Precipitation and Anthropogenic Activities Jointly Green the China–Mongolia–Russia Economic Corridor

Climate change and anthropogenic activities are widely considered the main factors affecting vegetation growth. However, their relative contributions are under debate. Within the non-climatic impact, detailed human activities, particularly government policy adjustments, are less investigated. In this study, we develop a fractional vegetation coverage (FVC) extraction method based on MODIS-EVI satellite data to analyze the spatiotemporal variation of vegetation and its attributions in the China–Mongolia–Russia Economic Corridor (CMREC). The average FVC has improved, with a general increase of 0.02/10a from 2000 to 2020. We construct a driving factor identification system for FVC change, based on partial and multiple correlation coefficients, and we divide the driving forces of FVC changes into seven climate-driven types and one non-climate-driven type. The results reveal that FVC changes caused by climatic factors account for 28.2% of CMREC. The most prominent greening (19.5%) is precipitation-driven, and is extensively distributed in Khentii Aimag, Mongolia; southeast Inner Mongolia; west Jilin Province; and southwest Heilongjiang Province, China. Moreover, we quantify the relative contribution of climatic and non-climatic factors to significant FVC change using the first-difference multivariate regression method. The results indicate that the effects of non-climatic factors on vegetation change outweigh those of climatic factors in most areas. According to the land cover change and regional policy adjustment, anthropogenic activities such as afforestation, reclamation, and planting structure adjustment explain most vegetation improvement in the Northeast Plain; eastern Inner Mongolia; and the Hetao Irrigation District, China. Meanwhile, both vegetation improvement and degradation disperse concurrently in the Mongolian and Russian parts of CMREC, where climate change and anthropogenic activities positively and negatively affect vegetation change, respectively. Despite the greening in most CMREC, it must be noted that human-induced greening is unsustainable to some degree. The overdevelopment of black soil area and sandy land, adverse effects of afforestation projects, and natural hazards related to weather and climate extremes altogether threaten the local ecological security in the long run. Therefore, governments should develop new desertification countermeasures in accordance with the laws of nature, and enhance international cooperation to guarantee the ecological safety of CMREC.

Study on paddy phenomics ecosystem and yield estimation using space-borne multi sensor remote sensing data

In the present study three phenological stages of rice namely transplanting stage, heading stage and harvesting stages were derived from MODIS EVI data. SMAP L-band was used to identify the puddling field. The performance of the estimated phenological stages from MODIS EVI and SMAP were evaluated with field data and root mean square error (RMSE) was calculated. The rice yield estimation was also performed by application of second order polynomial method. The performance of the polynomial model showed good results with the coefficient of determination of 0.74.

Greening of the Qinghai–Tibet Plateau and Its Response to Climate Variations along Elevation Gradients

The vegetation of the Qinghai–Tibet Plateau (QTP) is vital to the global climate change and ecological security of China. However, the impact of climate variation on the spatial pattern and zonal distribution of vegetation in the QTP remains unclear. Accordingly, we used multisource remote-sensing vegetation indices (GIMMS-LAI, GIMMS NDVI, GLOBMAP LAI, MODIS EVI, MODIS NDVI, and MODIS NIRv), climate data, a digital elevation model, and the moving window method to investigate the changes in vegetation greenness and its response to climate variations in the QTP from 2001 to 2016. Results showed that the vegetation was greening in the QTP, which might be attributed to the increases in temperature and radiation. By contrast, the browning of vegetation may be caused by drought. Notably, the spatial patterns of vegetation greenness and its variations were linearly correlated with climate at low altitudes, and vegetation greenness was non-linearly correlated with climate at high altitudes. The Northwestern QTP needs to be focused on in regard to positive and decreased VGEG (vegetation greenness along the elevation gradient). The significantly positive VGEG was up to (34.37 ± 2.21) % of the QTP, which indicated a homogenization of vegetation greenness on elevation. This study will help us to understand the spatial distribution of vegetation greenness and VGEG in the QTP under global warming, and it will benefit ecological environment management, policymaking, and future climate and carbon sink (source) prediction.

Spatial variation characteristics of vegetation phenology and its influencing factors in the subtropical monsoon climate region of southern China

Understanding the response mechanism of ecosystems to climate change and human disturbance can be improved by analyzing the spatial patterns of vegetation phenology and its influencing factors. Because the diverse phenological patterns are impacted by cloud cover contamination issues in the satellite observations, there are few remote sensing phenological research data in subtropical monsoon climate regions. To better understand the horizontal and vertical changes of vegetation phenology in these regions and how it may be affected by climatic factors and topographical features, we first extracted vegetation phenological information (such as start of growth season (SOS), end of growth season (EOS) and length of growth season (LEN)) from a reconstructed MODIS EVI time-series data. We then used geographic detectors to identify the influencing factors of phenology in different elevation zoning areas. We have found that in the Xiangjiang River Basin: 1) gradual changes in the longitudinal or latitudinal gradient of vegetation phenology were not obvious. Instead of horizontal changes, the variation pattern of phenology was similar to the striped river network of the Xiangjiang River. Earlier SOS mainly appeared in the areas far away from the river; later SOS appeared in the midstream and downstream reaches.2) Elevation played an important role in the regional differentiation of phenology. Boundaries at elevations of 320 m and 520 m distinctly separated the region into plain, hilly, and mountain vegetation phenological characteristics. 3) The impacts of climatic factors were quite different in the three vertical zoning areas. Precipitation was the most crucial factor affecting SOS both in plain and mountain areas. There was no significant factor affecting EOS in the plain area, but temperature had an essential effect on EOS in the mountain area. The hilly areas had a concentrated growth period with no significant factors affecting phenology. These findings highlight the importance of elevation in phenology at a watershed scale, enhance our understanding of the impact of climate changes on subtropical ecosystems, and provide a reference for further land-use change monitoring.

Improving the monitoring of deciduous broadleaf phenology using the Geostationary Operational Environmental Satellite (GOES) 16 and 17

Monitoring leaf phenology tracks the progression of climate change and seasonal variations in a variety of organismal and ecosystem processes. Networks of finite-scale remote sensing, such as the PhenoCam network, provide valuable information on phenological state at high temporal resolution, but they have limited coverage. Satellite-based data with lower temporal resolution have primarily been used to more broadly measure phenology (e.g., 16 d MODIS normalized difference vegetation index (NDVI) product). Recent versions of the Geostationary Operational Environmental Satellites (GOES-16 and GOES-17) can monitor NDVI at temporal scales comparable to that of PhenoCam throughout most of the western hemisphere. Here we begin to examine the current capacity of these new data to measure the phenology of deciduous broadleaf forests for the first 2 full calendar years of data (2018 and 2019) by fitting double-logistic Bayesian models and comparing the transition dates of the start, middle, and end of the season to those obtained from PhenoCam and MODIS 16 d NDVI and enhanced vegetation index (EVI) products. Compared to these MODIS products, GOES was more correlated with PhenoCam at the start and middle of spring but had a larger bias (3.35 ± 0.03 d later than PhenoCam) at the end of spring. Satellite-based autumn transition dates were mostly uncorrelated with those of PhenoCam. PhenoCam data produced significantly more certain (all p values ≤0.013) estimates of all transition dates than any of the satellite sources did. GOES transition date uncertainties were significantly smaller than those of MODIS EVI for all transition dates (all p values ≤0.026), but they were only smaller (based on p value <0.05) than those from MODIS NDVI for the estimates of the beginning and middle of spring. GOES will improve the monitoring of phenology at large spatial coverages and provides real-time indicators of phenological change even when the entire spring transition period occurs within the 16 d resolution of these MODIS products.

GRACE/GRACE-FO observed terrestrial water storage influencing global grassland growth

&lt;p&gt;Land water supply for plant growth directly links the water and carbon cycles. The abundance or shortage of water storage influences plant water consumption strategies and have important implications for ecosystem drought resistance and resilience, especially for the grassland ecosystem where water is the primary factor limiting plant production. However, plant-accessible water is rarely quantified due to the lack of regional to global scale observations of deeper water storage, and the influence of deeper water supply on plant-water relation remains unknown. In this study, we evaluate the capacity of GRACE/GRACE-FO total terrestrial water storage (TWS) estimates to capture plant-accessible water supply at depth. We use ESA CCI surface soil moisture (SM) estimates to represent shallow water storage and MODIS EVI as a proxy for grassland productivity. We calculate the inter-annual correspondence of EVI against both TWS and SM over 24 GRACE mascons covering the majority of the global grassland areas. Our results show that complementary to SM measurements, TWS provides unique information about deeper water storage limiting grassland growth. We find that the seasonal change of TWS constrains plant-accessible water storage and leads to different plant-water relations in the grassland regions across the globe.&lt;/p&gt;

Vegetation Growth Status and Topographic Effects in the Pisha Sandstone Area of China

Fractional vegetation coverage (FVC) plays an important role in monitoring vegetation growth status and evaluating restoration efforts in ecological environments. In this study, FVC was calculated using a binary pixel model and analyzed in the Pisha Sandstone area of China, using MODIS-EVI data from 2000 to 2019. Topographic effects were analyzed from elevation, slope and aspect using a terrain niche index model. The results were as follows. (1) From 2000 to 2019, FVC in the Pisha Sandstone area of China gradually increased at a mean rate of 0.0074/a, and the growth status of vegetation gradually improved. (2) The spatial distribution of FVC steadily decreased from southeast to northwest. FVC was the lowest in bare parts of the Pisha Sandstone area, whereas those in the sand- and soil-covered parts were the middle and highest, respectively. (3) With increasing elevation, the inferior coverage area and terrain niche index increased, and inferior coverage distribution changed from non-dominant to dominant. Meanwhile, the low, medium and high coverage areas decreased, and their distributions changed from dominance to non-dominance. (4) With increasing slope, distributions of the inferior, medium and high coverage areas changed from dominant to non-dominant, while the low coverage area had a dominant distribution. (5) Analyses of aspect effects revealed that the inferior coverage area was the dominant distribution in shady slopes but was non-dominant in semi-shady, semi-sunny and sunny slopes. The low, medium and high coverage areas were non-dominant in shady slopes, but dominant in semi-shady, semi-sunny and sunny slopes.