Dynamic Drought Monitoring in Guangxi Using Revised Temperature Vegetation Dryness Index

Moderate resolution imaging spectroradiometer （MODIS） data are very suitable for vast extent, long term and dynamic drought monitoring for its high temporal resolution, high spectral resolution and moderate spatial resolution. The composite Enhanced Vegetation Index （EVI） and composite land surface temperature （Ts） obtained from MODIS data MOD11A2 and MOD13A2 were used to construct the EVI-Ts space. And Temperature Vegetation Dryness Index （TVDI） was calculated to evaluate the agriculture drought in Guangxi province, China in October of 2006. The results showed that the drought area in Guangxi was evidently increasing and continuously deteriorating from the middle of September to the middle of November. The TVDI, coming from the EVI-Ts space, could effectively indicate the spatial distribution and temporal evolution of drought, so that it could provide a strong technical support for the forecasting agricultural drought in south China.

Coupled effects of climate change and human activities on vegetation dynamics in the Southwestern Alpine Canyon Region of China

The driving effects of climate change and human activities on vegetation change have always been a focal point of research.However,the coupling mechanisms of these driving factors across different temporal and spatial scales remain controversial.The Southwestern Alpine Canyon Region of China(SACR),as an ecologically fragile area,is highly sensitive to the impacts of climate change and human activities.This study constructed a vegetation cover dataset for the SACR based on the Enhanced Vegetation Index(EVI)from 2000 to 2020.Spatial autocorrelation,Theil-Sen trend,and Mann-Kendall tests were used to analyze the spatiotemporal characteristics of vegetation cover changes.The main drivers of spatial heterogeneity in vegetation cover were identified using the optimal parameter geographic detector,and an improved residual analysis model was employed to quantify the relative contributions of climate change and human activities to interannual vegetation cover changes.The main findings are as follows:Spatially,vegetation cover exceeds 60%in most areas,especially in the southern part of the study area.However,the border area between Linzhi and Changdu exhibits lower vegetation cover.Climate factors are the primary drivers of spatial heterogeneity in vegetation cover,with temperature having the most significant influence,as indicated by its q-value,which far exceeds that of other factors.Additionally,the interaction q-value between the two factors significantly increases,showing a relationship of bivariate enhancement and nonlinear enhancement.In terms of temporal changes,vegetation cover shows an overall improving trend from 2000 to 2020,with significant increases observed in 68.93%of the study area.Among these,human activities are the main factors driving vegetation cover change,with a relative contribution rate of 41.31%,while climate change and residual factors contribute 35.66%and 23.53%,respectively.By thoroughly exploring the coupled mechanisms of vegetation change,this study provides important references for the sustainable management and conservation of the vegetation ecosystem in the SACR.

Response of vegetation variation to climate change and human activities in the Shiyang River Basin of China during 2001-2022

Understanding the response of vegetation variation to climate change and human activities is critical for addressing future conflicts between humans and the environment,and maintaining ecosystem stability.Here,we aimed to identify the determining factors of vegetation variation and explore the sensitivity of vegetation to temperature(SVT)and the sensitivity of vegetation to precipitation(SVP)in the Shiyang River Basin(SYRB)of China during 2001-2022.The climate data from climatic research unit(CRU),vegetation index data from Moderate Resolution Imaging Spectroradiometer(MODIS),and land use data from Landsat images were used to analyze the spatial-temporal changes in vegetation indices,climate,and land use in the SYRB and its sub-basins(i.e.,upstream,midstream,and downstream basins)during 2001-2022.Linear regression analysis and correlation analysis were used to explore the SVT and SVP,revealing the driving factors of vegetation variation.Significant increasing trends(P<0.05)were detected for the enhanced vegetation index(EVI)and normalized difference vegetation index(NDVI)in the SYRB during 2001-2022,with most regions(84%)experiencing significant variation in vegetation,and land use change was determined as the dominant factor of vegetation variation.Non-significant decreasing trends were detected in the SVT and SVP of the SYRB during 2001-2022.There were spatial differences in vegetation variation,SVT,and SVP.Although NDVI and EVI exhibited increasing trends in the upstream,midstream,and downstream basins,the change slope in the downstream basin was lower than those in the upstream and midstream basins,the SVT in the upstream basin was higher than those in the midstream and downstream basins,and the SVP in the downstream basin was lower than those in the upstream and midstream basins.Temperature and precipitation changes controlled vegetation variation in the upstream and midstream basins while human activities(land use change)dominated vegetation variation in the downstream basin.We concluded that there is a spatial heterogeneity in the response of vegetation variation to climate change and human activities across different sub-basins of the SYRB.These findings can enhance our understanding of the relationship among vegetation variation,climate change,and human activities,and provide a reference for addressing future conflicts between humans and the environment in the arid inland river basins.

Effect of Topography and Accessibility on Vegetation Dynamic Pattern in Mountain-hill Region

Knowledge of both vegetation distribution pattern and phenology changes is very important.Their complicated relationship with elevation and accessibility were explored through a geographically weighted regression(GWR) framework in Fujian province,China.The 16-day time series of 250 m Moderate Resolution Imaging Spectroradiometer(MODIS) Enhanced Vegetation Index(EVI) dataset from 2000 to 2010 was applied.Wavelet transform method was adopted to decompose the original time series and construct the annual maximum EVI and amplitude of the annual phenological cycle(EVI).Candidate explaining factors included topographic conditions,accessibility variables and proportions of primary vegetation types.Results revealed very strong positive influence from parameters of elevation and accessibility to big rivers and negative effect from accessibility to resident on both maximum EVI and phenological magnitude through ordinary linear least square(OLS) regression analysis.GWR analysis revealed that spatially,the parameters of topography and accessibility had a very complex relationship with both maximum EVI and phenology magnitude,as a result of the various combinations of environmental factors,vegetation composition and also intensive anthropogenic impact.Apart from the continuously increasing trend of phenology magnitude with increasing altitude,the influence of topography and accessibility on maximum EVI and phenological magnitude generally decreased,even from strongly positive to negative,with increasing altitude or distance.Specially,the most rapid change of correlation coefficient between them was observed within a low elevation or close distance;less variation was discovered within a certain range of medium altitude or distance and their relationship might change above this range.Non-stationary approaches are needed to better characterize the complex vegetation dynamic pattern in Mountain-hill Region.

Climate Change and Ecological Projects Jointly Promote Vegetation Restoration in Three-River Source Region of China

As the source of the Yellow River,Yangtze River,and Lancang River,the Three-River Source Region(TRSR)in China is very important to China’s ecological security.In recent decades,TRSR’s ecosystem has degraded because of climate change and human disturbances.Therefore,a range of ecological projects were initiated by Chinese government around 2000 to curb further degradation.Current research shows that the vegetation of the TRSR has been initially restored over the past two decades,but the respective contribution of ecological projects and climate change in vegetation restoration has not been clarified.Here,we used the Moderate Resolution Imaging Spectroradiometer(MODIS)Enhanced Vegetation Index(EVI)to assess the spatial-temporal variations in vegetation and explore the impact of climate and human actions on vegetation in TRSR during 2001–2018.The results showed that about 26.02%of the TRSR had a significant increase in EVI over the 18 yr,with an increasing rate of 0.010/10 yr(P<0.05),and EVI significantly decreased in only 3.23%of the TRSR.Residual trend analysis indicated vegetation restoration was jointly promoted by climate and human actions,and the promotion of human actions was greater compared with that of climate,with relative contributions of 59.07%and40.93%,respectively.However,the degradation of vegetation was mainly caused by human actions,with a relative contribution of71.19%.Partial correlation analysis showed that vegetation was greatly affected by temperature(r=0.62,P<0.05)due to the relatively sufficient moisture but lower temperature in TRSR.Furthermore,the establishment of nature reserves and the implementation of the Ecological Protection and Restoration Program(EPRP)improved vegetation,and the first stage EPRP had a better effect on vegetation restoration than the second stage.Our findings identify the driving factors of vegetation change and lay the foundation for subsequent effective management.

Analysis of Climate Variability and Relation to Vegetation in Garamba National Park from 1990-2020

Climatic factors impact vegetation. Our study was to examine and analyze the climate variability and relationship to vegetation in Garamba National Park of the Democratic Republic of the Congo over the past 30 years (1990 to 2020), then to relate the climatic variables. Mann Kendall’s non parametric test, ANOVA, and p-value tests are used to analyze existing trends and relationships between vegetation cover, climatic factors, land surface temperature (LST) and normalized difference in temperature Vegetation index (NDVI), Enhanced vegetation index (EVI) in Garamba national park which is of particular importance for the network of protected areas of the Democratic republic of Congo because its position at the northern limit of the savanna-forest mosaics gives it a unique biodiversity. The southern part of the park is dominated by grassy shrub savannas. The results showed that: 1) In Garamba, the monthly correlation coefficient of Kendall and Pearsan between temperature and precipitation are negative respectively 0.763 and −0.876 (p-value < 0.00001). 2) Annually during the three decades in Garamba, the correlation between precipitation and NDVI is significant 0.416 (Kendall) and 0.496 (Pearsan);the same between precipitation and EVI 0.291 (Kendall) and 0.496 (Pearsan) while LST and precipitation are negatively correlated (p-value < 0.00001).

Increased Browning of Woody Vegetation due to Continuous Seasonal Droughts in Yunnan Province, China

In this paper, based on the analysis of satellite measurements, the authors conclude that the continuous seasonal droughts intensify the browning of woody vegetation and that evergreen needleleaf forest(ENF) shows a larger browning percentage than other woody vegetation types over Yunnan Province. Based on the Tropical Rainfall Measuring Mission(TRMM) precipitation standardized anomaly, in the dry season, which is from October to March, the 2010 drought affected an area of Yunnan Province 1.77 times larger than the 2012 drought, but in the post-drought months(April to June), the browning area of all woody vegetation in 2012 was 1.11 times larger than that in 2010 on the basis of the enhanced vegetation index(EVI) standardized anomaly. The reduction of vegetation greenness over large areas of Yunnan Province represents a photosynthetic capacity loss which will have an impact on carbon fluxes to the atmosphere.

Vegetation recovery trends under dual dominance of climate change and anthropogenic factors in the severely damaged areas of the Wenchuan earthquake

The occurrence of the Wenchuan earthquake caused the degradation of regional ecosystems,including vegetation destruction.However,the post-seismic vegetation recovery and its driving forces on the spatial-temporal scale are still vague,especially in the severely damaged areas(including Wenchuan,Beichuan,Mianzhu,Shifang,Qingchuan,Maoxian,Anzhou,Dujiangyan,Pingwu and Pengzhou).Here,we detected vegetation recovery in the severely damaged areas by using Ensemble Empirical Mode Decomposition(EEMD)to analyze the time series characteristics of the Enhanced Vegetation Index(EVI),and explored the driving effects of climate,land use types,nighttime light,water system,slope,and clay content on vegetation recovery based on Geographically and Temporally Weighted Regression(GTWR)model.The results indicated that the post-seismic vegetation recovery rate increased rapidly(acceleration>0)but slowed down after 2013.And the areas of best vegetation recovery(EVI increments>0.1)were distributed in the north of the study area,the Minjiang River Basin,and front fault and central fault of the Longmenshan Fault Zone.While the areas with the worst vegetation recovery(EVI increments<-0.1)were concentrated in the southern high-altitude areas and the Chengdu Plain.Additionally,a process attribution of the driving forces of vegetation recovery indicated that accumulated precipitation and maximum temperature promoted vegetation recovery(regression coefficients>0),but the impacts weakened after the earthquake,possibly due to the increase of secondary disasters induced by precipitation and the rise in maximum temperature.The impact of cultivated land on vegetation recovery was mostly positive(regression coefficients>0),which may be related to the implementation of the Grain for Green Project.The nighttime light inhibited vegetation recovery(regression coefficients<0),which could be closely associated with urbanization.The results indicated that more attention should be paid to the nonlinear variations of post-earthquake vegetation recovery trends,and the effects of climatic and anthropogenic factors on vegetation recovery also should not be underestimated.

Multilevel Assessment of Spatiotemporal Variability of Vegetation in Subtropical Mountain-hill Region

The complex spatiotemporal vegetation variability in the subtropical mountain-hill region was investigated through a multi-level modeling framework. Three levels - parcel, landscape, and river basin levels- were selected to discover the complex spatiotemporal vegetation variability induced by climatic, geomorphic and anthropogenic processes at different levels. The wavelet transform method was adopted to construct the annual maximum Enhanced Vegetation Index and the amplitude of the annual phenological cycle based on the 16-day time series of a5om Moderate Resolution Imaging Spectroradiometer Enhanced Vegetation Index datasets during 2OOl-2OlO. Results revealed that land use strongly influenced the overall vegetation greenness and magnitude of phenological cycles. Topographic variables also contributed considerably to the models, reflecting the positive influence from altitude and slope. Additionally, climate factors played an important role： precipitation had a considerable positive association with the vegetation greenness, whereas the temperature difference had strong positive influence on the magnitude of vegetation phenology. The multilevel approach leads to a better understanding of the complex interaction of the hierarchical ecosystem, human activities and climate change.

Change of Vegetation Cover and Its Relationships with Climatic Factors in Large-area Eucalyptus Introduced Region of Yunnan

Taking Lancang County as a study area with a large area of eucalyptus introduction in Yunnan, spatiotemporal change characteristics of vegetation cover, as well as the relationships between Enhanced Vegetation Index（EVl） and climatic factors （temperature and precipitation） were analyzed by using the data of MODIS-EVI from 2005 to 2010. The results indicated that： （1） The vegetation cover was overall good, and the annual average values of EVl were greater than 0.395 and showed a slow increasing trend from 2005 to 2010 in study area; the monthly average values of EVl ranged from 0.296 to 0.538, and seasonal variability was obvious. Monthly average values of EVl usually fell to the lowest level in February and March, and reached the peak in July and August. From the perspective of space, average EVl over the years significantly varied in different towns of Lancang County. During 2005 -2010, in 92.534% area of total, vegetation coverage change were not obvious; in 7.25% area of total, vegeta- tion becoming better; only in 0.02% area of total, vegetation cover were getting worse. （2） Monthly average values of EVl were significantly correlated with monthly average rainfall in Lancang County. The maxima of monthly average EVI and rainfall appeared in August on summer, while the minima of monthly average EVl and rainfall appeared in February and January on winter respectively. （3） Monthly average EVl was somewhat relative with monthly average temperature. The maxima of monthly average EVl and temperature appeared in June and August respectively, while the minima appeared in January and February respectively.

Soil Moisture Monitoring Based on Land Surface Temperature-Vegetation Index Space Derived from MODIS Data

Soil moisture has been considered as one of the main indicators that are widely used in the fields of hydrology, climate, ecology and others. The land surface temperature-vegetation index （LST-VI） space has comprehensive information of the sensor from the visible to thermal infrared band and can well reflect the regional soil moisture conditions. In this study, 9 pairs of moderate-resolution imaging spectroradiometer （MODIS） products （MOD09A1 and MODllA2）, covering 5 provinces in Southwest China, were chosen to construct the LST-VI space, and then the spatial distribution of soil moisture in 5 provinces of Southwest China was monitored by the temperature vegetation dryness index （TVDI）. Three LST-VI spaces were constructed by normalized difference vegetation index （NDVI）, enhanced vegetation index （EVI）, and modified soil-adjusted vegetation index （MSAVI）, respectively. The correlations between the soil moisture data from 98 sites and the 3 TVDIs calculated by LST-NDVI, LST-EVI and LST-MSAVI, respectively, were analyzed. The results showed that TVDI was a useful parameter for soil surface moisture conditions. The TVDI calculated from the LST-EVI space （TVDIE） revealed a better correlation with soil moisture than those calculated from the LST-NDVI and LST-MSAVI spaces. From the different stages of the TVDIE space, it is concluded that TVDIE can effectively show the temporal and spatial differences of soil moisture, and is an effective approach to monitor soil moisture condition.

Monitoring Perennial Sub-Surface Waterlogged Croplands Based on MODIS in Jianghan Plain, Middle Reaches of the Yangtze River

Perennial waterlogged soil（PWS） is induced by the high level of groundwater, and has a persistent impact on natural ecosystems and agricultural production. Traditionally, distribution information regarding PWS is mainly collected from in situ measurements through groundwater level surveys and physicochemical property analyses. However, in situ measurements of PWS are costly and time-consuming, only rough estimates of PWS areas are available in some regions. In this paper, we developed a method to monitor the perennial waterlogged cropland using time-series moderate resolution imaging spectroradiometer（MODIS） data. The Jianghan Plain, a floodplain located in the middle reaches of the Yangtze River, was selected as the study area. Temporal variations of the enhanced vegetation index（EVI）, night land surface temperature（LST）, diurnal LST differences（ΔLST）, albedo, and the apparent thermal inertia（ATI） were used to analyze the ecological and thermodynamic characteristics of the waterlogged croplands. To obtain pure remote sensing signatures of the waterlogged cropland from mixed pixels, the croplands were classified into different types according to soil and land cover types in this paper, and a linear mixing model was developed by fitting the signatures using the multiple linear regression approach. Afterwards, another linear spectral mixing model was used to get the proportions of waterlogged croplands in each 1 km×1 km pixel. The result showed an acceptable accuracy with a root-mean-square error of 0.093. As a tentative method, the procedure described in this paper works efficiently as a method to monitor the spatial patterns of perennial sub-surface waterlogged croplands at a wide scale.

Geographic range size patterns across plants and animals of Xinjiang,China

Patterns in species geographic range size are relatively well-known for vertebrates,but still poorly known for plants.Contrasts of these patterns between groups have rarely been investigated.With a detailed flora and fauna distribution database of Xinjiang,China,we used regression methods,redundancy analysis and random forests to explore the relationship of environment and body size with the geographic range size of plants,mammals and birds in Xinjiang and contrast these patterns between plants and animals.We found positive correlations between species range size and body size.The range size of plants was more influenced by water variables,while that of mammals and birds was largely influenced by temperature variables.The productivity variable,i.e.,Enhanced Vegetation Index(EVI)was far more correlated with range size than climatic variables for both plants and animals,suggesting that vegetation productivity inferred from remote sensing data may be a good predictor of species range size for both plants and animals.

The Influence of Tropical Cyclones to the Plant Productivity Indices along the Coast of Tanzania

The study investigated the influence of Tropical cyclone (TCs) to the plant productivity indices along the coast of Tanzania using both field observations and change detection methods. These indices are normally designed to maximize the sensitivity of the vegetation characteristics and are very crucial in monitoring droughts intensity, yield and biomass amongst others. The study used three types of satellite imageries including the 16 days Moderate Resolution Imaging Spectroradiometer (MODIS) of 250 × 250 m resolution;8 days Landsat 7 enhanced thematic mapper (ETM) with resolution of 30 × 30 m composites, and 5 Landsat 8 (LC8) images, to determine the patterns and the variability of the Normalized Difference Vegetation Index (NDVI) and Enhanced Vegetation Index (EVI) and TCs impacts on vegetation. Moreover, we used Tropical Rainfall Measuring Mission (TRMM) data and the daily to monthly rainfall data from Tanzanian Meteorological Authority (TMA). The change detection between the pre and post storm (TCs) conditions was used to analyse inter annual variability of EVI over Chwaka, Rufiji and Pugu— Kazimzumbwi. The changes in NDVI and EVI and monthly rainfall at the coastal stations were calculated, plotted and analyzed. The results revealed that, highest EVI values over coastal Tanzania were observed during March and April, and minimum (low) values in November. The results for EVI changes based on pre and post storm conditions revealed that most observed stations and most TCs led to significant EVI changes which ranged from -0.05 to 0.19, and -0.3 to 0.22, for MODIS and L7 ETM data, respectively. As for the spatial changes in NDVI results revealed that, TCs (Besija and Fobane) were associated with positive NDVI changes i.e. (enhancement) of >0.51 and >0.31, and NDVI reduction (i.e. negative changes) of <0.02 and <-0.19 for Chwaka and Rufiji, respectively. Besides the results revealed that, TCs episodes have induced a land cover changes from i.e. water covered areas changed to be vegetation covered especially over the shorelines and inter tidal areas. Indeed, these results were consistent with the analysis of rainfall patterns which indicated that low rainfall occurred in low NDVI areas and vice versa.

Satellite remote sensing reveals overwhelming recovery of forest from disturbances in Asia

Forest ecosystems play key roles in mitigating human-induced climate change through enhanced carbon uptake;however,frequently occurring climate extremes and human activities have considerably threatened the stability of forests.At the same time,detailed accounts of disturbances and forest responses are not yet well quantified in Asia.This study employed the Breaks For Additive Seasonal and Trend method-an abrupt-change detection method-to analyze the Enhanced Vegetation Index time series in East Asia,South Asia,and Southeast Asia.This approach allowed us to detect forest disturbance and quantify the resilience after disturbance.Results showed that 20%of forests experienced disturbance with an increasing trend from 2000 to 2022,and Southeast Asian countries were more severely affected by disturbances.Specifically,95%of forests had robust resilience and could recover from disturbance within a few decades.The resilience of forests suffering from greater magnitude of disturbance tended to be stronger than forests with lower disturbance magnitude.In summary,this study investigated the resilience of forests across the low and middle latitudes of Asia over the past two decades.The authors found that most forests exhibited good resilience after disturbance and about two-thirds had recovered to a better state in 2022.The findings of this study underscore the complex relationship between disturbance and resilience,contributing to comprehension of forest resilience through satellite remote sensing.

Mapping paddy rice with multi-date moderate-resolution imaging spectroradiometer (MODIS) data in China

The objective of this study was to obtain spatial distribution maps of paddy rice fields using multi-date moderate-resolution imaging spectroradiometer(MODIS) data in China.Paddy rice fields were extracted by identifying the unique char-acteristic of high soil moisture in the flooding and transplanting period with improved algorithms based on rice growth calendar regionalization.The characteristic could be reflected by the enhanced vegetation index(EVI) and the land surface water index(LSWI) derived from MODIS sensor data.Algorithms for single,early,and late rice identification were obtained from selected typical test sites.The algorithms could not only separate early rice and late rice planted in the same fields,but also reduce the uncertainties.The areal accuracy of the MODIS-derived results was validated by comparison with agricultural statistics,and the spatial matching was examined by ETM+(enhanced thematic mapper plus) images in a test region.Major factors that might cause errors,such as the coarse spatial resolution and noises in the MODIS data,were discussed.Although not suitable for monitoring the inter-annual variations due to some inevitable factors,the MODIS-derived results were useful for obtaining spatial distribution maps of paddy rice on a large scale,and they might provide reference for further studies.

Variations of root and heterotrophic respiration along environmental gradients in China’s forests

Aims Root and heterotrophic respiration may respond differently to environmental variability,but little evidence is available from largescale observations.Here we aimed to examine variations of root and heterotrophic respiration across broad geographic,climatic,soil and biotic gradients.Methods We conducted a synthesis of 59 field measurements on root and heterotrophic respiration across China’s forests.Important Findings Root and heterotrophic respiration varied differently with forest types,of which evergreen broadleaf forest was significantly different from those in other forest types on heterotrophic respiration but without statistically significant differences on root respiration.The results also indicated that root and heterotrophic respiration exhibited similar trends along gradients of precipitation,soil organic carbon and satellite-indicated vegetation growth.However,they exhibited different relationships with temperature:root respiration exhibited bimodal patterns along the temperature gradient,while heterotrophic respiration increased monotonically with temperature.Moreover,they showed different relationships with MOD17 GPP,with increasing trend observed for root respiration whereas insignificant change for heterotrophic respiration.In addition,root and heterotrophic respiration exhibited different changes along the age sequence,with insignificant change for root respiration and decreasing trend for heterotrophic respiration.Overall,these results suggest that root and heterotrophic respiration may respond differently to environmental variability.Our findings could advance our understanding on the different environmental controls of root and heterotrophic respiration and also improve our ability to predict soil CO_(2) flux under a changing environment.

Modeling Aboveground Biomass Using MODIS Images and Climatic Data in Grasslands on the Tibetan Plateau

Accurate quantification of aboveground biomass of grasslands in alpine regions plays an important role in accurate quantification of global carbon cycling.The monthly normalized difference vegetation index（NDVI）,enhanced vegetation index（EVI）,mean air temperature（Ta）,≥5℃ accumulated air temperature（AccT）,total precipitation（TP）,and the ratio of TP to AccT（TP/AccT） were used to model aboveground biomass（AGB） in grasslands on the Tibetan Plateau.Three stepwise multiple regression methods,including stepwise multiple regression of AGB with NDVI and EVI,stepwise multiple regression of AGB with Ta,AccT,TP and TP/AccT,and stepwise multiple regression of AGB with NDVI,EVI,Ta,AccT,TP and TP/Acc T were compared.The mean absolute error（MAE） and root mean squared error（RMSE） values between estimated AGB by the NDVI and measured AGB were 31.05 g m^（-2） and 44.12 g m^（-2）,and 95.43 g m^（-2） and 131.58 g m^（-2） in the meadow and steppe,respectively.The MAE and RMSE values between estimated AGB by the AccT and measured AGB were 33.61 g m^（-2） and 48.04 g m^（-2） in the steppe,respectively.The MAE and RMSE values between estimated AGB by the vegetation index and climatic data and measured AGB were 28.09 g m^（-2） and 42.71 g m^（-2）,and 35.86 g m^（-2） and 47.94 g m^（-2）,in the meadow and steppe,respectively.The study finds that a combination of vegetation index and climatic data can improve the accuracy of estimates of AGB that are arrived at using the vegetation index or climatic data.The accuracy of estimates varied depending on the type of grassland.

Multi-year monitoring of paddy rice planting area in Northeast China using MODIS time series data

e The objective of this study was to investigate the tempo-spatial distribution of paddy rice in Northeast China using moderate resolution imaging spectroradiometer （MODIS） data. We developed an algorithm for detection and estimation of the transplanting and flooding periods of paddy rice with a combination of enhanced vegetation index （EVI） and land surface water index with a central wavelength at 2130 nm （LSW12130）. In two intensive sites in Northeast China, fine resolution satellite imagery was used to validate the performance of the algorithm at pixel and 3x3 pixel window levels, respectively. The commission and omission errors in both of the intensive sites were approximately less than 20%. Based on the algorithm, annual distribution of paddy rice in Northeast China from 2001 to 2009 was mapped and analyzed. The results demonstrated that the MODIS-derived area was highly correlated with published agricultural statistical data with a coefficient of determination （R^2） value of 0.847. It also revealed a sharp decline in 2003, especially in the Sanjiang Plain located in the northeast of Heilongjiang Province, due to the oversupply and price decline of rice in 2002. These results suggest that the approaches are available for accurate and reliable monitoring of rice cultivated areas and variation on a large scale.

Synergy of remotely sensed data in spatiotemporal dynamic modeling of the crop and cover management factor

Soil erosion is a threat to the water quality constituents of sediments and nutrients and can cause long-term environmental damages.One important parameter to quantify the risk of soil loss from erosion is the crop and cover management factor(C-factor),which represents how cropping and management practices affect the rates and potential risk of soil erosion.We developed remotely sensed data-driven models for dynamic predictions of C-factor by implementing dynamic land cover modeling using the SWAT(Soil and Water Assessment Tool)model on a watershed scale.The remotely sensed processed variables included the enhanced vegetation index(EVI),the fraction of photosynthetically active radiation absorbed by green vegetation(FPAR),leaf area index(LAI),soil available water content(AWC),slope gradient(SG),and ratio of area(AR)of every hydrologic response unit(HRU)to that of the total watershed,comprising unique land cover,soil type,and slope gradient characteristics within the Fish River catchment in Alabama,USA between 2001 and 2014.Linear regressions,spatial trend analysis,correlation matrices,forward stepwise multivariable regression(FSMR),and 2-fold cross-validation were conducted to evaluate whether there were possible associations between the C-factor and EVI with the successive addition of remotely sensed environmental factors.Based on the data analysis and modeling,we found a significant association between the C-factor and EVI with the synergy of the environmental factors FPAR,LAI,AWC,AR,and SG(predicted R^(2)(R^(2)_(pred))=0.51;R^(2)=0.68,n=3220,P<0.15).The results showed that the developed FSMR model constituting the non-conventional factors AWC(R^(2)_(pred)=0.32;R^(2)=0.48,n=3220,P<0.05)and FPAR(R^(2)_(pred)=0.13;R^(2)=0.28,n=3220,P=0.31)was an improved fit for the watershed C-factor.In conclusion,the union of dynamic variables related to vegetation(EVI,FPAR,and LAI),soil(AWC),and topography(AR and SG)can be utilized for spatiotemporal C-factor estimation and to monitor watershed erosion.