 0.14–0.15 and RMSE decreased by 0.42–0.44 gC$\cdot \text{m}^{-2}\cdot $day−1. After correcting the RS-related topographic effect, EVI and NIRv achieved an obvious improvement (${R}^{2}$= 0.71 and RMSE = 2.00 gC$\cdot \text{m}^{-2}\cdot $day−1), while NDVI showed little sensitivity to topography. Subsequently, EVI and NIRv showed a notable improvement (${R}^{2}$= ~0.77 and RMSE = ~1.82 gC$\cdot \text{m}^{-2}\cdot $day−1) after integrating the micrometeorology-related topographic effect, and the performance of NDVI was also improved (${R}^{2}$= 0.73 and RMSE = 1.94 gC$\cdot \text{m}^{-2}\cdot $day−1). This study suggests that integrating the micrometeorology-related topographic effect on vegetation photosynthesis into topographically corrected VIs (TCVIs) is an effective way to improve mountain vegetation GPP estimation.

Soil moisture (SM) plays a significant role in many natural and anthropogenic systems. Thus, accurate assessment of changes in SM globally is of great value, including long-term historical assessment. The European Space Agency established the Climate Change Initiative (CCI) program to produce long time-series surface SM datasets starting from 1978 to the present. However, the Soil Moisture Active Passive (SMAP) mission, launched in 2015, has shown more satisfactory performance in both spatial accuracy and in capturing the pattern of temporal changes. In this paper, a random forest (RF) model was proposed to extend the SMAP dataset historically (named Hist_SMAP), using the corresponding CCI SM time-series. We assumed that the temporal changes in the SMAP SM dataset are similar generally to those in the available CCI dataset. Accordingly, the RF model was constructed using the temporal (extracted from the CCI SM data), coupled with terrain and location characteristics, and migrated to predict the Hist_SMAP dataset. The availablein-situand the real SMAP data were used as references for validation. Compared with the CCI dataset, the predicted Hist_SMAP dataset is closer to thein-situSM data and the real SMAP data. Moreover, the historical Hist_SMAP dataset is more accurate than the widely used Global Land Evaporation Amsterdam Model (GLEAM) dataset. Thus, the Hist_SMAP dataset was shown to be a reliable substitute for the historical CCI dataset. The new long time-series Hist_SMAP dataset is provided with free access and will be of great value for research and practical application in a range of fields.

In the face of rapid global climate change and increasing occurrence of extreme weather events, acquiring seamless land surface temperature (LST) with high spatial and temporal resolution on a global scale has become increasingly crucial. However, the limited ability of Thermal Infrared (TIR) Remote Sensing to penetrate cloud cover has hindered the widespread application of TIR LST datasets. To address this limitation, we propose a novel reconstruction approach for cloud-covered pixels, which is established based on the annual surface temperature cycle. It shifted previous reconstruction from directly modelling LST to indirectly modelling the residual term derived from the LST observations and the annual surface temperature cycle (ATC) model fitted values. A random forest regression was used to build this estimation model and the model was applied to cloud-covered pixels to derive their LSTs. Taking the Iberia Peninsula as the study area, the proposed method was applied to generate the all-weather LST product of whole year 2021. The visual assessment demonstrates its robust performance across different seasons and weather conditions. Additionally, through the validation with the masked clear-sky LST observations, it reveals that the proposed method achieves a stable estimation accuracy, with the average value of the coefficient of determination (R2) and Root Mean Squared Error (RMSE) of above 0.8 and 1.08 K under different climatic conditions. In comparison, the validation with the ERA-5 land reanalysis data also indicates a relatively good consistency between the performance of the reconstructed LST and the clear-sky LST, although with a slight decline in R2and RMSE. Additionally, the indirect validation with near surface air temperature (NSAT) also shows the comparable ability of the reconstructed LST in NSAT estimation as the clear-sky LST, with an increase of RMSE no more than 0.95 K. In general, the proposed method shows good potentials in reconstructing cloud-covered LSTs with relatively stable performance under different cloud cover conditions and it can be applied for generating all-weather LST product.

In regions with complex terrain, high-spatial-resolution downward surface shortwave radiation (DSSR) is critical for monitoring mountain ecological processes and for environmental management. However, currently available DSSR products are often too coarse (from a kilometer to tens of kilometers) to capture the spatial heterogeneity of DSSR in topographically complex regions. To address this issue, this study proposes a new downscaling method for all-sky instantaneous DSSR, employing a machine learning (ML) method, top-of-atmosphere reflectance, and topographic data. The method is used to downscale the 5-km Himawari-8 (H-8) DSSR product to the Sentinel 10 m scale. A region of Southwest China was chosen as a case study. Validated by field measurements from nine stations in 2020, the downscaled DSSR showed improvements in the mean bias error (MBE), mean absolute error (MAE), and root-mean-square error (RMSE) of 32.74%, 9.31%, and 6.34%, respectively, when compared with the original product. The downscaled DSSR can be generated in all-sky conditions. In general, this method successfully captures high-resolution DSSR over complex terrain and should be helpful for related studies.

Accurate estimates of downward surface shortwave radiation (DSSR) are critical for hydrological, biogeochemical, and ecological studies and remote sensing-based estimation of DSSR is an important way to derive DSSR at different spatio-temporal ranges. However, current estimation algorithms usually somewhat rely on atmospheric parameters or in-situ measurements, further blocking the application of these methods. Inspired by the emerging DSSR reanalysis data from the model simulation, this study proposed an integrated method by initializing the estimation model with ERA5 reanalysis data and further refining the estimation through iterative training. The random forest regression method was applied in the estimation model to build the connection between DSSR with the MODIS top-of-atmosphere reflectance, cloud flag, geometry information, elevation, latitude, and coefficient of Sun-Earth distance as input features. To separately consider the impact from cloud cover, the estimation model was established for clear-sky and cloudy-sky conditions, respectively. The proposed method was applied to estimate instantaneous DSSR of MODIS daytime overpasses in the Southwest part of China in 2020. Comparison between the estimates of the initialized model and the finalized model shows that the iterative process improves the DSSR estimates on both spatial distribution and accuracy. Validated by the measurements from nine sites in the study area, the DSSR estimates of the finalized model show a 0.02 higher correlation coefficient (CC) and 7.35 W m-2lower root mean squared error (RMSE) than that of the initialized model. To better evaluate the performance of the proposed method, three popular DSSR products including ERA5, MCD18A1, and Himawari-8 were introduced to make an inter-comparison with the estimation of this study. The validation results showed that the all-sky DSSR estimated in this study had the best accuracy, with a CC of 0.90, a mean bias error of 37.80 W m-2, a RMSE of 125.30 W m-2, and a relative root mean squared error of 42.73%. Obvious improvements can be observed under cloudy-sky and clear-sky conditions, respectively. Because of the simplicity and reliable performance of the proposed method, it shows good potential for DSSR estimation.

Land surface temperature (LST) is a crucial parameter in the study of Land Surface processes. Currently, there are great progresses in LST retrieval based on thermal infrared (TIR) remote sensing. However, TIR-based LST suffers from serious spatial discontinuities due to clouds. Although there are methods developed to address this issue, the methods show high uncertainty in days with extremely clouds. Therefore, this study proposed an integrated method to reconstruct cloudy LSTs using Terra Moderate Resolution Imaging Spectroradiometer (MODIS) and the China Land Data Assimilation System (CLDAS) LST. This method was separated into two parts according to the ratio of clear-sky pixels (RCP). On days with RCP more than 30%, a random forest reconstruction method was used to establish the complicated relationship between LST and its predicting variables, including solar radiation factor, vegetation index, water index, topographic information and latitude, and then applied to cloudy pixels to derive LSTs. For the rest days, the CLDAS LST was selected to assist the reconstruction via downscaling it to 1 km and then merged with clear-sky data to generate spatially continuous results. The proposed method was applied to the Southwest China and generate daily LST product in 2019. Validation with ground measurements demonstrated a high accuracy with the correlation coefficient changing from 0.73 to 0.88. Additionally, the reconstructed LST dataset exhibits similar temporal variability as existing all-weather satellite-based and reanalysis LST products. The findings reveal that this method shows good potential in generating gap-free LST dataset, especially for the mountain regions with heavy clouds.

Downward shortwave radiation (DSR) is an essential parameter in land surface energy budget. However, current DSR products are mainly generated at coarse-resolution scales (more than 5 km) and fail to accurately depict DSR distribution over different topographic and land cover conditions. Meanwhile, the existence of frequent cloud cover constrains the high-resolution DSR estimation. To overcome the above issues, a novel spatial downscaling method for high-resolution DSR estimation was proposed in this study by incorporating coarse-resolution Meteosat Second Generation (MSG) DSR product and Landsat-8 observations. Through decomposing the downscaling scheme into three separate models: fully cloudy, partial cloudy, and cloud-free, the 3 km MSG DSR data was spatially downscaled to 30 m scale under all-sky conditions, based on the assumption of scale-invariant of the models established at 3 km scale. An empirical model for DSR estimation under cloud cover condition was constructed between the top of atmosphere radiance from Landsat-8 and MSG DSR. The downscaled results showed reasonable DSR values under different cloud cover conditions and the spatial heterogeneity of the downscaled DSR was also well depicted with the variation of surface topography. Meanwhile, the validation within-situmeasurements also revealed the significant improvement in terms of the coefficient of determination (R2) (from 0.53 to 0.79) and the root mean squared error (RMSE) (from 198.5 to 140.41 W/m2). In general, the proposed downscaling method in this study show good potential for high-resolution DSR estimation without regard to the atmospheric information required in traditional DSR estimation under all-sky condition.

The spatial downscaling of soil moisture (SM) provides a technical tool to solve the problem of coarse resolution of passive microwave products. However, conventional methods are developed based on the kilometer scale grid pixels of remote sensing images. The regular rough grids will lead to the mixing and uncertainty of SM information. In this paper, we formulate a novel land geoparcel-based spatial downscaling technique for the Soil Moisture Active Passive (SMAP) satellite products. It is developed by combining XGBoost (eXtreme Gradient Boosting) machine learning algorithm with the support of geoparcel vector data and a variety of auxiliary raster data. The downscaling effect is evaluated by using SMAP 9km products and site measured data in Tongnan District of Chongqing, China. The experiments show that the geoparcel-based downscaling method maintains the dynamic range of the original SM product, and conserves energy before and after downscaling. It is proved that our method effectively increases the spatial details of the original SM product with complete spatial coverage. The comparison and analysis with the ground verification data demonstrate that the formalized procedure with geoparcel-based spatial downscaling allows better results than those of using km-scale regular grids.

Land surface temperature (LST) is an important parameter in land surface processes with strong relationship between surface energy and water exchange. To effectively capture the surface thermal dynamics, the annual temperature cycle model is a good option by depicting the annual variation as a constant term plus a sine function. However, this type model suffers from the assumption of constant surface thermal property which is hardly satisfied due to the changes in vegetation cover. To well address this issue, the normalized difference vegetation index (NDVI) is introduced as an indicator to characterize the variation in surface thermal property and added to the original form to propose an improved version. Through comparison between the fitting effects of the proposed model with the original one, the improvement shows good performance in suppressing the annual maximum temperature and elevating the annual minimum temperature with the increase in vegetation cover. The difference in the annual maximum and minimum temperature between the estimates from the proposed model and the original model shows good linear regression with NDVI difference when compared with the annual mean value, with the speed of −3.22 and 4.84, respectively. In addition, the fitting accuracy is also improved with a slight increase in the coefficient of determination (0.002) and a decrease in the root mean squared error (0.018 K). The application of the proposed model also provides reasonable distribution of the annual temperature parameters in the southwest of Europe and part of North Africa, confirming its potential effect in thermal dynamic monitoring.

The near-infrared reflectance of vegetation (NIRv) has been increasingly used as a proxy of gross primary production (GPP) across various temporal scales, ecosystems, and climate conditions. However, topography significantly distorts NIRv and GPP estimations over mountainous areas. We evaluated the topographic effects on NIRv and applied a path length correction (PLC) for improving its performance over mountainous areas. The proposed topographically corrected NIRv (referred to TCNIRv) was evaluated by multiple Landsat-8 operational land imager (OLI) images with concurrent${ in}~{ situ}$GPP measurements over the Lägeren mountainous forest area. TCNIRv reduced topographic effects in the original NIRv and it was comparable to the normalized difference vegetation index (NDVI) and the green normalized difference vegetation index (GNDVI), which are often deemed to be independent of topographic effects. In addition, TCNIRv better agreed with GPP than the other vegetation indices (VIs): coefficient of determination$R^{2} $= 0.90 and root mean square error RMSE = 1.40$\text{g}\cdot $Cm$^{-2} \cdot \text{d}$−1for TCNIRv compared to$R^{2} $= 0.71 and RMSE = 2.47$\text{g}\cdot $Cm−2$\cdot \text{d}$−1for NIRv. The evaluation shows that TCNIRv is a reliable proxy of GPP, and because of its simplicity and physical soundness, it will facilitate vegetation monitoring over complex topography mountainous areas.

Spatiotemporally continuous monitoring of aboveground biomass (AGB), an important indicator of grassland productivity, is crucial for achieving sustainable grassland development. Most existing grassland AGB estimation methods are empirical, and their temporally and spatially specific nature hinders operational application at large scales. Grass is herbaceous, so its AGB can be represented as the product of leaf area index (LAI) and dry matter content ($C_{m}$), both are the inputs of PROSAIL model. We, therefore, proposed a novel physical-based method through PROSAIL model inversion. Results showed that the estimated AGB presented good consistency with field-measured one, with$R^{2}= 0.87$and RMSE = 14.29 g/m2. We then implemented our method on the Google Earth Engine platform and generated daily and monthly AGB products covering the Tibetan Plateau (TP) and spanning from 2000 to 2021. These products characterized the spatiotemporally continuous dynamics of AGB on the TP. For example, it captured the decrease in dry matter caused by grazing during grassland dormancy, which is impossible for other existing AGB retrieval methods. Our method provides a promising tool to generate spatiotemporally continuous grassland AGB, which would inform the decision making for the conservation and restoration of grassland.

The complex terrain in mountainous areas distorts solar illumination, which brings a strong topographic effect on optical remote-sensing observations. Although many efforts have been done to correct this effect via normalizing solar illumination induced differences, there are still high uncertainty, especially for poor illuminated surfaces. In this study, a downwelling shortwave radiation (DSR)-based correction (DSRC) method was proposed. The topographic effects were accounted by normalizing DSR differences at different topographic conditions, and a stratified correction strategy was applied by separating the image into different groups according to normalized difference vegetation index (NDVI) to consider the spectral differences of different land-cover types. The DSRC method was applied to nine Landsat 8 scenes with high-resolution DSR data acquired by downscaling the Meteosat Second Generation (MSG) DSR product. The performance analysis indicates that the correlation coefficient between the corrected surface reflectance and illumination conditions notably decreased. Compared with SCS + C, empirical rotation, Statistical-Empirical, and Modified Minnaert methods, the DSRC method well retains inherent spectral pattern and provides good advantages in normalizing the aspect differences of surface reflectance. Furthermore, the comparison of NDVI values before and after correction indicated that DSRC preserved the original values and successfully corrected the overestimated NDVI values of poor illuminated surfaces. The corrected NDVI time series provide more reasonable cycle of the phenology of vegetated surfaces than the original series. In summary, the DSRC method has a strong potential for reducing topographic effects that currently limit the applications of remotely sensed data in mountainous areas.

Accurate estimation of land surface temperature (LST) is crucial for ecological environment monitoring and climate change studies in mountainous areas. The current LST retrieval algorithms were developed without accounting for the topographic effect, which can only be used to retrieve LST over relatively flat surfaces. Due to the impact of 3-D structure of mountainous surfaces, rugged terrain makes the processes of thermal radiation more complex. In this study, a radiative transfer equation (RTE)-based single-channel algorithm was proposed to retrieve LST with topographic effect correction from the Landsat 8 thermal infrared (TIR) data in mountainous areas. This algorithm accounts for the changes in the thermal radiation components in the TIR RTE caused by the topographic effect. According to the analysis of simulation data, sky-view factor (SVF), atmospheric water vapor content, surface emissivity of target pixel, and average LST of the surrounding terrain have significant influence on the magnitude of the topographic effect. The differences between the LST retrieved without/with topographic effect correction from the Landsat 8 TIR data are related to SVF. The topographic effect should be taken into account in the LST retrieval algorithm when SVF is smaller than 0.7. The largest LST difference of approximately 1 K occurs in the deep valley. The results indicate that LST without topographic effect correction could be overestimated to be as high as 1 K. Due to a lack ofin situLST measurements, the performance of the LST retrieval algorithm in mountainous areas was only evaluated by comparing the brightness temperature (BT) at the top of the atmosphere (TOA) simulated by the DART+MODTRAN model and the TIR RTE over mountainous surfaces at three subregions. There is a good consistency between BT at the TOA simulated by the DART+MODTRAN model and the TIR RTE over mountainous surfaces at the three subregions, with a root-mean-squared error (RMSE) of less than 0.23 K.

Based on time series of MODIS/Terra daily LST product from 2000 to 2018, the spatial and temporal variations in the land surface temperature (LST) of the Tibetan Plateau was analyzed in this study. Due to the influence of cloud cover, it is not possible to obtain daily LST with full spatial coverage. Therefore, the annual temperature cycle (ATC) model was used to extract the mean annual surface temperature (MAST) during the daytime and nighttime to reflect changes in LST. The average value of the MASTs from 2000 to 2018 showed a cross-distribution characteristic, and there are significant differences between daytime and nighttime. Combining the elevation map and the land cover map, it was found that terrain and land cover strongly affected the spatial distribution in LST. According to the interannual change rate of the estimated MAST over the 19 years, it revealed that the average change rate at the nighttime was higher than that at the daytime. The trend analysis based on linear regression also indicated that the LSTs of both daytime and nighttime have an increasing trend.

To reduce the topographic influence on the high-resolution optical remote sensing data, a radiation based topographic correction method was developed in this study by normalizing the solar illumination differences in mountain areas with the use of high-resolution downward shortwave radiation data (DSR). The coarse-resolution Meteosat Second Generation (MSG) SEVIRI DSR product was downscaled to the same spatial scale as the Landsat 8/OLI data to obtain the high-resolution DSR. The correction results indicated that the corrected spectral reflectance is poorer correlated with solar illumination than the original one. In addition, the corrected images also maintain the spectral characteristics with few overcorrections. Therefore, the proposed method will be of good potential in mountain remote sensing data process to reduce the topographic impacts.

Downward shortwave radiation (DSR) plays a very important role in land surface radiation budget and land-surface processes modeling. Although there are several radiation products developed based on satellite observations, the coarse spatial resolution greatly limits their applications in regional or local scales. To get high-resolution and accuracy-reliable DSR data, a practical downscaling method for clear-sky condition was proposed by using the scale-invariant relationship of the radiative transfer process to decompose the global radiation into direct and diffuse components at horizontal level and conducting topographic correction finally. Based on this method, the time series of Meteosat Second Generation (MSG) DSR product covering part of Navarre province in the northern Spain was disaggregated into 30-m level with the use of the ALOS World 3D-30m digital elevation model (DEM) data. The downscaled results not only presented high spatial heterogeneity with respect to the changes in surface topography but also showed reasonable values at different times over different days in one year. The in situ validation indicated that the hourly downscaled DSR had quite high correlation with the surface measurements at each day with the coefficient of determination above 0.97 and the root-mean-squared error lower than 35 W/m2. Overall, the evaluation allows concluding on the proposed method that can be a good way to get reliable and high-resolution DSR data from coarse-resolution DSR product under clear-sky condition.

Many remote sensing soil moisture (SM) products have been developed with global coverage. However, most of them are derived from passive microwave observations with very coarse resolution, greatly constraining the applications at regional scales. To increase the spatial resolution, a downscaling method is developed to downscale the 36-km Soil Moisture Active Passive L3 SM (SMAP SM) product to 1 km using the Moderate Resolution Imaging Spectroradiometer (MODIS) products (8-d land surface temperature, LST, and 16-d normalized difference vegetation index, NDVI). In this method, a linking model is first established between SM and LST and NDVI, and a self-adaptive window method is applied with the use of the geographically weighted regression (GWR) method to obtain an optimal local regression. Then, the uncertainty of the linking model, expressed as the regression residual, is redistributed to fine-resolution pixels to analyze the consistency before and after downscaling. The method was applied to the Iberian Peninsula to produce the 8-d downscaled SM product in 2016. The downscaled SM was validated with the in-situ SM network (REMEDHUS). A good agreement was found between the two data sets, with a correlation coefficient (R) of 0.87 and an unbiased root-mean-squared error (ubRMSE) of 0.043 m3/m3at a network level. At station level, the R is larger than 0.6 for all the REMEDHUS stations, with an ubRMSE smaller than 0.06 m3/m3. The evaluation indicates the good potential of the proposed method in the SM downscaling, which achieves a robust consistency and provides rich spatial information while maintaining good accuracy.

The in situ measurement of the leaf area index (LAI) from gap fraction is often affected by terrain slope. Path length correction (PLC) is commonly used to mitigate the topographic effect on the LAI measurements. However, the terrain-induced uncertainty and the accuracy improvement of the PLC for LAI measurements have not been systematically analyzed, hindering the establishment of an appropriate protocol for LAI measurements over mountainous regions. In this article, the above knowledge gap was filled using a computer simulation framework, which enables the estimated LAI before and after PLC to be benchmarked against the known and precise model truth. The simulation was achieved by using CANOPIX software and a dedicatedly designed ray-tracing method for continuous and discrete canopies, respectively. Simulations show that the slope distorts the angular pattern of the gap fraction, i.e., increasing the gap fraction in the down-slope direction and reducing it in the up-slope direction. The horizontally equivalent hemispheric gap fraction from the PLC can reconstruct the azimuthally symmetric angular pattern of the real horizontal surface. The azimuthally averaged gap fraction for sloping terrain can both be underestimated or overestimated depending on the LAI and can be successfully corrected through PLC. The topography-induced uncertainty in LAI measurements is found to be ~14.3% and >20% for continuous and discrete canopies, respectively. This uncertainty can be, respectively, reduced to ~1.8% and <; 7.3% after PLC, meeting the up-to-date uncertainty threshold of 15% established by the Global Climate Observing System (GCOS). Closer analysis shows that the topographic effect is influenced by fractional crown cover, and the largest uncertainty which corresponds to extensively clumping canopy can reach nearly up to 50%. The accuracy of the estimated LAI after PLC safely meets the GCOS uncertainty threshold even for this extreme case. This study demonstrates the necessity of a topographic correction for LAI measurements and the applicability of PLC for reconstructing the horizontally equivalent gap fraction and improving the LAI measurements over sloping terrains. The results of this article throw light on the design of a protocol for LAI measurements over mountainous regions.

Topographic correction is a prerequisite for generating radiometrically consistent Landsat 8 OLI vegetation reflectances in support of temporally continuous and spatially mosaicked applications. Path length correction (PLC) is a physically solid topographic correction method that avoids the involvement of any empirical parameter and is therefore suitable for reproducing the inherent reflectance of vegetation. This article compared two different implementation pathways of PLC, i.e., the explicit method (EM) and the implicit method (IM), which are based on the numerical inverse and analytical approximation of the PLC model, respectively. The results show that both EM and IM can obviously reduce the topographic effects on Landsat 8 OLI vegetation reflectances. EM performed slightly better than IM in eliminating the correlation between the topographic characteristics and the vegetation reflectances: the coefficient of determination between the green/red/near-infrared (Nir) band reflectance and the local illumination was reduced from 0.257/0.148/0.467 for the uncorrected (UNCORR) case to 0.016/0.004/0.012 and 0.027/0.014/0.094 for the EM and IM corrected results, respectively. The coefficient of variation of the three band reflectances across different aspects was reduced from 16.5%/18.5%/18.7% for the UNCORR case to 3.2%/1.8%/0.9% and 5.3%/7.1%/7.3% for the EM and IM corrected results, respectively. In addition, the intraclass reflectance variability was also reduced after both the EM and IM corrections. Nevertheless, due to the ill-posed nature of the numerical inverse process, EM cannot fully reproduce the inherent vegetation reflectances, and the reflectances after topographic correction overestimated the inherent vegetation values. In contrast, the IM can achieve an appropriate tradeoff between topographic effect elimination and vegetation inherent reflectance preservation. In addition, IM is computationally very efficient compared to EM: using an ordinary laptop, IM can finish the topographic correction for a Landsat OLI image within several seconds, while this would take more than 20 h for EM. This article highlights the potential of using IM for generating radiometrically consistent Landsat 8 OLI vegetation reflectances.

Soil moisture (SM) downscaling has become more and more crucial for assisting the application of the coarse-resolution SM product, such as climate change, sustainable development of agroforestry, efficient management of water resources, and monitoring of natural hazards. The main idea of downscaling methods lies on the help of fine-resolution auxiliary data, such as the widely used, land surface temperature (LST) and normalized difference vegetation index (NDVI). However, in the downscaling process, the ancillary data, especially for the daily LST, is strongly affected by cloud cover, resulting high frequency of blank areas in the final downscaled SM products. By contrast, the impact is usually omitted or paid less attention in current downscaling studies. To obtain the spatially continuous fine-resolution SM product, this study firstly introduced an annual temperature cycle (ATC) model to fill the gaps in daily Moderate Resolution Imaging Spectroradiometer (MODIS) LST product induced by cloud cover. Then the 36-km SM product from Soil Moisture Active Passive (SMAP) satellite mission was downscaled from 36-km to 1-km spatial resolution with the synergistic use of the filled LST and MODIS NDVI to estimate spatially continuous fine-resolution SM product.

To solve the limitation of coarse spatial resolution of passive microwave surface soil moisture (SSM) product, many spatial downscaling methods has been proposed under the theoretic basis of the land surface temperature (LST)/vegetation index triangle space. However, in most studies, the topographic influences on the downscaling results is rarely analyzed but the impacts should be significant due to the strong effect of topographic changes on LST. To effectively solve this issue, a downscaling approach was developed in this study to disaggregate the Soil Moisture Active and Passive (SMAP) SSM product with the use of the Optical/Thermal infrared (TIR) observations from the Moderate-Resolution Imaging Spectro-radiometer (MODIS) onboard the Terra satellite for a typical mountain region located in the west of U.S. Two steps are included in the approach: (1) normalizing the terrain effect on LST and (2) downscaling passive SSM product based on a machine learning approach. The SNOTEL soil moisture observation network located in the study area was used to validate the downscaled SSM.

In mountainous areas, land surface temperature (LST) shows significant terrain effect, which can be directly reflected by the spatial distribution associated with the change of topographic factors (elevation, slope, and aspect). By the way, the terrain effect diminishes the impacts from the differences in surface water and heat fluxes, and influences their comparison or estimation over complex terrain. In this study, a practical way to reduce the terrain effect is proposed based on the random forest method with datasets from MODIS products, which is used to build a LST prediction model instead of the previous model developed based on some numerical model or empirical method. The results indicates that the constructed LST model shows a good performance in predicting LST with the R2 of 0.93 and the RMSE lower than 2.0 K for four selected days. Corrected LST maps are compared with the original LST map, which presents a preliminary correction results with an obvious correction on pixels with significant terrain effect.

Together with the continuous development of passive microwave surface soil moisture (SSM) products from newly launched satellites, it is necessary to perform reliable validations to assess their accuracy. With this aim, a new “bottom-up” validation approach is proposed based on the synergistic use of in situ SSM measurements from the soil moisture measurement station network (REMEDHUS) of the University of Salamanca, Salamanca, Spain, and optical/thermal infrared observations over 18 cloud-free days from Landsat-8. An SSM estimation method using the boundary information from the land surface temperature and normalized difference vegetation index triangle space was developed for regional SSM mapping. The retrieved SSM reached a relatively good performance (mean R2and rootmean-squared error of 0.64 and 0.033 m3/m3, respectively). Then, the regional SSM was aggregated into the grid-cell scale of the Advanced Microwave Scanning Radiometer 2 (AMSR2) L3 high-resolution (0.1° /10 km) soil moisture product to validate it both at network and grid-cell levels. At the network level, the derived regional SSM showed a good agreement with the averaged in situ measurements over the network (R = 0.731). However, at the grid-cell level, small variations were observed for the cells over the network between the estimates and the AMSR2 product, with negative biases (-0.044 to -0.090 m3/m3) and positive correlations (R > 0.25) for most cells. In addition, it is shown that the descending product has a slightly better performance than does the ascending one. The preliminary assessments suggested that the proposed method provides new insights into the validation of passive microwave soil moisture products, while avoiding the common issue related to the big disparity in spatial scales between satellite observation and in situ measurements.

GF-4 is the first Chinese high resolution geostationary orbit satellite. It has great application potential in many earth-related studies. Given the low geometric accuracy of GF-4 images in mountain area, in this paper, a new operational and practical automatic orthorectification approach was proposed to improve the orthorectification accuracy of GF-4 images. The new approach adopted a two-level area-based algorithm to automatically search tie points between GF-4 and the base Landsat images. Then the images was further orthorectifyed using the improved rational polynomial coefficients model optimized by tie points. Results demonstrated that the new orthorectification approach could significantly improve the orthorectification accuracy. It is also suitable for orthorectification of GF-4 images with different clouds coverage.

Ecosystems is survived in the suitable environment which provide appropriately abiotic resources for organism and IUCN have applied abiotic degradation as Criterion C to assess the risk of ecosystems. However, the origin and collapse status of the criterion is vague for assessors, and the results are inconsistent as the response of ecosystems to environment are different. Therefore, the ecological amplitude of ecosystem to environment stress was introduced in the ecosystems risk assessment with the relationship between criterion and ecological amplitude. To put this concept into practice, remote sensing was applied to acquire the optimum and tolerance of each ecosystem. The natural forest in xishuangbanna was assessed by this proposed method to identify the stress of temperature. The result show that the status of natural forest in the past is least concern (LC), and vulnerable (VU) in the feature. With temperature in the future significantly increasing, natural forest may be suffered with heat stress. The proposed method describe the risk derived from degradation of environment in mechanism greatly improved the consistency and feasibility of Criterion C in the ecosystems risk assessment.

Aiming to solve the limitation of coarse spatial resolution of passive microwave soil moisture product, a soil moisture relationship model based on random forest method was constructed with land surface temperature (LST), normalized difference vegetation index (NDVI), and surface albedo (ALB) from MODIS products and surface soil moisture (SSM) from AMSR-E soil moisture product in the study area at the east edge of the Tibetan Plateau. The results show better performance of the proposed compared with the commonly used purely-empirical method, with the R2values above 0.88 and the RMSE values lower than 0.05m3/m3, respectively. It suggested that the proposed soil moisture relationship is able to capture the spatio-temporal variation of surface moisture well. There should be great potential to improve the downscaling soil moisture accuracy when the model is used in the passive microwave soil moisture downscaling scheme.

In mountainous areas, surface solar radiation (SSR) exhibits high spatiotemporal variation at different slopes and aspects due to its great topographic relief. To get mountain SSR spatial distribution, remote sensing-based methods have been popularly used, which separate SSR into direct solar radiation, diffuse sky radiation, and adjacent terrain radiation. However, the methods are highly depended on the atmospheric and angular information derived from different data sources. To clearly address the uncertainties associated with the estimation, this letter conducted a preliminary comparison study by using different atmospheric transmittance models and digital elevation model (DEM) data to retrieve SSR in the Mt. Gongga region. The comparison results indicated that the uncertainty of the atmospheric constituent data greatly limited the performance of the physical atmospheric transmittance models. The resolution of DEM data also played an important role in SSR determination because of the determination of surface angular information. High-resolution (30-m) DEM data showed better performance than low one (90 m). In addition, the systematic underestimation of SSR estimation with the empirical model was significantly improved by using the averaging method with nearby pixel values. It indicated that the geometric errors of satellite image and DEM data should be considered in the estimation.

Sloping terrain induces distortion of canopy reflectance (CR), and the retrieval of biophysical variables from remote sensing data needs to account for topographic effects. We developed a 1-D model (the path length correction (PLC)based model) for simulating CR over sloping terrain. The effects of sloping terrain on single-order and diffuse scatterings are accounted for by PLC and modification of the fraction of incoming diffuse irradiance, respectively. The PLC model was validated via both Monte Carlo and remote sensing image simulations. The comparison with the Monte Carlo simulation revealed that the PLC model can capture the pattern of slopeinduced reflectance distortion with high accuracy (red band: R2= 0.88; root-mean-square error (RMSE) = 0.0045; relative RMSE (RRMSE) = 15%; near infrared response (NIR) band: R2= 0.79; RMSE = 0.041; RRMSE = 16%). The comparison of the PLC-simulated results with remote sensing observations acquired by the Landsat8-OLI sensor revealed an accuracy similar to that with the Monte Carlo simulation (red band: R2= 0.83; RMSE = 0.0053; RRMSE = 13%; NIR band: R2= 0.77; RMSE = 0.023; RRMSE = 8%). To further validate the PLC model, we used it to implement topographic normalization; the results showed a large reduction in topographic effects after normalization, which implied that the PLC model captures reflectance variations caused by terrain. The PLC model provides a promising tool to improve the simulation of CR and the retrieval of biophysical variables over mountainous regions.

Surface soil moisture (SSM) is an important parameter at the land-atmosphere interface. In past decades, passive microwave remote sensing offers a good opportunity for obtaining SSM on a global scale, and many downscaling methods have been proposed using the triangle-based empirical soil moisture relationship models to overcome the limitation of coarse spatial resolution of its SSM products for regional applications. This paper aimed to examine and compare the effectiveness of five typical triangle-based empirical soil moisture relationship models for estimating SSM with Landsat-5 data and in situ measurements from the Maqu network on the northeastern part of the Tibetan Plateau for nine cloud-free days. The results showed that the model that treats the SSM as a second-order polynomial with land surface temperature, vegetation indices (VIs), and surface albedo as inputs exhibited the best performance compared with the results of other models. The VI comparison indicated that the use of the normalized difference VI or the fractional vegetation cover in this model outperformed other VIs, with the root-mean-square deviation of approximately 0.055 m3/m3and the coefficient of determination ($\text{R}^{2}$ ) above 0.78 at the nine-day average level. In addition, a significant spatial scale effect of the model was also found through analyzing the model fitting results at different window sizes. The study provides important insight into the best empirical relationship models for capturing soil moisture dynamics. These models can support the passive microwave soil moisture data spatial downscaling and validation applications in future studies.

Surface soil moisture (SSM) is a key state variable in controlling land surface energy balance and hydrological process. Based on the mechanism behind land surface temperature (LST)-vegetation index (VI) triangle space, an empirical relationship model has been proposed for SSM estimation with LST, NDVI, and surface albedo, and it has been applied in downscaling the coarse resolution microwave soil moisture product. In this paper, three soil moisture observation networks (REMEDHUS, MAQU, and MURRUMBIDGEE) were selected to evaluate the performance of this model at different climate and land cover conditions with in situ soil moisture measurements and Landsat satellite observations. According to the estimation results from different days for each network, it was found that the model was able to capture SSM variation with a satisfied accuracy [overall root-mean-squared error (RMSE) ranging from 0.025 to 0.055 m3/m3], and the R2can reach 0.9 on some individual days. However, the performance has high daily variability with some poor ones. The reason is partly attributed to the high sensitivity of the coefficients of the model to the variation degrees of the input LST, normalized difference vegetation index (NDVI), and SSM. Meanwhile, the spatial scale differences between the point measurement and satellite footprint observation are another important issue. To improve the model performance, a new relationship model was proposed by introducing the modified normalized difference water index, and the estimation results had a pronounced improvement (overall RMSE ranging from 0.021 to 0.049 m3/m3) compared with the previous model. The application effect of the proposed model showed that the model coefficient calibration accuracy greatly determined the uncertainty level of the estimation results.

Fractional vegetation cover (FVC) is one of the most critical indicators for herbaceous wetland vegetation status, degradation and desertification process simulations. The dense in time series and high spatial resolution of FVC is required for wetland ecosystem monitoring because of its heterogeneous landscapes and rapid spatio-temporal variations. However, due to the tradeoff in satellite sensor designs, it is hard to acquire both high temporal and spatial satellite images for FVC estimation for wetland ecosystem. In this paper, the dense in time series HJ composites at 30-m spatial resolution and UAV platform were used for the estimation of time series FVC in Zoige wetland area. Considering the spatial variability of soil backgrounds for peat wetland area, an improved adaptive endmember selection linear spectral mixture (LSMM) model was proposed in this paper. The results revealed that the proposed method can provide a higher estimation accuracy than the background invariant LSMM model, and the time series FVC estimation result is helpful to reflect both the spatial pattern and temporal variation characteristics of heterogeneous wetland regions.

Mapping and modeling the complex ecosystems and their changes over time are key issues in spatial ecology, biogeography, ecosystem ecology and biodiversity researches. This paper attempts to propose a simple, practical and automatic method to produce the ecosystem map in mountainous areas by fusing multi-source data and the related knowledge. The multi-source data included the 30m-resolution land cover map and the vegetation map of China (1:1 000 000). Three fusion strategies were contained in the proposed approach: hard matching, buffer matching and merged categories matching. Meanwhile, the related spatial distribution knowledge and the law of spatial distance decay were used to determine the optimal vegetation type, when more than two vegetation types are matched simultaneously. Taking the Southwestern China as study area, a new 30m-resolution ecosystem map with 144 ecosystem types was generated by the proposed method, which was used to establish the red list of ecosystems and evaluate the condition of biodiversity in the White Paper: China's Biodiversity.

Based on analyzing the influence of atmospheric water vapor content (WVC) on coefficients determination in the generalized split-window (GSW) algorithm, it is found that the coefficients are relatively monotonic variable with the increasing of WVC, which were proposed to determine the coefficients as implicit linear functions. To improve the land surface temperature (LST) retrieval accuracy in the GSW algorithm, the WVC is proposed to determine the coefficients as an explicit parameter in this work. The results show that the proposed method can acquire relatively high accurate LST if WVC is known. The root mean square errors (RMSEs) between the actual LST and those estimated with the proposed method are lower than those retrieved with the coefficients in the GSW algorithm.

How to accurately detect cloud and snow in the remote sensing imagery is an open problem for the remote sensing application. For only visible and near infrared band in HJ-1A/B CCD images, the cloud detection algorithm using the shortwave infrared and thermal infrared band is restricted by the band-lacking problem. Based on the multi-temporal information of the HJ-1A/B CCD images, a new algorithm is proposed in this paper. Using available images in one month, a cloud-free reference image was firstly composed. Then, the cloud and snow pixel are separated through the difference of the blue band between the reference and each date. Subsequently, the regional covariance matrix is computed further to eliminate the non-cloud pixels. The test result shows that, the overall accuracy is about 85.96% to 93%. It indicates that the proposed method can integrate the temporal and texture information to improve detection accuracy for the cloud and snow.

Based on the HANTS and dynamic threshold method, the spatio-temporal changes of the alpine grassland phenology in the Zoigê plateau was analyzed by using MODIS EVI data from 2001 to 2013. The results were found as follows: (1) The spatial distribution of the average vegetation phenology from 2001 to 2013 is closely related to the water and heat conditions. Accompanying the deterioration in heat and water conditions from low altitude to high altitude and south to north, SOG(start of growing season) was delayed gradually, EOG(end of growing season) advanced slowly, and LOG(long of growing season) shortened gradually. Water played an important role in the regional differentiation of phenology (2) From 2001 to 2013, SOG came earlier by 0.6d/a, EOG was late by 0.2d/a, and LOG lengthened by 0.8d/a. The inter-annual phenology changes of the vegetation exhibited significant differences at different elevations and water condition. (3) Heat and moisture is the main ecological factor influencing the growth of plant. Temperature responses of phenology significantly became stronger with increasing cumulative preseason precipitation.

Evapotranspiration (ET) is an important part of land surface water cycle and plays a key role in water resource management. Because of economy development and climate change, water resource shortage has become a looming crisis threatening the countries' security and social stability. Therefore, understanding the spatial and temporal pattern of ET change in South Asia has a significant impact. In this study, time series daily ET was estimated for South Asia in 2008 by using MODIS data combined with station observations and GLDAS data with SEBS energy balance model. Monthly ET estimation was calculated based on daily ET. Spatial and temporal analysis was conducted to analyze the daily ET temporal change for different land cover types located at different part of South. The results showed that the temporal change of rainfall has large impart on ET temporal variation. Quantification of ET in South Asia also suggested that the water availability is the major limitation to the evaporation and transpiration processes.

Recent years, the human activity and climate change greatly threaten the water resource security in South Asia. As an important parameter in land surface, evapotranspiration (ET) is essential to understand water cycle, estimate surface runoff and groundwater, and manage water resource. Satellite based ET derivation has been applied widely in many studies and become a popular way to estimate ET. In this study, time series ET in South Asia was derived with SEBS model with MODIS/Terra satellite data and field site observation data for the period March 2008-June 2008. Due to the lack of field flux observation, the derived ET was cross-validated by MOD16 ET product. The analysis results suggested that the proposed method was able to capture the reasonable spatial and temporal variation of ET more effectively than MOD16 product.

Land surface temperature (LST) and vegetation index or Fraction of Vegetation Cover (FVC) triangle space in regional scale has been demonstrated to be an effective way to monitor surface soil moisture condition. In this study, LST mid-morning rising rate from geostationary satellite data is applied instead of LST in the triangle space. A new soil water dryness index (Temperature Rate Vegetation Dryness Index, TRVDI) is presented from the LST mid-morning rising rate - FVC space to reflect surface soil moisture condition. Regional TRVDI is calculated over a region of the Iberian Peninsula using MSG SEVIRI data recorded on July 2006. The validation was performed with AMSR-E soil moisture product and Anticipant Precipitation Index (API) for two meteorological stations in the area. Results indicate that TRVDI reflects the variation in soil moisture to some extent and is suitable to monitor regional surface soil moisture and temporal variation.