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In South Asia, key differences in annual land use and land cover (LULC) take place due to climate change, global warming, human activity, biodiversity, and hydrology. So, it is very important to get accurate land cover information for this region. An annual LULC map that covers a comprehensive period is a major dataset for climatologically study. While yearly worldwide maps of LULC are produced from Moderate Resolution Imaging Spectroradiometer (MODIS) dataset, in 2001, the first LULC map of MODIS is generated which restrictions the perspective climatologically analysis. This research work generated a time series of yearly LULC maps of South Asia from 2001 to 2015 by using random forest classification from AVHRR GIMMS NDVI3g data. The MODIS land cover product such as (MCD12Q1) was used as a reference data for the trained classifier. The result was validated by using time series of annual LULC maps, and the spatiotemporal dynamic of LULC maps was illustrated in the last 15 years from 2001 to 2015. The simplified sixteen class versions of our 15-year overall accuracy of a land cover map are 86.70%, and 1.23% higher than that of MODIS maps. The change detection indicated that, for the last 15 years, the class of closed shrublands, savannas, croplands, urban and built-up land, barren, and cropland per natural vegetation mosaics increase notably during the 2001 to 2015, and in contrast, the class of woody savannas, evergreen needleleaf forests, open shrublands, grasslands, mixed forests, permanent wetlands, permanent snow and ice, evergreen broadleaf forests, and water bodies decrease notably during 2001 to 2015. These yearly land cover maps will be an essential dataset for the upcoming climate study, where time series of LULC maps accessibility is restricted.

This study explored the applicability of Temperature Vegetation Dryness Index (TVDI) derived from MODIS (Moderate Resolution Imaging Spectroradiometer) normalized difference vegetation index (NDVI) and land surface temperature (LST) data for drought monitoring in Eurasia from April to September during 2005 - 2014. TVDI was calculated based on the vegetation index/temperature trapezoid eigenspace (VITT) in this study. The Standardized Precipitation Evapotranspiration Index in three time scales (SPEI-01, SPEI-03, SPEI-06) and the Essential Climate Variable surface soil moisture product (ECV-SM) was used to evaluate the performance of TVDI over Eurasia, and to assess the sensitivity of TVDI to different land-cover types. The records of drought events from Emergency Events Database (EM-DAT) were also used to validate TVDI. The results indicated that TVDI negatively responded to SPEIs about 77.43%, 72.01%, and 75.95% of lands respectively, of which 29.84%, 33.93%, and 25.59% were subjected significant relationships (p 0.86. A comparison of TVDI with drought records demonstrated that the TVDI could capture drought events in study area.

Evaluating the potential productivity of the terrestrial ecosystem is extremely important to ascertain the threshold of vegetation productivity, to maximize the utilization of regional climate resources, carbon sequestration and to mitigate climate warming caused by rising CO2 concentrations. However, most previous studies neglected the optimum state of natural vegetation without human intervention and regional change trend of vegetation under future climate change. In this study, variations in spatio-temporal distributions of climate potential productivity (CPP) over China from 1980 to 2018 are analyzed with the synthetic estimating model. A comprehensive regionalization method (Principal components analysis, PCA) based on standardized precipitation evapotranspiration index (SPEI), and statistical analysis methods are adopted to assess CPP and its response to the climate change in different regions of China. The results demonstrate that the global temperature rising and precipitation decreasing have obvious effects on the productivity of terrestrial ecosystem and its spatio-temporal distribution in different sub-regions and ecosystems. Among them, precipitation is the dominant factor, and temperature significantly affects some regions such as Tibetan Plateau (TP) and Northeast China (NE) with high-altitude or high-latitude. The optimum temperature for the CPP in Xinjiang (XJ) region and Northwest China (NW) is 7.5°C and 8°C, respectively. With regards to the ecosystems, the CPP of grassland shows complex trends in XJ, Southwest China (SW), NE, and TP; especially in XJ (NE), the CPP shows a decreasing (an increasing) trend when the temperature is more than 7.5°C (0°C). Linear correlations occur between farmland CPP and temperature in each sub-region except for XJ. The same situation also exists at forest CPP, especially in TP, NE and NC regions. However, under the temperature increasing and precipitation decreasing, there are slight adverse impacts on the CPP of vegetation at the national scale, indicating that drier and warmer climate are detrimental for vegetation growth.

This study investigates the interdecadal variations of rapid intensification (RI) of tropical cyclones (TCs) over Western North Pacific (WNP) during 1960-2016, TC variabilities associated with RI and the possible mechanisms for such variations are also explored. Two RI-active periods (P1:1960-1972, P3:2000-2016) and one RI-inactive period (P2:1973-1999) are identified using Regime Shift Detection method based on the RI-TC ratio data. RI spatial distributions all exhibit a common Main Development Region. Significant different characteristics of the TC genesis and lifetime maximum intensity location are observed between RITCs and Non-RI TCs and in each period. The majority of RITCs starts with initial intensity at 35-80 kts, and P2 shows an obvious displacement of these TC occurrences. The longitudinal distributions of RI ratio demonstrate totally different patterns. The higher SST and relative humidity, as well as stronger VWS, lead to the highest RI ratio in P3. TCs in P2 have less chance to undergo RI due to the eastward extension of monsoon trough associated with a more eastward movement of TC occurrence away from MDR. The environmental conditions are obviously unfavorable for RI in the Western WNP in P2, as well as in P1. Our findings imply that the TC variabilities are affected by the decadal variations of RI events and the changes in TC occurrence along with the RI-favored environmental fields all contribute to the RI variations at interdecadal scales.

Rapid intensification (RI) progress is the main challenge that precludes the improvement of tropical cyclone (TC) intensity estimates. In this paper, a composite study of the observable characteristics of 11-year TCs undergoing RI and non-RI in the Western North Pacific were conducted using CloudSat tropical cyclone (CSTC) dataset. 2B-GEOPROF, 2B-CLDCLASS, and 2B-CWC-RO products in the CSTC dataset were used to construct radar reflectivity, cloud class, and cloud water-content distributions for each category, respectively. The results show that radar echo statistics have an arc-like contour profile for all five categories, with two distinct modes of reflectivity distributions separated by the melting layer. RI has the highest frequency expanding through the whole reflectivity range along with the broadest coverage in the upper branch, and a “continuity” of reflectivity distribution from −10 to 10 dBZ at 6–11 km could be a vital indicator of TC intensification. A sharp slope can be observed in the lower branch due to heavy precipitation attenuation. Vertical distributions of cloud types show that all categories have similar cloud compositions, and deep convective clouds are apparently to play an important role in maintaining the TC intensification. Liquid water content (LWC) curve exhibits a bimodal distribution with two peaks at around 1.5 and 5 km, while ice water content (IWC) curve is much smoother and has one peak at around 9 km. It is suggested that TCs with larger LWC around freezing level or/and larger IWC at upper level tend to intensify within the next 24 hours.

Satellite-derived Normalized Difference Vegetation index (NDVI) data records offer important sources for long term correlation modelling over West Africa. In this study, we assessed long range correlations in half monthly NDVI records over West Africa from 1982 to 2011 using GIMMS NDVI. In our analysis, we assessed (a) the annual and seasonal trends obtained using Ordinary Linear Regression, (b) the detrended lag-1-autocorrelation C(1), (c) the Detrended Fluctuation Analysis (DFA) scaling Hurst exponent h and (d) the Multifractal (MF) characteristics of NDVI. Results show that there exist some patterns or trends in the records that persist over time. The value of C(1) for NDVI was obtained as 0.989 is significant at 95% confidence interval. Consequently, the scaling h values of the Hurst DFA showed that about 37.4, 20.5, 41.7 and 0.5% of the vegetated areas are anti-correlated (h

The accurate and timely spatial distribution information of various crop types is vital for food security. In this study, the 2-band enhanced vegetation index (EVI2) data from Moderate Resolution Imaging Spectroradiometer (MODIS) were combined with sparse representation approach to identify the distribution of various crop types in Shandong Province, China. Three groups of input variables derived from EVI2 including annual time series EVI2 (TS), harmonic features (HF), and combined vector formed by harmonic features and texture features (HFT) were used. The online dictionary learning and orthogonal matching pursuit algorithms were applied to generate the dictionary and solve the sparse coefficients of the identified samples, respectively. Then, the label of the identified samples can be obtained according to the minimum residuals between the dictionary and the sparse coefficients of the identified samples. At the provincial level, the validation based on the statistical data showed that three groups of input variables presented lower than ±25% at relative errors, and input variables of HFT performed better than the other two. At the municipal level, the results achieved by using input variables of HFT also agreed well with the statistics with the coefficient of determination R2 > 0.85 for wheat and maize, as well as R2 > 0.71 for peanut and cotton during 2014-2016. These results demonstrate that the combination of the input variables of HFT derived from time series MODIS EVI2 data and sparse representation approach can be used for crop identification in the study area.

Drought, a climate-related disaster impacting a variety of sectors, poses challenges for millions of people in South Asia. Accurate and complete drought information with a proper monitoring system is very important in revealing the complex nature of drought and its associated factors. In this regard, deep learning is a very promising approach for delineating the non-linear characteristics of drought factors. Therefore, this study aims to monitor drought by employing a deep learning approach with remote sensing data over South Asia from 2001–2016. We considered the precipitation, vegetation, and soil factors for the deep forwarded neural network (DFNN) as model input parameters. The study evaluated agricultural drought using the soil moisture deficit index (SMDI) as a response variable during three crop phenology stages. For a better comparison of deep learning model performance, we adopted two machine learning models, distributed random forest (DRF) and gradient boosting machine (GBM). Results show that the DFNN model outperformed the other two models for SMDI prediction. Furthermore, the results indicated that DFNN captured the drought pattern with high spatial variability across three penology stages. Additionally, the DFNN model showed good stability with its cross-validated data in the training phase, and the estimated SMDI had high correlation coefficient R2 ranges from 0.57~0.90, 0.52~0.94, and 0.49~0.82 during the start of the season (SOS), length of the season (LOS), and end of the season (EOS) respectively. The comparison between inter-annual variability of estimated SMDI and in-situ SPEI (standardized precipitation evapotranspiration index) showed that the estimated SMDI was almost similar to in-situ SPEI. The DFNN model provides comprehensive drought information by producing a consistent spatial distribution of SMDI which establishes the applicability of the DFNN model for drought monitoring.

Evaluating the climate potential productivity (CPP) of terrestrial vegetation is crucial to ascertain the threshold of vegetation productivity, to maximize the utilization of regional climate resources, and to fully display the productivity application level. In this study, the maximum net primary productivity (NPPmax) representing the highest possible productivity of vegetation was calculated using the FLUXNET maximum gross primary productivity (GPPmax) from 177 flux towers. The relationships between NPPmax and a set of climate variables were established using the classification and regression tree (CART) modeling framework. The CART algorithm was used to upscale the CPP to the global scale under the current climate baseline (1980–2018) and future climate scenarios. The spatiotemporal variations in CPP over the globe were analyzed and the impacts of climate factors on it were assessed. The results indicate that global CPPs range from 0 to 2000 g C/m2. The tropical rainforest area is the region with the highest CPP, whereas the lowest CPP occurs in arid/semiarid areas. These two regions were identified as the areas with the largest CPP reductions in the future. The findings reveal that CPP shows signs of productivity saturation and that future climate is not conducive to the increases in vegetation productivity in these regions. The increases in average annual temperature, minimum temperature, and solar radiation are beneficial to CPP increase in most parts of the globe under climate change. However, the negative contribution of maximum temperature increase and precipitation reduction to CPP is higher than the positive contribution of the above three rising factors to CPP in tropical and arid/semiarid areas. Our study is important to aid in creating targeted policies for future sustainable development, resource allocation, and vegetation management.

Proper estimation and spatiotemporal trend analysis of net primary productivity (NPP) are very important for forest ecosystem monitoring and management. In this article, we investigate the spatiotemporal trend and variability of the Boreal Ecosystem Productivity Simulator (BEPS)-derived forest NPP over Nepal during the period 2000–2015. This study is the first attempt to simulate nation-wide NPP at 500 m spatial resolution at the daily step for the recent 16-year period. In our work, we use a process-based model BEPS, to estimate the NPP in the forest of Nepal. All the meteorological, forest cover, soil data, leaf area index (LAI), tree cover, biomass, and biophysical parameters, required for the model, were downloaded from different sources or prepared, pre-processed and used in the BEPS model to come up with the daily NPP. In spite of some fluctuations in NPP in some years, there is an overall positive change in the NPP throughout the study period. The annual average national NPP has been raised from 1.34 g m−2 day−1 to 1.74 g m−2 day−1 from 2000 to –2015. Examining the inter-annual variation, we found almost 87% of the total forested area has a positive inter-annual variation and 13% negative. The overall average daily NPP per m2 over the entire forest in the entire period ranges from 0.1 to 4.7 g m−2 day−1 with an average of 1.65 g m−2 day−1. Similarly, by physiographic regions, the average NPP in the plain was 2.43 g m−2 day−1, hills 1.83 g m−2 day−1, and mountain 1.41 g m−2 day−1. The total daily NPP for the entire forest area ranges from 3 to 257 tons with an average of 96 tons over the study period. NPP was found to be highly influenced by rainfall, solar radiation, and temperature. While in case of the variation of NPP on different forest types, broad-leaved forest was found to have almost 1.7 times more (NPP 1.97 g m−2 day−1) than that of needle-leaved forest (NPP 1.18 g m−2 day−1). The slope per cent of −2 day−1) for higher NPP among different slope per cent. We found that this method could be more convenient for estimation and studies of the NPP in Nepal. The result from this study gives us important information on intra and inter-annual spatiotemporal trends and variability of NPP in the forest of overall Nepal of different physiographic regions. It also gives us information on the variation in trends of NPP with the change in various climatic and vegetation parameters, which can be important for the proper forest ecosystem monitoring, management, and planning operations.

Forests account for 80% of the total carbon exchange between the atmosphere and terrestrial ecosystems. Thus, to better manage our responses to global warming, it is important to monitor and assess forest aboveground carbon and forest aboveground biomass (FAGB). Different levels of detail are needed to estimate FAGB at local, regional and national scales. Multi-scale remote sensing analysis from high, medium and coarse spatial resolution data, along with field sampling, is one approach often used. However, the methods developed are still time consuming, expensive, and inconvenient for systematic monitoring, especially for developing countries, as they require vast numbers of field samples for upscaling. Here, we recommend a convenient two-scale approach to estimate FAGB that was tested in our study sites. The study was conducted in the Chitwan district of Nepal using GeoEye-1 (0.5 m), Landsat (30 m) and Google Earth very high resolution (GEVHR) Quickbird (0.65 m) images. For the local scale (Kayerkhola watershed), tree crowns of the area were delineated by the object-based image analysis technique on GeoEye images. An overall accuracy of 83% was obtained in the delineation of tree canopy cover (TCC) per plot. A TCC vs. FAGB model was developed based on the TCC estimations from GeoEye and FAGB measurements from field sample plots. A coefficient of determination (R2) of 0.76 was obtained in the modelling, and a value of 0.83 was obtained in the validation of the model. To upscale FAGB to the entire district, open source GEVHR images were used as virtual field plots. We delineated their TCC values and then calculated FAGB based on a TCC versus FAGB model. Using the multivariate adaptive regression splines machine learning algorithm, we developed a model from the relationship between the FAGB of GEVHR virtual plots with predictor parameters from Landsat 8 bands and vegetation indices. The model was then used to extrapolate FAGB to the entire district. This approach considerably reduced the need for field data and commercial very high resolution imagery while achieving two-scale forest information and FAGB estimates at high resolution (30 m) and accuracy (R2 = 0.76 and 0.7) with minimal error (RMSE = 64 and 38 tons ha−1) at local and regional scales. This methodology is a promising technique for cost-effective FAGB and carbon estimations and can be replicated with limited resources and time. The method is especially applicable for developing countries that have low budgets for carbon estimations, and it is also applicable to the Reducing Emissions from Deforestation and Forest Degradation (REDD +) monitoring reporting and verification processes.

The study assessed the performance of NASA’s Modern-Era Retrospective Analysis for Research and Applications (MERRA) and MERRA-2 aerological (P–E*) model in reproducing the salient features of West Africa water balance including its components from 1980 to 2013. In this study we have shown that recent reanalysis efforts have generated imbalances between regional integrated precipitation (P) and surface evaporation (E), and the effect is more in the newly released MERRA-2. The atmospheric water balance of MERRA and MERRA-2 were inter-compared and thereafter compared with model forecast output of European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis (ERA-I) and Japanese 55-year Reanalysis (JRA-55). Results indicated that a bias of 12–20 (5–13) mm/month in MERRA-2 (ERA-I) leads to the classification of the Sahel (14°N–20°N) as a moisture source during the West African Summer Monsoon. Comparisons between MERRA/MERRA-2 and prognostic fields from two ERA-I and JRA-55 indicated that the average P–E* in MERRA is 18.94 (52.24) mm/month which is less than ERA-I (JRA-55) over Guinea domain and 25.03 (4.53) mm/month greater than ERA-I (JRA-55) over the Sahel. In MERRA-2, average P–E* indicated 25.76 (59.06) mm/month which is less than ERA-I (JRA-55) over Guinea and 73.72 (94.22) mm/month less than ERA-I (JRA-55) over the Sahel respectively. These imbalances are due to adjustments in data assimilation methods, satellite calibration and observational data base. The change in convective P parameterization and increased re-evaporation of P in MERRA-2 is suggestive of the cause of positive biases in P and E. The little disagreements between MERRA/MERRA-2 and CRU precipitation highlights one of the major challenges associated with climate research in West Africa and major improvements in observation data and surface fluxes from reanalysis remain vital.

Drought is pervasive global hazard and seriously impacts ecology. Particularly, vegetation drought, which is chiefly driven by soil moisture (SM) deficiency, has a direct bearing on grain production and human livelihoods. Various drought indices associated with vegetation and SM conditions have been proposed to monitor and detect vegetation drought. In this study, we evaluated the performance of eight drought indices, including Drought Severity Index (DSI), Evaporation Stress Index (ESI), Normalized Vegetation Supply Water Index (NVSWI), Temperature-Vegetation Dryness Index (TVDI), Temperature Vegetation Precipitation Dryness Index (TVPDI), Vegetation Health Index (VHI), Self-calibrating Palmer Drought Severity Index (SC-PDSI) and Standardized Precipitation Evapotranspiration Index (SPEI), for capturing SM dynamic (derived from Copernicus Climate Change Service) across the six main vegetation coverage types of China. Our results showed DSI and ESI had the best overall performance. When exploring the reasons for the uncertainty of these indices (except SC-PDSI and SPEI) in the evaluation, we found that, in the non-arable regions, the time lag effect of drought indices on SM, the average state and rangeability of corresponding variables and the climatic conditions (precipitation and temperature) all impacted the performance of DSI, ESI, NVSWI, TVPDI and VHI. In the arable region, cropland types (paddy field and non-paddy field) and the uncertainty of SM data mainly caused the uncertainties of the above five indices. With regard to the TVDI, abnormalities of dry and wet edges fitting may be the primary factor affecting its performance. These results demonstrated that these drought indices with reliable and robust performance of capturing SM dynamics can be suggested to characterize the trend of SM. Certainly, this study can provide a reference for the improvement of existing drought indices and the establishment of new drought indices.

Aerosols can affect the atmospheric radiation balance through direct and indirect effects. The formation and development of cloud and precipitation influenced by aerosols differ significantly from each other in different meteorological conditions. In this work, we used the MODIS’s daily Aerosol Optical Depth (AOD), Cloud Effective Radius (CER), Cloud Top Temperature (CTT), Cloud Water Path (CWP) and ECMWF’s Relative Humidity (RH), Vertical Velocity (VV) and Horizontal Wind (HW) (from 2005 to 2008) to reveal the influence of meteorological factors on the distribution of aerosols, and also the correlation between aerosols and clouds. The study was designed in such a way that, the RH, VV, Upwind (UW), Downwind (DW) and CWP were divided into several intervals, to quantify the relationship between AOD and CER by controlling one single variable or two comprehensive variables over the mountains and plains. At the same time, the effect of wind speed and direction on polluted conditions was analyzed through the superposed spatial distribution map of wind and AOD. The conclusions are as follows: (1) The wind coming from mountains dispelled aerosols while the sea breeze invigorated aerosols, and the upwind showed a markedly negative relevance with AOD. (2) The strong upwind contributed to the positive relationship between AOD and CER, and the correlation rose by 38% after excluding the condition where CWP

Recent studies have shown that global Penman-Monteith equation based (PM-based) models poorly simulate water stress when estimating evapotranspiration (ET) in areas having a Mediterranean climate (AMC). In this study, we propose a novel approach using precipitation, vertical root distribution (VRD), and satellite-retrieved vegetation information to simulate water stress in a PM-based model (RS-WBPM) to address this issue. A multilayer water balance module is employed to simulate the soil water stress factor (SWSF) of multiple soil layers at different depths. The water stress factor (WSF) for surface evapotranspiration is determined by VRD information and SWSF in each layer. Additionally, four older PM-based models (PMOV) are evaluated at 27 flux sites in AMC. Results show that PMOV fails to estimate the magnitude or capture the variation of ET in summer at most sites, whereas RS-WBPM is successful. The daily ET resulting from RS-WBPM incorporating recommended VI (NDVI for shrub and EVI for other biomes) agrees well with observations, with R2=0.60 ( RMSE = 18.72 W m−2) for all 27 sites and R2=0.62 ( RMSE = 18.21 W m−2) for 25 nonagricultural sites. However, combined results from the optimum older PM-based models at specific sites show R2 values of only 0.50 ( RMSE = 20.74 W m−2) for all 27 sites. RS-WBPM is also found to outperform other ET models that also incorporate a soil water balance module. As all inputs of RS-WBPM are globally available, the results from RS-WBPM are encouraging and imply the potential of its implementation on a regional and global scale.

Dust storms are one of the major disasters in arid and semi-arid regions. Understanding the impact factors is crucial for early warning and disaster mitigation. Many factors have been affecting the spatiotemporal patterns of dust storms. However, the relative importance of those factors to dust emissions in recent 40 years over the whole dust belt has not been well documented. This study explored the relative importance of those factors to the interannual variation in dust emissions over the whole dust belt. The difference in the primary contributors over two global warming phases was compared to investigate the association of dust emission trend with global warming. The results indicated that the wind regimes, such as the nocturnal low-level jet, were key factors to the wintertime dust emissions over the Sahel. The springtime dust storms related to cold air and cyclones primarily occurred in the southern coast of the Mediterranean and northwestern China. The cold high and heat low were typical mechanisms for the summertime dust emissions, which frequently formed in western North Africa, the Middle East, and northwestern Indian subcontinent. Whereas the land cover and drought conditions play significant roles in the relatively wetter regions, i.e., the southern coast of the Mediterranean, the Ustyurt Plateau, the northwest coast of Indian Ocean, the Thar desert and the Taklimakan desert. The wintertime global warming coincided well with the decreasing trend of dust emissions over the dust sources inland with a more significant effect seen in Asia. The positive anomalies of summertime dust emissions were primarily found over the dust sources in the low-lying coastal areas on the foot of high mountains. Understanding the relative importance of those drivers to dust emission trends and their variation under different warming periods can improve the prediction of dust storm evolutions and mitigate their impacts under future climate change.

Phenology is a sensitive and critical feature of vegetation and is a good indicator for climate change studies. The global inventory modelling and mapping studies (GIMMS) normalized difference vegetation index (NDVI) has been the most widely used data source for monitoring of the vegetation dynamics over large geographical areas in the past two decades. With the release of the third version of the NDVI (GIMMS NDVI3g) recently, it is important to compare the NDVI3g data with those of the previous version (NDVIg) to link existing studies with future applications of the NDVI3g in monitoring vegetation phenology. In this study, the three most popular satellite start of vegetation growing season (SOS) extraction methods were used, and the differences between SOSg and SOS3g arising from the methods were explored. The amplitude and the peak values of the NDVI3g are higher than those of the NDVIg curve, which indicated that the SOS derived from the NDVIg (SOSg) was significantly later than that derived fr...

Drought is an extreme climate phenomenon that mainly results from abnormally low rainfall leading to water scarcity in an ecological system. Pakistan is already facing a serious threat of water shortages. The situation could further be intensified due to the prevailing drought conditions. Therefore, there is a need of consistent drought monitoring to observe drought severity; its duration and spread, to ensure effective planning to help mitigate its possible adverse effects. This study has utilized both satellite and in-situ data for consistent and accurate drought monitoring over Pakistan. Three major drought-intensive periods such as 1968–1975, mid-1980s and 1999–2003 were reflected in the standardized precipitation index (SPI) time series. The deviations of MODIS-NDVI values from their long-term mean were used as a tool to identify wetness and dryness conditions. The tropical rainfall measuring mission (TRMM) precipitation (PPTN) data were used to derive monthly and annual accumulated rainfall. The deviations from the long-term mean (2001–2012) were calculated and mapped to identify drought-prone regions on country scale. It is hard to detect drought signal just from NDVI anomaly values on regional scale. Therefore, DSI index with high resolution and incorporated with MODIS-derived NDVI and ET/PET data was chosen as a tool for regional drought monitoring. The performance of MODIS-derived DSI was evaluated with satellite and in-situ precipitation data. The spatial correlation maps between DSI, TRMM-PPTN and SPI-3 were generated to evaluate the performance of DSI. Significant positive correlation values were present in winter (DJF); spring (MAM) and autumn (SON) seasons, in central parts of the country, portraying strong evidence that DSI is a good indicator for drought conditions especially for agricultural land in plain areas during these seasons. Most of this region is characterized by agricultural land cover and plays an important role in agricultural productivity of the country. Therefore, good performance of DSI would help scientists and policy makers to implement planning and risk reduction strategies in the region .

Drought over Southwest China occurs frequently and has an obvious seasonal characteristic. Proper management of regional droughts requires knowledge of the expected frequency or probability of specific climate information. This study utilized k-means classification and copulas to demonstrate the regional drought occurrence probability and return period based on trivariate drought properties, i.e., drought duration, severity, and peak. A drought event in this study was defined when 3-month Standardized Precipitation Evapotranspiration Index (SPEI) was less than −0.99 according to the regional climate characteristic. Then, the next step was to classify the region into six clusters by k-means method based on annual and seasonal precipitation and temperature and to establish marginal probabilistic distributions for each drought property in each sub-region. Several copula types were selected to test the best fit distribution, and Student t copula was recognized as the best one to integrate drought duration, severity, and peak. The results indicated that a proper classification was important for a regional drought frequency analysis, and copulas were useful tools in exploring the associations of the correlated drought variables and analyzing drought frequency. Student t copula was a robust and proper function for drought joint probability and return period analysis, which is important for analyzing and predicting the regional drought risks.

Ecosystem water-use efficiency (WUE) is a critical indicator to investigate the interaction between the terrestrial ecosystem carbon and water cycles. WUE, estimated from gross primary productivity (GPP) and evapotranspiration (ET) based on remote sensing (RS)-based ecosystem models and algorithms (e.g., MODIS (MODerate resolution Imaging Spectroradiometer), BESS (Breathing Earth System Simulator)), have been used to quantify the spatiotemporal dynamics of WUE and its responses to environmental changes. However, few studies have assessed the ability of RS-based ecosystem models and algorithms on global WUE estimation. In this study, we evaluated 8-day and annual WUE from MODIS and BESS among different sites, land cover types and climate zones using the FLUXNET2015 dataset as reference, and conducted spatial intercomparisons of annual WUE between MODIS, BESS and an upscaled FLUXNET dataset (MTE). The site level evaluation results showed that BESS WUE had better performance than MODIS WUE at both 8-day and annual scales. Among different land cover types and climate zones, MODIS and BESS WUE performed unsatisfactorily, especially for MODIS WUE in open shrublands and savannas and for BESS WUE in closed shrublands. Additionally, both MODIS and BESS WUE performed poorly in the hot semi-arid climate zone. The spatial intercomparisons over 2001-2011 revealed that BESS WUE had similar spatial patterns of annual WUE and linear trends with MTE WUE over the globe, except at the high latitudes. However, the spatiotemporal patterns of MODIS WUE were different from those of MTE and BESS WUE, particularly in the (sub) tropical arid and semi-arid regions. Our evaluations results suggested that coupling carbon and water cycles into RS-based models could improve their performance on global WUE estimation. Moreover, the performance of MODIS and BESS on global WUE estimation should be further improved, especially for their performance on temporal variation and their performance at the (semi) arid areas and the high latitudes.

Yield gap (YG) analysis is a powerful method to reveal the possible opportunities for fulfilling the growing food demand. It used to be restricted to a small geographic area, and thus the scope of untapped yield often remains unclear at macro scale. To this end, this study presents an improved remote sensing approach to assessing regional rice YG in northeast China (NEC) during the period from 2006 to 2017. A satellite-based biophysical model (BEPS) was used to derive the actual yield (Ya) and its spatial patterns. A machine-learning zonation scheme was further developed to estimate the potential yield for farmers (Yp) within domains having similar climatic, geomorphic, and edaphic context. Results indicate that the BEPS model can provide reliable estimates of rice yields in this region, with RMSE below 20 % at the county level; and the novel zonation scheme enables better portrayal of the spatial variation in Yp. To identify areas with the greatest potential to narrow the YG, we proposed a quantitative method for dividing NEC into four parts with different priorities for future yield improvement. In general, the exploitable YG in NEC was 2599 kg ha−1, amounting to 24.7 % of Yp. Southern NEC possessed substantial YG with the primary priority to be explored; whereas in the north, Ya already approximating Yp, further yield increase was limited and challenging. Growing degree days (≥10 °C), cumulative solar radiation and elevation were all significant limiting factors of Yp. This study demonstrates the ability of remote sensing approach to assessing regional YG. Meanwhile, this regional assessment can support planners to set practical yield target and to prioritize regions and needs for future exploitation and researches.

The temporal variation of dust emissions over Central Asia in recent decades and the linkages with atmospheric factors have not been well documented. This study investigated the annual and interannual variations of dust emissions in the six main dust emission regions (i.e., Sor Mertvyy Kultuk (SMK), Ustyurt Plateau (UP), Southeast of Aralkum (SA), Eastern shore of the southern Caspian (ESC), Central Karakum desert (CK), and Trans-Unguz Karakum desert (TK)), using the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) data from 1980 to 2018. The climate linkages with the variation trend were explored using reanalysis data and the climate indices from NOAA Climate Prediction Center (CPC). The results indicated that, in early spring, dust emissions are distinct in all these sources (except for ESC) due to the cold air intrusions from north, northwest, and northeast directions, and the effect of precipitation was limited by the high evaporation. Whereas in summer and/or early autumn, dust emissions were primarily in SA, ESC, CK, and TK because of the strong northeasterly winds, low vegetation, and dry soils. The overall interannual change of dust emissions in Central Asia showed that relatively larger emissions occurred during 1980–1986 and 1993–2003, especially in April. The dust emissions in SA, ESC, and TK in summer were positively correlated with the pressure gradient between northwestern Kazakhstan and Hindu Kush (NWKHKI), while those in SMK were negatively correlated with Caspian Sea High. The dust emissions in the six dust sources were highly correlated with the maximum wind speeds but showed different sensitivities to drought. The Multivariate El Nino-Southern Oscillation Index (MEI) was related to dust emissions in April over coastal Caspian-Sea areas (SMK and ESC), while those in February over the southern parts (ESC, CK, and TK) were significantly correlated with North Atlantic Oscillation/Arctic Oscillation (NAO/AO). These conclusions could contribute to the meteorology research related to dust emissions and also to the decisions made for environmental protection.

The temporal dynamics of optimum stomatal conductance (gsmax), as well differences between C3 and C4 crops, have rarely been considered in previous remote sensing (RS)-based Jarvis-type canopy conductance (Gc) models. To address this issue, a RS-based two-leaf Jarvis-type Gc model, RST-Gc, was optimized and validated for C3 and C4 crops using 19 crop flux sites across Europe, North America, and China. RST-Gc included restrictive functions for air temperature, vapor pressure deficit, and soil water deficit, and it used satellite-retrieved NDVI to formulate the temporal variation of gsmax defined at a photosynthetic photon flux density (PPFD) of 2000 μmol m−2 s−1 (gsm, 2000). Results showed that the parameters of RST-Gc differed between C3 and C4 crops. RST-Gc successfully simulated variations in Penman–Monteith (PM)-derived daytime Gc with R2 = 0.57 for both C3 and C4 crops. RST-Gc was incorporated into a revised evapotranspiration (ET) model and a new gross primary productivity (GPP) model. The two models were validated at 19 crop flux sites. Daily mean inputs were generally incorporated into a PM approach to model daily transpiration. This is inappropriate because available energy and stomatal conductance vary significantly on a diurnal basis, with both non-linearly regulating transpiration rate. The PM approach with daily mean inputs produced unreasonable transpiration rate estimates. Efforts were made in the revised ET model (denoted as RS-WBPM2), which was modified from the water balance based RS-PM (RS-WBPM) model of Bai et al. (2017), to address this issue by calculating transpiration using daytime inputs. The photosynthesis-based stomatal conductance model, developed by Ball et al. (1987a) and improved by Leuning (1995) (BBL model), was inverted to calculate GPP using canopy conductance; the inverted model was denoted as IBBL. Cross validation showed good agreement between flux tower measurements and modeled ET (R2 = 0.79, RMSE (root mean standard error) = 20.66 W m−2 for daily ET and R2 = 0.87, RMSE = 15.32 W m−2 for 16-day ET) and GPP (R2 = 0.83, RMSE = 2.49 gC m−2 d−1 for daily GPP and R2 = 0.86, RMSE = 1.96 gC m−2 d−1 for 16-day GPP) for the two models. Within-site validations demonstrated the successful performance of the two models at 18 sites (albeit with one outlier). Inter-site variations in ET and GPP were also successfully reproduced by the models. NDVI-derived gsm, 2000 outperformed the fixed gsm, 2000 in both ET and GPP estimates. The results imply that the RS-WBPM2 and IBBL models are useful tools for modeling regional and global ET and GPP.

Studying the response of vegetation phenology to climate change at different temporal and spatial scales is important for understanding and predicting future terrestrial ecosystem dynamics and the adaptation of ecosystems to global change. In this study, satellite-derived phenology metrics were used to explore spatial and temporal changes in climatic influences on the start of the vegetation greenup season (SOS) across the Tibetan Plateau during 1982–2012. The results showed that SOS changed to a greater degree in areas where the SOS started earlier. In areas where the spring temperature (T-spring) increased at a faster rate, SOS was more likely to be advanced, and the faster the T-spring increase the more obvious the advance of SOS was. The T-spring had a stronger impact on SOS in relatively moist eco-geographic regions, while spring precipitation (P-spring) had a stronger impact on SOS in relatively warm regions. The results also indicated that P-spring effect on SOS tend to get weaker from west to east, while T-spring effect on SOS would produce no significant variation along both altitude and latitude. The T-spring effect on SOS became gradually stronger from west to east, while the P-spring effect on SOS tended to become weaker. In addition, the thermal impacts on SOS became weaker from 1982 to 2012 which may attribute to the vegetation acclimation to environment changes.

Vegetation dynamics in Nigeria were assessed using the GIMMS AVHRR NDVI3g bimonthly data and NASA’s Modern Era Retrospective Analysis for Research and Applications (MERRA) monthly climate data from 1982 to 2011. The climate data employed, included air temperature, precipitation and humidity (at 2 m). The results indicated that the annual mean NDVI increased by 0.03 × 10−4 NDVI/year of the Nigeria’s total vegetative area. About 94.7 % of the area showed positive trends mostly in the southern part of the country, while 5.3 % showed negative trends mostly in the northern part. At country scale, vegetation dynamics were significantly correlated with air temperature (p

This paper presents the development of index to detect haze from moderate resolution imaging spectroradiometer remote sensing data. Detection of haze over a large area has always been a problem. This study focuses on Beijing, Tianjin, and Shijiazhuang cities in China. These cities have suffered the worst hazy weather in recent years. The spectral influence of haze on surface features was determined through analysis of the spectral variations of surface covers between hazy and haze-free days. A spectral index known as modified normalized difference haze index (m-NDHI) is developed that can be used to monitor haze distribution and intensity. Correlation analysis of the derived m-NDHI and previously developed NDHI with in situ PM 2.5 (particulate matter with diameter μ m ) data reveals that m-NDHI over water bodies has a coefficient of 0.7096, 0.5864, and 0.4857 and NDHI has coefficient of 0.5625, 0.5321, and 0.4618 with PM 2.5 for Beijing, Tianjin, and Shijiazhuang, respectively, in winter. Moreover, the correlation of m-NDHI with PM 2.5 is 0.4097, 0.8092, and 0.5546 during the spring, summer, and autumn, respectively, in Beijing. This developed index can be a much easier and more effective method to detect haze in large scales from remotely sensing data and characterize the situation of urban atmospheric pollution.