Multiple Cropping Intensity in China Derived from Agro-meteorological Observations and MODIS Data

Double-and triple-cropping in a year have played a very important role in meeting the rising need for food in China.However,the intensified agricultural practices have significantly altered biogeochemical cycles and soil quality.Understanding and mapping cropping intensity in China′s agricultural systems are therefore necessary to better estimate carbon,nitrogen and water fluxes within agro-ecosystems on the national scale.In this study,we investigated the spatial pattern of crop calendar and multiple cropping rotations in China using phenological records from 394 agro-meteorological stations(AMSs)across China.The results from the analysis of in situ field observations were used to develop a new algorithm that identifies the spatial distribution of multiple cropping in China from moderate resolution imaging spectroradiometer(MODIS)time series data with a 500 m spatial resolution and an 8-day temporal resolution.According to the MODIS-derived multiple cropping distribution in 2002,the proportion of cropland cultivated with multiple crops reached 34%in China.Double-cropping accounted for approximately 94.6%and triple-cropping for 5.4%.The results demonstrat that MODIS EVI(Enhanced Vegetation Index)time series data have the capability and potential to delineate the dynamics of double-and triple-cropping practices.The resultant multiple cropping map could be used to evaluate the impacts of agricultural intensification on biogeochemical cycles.

Crop Classification Using MODIS NDVI Data Denoised by Wavelet: A Case Study in Hebei Plain, China

Time-series Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) data have been widely used for large area crop mapping.However,the temporal crop signatures generated from these data were always accompanied by noise.In this study,a denoising method combined with Time series Inverse Distance Weighted (T-IDW) interpolating and Discrete Wavelet Transform (DWT) was presented.The detail crop planting patterns in Hebei Plain,China were classified using denoised time-series MODIS NDVI data at 250 m resolution.The denoising approach improved original MODIS NDVI product significantly in several periods,which may affect the accuracy of classification.The MODIS NDVI-derived crop map of the Hebei Plain achieved satisfactory classification accuracies through validation with field observation,statistical data and high resolution image.The field investigation accuracy was 85% at pixel level.At county-level,for winter wheat,there is relatively more significant correlation between the estimated area derived from satellite data with noise reduction and the statistical area (R2 = 0.814,p < 0.01).Moreover,the MODIS-derived crop patterns were highly consistent with the map generated by high resolution Landsat image in the same period.The overall accuracy achieved 91.01%.The results indicate that the method combining T-IDW and DWT can provide a gain in time-series MODIS NDVI data noise reduction and crop classification.

Practical Model of Sea Ice Thickness of Bohai Sea Based on MODIS Data

Sea ice thickness is one of the most important input parameters in the studies on sea ice disaster prevention and mitigation. It is also the most important content in remote sensing monitoring of sea ice. In this study, a practical model of sea ice thickness(PMSIT) was proposed based on the Moderate Resolution Imaging Spectroradiometer(MODIS) data. In the proposed model, the MODIS data of the first band were used to estimate sea ice thickness and the difference between the second-band reflectance and the fifth-band reflectance in the MODIS data was calculated to obtain the difference attenuation index(DAI) of each pixel. The obtained DAI was used to estimate the integrated attenuation coefficient of the first band of the MODIS at the pixel level. Then the model was used to estimate sea ice thickness in the Bohai Sea with the MODIS data and then validated with the actual sea ice survey data. The validation results showed that the proposed model and corresponding parameterization scheme could largely avoid the estimation error of sea ice thickness caused by the spatial and temporal heterogeneity of sea ice extinction and allowed the error of 18.7% compared with the measured sea ice thickness.

Evapotranspiration Estimation Based on MODIS Products and Surface Energy Balance Algorithms for Land(SEBAL) Model in Sanjiang Plain,Northeast China

In this study,the Surface Energy Balance Algorithms for Land(SEBAL) model and Moderate Resolution Imaging Spectroradiometer(MODIS) products from Terra satellite were combined with meteorological data to estimate evapotranspiration(ET) over the Sanjiang Plain,Northeast China.Land cover/land use was classified by using a recursive partitioning and regression tree with MODIS Normalized Difference Vegetation Index(NDVI) time series data,which were reconstructed based on the Savitzky-Golay filtering approach.The MODIS product Quality Assessment Science Data Sets(QA-SDS) was analyzed and all scenes with valid data covering more than 75% of the Sanjiang Plain were selected for the SEBAL modeling.This provided 12 overpasses during 184-day growing season from May 1st to October 31st,2006.Daily ET estimated by the SEBAL model was misestimaed at the range of-11.29% to 27.57% compared with that measured by Eddy Covariance system(10.52% on average).The validation results show that seasonal ET from the SEBAL model is comparable to that from ground observation within 8.86% of deviation.Our results reveal that the time series daily ET of different land cover/use increases from vegetation on-going until June or July and then decreases as vegetation senesced.Seasonal ET is lower in dry farmland(average(Ave):491 mm) and paddy field(Ave:522 mm) and increases in wetlands to more than 586 mm.As expected,higher seasonal ET values are observed for the Xingkai Lake in the southeastern part of the Sanjiang Plain(Ave:823 mm),broadleaf forest(Ave:666 mm) and mixed wood(Ave:622 mm) in the southern/western Sanjiang Plain.The ET estimation with SEBAL using MODIS products can provide decision support for operational water management issues.

A MODIS Time Series Data Based Algorithm for Mapping Forest Fire Burned Area

Burned area mapping is an essential step in the forest fire research to investigate the relationship between forest fire and cli- mate change and the effect of forest fire on carbon budgets. This study proposed an algorithm to map forest fire burned area using the Moderate-Resolution Imaging Spectroradiameter （MODIS） time series data in Heilongjiang Province, China. The algorithm is divided into two steps： Firstly, the ＇core＇ pixels were extracted to represent the most possible burned pixels based on the comparison of the tem- poral change of Global Environmental Monitoring Index （GEMI）, Burned Area Index （BAI） and MODIS active fire products between pre- and post-fires. Secondly, a 15-km distance was set to extract the entire burned areas near the ＇core＇ pixels as more relaxed conditions were used to identify the fire pixels for reducing the omission error as much as possible. The algorithm comprehensively considered the thermal characteristics and the spectral change between pre- and post-fires, which are represented by the MODIS fire products and the spectral index, respectively. Tahe, Mohe and Huma counties of Heilongjiang Province, China were chosen as the study area for burned area mapping and a time series of burned maps were produced from 2000 to 2011. The results show that the algorithm can extract burned areas more accurately with the hiehest accuracy of 96.61%.

A Simple Method to Extract Tropical Monsoon Forests Using NDVI Based on MODIS Data:A Case Study in South Asia and Peninsula Southeast Asia

Distribution of monsoon forests is important for the research of carbon and water cycles in the tropical regions. In this paper, a simple approach is proposed to map monsoon forests using the Normalized Difference Vegetation lndex （NDVI） derived from the Moderate Resolution Imaging Spectroradiometer （MODIS） data. Owing to the high contrast of greenness between wet season and dry season, the monsoon forest can be easily discriminated from other forests by combining the maximum and minimum annual NDVI. The MODIS-based monsoon forest maps （MODMF） from 2000 to 2009 are derived and evaluated using the ground-truth dataset. The MODMF achieves an average producer accuracy of 80.0% and the Kappa statistic of 0.719. The variability of MODMF among different years is compared with that calculated from MODIS land cover products （MCD 12Q 1）. The results show that the coefficient of variation of total monsoon forest area in MODMF is 7.3%, which is far lower than that in MCD12Q1 with 24.3%. Moreover, the pixels in MODMv which can be identified for 7 to 9 times between 200l and 2009 account for 53.1%, while only 7.9% ofMCD12QI pixels have this frequency. Additionally, the monsoon forest areas estimated in MODMF, Global Land Cover 2000 （GLC2000）, MCDI2Q1 and University of Maryland （UMD） products are compared with the statistical dataset at national level, which reveals that MODMv has the highest R^2 of 0.95 and the lowest RMSE of 14 014 km^2. This algorithm is simple but reliable for mapping the monsoon forests without complex classification techniques.

Global Water Vapor Content Decreases from 2003 to 2012： An Analysis Based on MODIS Data

Water vapor in the earth′s upper atmosphere plays a crucial role in the radiative balance, hydrological process, and climate change. Based on the latest moderate-resolution imaging spectroradiometer(MODIS) data, this study probes the spatio-temporal variations of global water vapor content in the past decade. It is found that overall the global water vapor content declined from 2003 to 2012(slope b = –0.0149, R = 0.893, P = 0.0005). The decreasing trend over the ocean surface(b = –0.0170, R = 0.908, P = 0.0003) is more explicit than that over terrestrial surface(b = –0.0100, R = 0.782, P = 0.0070), more significant over the Northern Hemisphere(b = –0.0175, R = 0.923, P = 0.0001) than that over the Southern Hemisphere(b = –0.0123, R = 0.826, P = 0.0030). In addition, the analytical results indicate that water vapor content are decreasing obviously between latitude of 36°N and 36°S(b = 0.0224, R = 0.892, P = 0.0005), especially between latitude of 0°N and 36°N(b = 0.0263, R = 0.931, P = 0.0001), while the water vapor concentrations are increasing slightly in the Arctic regions(b = 0.0028, R = 0.612, P = 0.0590). The decreasing and spatial variation of water vapor content regulates the effects of carbon dioxide which is the main reason of the trend in global surface temperatures becoming nearly flat since the late 1990 s. The spatio-temporal variations of water vapor content also affect the growth and spatial distribution of global vegetation which also regulates the global surface temperature change, and the climate change is mainly caused by the earth's orbit position in the solar and galaxy system. A big data model based on gravitational-magmatic change with the solar or the galactic system is proposed to be built for analyzing how the earth's orbit position in the solar and galaxy system affects spatio-temporal variations of global water vapor content, vegetation and temperature at large spatio-temporal scale. This comprehensive examination of water vapor changes promises a holistic understanding of the global climate change and potential underlying mechanisms.

Identifying Sky Conditions in Iran from MODIS Terra and Aqua Cloud Products

Clouds can influence climate through many complex interactions within the hydrological cycle. Due to the important effects of cloud cover on climate, it is essential to study its variability over certain geographical areas. This study provides a spatial and temporal distribution of sky conditions, cloudy, partly cloudy, and clear days, in Iran. Cloud fraction parameters were calculated based on the cloud product （collection 6_L2） obtained from the Moderate Resolution Imaging Spectroradiorneter （MODIS） sensors on board the Terra （MOD06） and Aqua （MYD06） satellites. The cloud products were collected daily from January 1, 2003 to December 31, 2014 （12 years） with a spatial resolution of 5 km × 5 km. First, the cloud fraction data were converted into a regular geographic coordinate network over Iran. Then, the estimations from both sensors were analyzed. Results revealed that the maximum annual frequency of cloudy days occurs along the southern shores of the Caspian Sea, while the minimum annual frequency occurs in southeast Iran. On average, the annual number of cloudy and clear-sky days was 88 and 256 d from MODIS Terra, as compared to 96 and 244 d from MODIS Aqua. Generally, cloudy and partly cloudy days decrease from north to south, and MODIS Aqua overestimates the cloudy and partly cloudy days compared to MODIS Terra.

The study of estimation method of broadband emissivity from EOS/MODIS data

The broadband emissivity is an important parameter for estimating the energy balance of the Earth. This study focuses on estimating the window （8 -12 μm） emissivity from the MODIS （mod- erate-resolution imaging spectroradiometer） data, and two methods are built. The regression method obtains the broadband emissivity from MODllB1 - 5KM product, whose coefficient is developed by using 128 spectra, and the standard deviation of error is about 0.0118 and the mean error is about O. 0084. Although the estimation accuracy is very high while the broadband emissivity is estimated from the emissivity of bands 29, 31 and 32 obtained from MOD11B1 _ 5KM product, the standard deviations of errors of single emissivity in bands 29, 31, 32 are about 0.009 for MOD11B1 5KM product, so the total error is about O. 02 and resolution is about 5km × 5km. A combined radiative transfer model with dynamic learning neural network method is used to estimate the broadband emis- sivity from MODIS 1B data. The standard deviation of error is about 0.016, the mean error is about 0.01, and the resolution is about 1 km x 1 km. The validation and application analysis indicates that the regression is simpler and more practical, and estimation accuracy of the dynamic learning neural network method is higher. Considering the needs for accuracy and practicalities in application, one of them can be chosen to estimate the broadband emissivity from MODIS data.

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.

Regional fire monitoring and NASA MODIS fire products characterization using global in dry lands of Central Asia

Central Asian dry lands are grass- and desert shrub-dominated ecosystems stretching across Northern Eurasia. This region supports a population of more than 1 00 million which continues to grow at an average rate of 1.5% annually. Dry steppes are the primary grain and cattle growing zone within Central Asia. Degradation of this ecosystem through burning and overgrazing directly impacts economic growth and food supply in the region. Fire is a recurrent disturbance agent in dry lands contributing to soil erosion and air pollution. Here we provide an overview of inter-annual and seasonal fire dynamics in Central Asia obtained from remotely sensed data. We evaluate the accuracy of the Moderate Resolution Imaging Spectroradiometer （MODIS） global fire products within Central Asian dry lands and use these products to characterize fire occurrence between 2001 and 2009. The results show that on average -15 million ha of land burns annually across Central Asia with the majority of the area burned in August and September in grasslands. Fire is used as a common crop residue management practice across the region. Nearly 89% of all burning occurs in Kazakhstan, where 5% and 3% of croplands and grasslands, respec- tively, are burned annually.

Snow Cover Variation and Streamflow Simulation in a Snow-fed River Basin of the Northwest Himalaya

Snowmelt is an important component of any snow-fed river system.The Jhelum River is one such transnational mountain river flowing through India and Pakistan.The basin is minimally glacierized and its discharge is largely governed by seasonal snow cover and snowmelt.Therefore,accurate estimation of seasonal snow cover dynamics and snowmeltinduced runoff is important for sustainable water resource management in the region.The present study looks into spatio-temporal variations of snow cover for past decade and stream flow simulation in the Jhelum River basin.Snow cover extent(SCE) was estimated using MODIS(Moderate Resolution Imaging Spectrometer) sensor imageries.Normalized Difference Snow Index(NDSI) algorithm was used to generate multi-temporal time series snow cover maps.The results indicate large variation in snow cover distribution pattern and decreasing trend in different sub-basins of the Jhelum River.The relationship between SCE-temperature,SCE-discharge and discharge-precipitation was analyzed for different seasons and shows strong correlation.For streamflow simulation of the entire Jhelum basin Snow melt Runoff Model(SRM) used.A good correlation was observed between simulated stream flow and in-situ discharge.The monthly discharge contribution from different sub-basins to the total discharge of the Jhelum River was estimated using a modified version of runoff model based on temperature-index approach developed for small watersheds.Stream power - an indicator of the erosive capability of streams was also calculated for different sub-basins.

A Thin Cloud Removal Method from Remote Sensing Image for Water Body Identification

In this paper,a thin cloud removal method was put forward based on the linear relationships between the thin cloud reflectance in the channels from 0.4 μm to 1.0 μm and 1.38 μm.Channels of 0.66 μm,0.86 μm and 1.38 μm were chosen to extract the water body information under the thin cloud.Two study cases were selected to validate the thin cloud removal method.One case was applied with the Earth Observation System Moderate Resolution Imaging Spectroradiometer(EOS/MODIS) data,and the other with the Medium Resolution Spectral Imager(MERSI) and Visible and Infrared Radiometer(VIRR) data from Fengyun-3A(FY-3A).The test results showed that thin cloud removal method did not change the reflectivity of the ground surface under the clear sky.To the area contaminated by the thin cloud,the reflectance decreased to be closer to the reference reflectance under the clear sky after the thin cloud removal.The spatial distribution of the water body area could not be extracted before the thin cloud removal,while water information could be easily identified by using proper near infrared channel threshold after removing the thin cloud.The thin cloud removal method could improve the image quality and water body extraction precision effectively.

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.

Estimating emissions from crop residue open burning in China based on statistics and MODIS fire products

With the objective of reducing the large uncertainties in the estimations of emissions from crop residue open burning, an improved method for establishing emission inventories of crop residue open burning at a high spatial resolution of 0.25°× 0.25° and a temporal resolution of1 month was established based on the moderate resolution imaging spectroradiometer（MODIS） Thermal Anomalies/Fire Daily Level3 Global Product（MOD/MYD14A1）. Agriculture mechanization ratios and regional crop-specific grain-to-straw ratios were introduced to improve the accuracy of related activity data. Locally observed emission factors were used to calculate the primary pollutant emissions. MODIS satellite data were modified by combining them with county-level agricultural statistical data, which reduced the influence of missing fire counts caused by their small size and cloud cover. The annual emissions of CO2, CO, CH4,nonmethane volatile organic compounds（NMVOCs）, N2O, NOx, NH3, SO2, fine particles（PM2.5）,organic carbon（OC）, and black carbon（BC） were 150.40, 6.70, 0.51, 0.88, 0.01, 0.13, 0.07, 0.43,1.09, 0.34, and 0.06 Tg, respectively, in 2012. Crop residue open burning emissions displayed typical seasonal and spatial variation. The highest emission regions were the Yellow-Huai River and Yangtse-Huai River areas, and the monthly emissions were highest in June（37%）.Uncertainties in the emission estimates, measured as 95% confidence intervals, range from a low of within ±126% for N2O to a high of within ± 169% for NH3.

Validation of Global Evapotranspiration Product(MOD16) Using Flux Tower Data from Panjin Coastal Wetland,Northeast China

Recent advances in remote sensing technology and methods have resulted in the development of an evapotranspiration(ET) product from the Moderate Resolution Imaging Spectrometer(MOD16). The accuracy of this product however has not been tested for coastal wetland ecosystems. The objective of this study therefore is to validate the MOD16 ET product using data from one eddy covariance flux tower situated in the Panjin coastal wetland ecosystem within the Liaohe River Delta, Northeast China. Cumulative ET data over an eight-day period in 2005 from the flux tower was calculated to coincide with the MOD16 products across the same period. Results showed that data from the flux tower were inconsistent with that gained form the MOD16 ET. In general, results from Panjin showed that there was an underestimation of MOD16 ET in the spring and fall, with Biases of -2.27 and -3.53 mm/8 d, respectively(–40.58% and -49.13% of the observed mean). Results for Bias during the summer had a range of 1.77 mm/8 d(7.82% of the observed mean), indicating an overestimation of MOD16 ET. According to the RMSE, summer(6.14 mm/8 d) achieved the lowest value, indicating low accuracy of the MOD16 ET product. However, RMSE(2.09 mm/8 d) in spring was the same as that in the fall. Relationship between ET and its relevant meteorological parameters were analyzed. Results indicated a very good relationship between surface air temperature and ET. Meanwhile a significant relationship between wind speed and ET also existed. The inconsistent comparison of MOD16 and flux tower-based ET are mainly attributed to the parameterization of the Penman-Monteith model, flux tower measurement errors, and flux tower footprint vs. MODIS pixels.

Surface diurnal warming in the East China Sea derived from satellite remote sensing

Process of sea surface diurnal warming has drawn a lot of attention in recent years, but that occurs in shelf seas was rarely addressed. In the present work, surface diurnal warming strength in the East China Sea was calculated by the sea surface temperature(SST) data derived from the MODIS sensors carried by the satellites Aqua and Terra. Due to transit time difference, both the number of valid data and the surface diurnal warming strength computed by the MODIS-Aqua data are relatively larger than Terra. Therefore, the 10-year MODIS-Aqua data from 2005 to 2014 were used to analyze the monthly variability of the surface diurnal warming. Generally, the surface diurnal warming in the East China sea is stronger in summer and autumn but weaker in winter and spring, while it shows different peaks in different regions. Large events with ΔT≥5 K have also been discussed. They were found mainly in coastal area, especially near the Changjiang(Yangtze) River estuary. And there exists a high-incidence period from April to July. Furthermore, the relationship between surface diurnal warming and wind speed was discussed. Larger diurnal warming mainly lies in areas with low wind speed. And its possibility decreases with the increase of wind speed. Events with ΔT ≥2.5 K rarely occur when wind speed is over 12 m/s. Study on surface diurnal warming in the East China Sea may help to understand the daily scale air-sea interaction in the shelf seas. A potential application might be in the marine weather forecasts by numerical models. Its impact on the coastal eco-system and the activities of marine organisms can also be pursued.

Statistical Estimation of High-Resolution Surface Air Temperature from MODIS over the Yangtze River Delta,China

High-resolution surface air temperature data are critical to regional climate modeling in terms of energy balance,urban climate change,and so on.This study demonstrates the feasibility of using Moderate Resolution Imaging Spectroradiometer（MODIS）land surface temperature（LST）to estimate air temperature at a high resolution over the Yangtze River Delta region,China.It is found that daytime LST is highly correlated with maximum air temperature,and the linear regression coefficients vary with the type of land surface.The air temperature at a resolution of 1 km is estimated from the MODIS LST with linear regression models.The estimated air temperature shows a clear spatial structure of urban heat islands.Spatial patterns of LST and air temperature differences are detected,indicating maximum differences over urban and forest regions during summer.Validations are performed with independent data samples,demonstrating that the mean absolute error of the estimated air temperature is approximately 2.5°C,and the uncertainty is about 3.1°C,if using all valid LST data.The error is reduced by 0.4°C（15%）if using best-quality LST with errors of less than 1 K.The estimated high-resolution air temperature data have great potential to be used in validating high-resolution climate models and other regional applications.

Comparison of aerosol optical depth of UV-B Monitoring and Research Program （UVMRP）, AERONET and MODIS over continental United States

The concern about the role of aerosols as to their effect in the Earth-Atmosphere system requires observation at multiple temporal and spatial scales. The Moderate Resolution Imaging Spectroradiameters （MODIS） is the main aerosol optical depth （AOD） monitoring satellite instrument, and its accuracy and uncertainty need to be validated against ground based measurements routinely. The comparison between two ground AOD measurement programs, the United States Department of Agriculture （USDA） Ultmviolet-B Monitoring and Research Program （UVMRP） and the Aerosol Robotic Network （AERONET） program, confirms the consistency between them. The intercomparison between the MODIS AOD, the AERONET AOD, and the UVMRP AOD suggests that the UVMRP AOD measurements are suited to be an alternative ground-based validation source for satellite AOD products. The experiments show that the spatial-temporal dependency between the MODIS AOD and the UVMRP AOD is positive in the sense that the MODIS AOD compare more favorably with the UVMRP AOD as the spatial and temporal intervals are increased. However, the analysis shows that the optimal spatial interval for all time windows is defined by an angular subtense of around 1° to 1.25°, while the optimal time window is around 423 to 483 minutes at most spatial intervals. The spatial-temporal approach around 1.25° ＆ 423 minutes shows better agreement than the prevalent strategy of 0.25° ＆ 60 minutes found in other similar investigations.

Three distinct global estimates of historical land-cover change and land-use conversions for over 200 years

Earth＇s land cover has been extensively transformed over time due to both human activities and natural causes. Previous global studies have focused on developing spatial and temporal patterns of dominant human land-use activities （e.g., cropland, pastureland, urban land, wood harvest）. Process-based modeling studies adopt different strategies to estimate the changes in land cover by using these land-use data sets in combination with a potential vegetation map, and subsequently use this information for impact assessments. However, due to unaccounted changes in land cover （resulting from both indirect anthropogenic and natural causes）, heterogeneity in land-use/cover （LUC） conversions among grid cells, even for the same land use activity, and uncertainty associated with potential vegetation mapping and historical estimates of human land use result in land cover estimates that are substantially different compared to results acquired from remote sensing observations. Here, we present a method to implicitly account for the differences arising from these uncertainties in order to provide historical estimates of land cover that are consistent with satellite estimates for recent years. Due to uncertainty in historical agricultural land use, we use three widely accepted global estimates of cropland and pastureland in combination with common wood harvest and urban land data sets to generate three distinct estimates of historical land-cover change and underlying LUC conversions. Hence, these distinct historical reconstructions offer a wide range of plausible regional estimates of uncertainty and the extent to which different ecosystems have undergone changes. The annual land cover maps and LUC conversion maps are reported at 0.5°×0.5° resolution and describe the area of 28 land- cover types and respective underlying land-use transitions. The reconstructed data sets are relevant for studies addressing the impact of land-cover change on biogeo- physics, biogeochemistry, water cycle, and global climate.