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Changes in large-scale circulation, especially El Niño–Southern Oscillation (ENSO), have significant impacts on dust activities over the dust source and downwind regions. However, these impacts present an interdecadal pattern, and it remains less clear which factors lead to the interdecadal variability of the ENSO impact on dust activities over northwestern South Asia, although previous studies have discussed the response of interannual dust activities over northwestern South Asia to the ENSO circle. Based on the linear regression model and MERRA-2 atmospheric aerosol reanalysis data, this study investigated the interdecadal variability of the ENSO impact on dust activities as well as the associated possible atmospheric drivers under two different warming phases over northwestern South Asia. Results indicated that the relationship between ENSO and dust column mass density (DUCMASS) experienced an obvious shift from the accelerated global warming period (1982–1996) to the warming hiatus period (2000–2014). The change in Atlantic and Indian Ocean sea surface temperature anomaly (SSTA) patterns weakened the impact of ENSO on dust activities over northwestern South Asia during 1982–1996, while the change in Pacific Decadal Oscillation (PDO) strengthened ENSO's effect when it was in phase with ENSO. Both the Atlantic and Indian Ocean SSTA patterns were modulated by the duration of ENSO events (i.e., continuing and emerging ENSO). This study provides new insights into numerical simulation involving the influence of atmospheric teleconnections on the variability of dust activities and their influence mechanisms.

Cotton is an important cash crop in the world, as the main source of natural and renewable fiber for textiles. Accurate and timely monitoring of the cotton distribution is crucial for cotton cultivation management and international trade. However, most of the previous researches on cotton identification using remotely sensed images are highly dependent on training samples, and the collection of samples is time-consuming and expensive. To overcome this limitation, a new index, termed as Cotton Mapping Index (CMI), was developed in this study for automatic cotton mapping using time series of Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 Multispectral Instrument (MSI) satellite data. Four sites in the United States (U.S.) and four sites in China were selected to develop and assess the performance of the CMI. The spectral characteristics derived from Sentinel-2 and backscattering coefficients derived from Sentinel-1 for cotton and non-cotton crops during the cotton growth period were analyzed. Considering the phenology differences of crops in different regions, the features at an adaptive window were adopted to construct the CMI. The results showed that at the peak greenness period, the multiplication of red-edge 1 and red-edge 2 band for cotton samples were much larger than those for non-cotton samples, whereas the spectral angle at the red band as well as the absolute values of backscattering coefficients in vertical transmit and vertical receive (VV) polarization for cotton samples were much smaller than those for non-cotton samples. Based on these findings, the CMI was developed to identify cotton cultivated area within the cropland area. The overall accuracy of classification results for the sites in the U.S. was higher than 81.20%, and the mean relative error for the sites in Xinjiang of China was 26.69%. The CMI, which incorporated optical and radar features, had a better performance than the indices using optical features solely. The advantage of the CMI over supervised classifiers (i.e., k-nearest neighbors, support vector machine and random forest) is that no training samples are required. Moreover, the cotton distribution map can be obtained before the harvest using the CMI. These results indicated the potential of the CMI for cotton mapping. The applicability of CMI in other regions with different cropping systems and crop types needs to be further assessed in the future study.

Sea surface chlorophyll-a concentration (Chl-a) is a key proxy for phytoplankton biomass. Spatio-temporal continuous Chl-a data are important to understand the mechanisms of chlorophyll occurrence and development and track phytoplankton changes. However, the greatest challenge in utilizing daily Chl-a data is massive missing pixels due to orbital position and cloud coverage. This study proposes the application of a spatial filling method using the machine learning-based Extreme Gradient Boosting (BST) to reconstruct missing pixels of daily MODIS Chl-a data from 2007 to 2018. The approach is applied to different trophic biogeographical subregions of the Northwestern Pacific where it has complex phytoplankton dynamics and frequent data missing. Various environmental variables are taken into consideration, including meteorological forcing, geographic and topographic features, and oceanic physical components. The BST-reconstructed Chl-a (BST Chl-a) is validated using in-situ Chl-a measurements, VIIRS and Himawari-8 Chl-a products. The results show that the BST model is highly adaptive in reconstructing Chl-a data, and it performs well in pelagic, offshore and coastal with the best performance in pelagic. BST Chl-a improves coverage without significant quality degradation compared to the original MODIS Chl-a. BST Chl-a agrees better with in-situ data than that of MODIS, with CC of 0.742, RMSE of 0.247, MAE of 0.202 and Bias of 0.089. Cross-satellite validation using VIIRS and Himawari-8 Chl-a also shows promising results with the CC of 0.861 and 0.765, respectively, suggesting the high accuracy of BST Chl-a. The inter-annual trend of BST Chl-a decreases in coastal and increases in offshore and pelagic. BST Chl-a images present similar spatial patterns to MODIS Chl-a under different missing rates, with gradual decreases from coastal to pelagic. It indicates that phytoplankton bloom patterns can be identified by daily BST Chl-a images.

The change of large-scale circulation, especially El Niño-Southern Oscillation (ENSO), play an important role in the interdecadal variability of dust activities over the dust source and downwind regions. However, the detailed factors that lead to the interdecadal variability of the ENSO impact on dust activities over the northwestern South Asia remain less clear, although previous studies have discussed the response of the interannual dust activities over the northwestern South Asia to the ENSO circle. Based on the linear regression model and MERRA-2 atmospheric aerosol reanalysis data, this study investigated the interdecadal variability of the ENSO impact on dust activities as well as the associated possible atmospheric drivers under two different warming phases over the northwestern South Asia. Results indicated that the relationship between ENSO and surface dust mass concentration (DUSMASS) experienced an obvious shift from the accelerated global warming period (1982–1996) to the warming hiatus period (2000–2014). The change of Atlantic SSTA pattern weakened the impact of ENSO on dust activities over the northwestern South Asia, while that of Indian Ocean SSTA pattern and PDO tended to strengthen ENSO’s effect. Both the Atlantic and Indian Ocean SSTA patterns were modulated by the duration of ENSO events (i.e., continuing and emerging ENSO). The Eurasian continent and Indian Ocean thermal contrast was less likely to cause the shift of ENSO–DUSMASS relationship. This study provides new sights to numerical simulation involving the influence of atmospheric teleconnections on the variability of dust activities and their influence mechanisms.

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.

In South Asia, annual land cover and land use (LCLU) is a severe issue in the field of earth science because it affects regional climate, global warming, and human activities. Therefore, it is vital essential to obtain correct information on the LCLU in the South Asia regions. LULC annual map covering the entire period is the primary dataset for climatological research. Although the LULC annual global map was produced from the MODIS dataset in 2001, this limited the perspective of the climatological analysis. This study used AVHRR GIMMS NDVI3g data from 2001 to 2015 to randomly forests classify and produced a time series of the annual LCLU map of the South Asia. The MODIS land cover products (MCD12Q1) are used as data from reference for trained classifiers. The results were verified using of the annual map of LCLU time series, and the space-time dynamics of the LCLU map were shown in the last 15 years, from 2001 to 2015. The overall precision of our 15-year land cover map simplifies 16 classes, which is 1.23% and 86.70% significantly maximum as compared to the precision of the MODIS data map. Findings of the past 15 years shows the changing detection that forest land, savanna, farmland, urban and established land, arid land, and cultivated land have increased; by contrast, woody prairie, open shrub-lands, permanent ice and snow, mixed forests, grasslands, evergreen broadleaf forests, permanent wetlands, and water bodies have been significantly reduced over South Asia regions.

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.

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.

In South Asia, annual land cover and land use (LCLU) is a severe issue in the field of earth science because it affects regional climate, global warming, and human activities. Therefore, it is vital essential to obtain correct information on the LCLU in the South Asia regions. LULC annual map covering the entire period is the primary dataset for climatological research. Although the LULC annual global map was produced from the MODIS dataset in 2001, this limited the perspective of the climatological analysis. This study used AVHRR GIMMS NDVI3g data from 2001 to 2015 to randomly forests classify and produced a time series of the annual LCLU map of the South Asia. The MODIS land cover products (MCD12Q1) are used as data from reference for trained classifiers. The results were verified using of the annual map of LCLU time series, and the space-time dynamics of the LCLU map were shown in the last 15 years, from 2001 to 2015. The overall precision of our 15-year land cover map simplifies 16 classes, which is 1.23% and 86.70% significantly maximum as compared to the precision of the MODIS data map. Findings of the past 15 years shows the changing detection that forest land, savanna, farmland, urban and established land, arid land, and cultivated land have increased; by contrast, woody prairie, open shrub-lands, permanent ice and snow, mixed forests, grasslands, evergreen broadleaf forests, permanent wetlands, and water bodies have been significantly reduced over South Asia regions.

Dust deposition can supply nutrients to the ocean and affect phytoplankton growth. However, the impact of dust deposition on phytoplankton biomass in varying trophic regions remains poorly evaluated. The Northwestern Pacific is located in the downwind area of East Asian dust and includes eutrophic regions (Yellow Sea, YS; East China Sea, ECS), high-nutrient low-chlorophyll waters (subarctic Northwestern Pacific, SNWP) and low-nutrient low-chlorophyll waters (Northwestern Pacific subtropical gyre, NWPSG), which is an ideal region to explore the spatial heterogeneity of the dust fertilization effect. Here, the distribution and variation of dust deposition, high dust deposition events (HDDE) and Chlorophyll-a concentration (Chl-a, mg m

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.

The breakdown of nocturnal low-level jets (NLLJs), West African heat low (WAHL), and Harmattan Surges (HS) have been proved to be important meteorological drivers of the seasonal variation of dust emissions over North Africa. This study further investigated their relative contributions to the interannual variation of dust emissions from 1980 to 2018. Dust emissions from the Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2), precipitation data from TerraClimate, and wind speed, temperature, and geopotential from the European Centre for Medium-Range Weather Forecasts (ECMWF) were used to examine the roles of precipitation and wind speed in the dust emission trend as well as the spatiotemporal characteristics of the contributions of those three meteorological factors to the interannual variation of dust emissions. Results indicated that the dust emissions over Sahel and the southern coast of Mediterranean were more sensitive to precipitation rather than wind speed, while areas that were not influenced by rainfall were highly correlated with the cube of the wind speed at 10 m above surface with p < 0.001. The regional difference in the contribution of the three meteorological factors was significant. HS was the main contributor for dust emissions over the northern North Africa primarily in winter and spring. NLLJs primarily controlled the southern part (south of 20°N) in almost all seasons especially in winter and spring, while they contributed more to dust emissions north of 20° N from June to August. The contribution of WAHL started from the south of the Hoggar-Tibesti channel and the lee of Ethiopian Highlands in winter, then it moved northwestward in spring and reached their strongest states in summer.

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.

Achieving high crop yield with less irrigation is important to improve water productivity (WP) and irrigation water productivity (IWP) in water-limited regions in the North China Plain (NCP). Coupling the impacts of precipitation and irrigation on the crop yield is, therefore, an essential tool for understanding crop response to different water sources and forecasting irrigation water requirements. In this study, the CERES-Wheat model was used to simulate winter wheat yield, WP, and IWP under different irrigation schedules (no, deficit, full, and automatic irrigation) and three precipitation category years (wet, normal, and dry years). Results showed that the amount of precipitation fluctuated significantly over site-years, leading to considerably varied irrigation water requirements. For the wet year, deficit two irrigations at greening and jointing stages (treatment 11) alleviated water stress during key winter wheat growth periods, contributing to increasing biomass, yield, WP, and IWP. However, when reducing the irrigation amount by 50%, a significant increase in WP and a nonsignificant difference in yield were found at Fengqiu and Shangqiu. Two irrigation applications 11 improved pre-anthesis biomass and wheat yield while achieving high WP and IWP in normal and dry years (except for extreme drought conditions). The finding also indicated that the distribution of growing season precipitation exerted a significant impact on irrigation time. If the early season precipitation was low, shifting the irrigation to an earlier time to ensure pre-anthesis water requirements, which can synchronously achieve the goals of increasing WP and maintaining a higher yield. In conclusion, optimizing irrigation strategies to various precipitation conditions will be a promising and effective practice for wheat production and water conservation.

As climate change intensifies, surface vegetation productivity and carbon exchange between terrestrial ecosystems and the atmosphere are significantly affected by the variation of climatic factors. Due to the sensitivity of grasslands to these climatic factors, it is crucial to understand the response of vegetation greenness, or carbon exchange within grasslands, to environment factor dynamics. In this study, we used solar-induced chlorophyll fluorescence (SIF), precipitation (P), vapor pressure deficit (VPD), evaporative stress (ES), and root zone soil moisture (RSM) derived from remote sensing, reanalysis, and assimilation datasets to explore the response of vegetation greenness within Eurasian Steppe to climatic factors. Our results indicated deseasonlization based on the Seasonal-Trend decomposition using Loess (STL) method, which was an effective means to remove the seasonality disturbances that affect the qualification of the relationship between SIF and the four climatic factors. The response of SIF had a time lag effect on these climatic factors, and the longer the response period, the greater the impact on the correlation of SIF with P, VPD, ES, and RSM. We also found, among the four factors, that the response of SIF to ES was the timeliest. The findings of this study emphasized the impact of the seasonality and time lag effect on the dynamic response between variables, and provided references to the attribution and monitoring of vegetation greenness and ecosystem productivity.

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.