Severe wildfires increased and threaten in southwest Sichuan Province China, causing serious death and damage to this region. The local government launched a series of strict policies on wildfire management in 2014, including control of anthropogenic ignition sources and quick response on wildfire suppression when a fire is detected. This study aimed to examine the effectiveness of these policies in containing wildfire in this region based on multiple remotely sensed observation data. We examined the fire event count (FEC) change between 2001 and 2021 based on multiple satellite-based fire products and found the FEC markedly decreased since 2015. Since wildfire occurrence is a non-linear process resulting from interactions between weather, topography, fuel, and anthropogenic factors, to explore which factor led to such FEC decline, we examined three wildfire weather-related variables - the vapor pressure deficit (VPD), Canada forest fire weather index (FWI), synthetical wildfire danger index (WDI), and three fuel load-related variables - foliage fuel load (FFL), aboveground biomass (AGB), and solar-induced chlorophyll fluorescence (SIF). We found no significant (p>0.05) increase or decline in wildfire weather trend was observed, whereas a significant fuel load accumulation trend (p<0.05) was identified across this region between 2001 and 2021. As the topography factor is stable, this study, with the lengths of remote sensing observation, demonstrated the effectiveness of the management paradigm containing wildfire over southwest China.

Live fuel moisture content (LFMC) is one of the key variables affecting wildfire ignition and behavior. Remote sensing data-derived vegetation indices, meteorological indicators, and soil moisture have been reported to estimate LFMC in previous studies, but LFMC estimation from all these sources has not yet been attempted. This study pooled all these remotely sensed data in the construction of LFMC estimation models to this end. Based on the XGBoost (Extreme Gradient Boosting) algorithm, we built an empirical model and reached reasonable LFMC estimates (R2=0.56, RMSE=27.16%) across the western U.S. states. The best LFMC estimate was found for the closed shrublands (R2=0.66, RMSE=24.38%), followed by open shrublands (R2=0.60, RMSE=30.04%), grasslands (R2=0.58, RMSE=27.02%), savannas (R2=0.50, RmSe=24.23%) and woody savannas (R2=0.44, RMSE=25.94%). This study advances from previous research as it involved the combination and analysis of multi-source indicators and required the improvement of LFMC estimation accuracy using meteorological long-term temporal characteristics data for multiple vegetation cover types in a large-scale regional context.

P-band PolInSAR has the potential to map forest height and biomass at the global scale with the upcoming BIOMASS mission. However, because of the strong penetration of P-band and temporal decorrelation of repeat-pass observations, volume temporal decorrelation (γVT), ground temporal decorrelation (γGT), and residual ground scattering (mmin) largely influence forest height inversion. By integrating the random volume over ground (RVoG) model and Sum of Kronecker Products (SKP) decomposition, this study proposed a multi-baseline forest height inversion method to remove the joint influence of these problems. The theoretical simulation and empirical experiments using airborne P-band PolInSAR data in tropical forests, Nouragues, explicitly explored how each of the three problems affects the inversion. γVTandmmingenerally affect forest height inversion more than γGT, and the concurrence of them brought severe overestimation to RVoG inversions (RMSE ranges from 7.6 to 18.8 m). Stepwise comparisons show that compensating for each of γGT, γVT, andmmincould improve the inversion accuracy further. The proposed multi-baseline inversion simultaneously solved all three problems and produced the best accuracy (RMSE of 3.4 m), and it has a stable performance for another two more different multi-baseline datasets, producing similar inversion accuracies (RMSE of 3.3 and 3.6 m). The additional experiment using BIOSAR 2007 datasets with varied temporal baselines of 0 day, 30 days, and 56 days demonstrated that the proposed method has stability against temporal decorrelation. Consequently, the proposed method improved both the accuracy and robustness of forest height inversion.

This study presents a radiative transfer process and machine learning combined method to estimate the crown foliage fuel load (FFL), an important factor influencing the characteristics of crown fires. To this end, the GeoSail, SAIL and PROSPECT radiative transfer models (RTMs) were firstly coupled together to simulate the near realistic scenario of a two-layered forest structure. For the backward inversion process, the coupled RTMs were linked to the machine learning models of multi-layer perceptron (MLP) to train this model and then derive the FFL estimates from the Landsat products. The performance of retrieved FFL was validated and compared with the traditional look-up table (LUT) method. Results showed that the MLP performed better than LUT, revealing the reasonable skill of the combination of RTMs and machine learning modeling in deriving the FFL from remote sensing data and putting the insight into wildfire danger assessment from space.

With the deepening understanding of fire research and the rapid development of remote sensing technology, remote sensing data has become the main basis for fire monitoring. This study aimed to test the accuracy of four selected fire products based on fire events in Sichuan Province, including VNPI4IMGTDL_NRT (hereinafter referred to as VNPI4DL), MCD64Al, Fire_CCI, MCDI4ML. The reference data is provided by Sichuan Forestry and Grassland Bureau, and the fire products are verified by the spatio-temporal cluster analysis method and classification accuracy evaluation method. The results show VNP14DL has the highest fire detection accuracy, with Fl-score reaching 0.490 in 2014, followed by MCD14ML (Fl-score=0.457), MCD64Al (Fl-score=0.390), and Fire_CCI (Fl-score=0.330).

In the absence of historical fire records, end-users intend to adopt free satellite-derived fire products, including global burn area (BA) and active fire (AF) products, to understand the historical fire dynamics for better forest management. Previous literature evaluated the accuracy of these fire products in regions with different environments, but no study evaluated the performance of these fire products in fire-prone, cloudy and mountainous areas. This study contributed to filling this gap, through the first evaluation of four broadly used fire products: MODIS-based MCD64A1, MCD14ML, VIIRS-based VNP14DLIMGTDL_NRT (hereafter simplified as VNP14DL), and ESA Fire_CCI51 over subtropical China. Two methods were applied to this end, the spatio-temporal clustering algorithm based on official historical fire records and the density-based random sampling and estimation method from the Landsat 8 fire scenes. The results show that both the AF and BA products show poor fire detection ability in this area (with all the F1-Score < 0.5). Among them, the VNP14DL performed best, followed by MCD14ML, Fire_CCI51, and MCD64A1. The MCD14ML had the best detection capability for small fires (< 50 hectares). AF products have an overall higher fire detection ability than BA products. These findings provide insights for the improvement of fire detection algorithms of these fire products in the fire-prone, cloudy and mountainous area over subtropical China.

Dead fuel moisture content (DFMC) is of important significance for estimating and predicting forest wildfire risk, in which 1-h dead fuel is most critical as the easiest fuel to ignite. Current methodologies based on empirical and physical models rely heavily on meteorological data from uneven and sparse stations. In contrast, satellite data become a better choice since its continuous surface observations. Of all satellites, Himawari-8 is the most appropriate data to meet the rapid changes of DFMC throughout the day owing to its high time resolution. Thus, this study explored the application of 1-h DFMC predicting using machine learning from Himawari -8 data. Random forest was selected for the prediction and linear regression was used for comparison. The results showed that random forest has a satisfactory performance with higher R2 (0.53) and lower RMSE (3.15%) than that of linear regression (R2=0.21, RMSE=5.47%). The research suggested that predicting 1-h DFMC at regional scales using machine learning from Himawari -8 data is promising.

Aboveground biomass (AGB) is an important indicator of grassland state. Remote sensing estimation method of grassland biomass can provide important decision support for decision-makers and relevant personnel on grassland management, such as the rational development and utilization of grassland resources, the local ecological environment protection and the rational development of animal husbandry. In this study, based on Landsat 8 and Sentinel-2 A/B satellites data, PROSAILH radiative transfer model (RTM) and look up table (LUT) algorithm were applied to temporally retrieve and map the grassland AGB in Qinghai Province from September 2019 to August 2020, an important graziery region in China. Further analysis on the relation between the AGB and meteorological data showes that the precipitation and air temperature in Qinghai is consistent with the average biomass dynamic, indicating the considerable effect of the meteorological factors on the AGB in this region.

Forest Dead Fuel Load (FDFL) is vital for fire prevention and suppression since it affects the surface fire ignition and intensity directly. The accurate spatial distribution information of FDFL can provide decision support for fire managers. Remote sensing (RS) technology is a unique way to estimate FDFL on a large scale. However, most researches focus on the application of LiDAR data which is expensive to analyze fuel dynamics on spatiotemporal scale. Little attention has been devoted to freely accessible datasets such as Sentinel-1 and Sentinel-2 with global coverage. This study not only combined these two data but also the site conditions (i.e., elevation, slope and aspect) to estimate the FDFL in the southwest of Sichuan, China. The machine learning method, Random Forest Regression (RFR) was selected to manage the multiple and nonlinear relationships between RS data and FDFL. Results show that 1h and all FDFL (the sum of 1h, 10h, 100h and litter) can be more indicated by RS data (1h:$\mathrm{R}^{2}=0.57,\ \text{RMSE}=0.18$Tons/ha; All:$\mathrm{R}^{2}=0.59,\ \text{RMSE}=1.81$Tons/ha). The representation ability of RS data for 10h, 100h and litter is relatively weaker (10h:$\mathrm{R}^{2}=0.41,\ \text{RMSE}=0.47$Tons/ha; 100h:$\mathrm{R}^{2}=0.40,\ \ \text{RMSE}=1.07$Tons/ha; litter:$\mathrm{R}^{2}=0.29,\quad \text{RMSE}=1.66$Tons/ha). Hence, this study demonstrated the potential of multi-source RS data for FDFL estimation.

Wildfire is a typical complex nonlinear process, which has various interactions with weather, fuel, topography, and human variables. Previous studies on wildfire danger modeling mainly focused on the current climate or future scenarios of these variables. However, the outbreaks of wildfire are not only related to various factors at the current moment but also related to the change and trend of factors before the outbreak time point. The short-term features of weather and fuel dynamics$\text{(weather}\&\text{fuel}_{\text{short-term}_{-}\text{features}})$were therefore assumed to be of great importance in wildfire danger modeling within this context. To verify this hypothesis, the wildfire danger was modeled across the fire-prone area over southwest China from 2001 to 2020. The state-of-the-art eXtreme Gradient Boosting (XGBoost) model was applied to explore the relations between this topography, weather, fuel, and human variables and the wildfire occurrence, as well as to identify the role of five categories of$\text{weather}\&\text{fuel}_{\text{short}-} \text{term}_{-}\text{features}$, i.e., the$t^{C3}, t^{Entropy}$, Ricker's wavelet transformation$(f^{cwt})$, regular statistic description$(t^{RSD})$, and fast Fourier transform$(t^{FFT})$. Results showed that wildfire danger modeling can be improved by consideration of the$\text{weather}\&\text{fuel}_{\text{short-term}_{-}\text{features}}$which is proved to play an important role in driving the wildfire occurrence by factor importance analysis.

Wildfire, one of the most serious disasters in the world, making a huge threat to local economic development and residents' life and property safety. Therefore, it is crucial to realize near real time wildfire detection in specific high wildfire risk area. Himawari-8 is a state of art Geostationary Orbit Satellites (GOS), which can realize near-real time wildfire detection and dynamic monitoring of wildfire changes. In this study, three features strategies: spectral, spectral calculated and spectral with spatial were used to extract features from Himawari-8 and Topography data. Next, Random Forest models were trained by these features. And then, we evaluated models on Himawari-8 data generated in March 29, 2020 6:10 UTC, March 30, 2020 5:50 UTC, March 31, 2020 5:30 UTC and April 1,2020 5:50 UTC to detect specific wildfire events in Southwestern China. The results showed an overall precision of 64.24%, 64.08% and 68.07%, and an overall F1-score of 0.737, 0.677, 0.724 based on three different features strategies. This study proved that our models have the ability to detect wildfire point accurately, and model with strategy.3 performed the best when considering precision, omission and F1-score.

Spatiotemporal mapping of shrublands live fuel moisture content (LFMC) is of critical significance for wildfire risk assessment. Previous studies generally estimated the shrubland LFMC based on PROSAILH radiative transfer model (RTM), the model assuming that shrub canopies were homogenously and continuously distributed. We argued that this treatment was inaccurate since shrublands present diverse distribution and structure that normally can be classified into closed and open shrublands according to IGBP scheme. To this end, this study explored the effects of PROSAILH for open and closed shrublands LFMC retrieval based on National Fuel Moisture Database. The results showed that 1) PROSAILH performed poorly on open shrublands LFMC retrieval due to heterogeneity, whereas 2) better results were found for closed shrublands LFMC cases. Thus, this research suggested that PROSAILH is not suitable for all types of shrublands LFMC retrievals.

Wildfire is one of the major environmental issue in economic, social and environmental damages globally. Three major forces are essential for understanding the wildfire occurrence, fuel, weather and topography, as regulated by the `fire environment triangle'. Within this concept, a big database that involved all the satellite-derived fuel parameters (fuel moisture content and its derived variables, Leaf area index and vegetation type), weather parameters (relative humidity, air temperature, precipitation, wind speed), satellite-derived topography parameters (slope, aspect, elevation) and corresponding historical wildfires events that calculated from the MODIS burned area product (MCD64A1) was built from 2001-2018 over China. To explore the relations between wildfires and these parameters, the random forest model was developed to mine the relations between the explanatory variables (fuel, weather and topography parameters) and the dependent variable (historical wildfires events). Results showed that the models obtained a high accuracy level in fire spatial pattern distribution, indicating reasonable capacity in predicting wildfire occurrence in China.

Near-real-time monitoring live fuel moisture content (LFMC) from remote sensing is paramount to wildfire early management at a large scale since LFMC is a critical variable in affecting fire ignition and fire spread rate. The geostationary satellite Himawari-8 observes the land surface every 10 minutes, making near-real-time LFMC retrieval achievable. To this end, the potential of Himawari-8 data for LFMC retrieval using the radiative transfer model was explored in this study. The performance of retrieved LFMC was validated using 16 LFMC samplings located in Australia involving two land cover types: croplands and tree cover lands. Additionally, the MODIS data was also applied and compared for the LFMC retrieval. The results showed that Himawati-8 data performed poor accuracy level with R2 and RMSE of 0.26 and 42.16%, respectively. Whereas better accuracy level was found for MODIS data, R2 and RMSE were 0.67 and 29.17%, respectively. This result indicated that the LFMC estimated from Himawari-8 is challenged. Detailed fieldwork and methodology improvements adopted for this data are needed for improving the LFMC estimate in the future.

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.

Fuel moisture content (FMC) of vegetation canopy is a critical variable in affecting wildfire behavior. Methodologies based on multiple sources of remote sensing data have shown a prominent advantage for spatial and temporal FMC mapping. However, there is no study focused on FMC retrieval using the Sentinel-2A satellite data to date. This study is to evaluate the performance of this data for FMC retrieval under the framework of the multiple coupled radiative transfer models. Due to the limited field measurements and discontinuous satellite data, only 15 field measurements from USA, South Africa, Australia and France were available for the validation of the retrieved FMC. Results show that the retrieved FMCs were promising with R2= 0.64 and RMSE = 47.16%, which demonstrated the potential usage of the Sentinel-2A data for FMC mapping and further applications for early-warning of wildfire risk.

The objective of this study is to investigate the potential of multitemporal remote sensing images for crop classification. Multi-temporal Landsat 8 OLI/TIRS C1 Level-1 images were acquired. The surface reflectance of visible and near infrared bands was used to represent the characteristics of crops. A time series model of surface reflectance was constructed for crop classification. Cloud cover is critical for the accuracy of classification. In order to remove the influence of clouds, the cloud pixels were neglected by setting a constant. Pearson correlation coefficient was used in the time series model of surface reflectance to classify the crop type. Finally, the overall accuracy reaches 78.26% and Kappa reaches 71.33%. Therefore, the method has the operational potential for crop classification even in the special area with cloudy or foggy weather.

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.

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 R2and RMSE are 0.549, 0.354 dB, and 0.543, 13.579 %, respectively.

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.

This paper studies the applicability of multi-temporal synthetic aperture radar (SAR) images in detecting urban land use types change. In this paper, the study area is 128×358 pixels covers Shuangliu International Airport, Chengdu, China. Nine scenes of ALOS-PALSAR HV images from July 2007 to October 2010 were collected. the logarithmic ratio operator was used to generate the intensity and texture feature difference images. Texture features were extracted by the gray level co-occurrence matrix (GLCM). Then the redundant difference information was compressed by PCA transformation. The index of dynamic change image was generated to represent the change in land use type in Chengdu Shuangliu International Airport.

Fuel biomass burning plays an important role in shaping many ecosystems worldwide and produces gaseous emissions which ultimately alter global climatic processes. As for grass, we assumed the fuel biomass could be approximately estimated from the total dry weight of aboveground grass live and dead organs. Remote sensing techniques adapted methods for estimating the aboveground biomass (the live organs) were wildly explored, yet limited studies used the remote sensing technique to estimate the dry weight of aboveground dead organs. For this end, a method of assimilating leaf area index (LAI) derived from radiative transfer model into the WOrld FOod STudies (WOFOST) model was presented to simultaneously estimate the total dry weight of aboveground grass live and dead organs (i.e., the grass fuel biomass). Validation between measured and estimated fuel biomass showed that the estimated fuel biomass presents a reasonable accuracy with the R2= 0.77 and the RMSE = 223.07 gm-2.

Nonvolume decorrelation (γNonvol) united with the unknown ground contribution will bring a 2-D ambiguity to volume-only coherence, making the inversion underdetermined even when multiple baselines are available. In the context of random volume over ground (RVoG) model and three-stage algorithm, this paper theoretically presented the varied response of different baselines to both γNonvoland ground contribution, and then proposed a new multi-baseline inversion method to reduce the 2-D ambiguity. The proposed method includes two steps, calculating the common overlapped ambiguity from different baselines and fixing the extinction coefficient, to more accurately retrieve the volume-only coherence and forest height. It makes no assumptions on γNonvoland ground contribution. The method was validated and compared with three single-baseline inversions and two published multi-baseline inversions by using the airborne P-band polarimetric SAR interferometry (PolInSAR) data and the reference data of LiDAR canopy height model (CHM) over a dense rainforest site. Results showed that the developed multi-baseline method successfully reduced the combined influence of both γNonvoland ground contribution, and performed better than any single baseline, improving the R2from 0.60 to 0.77 and unbiased root-mean-square error (RMSE) from 1.32 to 1.04 m at the scale of ca. 100 × 140 m2. Moreover, the multi-baseline scheme is relatively robust among different baseline combinations.

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.

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.

This study focuses on the forest fire risk assessing using entirely remote sensing derived variables. These variables include Fuel moisture content (FMC), Normalized Difference Vegetation Index (NDVI), Leaf Area Index (LAI), Elevation and Slope. The Difference and Anomaly of FMC in time series are also calculated since FMC is one of the critical factors in assessing the wildfire risk. The logistic regression model is used to integrate all the variables in the fire occurred and none-occurred areas to derive the Fire Risk Index (FRI). A case study of the above methodology is applied to assess the FRI in Yunnan Province in China. The result shows that the AUC is to 0.8 for grassland and 0.81 for woodland, which indicates the good performance of the presented methodology in assessing forest fire risk.

We present the first continental-scale methodology for estimating Live Fuel Moisture Content (FMC) and flammability in Australia using satellite observations. The methodology includes a physically-based retrieval model to estimate FMC from MODIS (Moderate Resolution Imaging Spectrometer) reflectance data using radiative transfer model inversion. The algorithm was evaluated using 363 observations at 33 locations around Australia with mean accuracy for the studied land cover classes (grassland, shrubland and forest) close to those obtained elsewhere (r2=0.57, RMSE = 40%) but without site-specific calibration. Logistic regression models were developed to predict a flammability index, trained on fire events mapped in the MODIS burned area product and four predictor variables calculated from the FMC estimates. The selected predictor variables were actual FMC corresponding to the 8-day and 16-day period before burning; the same but expressed as an anomaly from the long-term mean for that date; and the FMC change between the two successive 8-day periods before burning. Separate logistic regression models were developed for grassland, shrubland and forest, obtaining performance metrics of 0.70, 0.78 and 0.71, respectively, indicating reasonable skill in fire risk prediction.

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.

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 R2reached 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.

Leaf area index (LAI) is an important biophysical vegetation variable in many models describing vegetation atmosphere interactions. This study presents a method to assimilation leaf area index(LAI) obtained by a simple downscaling technique into the World Food Studies(WOFOST) to improve simulation LAI in time series in plateau grasslands located in northeast of Qinghai province, China. Using the Sobol' global sensitivity analysis approach, serval local and regional crop parameters of WOFOST model were identified to be recalibrated. Then, the ensemble Kalman filter algorithm was introduced to assimilate the 30m LAI into the WOFOST model to update the model state for improving simulation effect.

Understanding the dynamics of vegetation activity and its interaction with climate factors becomes an important issue in global change research. In this study, using satellite-derived normalized difference vegetation index (NDVI) data, we analyzed changes in vegetation growth at the region scale from 2000 to 2013 in Sichuan Province, China. And then explored the relationship between NDVI trends and the temperature and precipitation. The results showed: (1) The NDVI of the vegetated area in Sichuan was decreasing with a trend about −2.3 ×10−3NDVI.yr−1. About 2/3 of the vegetated area showed a negative trend of annual mean NDVI. (2) In larger areas vegetation growth was controlled by temperature. (3) Monthly NDVI was significantly correlated with the preceding month's temperature and precipitation. (4) The spatial pattern of relationship between NDVI and climatic factors was likely correlated with terrain and vegetation type.

Grassland, which has a great significance to human society, is susceptible to the drought. An efficient way to monitor the drought scale of grassland is imperative. In this paper, we proposed a new drought index named Grassland Drought Index (GDI) to monitor the drought conditions of the grassland ecosystem. The GDI was constructed by comprehensively integrating the soil moisture content, canopy water content (CWC) and precipitation together with different weights. The soil moisture with 1km spatial resolution was estimated by downscaling the AMSR-E soil moisture from 25km to 1km. The CWC was retrieved by the 1km MODIS products based on the PROSAIL model. And the precipitation was acquired by CRU data. Besides, the global distribution product of GDI in July 2010 was produced. By validation, the correlation coefficient R between the scaled GDI and the Standard Precipitation Index (SPI) was 0.6109, and the root-mean-square error (RMSE) was 0.7774.

A novel way to estimate the live fuel moisture content (LFMC) was explored from the ratio of canopy water content (CWC) and foliage dry biomass (FDB). The CWC was estimated using the PROSAIL (PROSPECT + SAIL) radiative transfer model from the Landsat 8 product. A weak constraint 4-D variational data assimilation method was employed to assimilate the temporally estimated leaf area index into a soil-water-atmosphere-plant (SWAP) model for optimizing the model control variables. Then, the SWAP model was reinitialized with this optimum set of control variables, and better prediction of FDB was obtained. Results showed that a high accuracy level was achieved for the estimated CWC (R2= 0.91, RMSE = 84.74 g/m2) and FDB (R2= 0.88, RMSE = 48.54 g/m2) when compared with in situ measured values. However, the accuracy level of estimated LFMC was poor (R2= 0.59, RMSE = 30.85%). Further analyses find that the estimated LFMC is reliable for low LFMC but challenged for high LFMC, which indicates that the presented method still makes sense to the assessment of wildfire risk since the wildfire generally occurs when the vegetation is in low LFMC condition.

Retrieval of vegetation parameters from remotely sensed data using a radiative transfer model is generally hampered by the ill-posed inverse problem, which dramatically decreases the precision level of retrieved parameters. The purpose of this study was to use a Bayesian network-based method to allow the alleviation of the ill-posed inverse problem. This was achieved by introducing the correlations between the model free parameters into their prior joint probability distribution (PJPD), allowing the reduction of the probabilities of unrealistic combinations. Three sampling strategies intended to design three types of PJPDs that considered different correlations (represented by a correlation matrix) were presented. They were multivariate uniform distribution composed by independent free parameters, multivariate uniform distribution based on a simple correlation matrix, and multivariate Gaussian distribution based on a complicated correlation matrix, respectively. A case study of the presented method to retrieve leaf area index (LAI) and canopy water content (CWC) using the PROSAIL_5B (PROSPECT-5 + 4SAIL) model from Landsat 8 products was implemented. Results indicate that the presented method greatly improves the precision level of target parameters, with the coefficient of determination R2of 0.69, 0.77, and 0.82 and root-mean-square error (RMSE) of 0.55, 0.51, and 0.44 m2· m-2for LAI and R2= 0.68, 0.78, and 0.84 and RMSE = 230, 198, and 166 g · m-2for CWC, respectively. Hence, the ill-posed inverse problem can be alleviated by the presented method, which can be widely applied for vegetation parameters retrieval.

The retrieval of parameters through a physical mechanism model is promising for its generality but is challenged by the ill-posed inversion problem. This study focused on the use of multiple priori information to alleviate the ill-posed inversion problem. The priori information included the products of satellite images, the correlations among model free parameters, field survey, and the achievements of previous studies. However, the priori information was of uncertainty, which was described by multi-variables probability distribution in this study. A Bayesian network algorithm was used to retrieve the canopy water content (CWC) by calculating the posterior probability distribution of CWC based on the priori information, the HJ-1B product, and the PROSAIL model. The retrieval results showed that the R2 = 0.83 and RMSE = 0.18 compared to the field measured CWC, which confirmed the feasibility to alleviate the ill-posed inversion problem by the multiple priori information.

A synergistic method of SAR and optical remote sensing data for retrieval of soil moisture was developed in this paper. Vegetation coverage, which can be easily estimated from optical data, was combined in the backscattering model. The total backscattering was divided into the amount attributed to areas covered with vegetation and that attributed to areas of bare soil. Backscattering coefficients were simulated using the established backscattering model. Then, soil moisture was estimated using the inverted model. The results showed that the predicted soil moisture correlated with the measured soil moisture (R2= 0.7075, RMSE = 3.3219 m2/m2).

Leaf area index (LAI) is one of the key vegetation indices for many biological and physical processes in plant canopies. In this study, an assimilation technique was used to simulate the LAI's varying in time series in an alpine wetland located in western China. The Terra MODIS 16 day composite surface reflectance products at 250 m resolution in 2010 with high quality were used. LAI was retrieved based on the ACRM canopy reflectance model and LUT algorithm. An experiential LOGISTIC model was fitted using the retrieved LAI, and the ensemble Kalman filter algorithm was introduced to assimilate the estimated LAI into the LOGISTIC model to update the model state.