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When a coal mine fire occurs, the rescue of trapped mine workers is difficult, because some efficient fire-fighting measures (such as nitrogen injection) cannot be implemented. The development and severity of a fire is difficult to determine, and the integrity of the auxiliary ventilation system may be unknown. These situations pose additional challenges to the trapped mine workers and increase the difficulty level for the rescue team. A novel method using the tracer gas technique is developed to remotely gather information to determine the location and severity of the fire. Laboratory experiments were conducted to understand the tracer gas distribution characteristics when the fire causes damage to the ventilation duct. A mathematical model was developed that uses the tracer gas concentration curve to determine the location and severity of the fire. The model combines the Expectation Maximization (EM) algorithm with the Gaussian Mixture (GM) model. Validated using experimental data, it is demonstrated that the method can determine the fire location with low error. The information collected using this method reflects basic living environmental conditions of the trapped mine workers and provides important input for quickly formulating an effective fire rescue plan that saves lives.

Foliage fuel load (FFL) is a critical factor affecting crown fire intensity and rate of spread. Satellite observations provide the potential for monitoring FFL dynamics across large areas. Previous studies commonly used empirical methods to estimate FFL, which potentially lacks reproducibility. This study applied Landsat 7 ETM+ and 8 OLI data for FFL retrieval using radiative transfer model (RTM) and machine learning method. To this end, the GeoSail, SAIL, and PROSPECT RTMs were first coupled together to model the near-realistic scenario of a two-layered forest structure. Second, available ecological information was applied to constrain the coupled RTM modeling phases in order to decrease the probability of generating unrealistic simulations. Third, the coupled RTMs were linked to three machine learning models—random forest, support vector machine, and multilayer perceptron—as well as the traditional lookup table. Finally, the performance of each method was validated by FFL measurements from Southwest China and Sweden. The resulting multilayer perceptron ( R 2 = 0.77, RMSE = 0.13, and rRMSE = 0.43) outperformed the other three methods. The evaluation of the applicability of the FFL estimates was conducted in a southwest China forest where two occurred in 2014 and 2020. The FFL dynamics from 2013 through 2020 showed that the fire was likely to occur when the FFL accumulated to a critical point (around 27 × 106 kg), highlighting the relevance of remote sensing derived FFL estimates for understanding potential fire occurrence.

Soil moisture ( Mv ) estimation and monitoring over agricultural areas using Synthetic Aperture Radar (SAR) are often affected by vegetation cover during the growing season. Volume scattering and vegetation attenuation can complicate the received SAR backscatter signal when microwave interacts with the vegetation canopy. To address the existing problems, this article employed the model-based polarimetric decomposition method considering the two-way attenuation to remove the volume scattering and vegetation attenuation. A deorientation process of SAR data was applied to remove the influence of randomly distributed target orientation angles before the polarimetric decomposition. To parameterize the two-way attenuation, Radar Vegetation Index derived from the SAR intensity images was adopted. The Dubois model was used to describe backscattering from the underlying bare soil. Since the soil roughness parameters are difficult to measure under vegetation cover, the optimum surface roughness method was used to parameterize the Dubois model. This soil moisture retrieval algorithm was applied to the polarimetric multitemporal RADARSAT-2 SAR data over soybean fields. The validation indicates the root-mean-square error of 9.2 vol.% and 8.2 vol.% at HH and VV polarization, respectively, over the entire soybean growing period, suggesting that the proposed method is capable of reducing the effect of vegetation cover for soil moisture monitoring over the soybean field.

Xinjiang is located in the core China’s ‘Belt and Road’ development, and northern Xinjiang is an important region for economic development. In recent years, due to the strong influence of global climate change and human disturbance, regional climate instability and ecological-economic-social system sensitivity have grown. In this paper, seasonal, interannual, interdecadal, spatial, abrupt, and periodic variations of temperature and precipitation in northern Xinjiang were analyzed using daily surface air temperature and precipitation data from 49 meteorological stations during 1961–2017. At the same time, the driving factors of climate change are discussed. Methods included linear regression, cumulative anomaly, the Mann-Kendall test, and Morlet wavelet analysis. The results indicated that during the study period, annual mean temperature and annual precipitation increased significantly at rates of 0.35°C/10 yr and 13.25 mm/10 yr, respectively, with abrupt changes occurring in 1994 and 1986. Annual mean temperature and annual precipitation in all four seasons showed increasing trends, with the maximum increases in winter of 0.42°C/10 yr and 3.95 mm/10 yr, respectively. The general climate in northern Xinjiang showed a trend towards increasingly warm and humid. In terms of spatial distribution, the temperature and precipitation in high mountainous areas increased the most, while basins areas increased only slightly. Periodic change analysis showed that annual mean temperature and annual precipitation experienced two climatic shifts from cold to warm and dry to wet, respectively. Population change, economic development and land use change are important factors affecting climate change, and more research should be done in this field.

As one of the important coastal cities in China, Tianjin has been urbanized dramatically over the past 40 years, and the urbanization rate has been up to 83.15% by 2018. In this study, we used the continuous change detection and classification algorithm to comprehensively understand the urban expansion processes in Tianjin based on the Landsat time series from 1985 to 2018 with 30-m resolution. Specially, we applied the c-factor approach with the Ross Thick-LiSparse-R model to correct the bidirectional reflectance distribution function (BRDF) effect for each Landsat image and calculated a spatial line density feature for improving the change detection and the classification. Based on the study in Tianjin, we found that BRDF correction can substantially improve the change detection (9.00% higher overall accuracy) and classification (1.08% higher overall accuracy); and the line density is also beneficial to classification (0.48% higher overall accuracy), especially for impervious surface (1.70% less commission errors and 1.49 % less omission errors). By analyzing the imperious surface change processes, we observed that Tianjin has undergone rapid urban expansion in the past decades, and the urban area was mainly transformed from cropland around the central area before 2005 and later from the coast.

Dead fuel moisture content (DFMC) is a key driver for fire occurrence and is often an important input to many fire simulation models. There are two main approaches to estimating DFMC: empirical and process-based models. The former mainly relies on empirical methods to build relationships between the input drivers (weather, fuel and site characteristics) and observed DFMC. The latter attempts to simulate the processes that occur in the fuel with energy and water balance conservation equations. However, empirical models lack explanations for physical processes, and process-based models may provide an incomplete representation of DFMC. To combine the benefits of empirical and process-based models, here we introduced the Long Short-Term Memory (LSTM) network and its combination with an effective physics process-based model fuel stick moisture model (FSMM) to estimate DFMC. The LSTM network showed its powerful ability in describing the temporal dynamic changes of DFMC with high R2 (0.91), low RMSE (3.24%) and MAE (1.97%). When combined with a FSMM model, the physics-guided model FSMM-LSTM showed betterperformance (R2 = 0.96, RMSE = 2.21% and MAE = 1.41%) compared with the other models. Therefore, the combination of the physics process and deep learning estimated 10-h DFMC more accurately, allowing the improvement of wildfire risk assessments and fire simulating.

In this study, urban stream sediment samples were collected in the Suzhou Industrial Park (SIP), one of the earliest national demonstration eco-industrial parks of China. PAHs were analyzed in these sediments, and concentrations of total PAHs were 180–81,000 ng g−1 (5700 ± 14,000 ng g−1). Medium molecular weight (4- ring) PAHs were predominant (42 ± 12%), followed by high molecular weight (5- and 6- ring) PAHs (31 ± 10%). No correlation was found between concentrations of PAHs and land uses of SIP in this study. Diagnostic ratios and a positive matrix factorization (PMF) model indicated that coal/biomass combustion might be the primary PAH source (61%), followed by non-combustion sources (21%) and vehicular emission (18%). According to the spatial analysis, PAHs in the sediments of SIP might be mainly associated with the coal/biomass combustion in the northeast industrial zone. Residential & commercial activities seem not to be the major causes of PAH contamination. Total PAH toxic equivalent concentrations, effect range low/effect range median values, and mean effects range–median quotient all showed that PAHs were present at a low toxicity risk level in most regions of the SIP. However, vigilance is required at some sampling sites with extremely high PAH concentrations or high mean effects range–median quotient.

Fuel load is the key factor driving fire ignition, spread and intensity. The current literature reports the light detection and ranging (LiDAR), optical and airborne synthetic aperture radar (SAR) data for fuel load estimation, but the optical and SAR data are generally individually explored. Optical and SAR data are expected to be sensitive to different types of fuel loads because of their different imaging mechanisms. Optical data mainly captures the characteristics of leaf and forest canopy, while the latter is more sensitive to forest vertical structures due to its strong penetrability. This study aims to explore the performance of Landsat Enhanced Thematic Mapper Plus (ETM+) and Advanced Land Observing Satellite (ALOS) Phased Arrayed L-band Synthetic Aperture Radar (PALSAR) data as well as their combination on estimating three different types of fuel load—stem fuel load (SFL), branch fuel load (BFL) and foliage fuel load (FFL). We first analyzed the correlation between the three types of fuel load and optical and SAR data. Then, the partial least squares regression (PLSR) was used to build the fuel load estimation models based on the fuel load measurements from Vindeln, Sweden, and variables derived from optical and SAR data. Based on the leave-one-out cross-validation (LOOCV) method, results show that L-band SAR data performed well on all three types of fuel load (R2 = 0.72, 0.70, 0.72). The optical data performed best for FFL estimation (R2 = 0.66), followed by BFL (R2 = 0.56) and SFL (R2 = 0.37). Further improvements were found for the SFL, BFL and FFL estimation when integrating optical and SAR data (R2 = 0.76, 0.81, 0.82), highlighting the importance of data selection and combination for fuel load estimation.

Fuel moisture content (FMC) of live vegetation is a crucial wildfire risk and spread rate driver. This study pre- sents the first daily FMC product at a global scale and 500 m pixel resolution from the Moderate Resolution Imaging Spectroradiometer (MODIS) and radiative transfer models (RTMs) inversion techniques. Firstly, multi- source information parameterized the PROSPECT-5 (leaf level), 4SAIL (grass and shrub canopy level) and GeoSail (tree canopy level) RTMs to generate three look-up tables (LUTs). Each LUT contained the most realistic model inputs range and combination, and the corresponding simulated spectra. Secondly, for each date and location of interest, a global landcover map classified fuels into three classes: grassland, shrubland and forest. For each fuel class, the best LUT-based inversion strategy based on spectral information, cost function, percentage of solutions, and central tendency determined the optimal model for the global FMC product. Finally, 3,034 FMC measurements from 120 worldwide sites validated the statistically significant results (R2 = 0.62, RMSE =34.57%, p < 0.01). Filtering out low quality field measurements achieved better accuracy (R2 = 0.71, RMSE =32.36%, p < 0.01, n = 2008). It is anticipated that this global FMC product can assist in wildfire danger modeling, early prediction, suppression and response, as well as improve awareness of wildfire risk to life and property., This work was supported by the National Natural Science Foundation of China (Contract No. 41801272 and U20A2090) and Sichuan Science and Technology Program (Contract No. 2020YFS0058). The authors are grateful to the Land Processes Distributed Active Archive Center (LPDAAC) at the U.S. Geological Survey (USGS) Earth Resources Observation and Science Center (EROS) (http://lpdaac.usgs.gov) for providing the MODIS dataset.

Burn severity mapping is critical to quantifying fire impact on key ecological processes and post-fire forest management. Satellite remote sensing has the advantages of high spatial-temporal resolution and large-scale monitoring and provides a more efficient way to evaluate forest fire burn severity than traditional field or aerial surveys. However, the proportion of tree canopy cover (TCC) affects the spectral signal received by remote sensing sensors from the background charcoal and ash. Consequently, not considering this factor normally leads a spectral confusion in burn severity retrieval. In this study, the burn severity of two Qinyuan forest fires was estimated using a coupled Radiative Transfer Model (RTM) and Sentinel-2A Multi-Spectral Instrument (MSI) reflectance data. A two-layer Canopy Reflectance Model (ACRM) RTM was coupled with the GeoSail RTM by replacing the spectra of the background input of GeoSail RTM to simulate the spectra of the three-layered forests for burn severity retrieval measured as the Composite Burn Index (CBI). The TCC data was then served to RTM parameterization and constrain the backward inversion procedure of the coupled RTM to alleviate spectral confusion. Finally, the inversion retrievals were evaluated using 163 field measured CBI. The coupled RTM can simulate the radiative transfer characteristics of three-layer vegetation and has greater potential to accurately estimate burn severity worldwide. To evaluate the merit of our proposed method, the CBI was estimated through coupled RTM inversion with TCC constraint (CP_RTM+TCC), coupled RTM inversion with global optimal search (CP-RTM+GOS), Forest Reflectance and Transmittance (FRT) RTM inversion with TCC constraint (FRT+TCC), and random forest (RF) algorithm. The results showed that the method proposed in this study (CP_RTM+TCC) yielded the highest estimation accuracy (R2 = 0.92, RMSE = 0.2) among the four methods used as benchmark, indicating its reasonable ability to assist forest managers to better understand post-fire vegetation regeneration and forest management.

Chlorophyll is an essential pigment for photosynthesis of crops, which indicates the growth status of crops. Accurate and robust information on the spatial dynamics of chlorophyll content is of critical importance for crop growth status assessment and corresponding response activities. However, previous studies mainly focus on the methodologies based on in situ hyperspectral data, which are not applicative for regional chlorophyll content mapping. In this content, Unmanned Aerial Vehicle (UAV) based hyperspectral imagery, with high spatial and spectral resolution, may provide the spatial distribution of chlorophyll content accurately over crop fields. In this study, an empirical model between wavelet features, derived from UAV based hyperspectral imagery by continuous wavelet transform (CWT), and chlorophyll content (measured by a portable soil–plant analysis development meter) is proposed by support vector regression (SVR). The results suggest that the UAV based hyperspectral imagery combined with CWT demonstrates good performance in rice chlorophyll content estimation with R and RMSE of 0.81 and 3.51, respectively. Moreover, the wavelet coefficients corresponding to two bands at red (640nm, 628nm) and one band at green (548nm) are the most effective wavelet features to estimate chlorophyll content of rice.

Previous studies have shown that an accurate prediction of frost heaves largely depends on the pore water pressure and hydraulic conductivity of frozen fringes, which are difficult to determine. The segregation potential model can avoid this problem, however, the conventional segregation potential is considered to be approximately unchanged at a steady state and only valid in an open system without dehydration in the unfrozen zone. Based on Darcy&rsquo, s law and the conventional segregation potential, the segregation potential was expressed as a function of the pore water pressure at the base of the ice lens, the pore water pressure at the freezing front, the freezing temperature, the segregation freezing temperature and the hydraulic conductivity of the frozen fringe. This expression indicates that the segregation potential under quasi-steady-state conditions is not a constant in a closed system, since the pore water pressure at the freezing front varies with the freezing time owing to the dehydration of the unfrozen zone, and that when the pore water pressure at the freezing front is equal to that at the base of the ice lens, the water migration and frost heave will be terminated. To analyze the possibility of applying the segregation potential model in a closed system, a series of one-sided frost heave tests under external pressure in a closed system were carried out in a laboratory, and the existing frost heaving test data from the literature were also analyzed. The results indicate that the calculated frost heave was close to the tested data, which shows the applicability of the model in a closed system. In addition, the results show the rationality of calculating the segregation potential from the frost heaving test by comparing the potential with that calculated from the numerical simulation results. This study attempted to extend the segregation potential model to freezing soil in a closed system and is significant to the study of frost heaves.

Solar-induced chlorophyll fluorescence (SIF) opens a new perspective on the monitoring of vegetation photosynthesis from space, and has been recently used to estimate gross primary productivity (GPP). However, previous studies on SIF were mainly based on satellite observations from the Greenhouse Gases Observing Satellite (GOSAT) and Global Ozone Monitoring Experiment-2 (GOME-2), and the evaluation of these coarse-resolution SIF measurements using GPP derived from eddy covariance (EC) flux towers has been hindered by the scale mismatch between satellite and tower footprints. We use new far-red SIF observations from the Orbiting Carbon Observatory-2 (OCO-2) with much finer spatial resolution and GPP data from EC flux towers from 2014 to 2016 to examine the relationship between GPP and SIF for temperate forests. The OCO-2 SIF tracked tower GPP well, and had strong correlation with tower GPP at both retrieval bands (757 nm and 771 nm) and both instantaneous (mid-day) and daily timescales, with the strongest relationship for the 757 nm band (SIF 757 ) at the daily timescale. Daily SIF 757 exhibited much stronger correlation with tower GPP compared to the enhanced vegetation index (EVI) and normalized difference vegetation index (NDVI) based on the surface reflectance product derived from the moderate resolution imaging spectroradiometer (MODIS) onboard either Terra or Aqua satellite and had a similarly strong relationship as EVI based on the bidirectional reflectance distribution function (BRDF) corrected reflectance product (MCD43A4) based on data from both Terra and Aqua. Absorbed photosynthetically active radiation (APAR) explained 85% of the variance in SIF 757 , while the product of APAR and two environmental scalars - f T min and f VPD (representing minimum temperature stress and water stress) explained higher variance (92%) in SIF 757 . This suggests that SIF mainly depends on APAR and is also affected by environmental stresses that determine photosynthetic light use efficiency. The BRDF-corrected EVI also has advantages over SIF in that EVI has spatially and temporally continuous coverage and has a much longer record (2000-present). The OCO-2 SIF 757 estimated GPP well (R 2 = 0.81, p − 2 d − 1 ), and its performance was comparable to or slightly better than that of the MODIS GPP algorithm, a GPP model based on the light use efficiency logic. With intensive OCO-2 observations acquired in the Target mode around Park Falls, a calibration/validation site for OCO-2, we also generated gridded SIF and then estimated GPP on a per-pixel basis at the landscape scale. Our findings demonstrate the strong ability of chlorophyll fluorescence observed by OCO-2 in estimating GPP for temperate forests and reveal its potential and limitations in future ecosystem functioning and carbon cycling studies.

We developed a new algorithm called MFmask (Mountainous Fmask) for automated cloud and cloud shadow detection for Landsats 4–8 images acquired in mountainous areas. The MFmask algorithm, built upon the success of the Fmask algorithm (Zhu and Woodcock, 2012; Zhu et al., 2015), is designed for cloud and cloud shadow detection in mountainous areas, where the Fmask algorithm is not performing well. The inputs of the MFmask algorithm include Landsat Top of Atmosphere (TOA) reflectance, Brightness Temperature (BT), and Digital Elevation Models (DEMs). Compared to Fmask, MFmask can separate water and land pixels better in mountainous areas with the aid of DEMs. Moreover, MFmask produces better cloud detection results than Fmask in mountainous areas after BT is linearly normalized by DEMs. To provide more accurate cloud shadow detection in mountainous areas, MFmask uses a double-projection approach to better predict cloud shadow shape on slope side. Additionally, MFmask applies a topographic correction to remove terrain shadows and estimates cloud base height with neighboring clouds. Both will reduce the possibility of cloud and cloud shadow mismatch and increase cloud shadow detection accuracy for places with large topographic gradient. To test the performance of the proposed MFmask algorithm, a total of 67 Landsat images acquired in mountainous areas from different parts of the world were selected for assessing the accuracy of cloud detection, in which 15 of them were used for assessing the accuracy of cloud shadow detection. Compared with Fmask, MFmask can provide substantial improvements in cloud and cloud shadow detection accuracies for places with large topographic gradient and also work well for relatively flat terrain.

Forest fuel moisture content (FMC) dynamics are paramount to assessing the forest wildfire risk and its behavior. This variable can be retrieved from remotely sensed data using a radiative transfer model (RTM). However, previous studies generally treated the background of forest canopy as soil surface while ignored the fact that the soil may be covered by grass canopy. In this study, we focused on retrieving FMC of such forestry structure by coupling two RTMs: PROSAIL and PRO-GeoSail. The spectra of lower grass canopy were firstly simulated by the PROSAIL model, which was then coupled into the PRO-GeoSail model. The results showed that the accuracy level of retrieved FMC using this coupled model was better than that when the PRO-GeoSail model used alone. Further analysis revealed that low FMC condition fostered by fire weather condition had an important influence on the breakout of a fire during the study period.

This paper presents a novel method to derive grassland aboveground biomass (AGB) based on the PROSAILH (PROSPECT + SAILH) radiative transfer model (RTM). Two variables, leaf area index (LAI, m2m−2, defined as a one-side leaf area per unit of horizontal ground area) and dry matter content (DMC, gcm−2, defined as the dry matter per leaf area), were retrieved using PROSAILH and reflectance data from Landsat 8 OLI product. The result of LAI × DMC was regarded as the estimated grassland AGB according to their definitions. The well-known ill-posed inversion problem when inverting PROSAILH was alleviated using ecological criteria to constrain the simulation scenario and therefore the number of simulated spectra. A case study of the presented method was applied to a plateau grassland in China to estimate its AGB. The results were compared to those obtained using an exponential regression, a partial least squares regression (PLSR) and an artificial neural networks (ANN). The RTM-based method offered higher accuracy (R2 = 0.64 and RMSE = 42.67 gm−2) than the exponential regression (R2 = 0.48 and RMSE = 41.65 gm−2) and the ANN (R2 = 0.43 and RMSE = 46.26 gm−2). However, the proposed method offered similar performance than PLSR as presented better determination coefficient than PLSR (R2 = 0.55) but higher RMSE (RMSE = 37.79 gm−2). Although it is still necessary to test these methodologies in other areas, the RTM-based method offers greater robustness and reproducibility to estimate grassland AGB at large scale without the need to collect field measurements and therefore is considered the most promising methodology.

In this study, the burn severity of several wildfires ignited at northern Australian tropical savannas area were estimated using the Forest Reflectance and Transmittance (FRT) radiative transfer model (RTM) and Sentinel-2A Multi-Spectral Instrument (MSI) satellite data. To alleviate the spectral confusion between severe (SV) and not-severe (NSV) burnt levels caused by sparse tree distribution, the MODIS Vegetation Continuous Fields (VCF) tree cover percentage data was used to constrain the inversion. The results showed that the accuracy of burn severity estimation significantly improves when considering the tree coverage, with overall accuracy for two study sites increasing from 65% to 81% and kappa coefficient from 0.35 to 0.55. Future work will focus on extending the methodology to other ecosystems.

Semantic segmentation is understanding images at pixel level, which becomes increasingly vital in unmanned aerial vehicles (UAV) imagery classification tasks. With the powerful calculating ability of GPU, a growing number of deep convolutional neural networks (DCNNs) are designed to address semantic segmentation challenges. However, compared with CPU, GPU is much more costly, and GPU relies on powerful supplementary equipments to support it. Therefore, GPU-based methods are hard to carry out in practical applications. In this study, we propose a CPU-based method named fully convolutional support vector machine (FCSVM) to address the semantic segmentation challenge in UAV images. On the one hand, we adopt the SVM kernel from the original convolutional SVM networks (CSVM), which completes classification at image level. On the other hand, FCSVM consists of two processes which are a compressed process and a extensive process. In the compressed process, the FCSVM has convolutional layers and reduction layers. In the extensive process, the FCSVM has convolutional layers and upsampling layers. This structure allows FCSVM to classify images at pixel level using limited number of training data. The experiments are implemented on one UAV dataset with very little training data. The result shows our FCSVM achieves competitive performance compared to modern state-of-the-art semantic segmentation methods.

Tianjin city, China has seen rapid urban expansion and economy development especially since the Chinese reform and opening up in 1978. Understanding their internal relationship is important for urban management. Considering of the long-history records with medium spatial resolution (30 meters), Landsat time series (LTS) has become a key remote sensing dataset for monitoring urban changes. As the impervious surface is an important indicator for assessing urban environment, in this study, we extracted the impervious surface maps in Tianjin between 1990 and 2017 based on LTS using a Continue Change Detection and Classification (CCDC) algorithm. On the other hand, we quantitatively explored the relationships between the impervious surface and Gross National Product (GDP). Results show that the impervious surface expansion in Tianjin experienced two stages between 1990 and 2017 with the area increased by 521.95 km2, and the correlation coefficient between the area of impervious surface and GDP value was as high as 0.9718.

Spatiotemporal monitoring of fuel moisture content (FMC) is vital to assessing the wildfire risk and its behavior. Optical remote sensing data-based FMC estimation have been wildly explored in previous studies. However, limited studies focused on FMC retrieval from the active microwave technique represented by synthetic aperture radar (SAR) data, which processes the advantage of higher sensitivity to surface moisture and better all-weather and all-time work capability than optical data. This is the first study to assess the performance of time series dual-polarization Sentinel-1A data for FMC estimation from coupled the bare soil backscatter Linear Model and the vegetation backscatter Water Cloud Model. The results show that the simulated backscattering coefficients and FMC are in line with the measured Sentinel-1A data and FMC with R2 and RMSE are 0.549, 0.354 dB, and 0.543, 13.579 %, respectively.

Fuel moisture content (FMC) is a critical variable in assessing wildfire risk and its behavior. Previous studies normally focused on the methodologies based on optical remote sensing data for FMC retrieval. However, active microwave technique, which processes the advantage of high sensitivity to surface moisture, all-weather and all-time work capability and strong penetrability, attracted more attention in surface parameter monitoring, particularly for the polarimetric SAR which provides more sufficient object scatter characteristic. In this paper, we retrieved the FMC for a grassland based on the multiple linear regression analysis of polarimetric decomposition parameters from Radarsat-2 data. The results show that the correlation coefficient (R) and root mean square error (RMSE) reached to 0.658 and 30.319% when compared to the measured FMC. Finally, the presented method was used for spatial and temporal mapping of FMC in the target study area.

Fuel Moisture Content (FMC) is a key indicator for assessing wildfire risk and fire spread rate since fire normally broke out under low FMC condition. However, how the FMC driving wildfire activities is still unclear. To understand the relation between FMC and fire occurrence, this study focused on analyzing the FMC retrieved from Radiative Transfer Model (RTM) and historical fires extracted from MODIS Burned Area (BA) product MCD64A1 to determine critical FMC thresholds for wildfire occurrences in southwest China. The results indicated that there were three significant FMC thresholds for forest: 138.9%, 119.0% and 53.1%, and three thresholds for grassland: 138.3%, 70.2%, and 10.6% that associated with the occurrence of wildfires. Large fires and high frequency of fires became vulnerable when FMC fell below those thresholds.

Paddy rice is one of the most important crops in the world. Information of the area and spatial distribution of paddy rice is significant for food security, water resources management, and greenhouse gas (methane) emissions. Paddy rice field is characterized by an initial period of flooding and transplanting, during which period open canopy (a mixture of rice crops and surface water) exists. The present algorithms for paddy rice recognition are mainly based on the unique physical features of paddy rice and the vegetation indices, which are sensitive to dynamics of the canopy and surface water content. In this study, the rice growth calendar data and the cropping pattern (e.g., single rice or double rice) information were used to improve present phenology-based rice recognition approach by determining the potential temporal window of flooding and transplanting periods over a year. Other land cover types (e.g., snow, evergreen vegetation and permanent water bodies) with potential influences on paddy rice identification were removed (masked out) due to different temporal profiles. The results showed that the estimated rice area is in line with the provincial agricultural statistics with R2 and slope are 0.951, 1.049, respectively, and the relative errors of the different province are between -15.81% to 25.75%.

In our recent study in Global Change Biology (Li et al., ), we examined the relationship between solar-induced chlorophyll fluorescence (SIF) measured from the Orbiting Carbon Observatory-2 (OCO-2) and gross primary productivity (GPP) derived from eddy covariance flux towers across the globe, and we discovered that there is a nearly universal relationship between SIF and GPP across a wide variety of biomes. This finding reveals the tremendous potential of SIF for accurately mapping terrestrial photosynthesis globally.

With the upcoming BIOMASS mission, P-band PolInSAR is expected to provide new perspectives on global forest aboveground biomass (AGB). However, its performance has not yet been fully evaluated for dense tropical forests with complex structure and very high biomass. Based on the TropiSAR campaign in French Guiana, we explored the challenges of the three most commonly used PolInSAR measures to capture AGB in tropical forests; coherence magnitude, interferometric phase, and backscatter. An improved AGB estimation approach was developed by integrating multiple information derived from single-baseline PolInSAR data. The approach involves ground-volume backscatter decomposition and combines volume backscatter with the retrieved forest height. Volume backscatter from the forest canopy was the best predictor of AGB for tropical forests, whereas the ground backscatter contribution was affected by the complex underlying surface and terrain slope. Both LiDAR- and PolInSAR-derived forest heights showed limited correlation with high AGB due to the varying forest basal area. The linear combination of PolInSAR-derived forest height and volume backscatter complemented each other and produced improved AGB estimates. Comparing three different PolInSAR data pairs, the proposed method produced an AGB map with an average R2 of 0.7 and RMSE of 34 tons/ha (relative RMSE of 9.4%) at a spatial resolution of 125 × 125 m2 for biomass between 250–500 tons/ha.

Globe-LFMC is an extensive global database of live fuel moisture content (LFMC) measured from 1,383 sampling sites in 11 countries: Argentina, Australia, China, France, Italy, Senegal, Spain, South Africa, Tunisia, United Kingdom and the United States of America. The database contains 161,717 individual records based on in situ destructive samples used to measure LFMC, representing the amount of water in plant leaves per unit of dry matter. The primary goal of the database is to calibrate and validate remote sensing algorithms used to predict LFMC. However, this database is also relevant for the calibration and validation of dynamic global vegetation models, eco-physiological models of plant water stress as well as understanding the physiological drivers of spatiotemporal variation in LFMC at local, regional and global scales. Globe-LFMC should be useful for studying LFMC trends in response to environmental change and LFMC influence on wildfire occurrence, wildfire behavior, and overall vegetation health., Design Type(s)database creation objective • cross validation objective • physiological process monitoring objectiveMeasurement Type(s)moisture content traitTechnology Type(s)digital curationFactor Type(s)geographic location • environmental featureSample Characteristic(s)Earth (Planet) • United States of America • French Republic Machine-accessible metadata file describing the reported data (ISA-Tab format)

The distribution of soil moisture is important for modeling hydrological and climatological processes to understand the Earth energy cycle and balance. The major difficulty for soil moisture retrieval in vegetated areas is how to separate the individual scattering contribution of soil moisture, vegetation, and surface roughness from the backscattered radar signal. In this paper, a semi-empirical method was proposed to retrieve soil moisture in the Ruoergai prairie using single temporal Radarsat-2 data. It was formulated by integrating the advanced integral equation model (AIEM), a semi-empirical ratio vegetation model, and optimum surface roughness parameters. The AIEM was run in the forward mode to simulate the backscattering coefficient of bare soil surface. The ratio vegetation model was applied for eliminating the vegetation effect from the observed backscattering coefficient. Meanwhile, four different vegetation parameters were used to characterize the change of vegetation: leaf area index, vegetation water content, normalized difference vegetation index, and enhanced vegetation index. A global search method was used to find out the optimum surface roughness parameters, which made the relationship between the observed and retrieved soil moisture reach up to the best. The collected in situ measurements and satellite data from the Ruoergai prairie were employed to validate the feasibility and effectiveness of the introduced method. From the analysis of experiment results, the optimum surface roughness parameters had a relative change when different vegetation parameters were used to parameterize the ratio vegetation model. In addition, the root-mean-square height had a significant impact on the accuracy of soil moisture retrieval compared with correlation length. The best retrieval result was obtained when EVI was used to remove the influence of vegetation, with a correlation coefficient of 0.84 and root-mean-square error of 4.05 $\mbox{vol}\cdot\%$ . Therefore, compared with other three vegetation parameters, EVI was recommended to characterize the change of vegetation in this experiment. It was evident that optimum surface roughness parameters were validated to be a promising tool for soil moisture retrieval in prairie areas using Radarsat-2 data.

Live fuel moisture content (LFMC) is an important factor in wildfire risk assessment, but the estimation of LFMC is affected by the leaf area index (LAI) if it is retrieved using a radiative transfer model. To reduce the influence of LAI in the LFMC retrieval process, we used the interval estimated LAI to parameterize the PROSAIL ( ${\mathbf{PROSPECT}} + {\mathbf {SAIL}}$ ) radiative transfer model. The LAI values were first estimated based on the MODIS LAI product (MOD15A2) using a simple downscaling technique, and then were constrained using an interval estimation method at the confidence levels of 80%, 90%, and 95%. To evaluate the reliability of this approach, another two LAI constraint strategies were applied, weakly constrained LAI over a wide range and strongly constrained LAI with fixed values. A look up table (LUT) was set up for the parameterized PROSAIL model. Furthermore, the LFMC can be retrieved based on the Landsat TM and 8 products. The results showed that the retrieved LFMC using the interval estimated LAI at the 95% confidence level resulted in the best accuracy level ( ${\mathbf{R}}^{\mathbf{2}} = {\mathbf{0.78}}$ , ${\mathbf{RMSE}} = {\mathbf{35.80}}\% $ ), which was slightly better than the LFMC obtained when using the interval estimated LAI at confidence levels of 90% ( ${\mathbf{R}}^{\mathbf{2}} = {\mathbf{0.75}},\;{\mathbf{RMSE}} = {\mathbf{39.38}}\% $ ) and 80% ( ${\mathbf{R}}^{\mathbf{2}} = {\mathbf{0.70}},\;{\mathbf{RMSE}} = {\mathbf{41.21}}\% $ ). The accuracy levels of the retrieved LFMC using weakly constrained LAI ( ${\mathbf{R}}^{\mathbf{2}} = {\mathbf{0.64}},\;{\mathbf{RMSE}} = {\mathbf{41.68}}\% $ ) and strongly constrained LAI ( ${\mathbf{R}}^{\mathbf{2}} = {\mathbf{0.68}},\;{\mathbf{RMSE}} = {\mathbf{45.53}}\% $ ) were poor. Thus, this study demonstrated that LFMC retrieval could be improved by using the interval estimated LAI.

In this study, an arid grassland was selected, and the chlorophyll content of the leaf and canopy level was estimated based on Landsat-8 Operational Land Imager OLI data using the PROSAIL radiative transfer RT model. Two vegetation indices green chlorophyll index, CIgreen, and greenness index, G were selected to estimate the leaf and canopy chlorophyll content LCC and CCC. By analysing the effect of soil background on the two indices, the LCC was divided into low and moderate-to-high levels. A different combination of the two indices was adopted at each level to improve the chlorophyll content estimation accuracy. The results suggested that the chlorophyll content estimated using the proposed method yielded a higher accuracy with coefficient of determination, R2 = 0.84, root-mean-square error, RMSE = 9.67 μg cm−2 for LCC and R2 = 0.85, RMSE = 0.43 g m−2 for CCC than that using CIgreen alone with R2 = 0.62, RMSE = 20.04 μg cm−2 for LCC and R2 = 0.85, RMSE = 0.71 g m−2 for CCC. The results also confirmed the validity of this approach to estimate the chlorophyll content in arid areas.

Detailed spatial information on the presence and properties of woody vegetation serves many purposes, including carbon accounting, environmental reporting and land management. Here, we investigated whether machine learning can be used to combine multiple spatial observations and training data to estimate woody vegetation canopy cover fraction (‘cover’), vegetation height (‘height’) and woody above-ground biomass dry matter (‘biomass’) at 25-m resolution across the Australian continent, where possible on an annual basis. We trained a Random Forest algorithm on cover and height estimates derived from airborne LiDAR over 11 regions and inventory-based biomass estimates for many thousands of plots across Australia. As predictors, we used annual geomedian Landsat surface reflectance, ALOS/PALSAR L-band radar backscatter mosaics, spatial vegetation structure data derived primarily from ICESat/GLAS satellite altimetry, and spatial climate data. Cross-validation experiments were undertaken to optimize the selection of predictors and the configuration of the algorithm. The resulting estimation errors were 0.07 for cover, 3.4 m for height, and 80 t dry matter ha-1 for biomass. A large fraction (89–94 %) of the observed variance was explained in each case. Priorities for future research include validation of the LiDAR-derived cover training data and the use of new satellite vegetation height data from the GEDI mission. Annual cover mapping for 2000–2018 provided detailed insight in woody vegetation dynamics. Continentally, woody vegetation change was primarily driven by water availability and its effect on bushfire and mortality, particularly in the drier interior. Changes in woody vegetation made a substantial contribution to Australia’s total carbon emissions since 2000. Whether these ecosystems will recover biomass in future remains to be seen, given the persistent pressures of climate change and land use.

Synthetic Aperture Radar (SAR) texture has been demonstrated to have the potential to improve forest biomass estimation using backscatter. However, forests are 3D objects with a vertical structure. The strong penetration of SAR signals means that each pixel contains the contributions of all the scatterers inside the forest canopy, especially for the P-band. Consequently, the traditional texture derived from SAR images is affected by forest vertical heterogeneity, although the influence on texture-based biomass estimation has not yet been explicitly explored. To separate and explore the influence of forest vertical heterogeneity, we introduced the SAR tomography technique into the traditional texture analysis, aiming to explore whether TomoSAR could improve the performance of texture-based aboveground biomass (AGB) estimation and whether texture plus tomographic backscatter could further improve the TomoSAR-based AGB estimation. Based on the P-band TomoSAR dataset from TropiSAR 2009 at two different sites, the results show that ground backscatter variance dominated the texture features of the original SAR image and reduced the biomass estimation accuracy. The texture from upper vegetation layers presented a stronger correlation with forest biomass. Texture successfully improved tomographic backscatter-based biomass estimation, and the texture from upper vegetation layers made AGB models much more transferable between different sites. In addition, the correlation between texture indices varied greatly among different tomographic heights. The texture from the 10 to 30 m layers was able to provide more independent information than the other layers and the original images, which helped to improve the backscatter-based AGB estimation.

Burn severity mapping greatly informs fire management and can be used to predict post-fire vegetation recovery. Satellite remote sensing is a cost-effective method for estimating burn severity, providing a comprehensive spatially explicit view of whole landscapes. However, the proportion of tree canopy cover (TCC) affects the reflectance signal, obscuring background char and ash. Consequently, traditional optical satellite remote sensing methods that do not account for variation in TCC misclassify burn severity, especially in areas with extremely low or high TCC. In this study, TCC data served to parameterize and constrain the inversion of the Forest Reflectance and Transmittance (FRT) radiative transfer model (RTM) to alleviate spectral confusion when retrieving burn severity. The methodology was evaluated using field measurements of burn severity for a series of wildfires in the fire-prone tropical savannas of northern Australia and the western United States. Burn severity classes were used for Australia while the Composite Burn Index (CBI) for US. Reflectance data from Sentinel-2A Multi-Spectral Instrument (MSI) and Landsat-5 Thematic Mapper (TM) corresponding to post-fire field survey dates were used to retrieve burn severity using FRT RTM (with and without using TCC information in its parameterization and inversion) and two standard empirical burn indices, dNBR and RdNBR, for comparison. Using FRT RTM without TCC constraint produced an overestimation for low burn severity in regions with low TCC and an underestimation for moderate and high burn severity in regions with high TCC. Burn severity estimation accuracy significantly improved by integrating TCC in the parameterization and inversion of FRT RTM. The overall accuracy in northern Australia increased from 65% to 81%, and the kappa coefficient increased from 0.35 to 0.55. In the western United States, R2 between estimated and observed CBI, increased from 0.33 to 0.54, root mean square error (RMSE) reduced from 0.53 to 0.43, and in all instances, the method performed better than dNBR and RdNBR. The method used in this study achieved more accurate burn severity mapping, thus assisting land managers to better understand post-fire vegetation resilience and forest management.

Frost heaving behavior of silty clay under different loading paths was experimentally investigated through unidirectional freezing tests in a closed system. The compression progression, freezing process, development of frost heave induced pressure (FHIP), and frost heave progression were observed and analyzed under three different loading paths. Considering the compression of unfrozen soil due to an additional force, experimental results show that frost heaving is a process of coordinated development between FHIP and frost heaving ratio (FHR). The variation trends of each variable under different loading paths are similar during freezing, but frost heaving behavior is different for each loading path. The dependence of FHR on FHIP decreases exponentially, and the effect of loading path on the FHR-FHIP (η−F) relationship decreases with increasing freezing time. The additional force restrains frost heaving by restraining moisture migration within the soil, and high freezing rates have little contribution to frost heave. Loading path has no significant effect on FHIP-constraining rate. FHIP increases exponentially with constraining rate, and F−θ commonly shows a banded distribution forming two edge envelopes, of which L2 is the most secure envelope. The frost heaving behavior under different loading paths presented herein may be used for reference in the designing of foundations to prevent frost heave in cold regions.

The change of water resources under global warming is one of the most important issues faced by national economy and social development. Accurate estimation and analysis of Terrestrial Water Storage Anomaly (TWSA) are important for understanding global and regional water cycle process. Gravity Recovery and Climate Experiment (GRACE) satellite data is used to inverse water storage change in China and the correlation between it and precipitation from Tropical Rainfall Measuring Mission (TRMM) is also analyzed. The study shows that GRACE TWSA and TRMM precipitation are consistent and linear. In the best regression model, R2 and RMSE are 0.8025 and 11.1469 respectively, which suggests that precipitation is the source of water supply and also the main impact of water storage change.

Fire Spread Rate (FSR) is one of the key factors for fire rescue and prevention. Remote sensing images have an advantage of acquiring intuitive information timely. To achieve extracting real-time FSR from remote sensing data, a method based on the movement rate of burned area centroid is presented in this study. The FSR extracted from geostationary Himawari-8 (H-8) data in two bushfires outbroke in Esperance, Western Australia in 2015. And the FSR estimated from CSIRO (Commonwealth Scientific and Industrial Research Organization) Grassland Fire Spread Model (CGFSM) were set as the benchmark to assess the presented approach. The results illustrated that the proposed method yield a promising accuracy by comparing with the FSR from CGFSM in that two fires, with the coefficient of determination (R2) reaches to 0.76 and root-mean-square error (RMSE) is 0.50 m·s−1. Furthermore, this study provides a potential application of geostationary satellite in extracting real-time wildfire behavior.

Dam construction is one of the solutions to the problem in water resources distribution and utilization caused by global climate change and the calculation of water energy reserves can provide scientific decision for it. The emergence of remotely sensed data and the application of Geographic Information System (GIS) make water reserves calculation a reality on a large scale. Hydro30 and measured runoff data are adopted to extract indicators of water energy reserves and the analysis of its spatial distribution is carried out in Anabar basin, Russia. The result shows that the most suitable site for waterpower development is concentrated in the upper reaches and the reserves of main rivers are larger than that in the source and tributary rivers.

Fuel moisture content (FMC) is a critical factor in assessing wildfire risk and its behaviour. Traditional field measurement of this variable is time-consuming and is impossible to extend to large-scale and dynamic applications. The canopy water has strong absorption characteristic in near and shortwave infrared spectra, allowing the near-real-time, multi-temporal and -spatial estimation of the FMC from remotely sensed data available. During last decade, numerous statistic- or physical model-based studies were carried out for the estimation of this variable. As FMC is responsive to weather variations, diurnal determination of this variable is essential for wildfire early-warning. With the launch of Himawari-8 in 2014, 10 mins images are available from this satellite, making real-time retrieval of the FMC achievable. Thus, this is the first study to retrieve diurnal FMC from Himawari-8 images, with the purpose for real-time wildfire risk assessment in near future.

Solar-induced chlorophyll fluorescence (SIF) has been increasingly used as a proxy for terrestrial gross primary productivity (GPP). Previous work mainly evaluated the relationship between satellite-observed SIF and gridded GPP products both based on coarse spatial resolutions. Finer resolution SIF (1.3 km × 2.25 km) measured from the Orbiting Carbon Observatory-2 (OCO-2) provides the first opportunity to examine the SIF-GPP relationship at the ecosystem scale using flux tower GPP data. However, it remains unclear how strong the relationship is for each biome and whether a robust, universal relationship exists across a variety of biomes. Here we conducted the first global analysis of the relationship between OCO-2 SIF and tower GPP for a total of 64 flux sites across the globe encompassing eight major biomes. OCO-2 SIF showed strong correlations with tower GPP at both midday and daily timescales, with the strongest relationship observed for daily SIF at the 757 nm (R2 = 0.72, p

The Multi Spectral Instrument (MSI) onboard Sentinel-2 can record the information in Vegetation Red-Edge (VRE) spectral domains. In this study, the performance of the VRE bands on improving land cover classification was evaluated based on a Sentinel-2A MSI image in East Texas, USA. Two classification scenarios were designed by excluding and including the VRE bands. A Random Forest (RF) classifier was used to generate land cover maps and evaluate the contributions of different spectral bands. The combination of VRE bands increased the overall classification accuracy by 1.40 %, which was statistically significant. Both confusion matrices and land cover maps indicated that the most beneficial increase was from vegetation-related land cover types, especially agriculture. Comparison of the relative importance of each band showed that the most beneficial VRE bands were Band 5 and Band 6. These results demonstrated the value of VRE bands for land cover classification.

Polarimetric Synthetic Aperture Radar Interferometry (PolInSAR) has shown potential for the retrieval of a forest canopy height model (CHM) and the underlying solid earth digital terrain model (DTM). However, because of non-volume decorrelation and other unavoidable errors, the robustness of retrieval heights is sensitive to the spatial baseline of the selected InSAR pairs, which relates forest parameters to measured coherence. Within the context of the random volume over ground (RVoG) model and the three-stage inversion method, we aimed to quantify the influence of spatial baseline on the inversions at P-band, which are distinct from the inversions at higher frequency due to the non-negligible ground contributions. This information assists in optimal baseline selection and the development of robust inversion schemes. Assumptions about the extinction coefficient and additional DTM or DEM were used to reduce the influence of ground contribution on CHM and DTM inversion, respectively. Inversions from published airborne repeat-pass P-band PolInSAR data with four different spatial baselines were validated against LiDAR-derived DTM and CHM data. The results show that a longer spatial baseline performed better in DTM inversion. The longest baseline produced the best R2 of 0.995 and RMSE of 0.555 m, much better than the smallest baseline with an R2 of 0.794 and RMSE of 3.74 m. A threshold height could be identified that determines the overestimation and underestimation of CHM inversion due to the non-volume decorrelation. Different baselines produced different threshold heights, making CHM inversion only accurate for a limited range of forest height around the threshold. The optimal baseline produced a CHM with R2 of 0.605 and RMSE of 2.67 m. Additionally, we found that using multiple baselines has the potential to improve CHM inversion, improving the R2 to 0.827 and RMSE to 0.876 m in our study.

The spatiotemporal distribution of soil moisture over the Tibetan Plateau is important for understanding the regional water cycle and climate change. In this paper, the surface soil moisture in the northeastern Tibetan Plateau is estimated from time-series VV-polarized Sentinel-1A observations by coupling the water cloud model (WCM) and the advanced integral equation model (AIEM). The vegetation indicator in the WCM is represented by the leaf area index (LAI), which is smoothed and interpolated from Terra Moderate Resolution Imaging Spectroradiometer (MODIS) LAI eight-day products. The AIEM requires accurate roughness parameters, which are parameterized by the effective roughness parameters. The first halves of the Sentinel-1A observations from October 2014 to May 2016 are adopted for the model calibration. The calibration results show that the backscattering coefficient (σ°) simulated from the coupled model are consistent with those of the Sentinel-1A with integrated Pearson’s correlation coefficients R of 0.80 and 0.92 for the ascending and descending data, respectively. The variability of soil moisture is correctly modeled by the coupled model. Based on the calibrated model, the soil moisture is retrieved using a look-up table method. The results show that the trends of the in situ soil moisture are effectively captured by the retrieved soil moisture with an integrated R of 0.60 and 0.82 for the ascending and descending data, respectively. The integrated bias, mean absolute error, and root mean square error are 0.006, 0.048, and 0.073 m3/m3 for the ascending data, and are 0.012, 0.026, and 0.055 m3/m3 for the descending data, respectively. Discussions of the effective roughness parameters and uncertainties in the LAI demonstrate the importance of accurate parameterizations of the surface roughness parameters and vegetation for the soil moisture retrieval. These results demonstrate the capability and reliability of Sentinel-1A data for estimating the soil moisture over the Tibetan Plateau. It is expected that our results can contribute to developing operational methods for soil moisture retrieval using the Sentinel-1A and Sentinel-1B satellites.

Solar energy (SE) is accepted as a key resource for easing the tense situation of global energy supply. It is urgent to figure out the potential for global solar energy utilization. In this paper, a multicriteria evaluation (MCE) model was constructed by comprehensively integrating resources richness, resources stability, resources utilization value, and natural environment together with weights. The resources richness, resources stability and resources utilization value derive from ERA-Interim reanalysis datasets with spatial resolution 0.125°. The natural environment comes from MODIS product MCD12C1 with spatial resolution 0.05°, and global multi-resolution terrain elevation data 2010 (GMTED 2010) with spatial resolution 30″. Besides, the global solar energy utilization potential map was produced, which shows most of the high potential region located in north of African. Additionally, 3% and 75% large photovoltaic (PV) power plants (LPVPP) falls on high and medium-high potential regions respectively.

Desertification is a severe issue that has received increasing attention currently. This paper aims at the desertification degree assessment and trend analysis between 2000 and 2014. Firstly, based on the negative correlation between albedo and Normalized Difference Vegetation Index (NDVI), desertification difference index (DDI) products are completed, which are applied to assess desertification degree. For the purpose of monitoring the tendency of global desertification, the time-series data from MODIS are applied to work out the normalized yearly albedo and NDVI. Then, based on DDI products and the trend analysis approach, the global desertification tendency products are produced. Preliminary analysis showed that the ranking of global desertification seriousness relates to the temperature zone. Desertification trended to be aggravated in central North America, eastern Asia, the Pacific Rim, and central Australia. The results above may promote the scientific decision, and help prevent and control the desertification.

In this study, an alpine watershed was selected, and four kinds of biophysical parameters (Leaf Area Index, LAI; Canopy Water Content, CWC; Canopy Chlorophyll Content, CCC; Fractional Vegetation Cover, FVC) were retrieved and their products were generated at the regional scale from 2013 to 2014. PROSAIL radiative transfer model and modified mixed sub pixel model were used to estimate these biophysical parameters based on Landsat 8 OLI surface reflectance data. The results suggested that the estimated biophysical parameters yielded a promising accuracy compared with the ground measurements. The inter-partition statistic was also conducted by analyzing the grassland peak growing season (July and August), and confirmed that the pasture yield and water stress condition of grassland in Lake Qinghai watershed of 2014 is better than 2013.

Previous studies have shown that Live Fuel Moisture Content (LFMC) is a crucial driver affecting wildfire occurrence worldwide, but the effect of LFMC in driving wildfire occurrence still remains unexplored over the southwest China ecosystem, an area historically vulnerable to wildfires. To this end, we took 10-years of LFMC dynamics retrieved from Moderate Resolution Imaging Spectrometer (MODIS) reflectance product using the physical Radiative Transfer Model (RTM) and the wildfire events extracted from the MODIS Burned Area (BA) product to explore the relations between LFMC and forest/grassland fire occurrence across the subtropical highland zone (Cwa) and humid subtropical zone (Cwb) over southwest China. The statistical results of pre-fire LFMC and cumulative burned area show that distinct pre-fire LFMC critical thresholds were identified for Cwa (151.3%, 123.1%, and 51.4% for forest, and 138.1%, 72.8%, and 13.1% for grassland) and Cwb (115.0% and 54.4% for forest, and 137.5%, 69.0%, and 10.6% for grassland) zones. Below these thresholds, the fire occurrence and the burned area increased significantly. Additionally, a significant decreasing trend on LFMC dynamics was found during the days prior to two large fire events, Qiubei forest fire and Lantern Mountain grassland fire that broke during the 2009/2010 and 2015/2016 fire seasons, respectively. The minimum LFMC values reached prior to the fires (49.8% and 17.3%) were close to the lowest critical LFMC thresholds we reported for forest (51.4%) and grassland (13.1%). Further LFMC trend analysis revealed that the regional median LFMC dynamics for the 2009/2010 and 2015/2016 fire seasons were also significantly lower than the 10-year LFMC of the region. Hence, this study demonstrated that the LFMC dynamics explained wildfire occurrence in these fire-prone regions over southwest China, allowing the possibility to develop a new operational wildfire danger forecasting model over this area by considering the satellite-derived LFMC product.

We developed the Function of mask (Fmask) 4.0 algorithm for automated cloud and cloud shadow detection in Landsats 4–8 and Sentinel-2 images. Three major innovative improvements were made as follows: (1) integration of auxiliary data, where Global Surface Water Occurrence (GSWO) data was used to improve the separation of land and water, and a global Digital Elevation Model (DEM) was used to normalize thermal and cirrus bands; (2) development of new cloud probabilities, in which a Haze Optimized Transformation (HOT)-based cloud probability was designed to replace temperature probability for Sentinel-2 images, and cloud probabilities were combined and re-calibrated for different sensors against a global reference dataset; and (3) utilization of spectral-contextual features, where a Spectral-Contextual Snow Index (SCSI) was created for better distinguishing snow/ice from clouds in polar regions, and a morphology-based approach was applied to reduce the commission error in bright land surfaces (e.g., urban/built-up and mountain snow/ice). The Fmask 4.0 algorithm showed higher overall accuracies for Landsats 4–8 imagery than the 3.3 version ( Zhu et al., 2015 ) (92.40% versus 90.73% for Landsats 4–7 and 94.59% versus 93.30% for Landsat 8), and much higher overall accuracies for Sentinel-2 imagery than the 2.5.5 version of the Sen2Cor algorithm ( Muller-Wilm et al., 2018 ) (94.30% versus 87.10%).

Surface soil moisture (SSM) retrieval over agricultural fields using synthetic aperture radar (SAR) data is often obstructed by the vegetation effects on the backscattering during the growing season. This paper reports the retrieval of SSM from RADARSAT-2 SAR data that were acquired over wheat and soybean fields throughout the 2015 (April to October) growing season. The developed SSM retrieval algorithm includes a vegetation-effect correction. A method that can adequately represent the scattering behavior of vegetation-covered area was developed by defining the backscattering from vegetation and the underlying soil individually to remove the effect of vegetation on the total SAR backscattering. The Dubois model was employed to describe the backscattering from the underlying soil. A modified Water Cloud Model (MWCM) was used to remove the effect of backscattering that is caused by vegetation canopy. SSM was derived from an inversion scheme while using the dual co-polarizations (HH and VV) from the quad polarization RADARSAT-2 SAR data. Validation against ground measurements showed a high correlation between the measured and estimated SSM (R2 = 0.71, RMSE = 4.43 vol.%, p < 0.01), which suggested an operational potential of RADARSAT-2 SAR data on SSM estimation over wheat and soybean fields during the growing season.

Fuel moisture content (FMC) is a crucial variable affecting fuel ignition and rate of fire spread. Much work so far has focused on the usage of remote sensing data from multiple sensors to derive FMC; however, little attention has been devoted to the usage of the C-band Sentinel-1A data. In this study, we aimed to test the performance of C-band Sentinel-1A data for multi-temporal retrieval of forest FMC by coupling the bare soil backscatter linear model with the vegetation backscatter water cloud model (WCM). This coupled model that linked the observed backscatter directly to FMC, was firstly calibrated using field FMC measurements and corresponding synthetic aperture radar (SAR) backscatters (VV and VH), and then a look-up table (LUT) comprising of the modelled VH backscatter and FMC was built by running the calibrated model forwardly. The absolute difference (MAEr) of modelled and observed VH backscatters was selected as the cost function to search the optimal FMC from the LUT. The performance of the presented methodology was verified using the three-fold cross-validation method by dividing the whole samples into equal three parts. Two parts were used for the model calibration and the other one for the validation, and this was repeated three times. The results showed that the estimated and measured forest FMC were consistent across the three validation samples, with the root mean square error (RMSE) of 19.53% (Sample 1), 12.64% (Sample 2) and 15.45% (Sample 3). To further test the performance of the C-band Sentinel-1A data for forest FMC estimation, our results were compared to those obtained using the optical Landsat 8 Operational Land Imager (OLI) data and the empirical partial least squares regression (PLSR) method. The latter resulted in higher RMSE between estimated and measured forest FMC with 20.11% (Sample 1), 26.21% (Sample 2) and 26.73% (Sample 3) than the presented Sentinel-1A data-based method. Hence, this study demonstrated that the good capability of C-band Sentinel-1A data for forest FMC retrieval, opening the possibility of developing a new operational SAR data-based methodology for forest FMC estimation.

The robust and accurate retrieval of biophysical vegetation variables from remotely sensed data using radiative transfer models (RTMs) has been generally hampered by the ill-posed inverse problem. To alleviate this issue and improve the retrieval accuracy, a two-step inversion method based on the PROSAIL (PROSPECT+SAIL) radiative transfer model has been developed to consider the spectral information from neighboring pixels (3 × 3 windows) and the spatial dependency between these pixels. A case study was conducted to retrieve canopy water content (CWC) values from Landsat-8 OLI data for a plateau grassland located in the northeastern Qinghai province, China. The result showed that the proposed inversion method that the combination of nearby information yields more accurate results than the standard pixel-based inversion methods. With the proposed method, the coefficient of determination R2 reached 0.82 and root-mean-square error (RMSE) was 65.89 g·m−2, which was superior to the standard pixel-based inversion method with the R2 = 0.77, and RMSE 76.58 g·m−2.

Landslide susceptibility assessment is necessary for disaster management and safe planning in mountainous regions. In this paper, we have used rough set theory to select ten landslide-related factors (slope, aspect, elevation and so on) derived from maps of geology and remote sensing data, and produced landslide susceptibility map in Beichuan Country, Sichuan Province of China based on a model integrating C4.5 decision tree and m-branch smoothing. The performance of method was evaluated by receiver operation characteristic (ROC) curve and the area under the curve (AUC). The result shows that the above initial controlling factors are very important for landslide activity in study area. When the parameter value of m-branch smoothing was set as 14, the method can get the best prediction accuracy with AUC value of 87.16%. These results demonstrated the proposed method for landslide susceptibility assessment is quite effective and can be applied to more disaster assessment problems.