Overlooked uneven progress across sustainable development goals at the global scale: Challenges and opportunities.

Differences in progress across sustainable development goals (SDGs) are widespread globally; meanwhile, the rising call for prioritizing specific SDGs may exacerbate such gaps. Nevertheless, how these progress differences would influence global sustainable development has been long neglected. Here, we present the first quantitative assessment of SDGs' progress differences globally by adopting the SDGs progress evenness index. Our results highlight that the uneven progress across SDGs has been a hindrance to sustainable development because (1) it is strongly associated with many public health risks (e.g., air pollution), social inequalities (e.g., gender inequality, modern slavery, wealth gap), and a reduction in life expectancy; (2) it is also associated with deforestation and habitat loss in terrestrial and marine ecosystems, increasing the challenges related to biodiversity conservation; (3) most countries with low average SDGs performance show lower progress evenness, which further hinders their fulfillment of SDGs; and (4) many countries with high average SDGs performance also showcase stagnation or even retrogression in progress evenness, which is partly ascribed to the antagonism between climate actions and other goals. These findings highlight that while setting SDGs priorities may be more realistic under the constraints of multiple global stressors, caution must be exercised to avoid new problems from intensifying uneven progress across goals. Moreover, our study reveals that the urgent needs regarding SDGs of different regions seem complementary, emphasizing that regional collaborations (e.g., demand-oriented carbon trading between SDGs poorly performed and well-performed countries) may promote sustainable development achievements at the global scale.

Correction of UAV LiDAR-derived grassland canopy height based on scan angle.

Grassland canopy height is a crucial trait for indicating functional diversity or monitoring species diversity. Compared with traditional field sampling, light detection and ranging (LiDAR) provides new technology for mapping the regional grassland canopy height in a time-saving and cost-effective way. However, the grassland canopy height based on unmanned aerial vehicle (UAV) LiDAR is usually underestimated with height information loss due to the complex structure of grassland and the relatively small size of individual plants. We developed canopy height correction methods based on scan angle to improve the accuracy of height estimation by compensating the loss of grassland height. Our method established the relationships between scan angle and two height loss indicators (height loss and height loss ratio) using the ground-measured canopy height of sample plots with 1×1m and LiDAR-derived heigh. We found that the height loss ratio considering the plant own height had a better performance (R2 = 0.71). We further compared the relationships between scan angle and height loss ratio according to holistic (25-65cm) and segmented (25-40cm, 40-50cm and 50-65cm) height ranges, and applied to correct the estimated grassland canopy height, respectively. Our results showed that the accuracy of grassland height estimation based on UAV LiDAR was significantly improved with R2 from 0.23 to 0.68 for holistic correction and from 0.23 to 0.82 for segmented correction. We highlight the importance of considering the effects of scan angle in LiDAR data preprocessing for estimating grassland canopy height with high accuracy, which also help for monitoring height-related grassland structural and functional parameters by remote sensing.

Challenges and opportunities in remote sensing-based crop monitoring: a review.

Building a more resilient food system for sustainable development and reducing uncertainty in global food markets both require concurrent and near-real-time and reliable crop information for decision making. Satellite-driven crop monitoring has become a main method to derive crop information at local, regional, and global scales by revealing the spatial and temporal dimensions of crop growth status and production. However, there is a lack of quantitative, objective, and robust methods to ensure the reliability of crop information, which reduces the applicability of crop monitoring and leads to uncertain and undesirable consequences. In this paper, we review recent progress in crop monitoring and identify the challenges and opportunities in future efforts. We find that satellite-derived metrics do not fully capture determinants of crop production and do not quantitatively interpret crop growth status; the latter can be advanced by integrating effective satellite-derived metrics and new onboard sensors. We have identified that ground data accessibility and the negative effects of knowledge-based analyses are two essential issues in crop monitoring that reduce the applicability of crop monitoring for decisions on food security. Crowdsourcing is one solution to overcome the restrictions of ground-truth data accessibility. We argue that user participation in the complete process of crop monitoring could improve the reliability of crop information. Encouraging users to obtain crop information from multiple sources could prevent unconscious biases. Finally, there is a need to avoid conflicts of interest in publishing publicly available crop information.

Analysis of coniferous tree growth gradients in relation to regional pollution and climate change in the Miyun Reservoir Basin, China.

Forests play a crucial role in regulating regional climate and mitigating local air pollution, but little is known about their responding to such changes. This study aimed to examine the potential responses of Pinus tabuliformis, the major coniferous tree species in the Miyun Reservoir Basin (MRB), along an air pollution gradient in Beijing. Tree rings were collected along a transect, and ring width (basal area increment, BAI) and chemical characteristics were determined and related to long-term climatic and environmental records. The results showed that Pinus tabuliformis showed an overall increase in intrinsic water-use efficiency (iWUE) at all sites, but the relationships between iWUE and BAI differed among the sites. The contribution of atmospheric CO2 concentration (ca) to tree growth was significant at the remote sites (> 90%). The study found that air pollution at these sites might have caused further stomatal closure, as evidenced by the higher δ13C levels (0.5 to 1‰ higher) during heavy pollution periods. The analysis of tree ring δ15N also revealed the potential of using δ15N to fingerprint major nitrogen (N) deposition, as shown in the increasing tree ring δ15N, and major nitrogen losses due to denitrification and leaching, as shown in the higher δ15N in tree rings during heavy rainfall events. Overall, the gradient analysis indicated the contributions of increasing ca, increasing water deficit and elevated air pollution to tree growth and forest development. The different BAI trajectories suggested that Pinus tabuliformis has the ability to adapt to the harsh environment in the MRB.

A framework for separating natural and anthropogenic contributions to evapotranspiration of human-managed land covers in watersheds based on machine learning.

Actual EvapoTranspiration (ET) represents the water consumption in watersheds; distinguishing between natural and anthropogenic contributions to ET is essential for water conservation and ecological sustainability. This study proposed a framework to separate the contribution of natural and anthropogenic factors to ET of human-managed land cover types using the Random Forest Regressor (RFR). The steps include: (1) classify land cover into natural and human-managed land covers and then divide ET, meteorological, topographical, and geographical data into two parts corresponding to natural and human-managed land cover types; (2) construct a natural ET (ETn) prediction model using natural land cover types of ET, and the corresponding meteorological, topographical and geographical factors; (3) the constructed ETn prediction model is used to predict the ETn of human-managed land cover types using the corresponding meteorological, topographical and geographical data as inputs, and (4) derive the anthropogenic ET (ETh) by subtracting the natural ET from the total ET (ETt) for human-managed land cover types. Take 2017 as an example, ETn and ETh for rainfed agriculture, mosaic agriculture, irrigated agriculture, and settlement in Colorado, Blue Nile, and Heihe Basin were separated by the proposed framework, with R2 and NSE of predicted ETn above 0.95 and RB within 1% for all three basins. In the semi-arid Colorado River Basin and arid Heihe Basin, human activities on human-managed land cover types tended to increase ET higher than humid Blue Nile Basin. The anthropogenic contribution to total water consumption is approaching 53.68%, 66.47%, and 6.14% for the four human-managed land cover types in Colorado River Basin, Heihe Bain and Blue Nile Basin, respectively. The framework provides strong support for the disturbance of water resources by different anthropogenic activities at the basin scale and the accurate estimation of the impact of human activities on ET to help achieve water-related sustainable development goals.

Assessment of environmentally sensitive areas to desertification in the Blue Nile Basin driven by the MEDALUS-GEE framework.

Assessing environmentally sensitive areas (ESA) to desertification and understanding their primary drivers are necessary for applying targeted management practices to combat land degradation at the basin scale. We have developed the MEditerranean Desertification And Land Use framework in the Google Earth Engine cloud platform (MEDALUS-GEE) to map and assess the ESA index at 300 m grids in the Blue Nile Basin (BNB). The ESA index was derived from elaborating 19 key indicators representing soil, climate, vegetation, and management through the geometric mean of their sensitivity scores. The results showed that 43.4%, 28.8%, and 70.4% of the entire BNB, Upper BNB, and Lower BNB, respectively, are highly susceptible to desertification, indicating appropriate land and water management measures should be urgently implemented. Our findings also showed that the main land degradation drivers are moderate to intensive cultivation across the BNB, high slope gradient and water erosion in the Upper BNB, and low soil organic matter and vegetation cover in the Lower BNB. The study presented an integrated monitoring and assessment framework for understanding desertification processes to help achieve land-related sustainable development goals.

Method for monitoring environmental flows with high spatial and temporal resolution satellite data.

Monitoring environmental flows is crucial to maintaining the function and stability of river and lake ecosystems. However, current methods for monitoring environmental flows are expensive and ground based, and the accuracy of the results needs to be verified to evaluate the environmental flows. This evaluation is hampered by the problem of data shortages, such as hydrological and ecological data. In this study, a method for monitoring environmental flows is proposed using multisource high spatial and temporal resolution satellite data. A case study in the Yongding River Basin demonstrates that the method is feasible for monitoring the environmental flows of rivers in semiarid and arid areas. The results show that the environmental flows and months with large water discharges and shortages in the three control sections of the Yongding River Basin were different. Moreover, the downstream river width rarely met the environmental water demand, achieving this only for one period from 2017 to 2019 according to the three typical types of years (an average water year, a dry year, and an extremely dry year). This method and the results have applications in planning environmental flows and could improve the comprehensive management of the ecological environment in river basins.

Evenness is important in assessing progress towards sustainable development goals.

Sustainable development goals (SDGs) emphasize a holistic achievement instead of cherry-picking a few. However, no assessment has quantitatively considered the evenness among all 17 goals. Here, we propose a systematic method, which first integrates both the evenness and the overall status of all goals, to distinguish the ideal development pathways from the uneven ones and then revisit the development trajectory in China from 2000 to 2015. Our results suggest that, despite the remarkable progress, a bottleneck has occurred in China since 2013 due to the stagnant developments in some SDGs. However, many far-reaching policies in China have been targeting these deficiencies since then, providing a perspective on how a country approaches sustainable development by promoting evenness among all SDGs. Our results also indicate that regions with the slowest progress are the developed provinces, owing to the persistent uneven status of all goals. Our study demonstrates the importance of adopting evenness in assessing and guiding sustainable development.

A canopy conductance model with temporal physiological and environmental factors.

Canopy conductance, one of the key variables in simulating evapotranspiration, is strongly influenced by the physiological status of a plant and environmental factors, including photosynthetically active radiation, vapor pressure deficit, air temperature, soil moisture and so on. However, the restrictive functions used to represent these factors rarely consider the dynamics of physiological and environmental factors. This study proposed an improved canopy conductance model by regarding radiation and vapor pressure deficit as the two main influencing factors, quantifying the temporal variation in stomatal responses to radiation that notably adjust stomatal behavior, parameterizing maximum stomatal conductance with plant type-specific functions and proposing a new restrictive function for the VPD. The improved canopy conductance model was incorporated in a surface conductance model for estimating surface conductance and evapotranspiration at 8 flux stations at the Heihe River Basin and the Haihe River Basin. The estimated results were the most accurate when comparing to two other models. Furthermore, the model performance was acceptable when most of the parameters were assumed to be constant across the sites except the reference canopy conductance Gc, ref and the soil evaporation parameter αs, which suggests that the improved canopy conductance model could be used as a parsimony model for improving canopy conductance predictions and water use efficiency over typical climate zones and underlying surface types in North of China.

Soil erosion assessment in the Blue Nile Basin driven by a novel RUSLE-GEE framework.

Assessment of soil loss and understanding its major drivers are essential to implement targeted management interventions. We have proposed and developed a Revised Universal Soil Loss Equation framework fully implemented in the Google Earth Engine cloud platform (RUSLE-GEE) for high spatial resolution (90 m) soil erosion assessment. Using RUSLE-GEE, we analyzed the soil loss rate for different erosion levels, land cover types, and slopes in the Blue Nile Basin. The results showed that the mean soil loss rate is 39.73, 57.98, and 6.40 t ha-1 yr-1 for the entire Blue Nile, Upper Blue Nile, and Lower Blue Nile Basins, respectively. Our results also indicated that soil protection measures should be implemented in approximately 27% of the Blue Nile Basin, as these areas face a moderate to high risk of erosion (>10 t ha-1 yr-1). In addition, downscaling the Tropical Rainfall Measuring Mission (TRMM) precipitation data from 25 km to 1 km spatial resolution significantly impacts rainfall erosivity and soil loss rate. In terms of soil erosion assessment, the study showed the rapid characterization of soil loss rates that could be used to prioritize erosion mitigation plans to support sustainable land resources and tackle land degradation in the Blue Nile Basin.

Surface mining caused multiple ecosystem service losses in China.

China's surface mining greatly supported the rapid socio-economic development; however, there was a scarcity in the systematic understanding of national changes in surface mining changes and associated ecosystem services (ESs) losses, which inevitably affected human well-being and limited sustainable ecosystem management and policy optimization. In this study, we quantified the areal changes in surface mining based on the ChinaCover database and performed further analysis of ES loss from expanded surface mining using multidimensional geospatial data from 1990 to 2015, including MODIS products, meteorological records, and statistical datasets. Our observations reveal that China's surface mining was estimated to be 4746 km2 in 2015 and that Inner Mongolia had the largest surface mining area (28%). Surface mining expanded remarkably from 1990 to 2015, with an increase by 2.7 times after 2000. In particular, Inner Mongolia, Shanxi, and Qinghai had the greatest increases in surface mining area. Rapid expansion of surface mining led to obvious declines in natural habitat area, water retention, net primary productivity, and grain production, and these ES losses showed apparent spatiotemporal variations. China has taken many measures to reclaim the abandoned surface mining sites. Given the rapid expansion of surface mining and related ES loss, China should continue to perform ecological restoration for its sustainability.

Identification of Crop Type in Crowdsourced Road View Photos with Deep Convolutional Neural Network.

In situ ground truth data are an important requirement for producing accurate cropland type map, and this is precisely what is lacking at vast scales. Although volunteered geographic information (VGI) has been proven as a possible solution for in situ data acquisition, processing and extracting valuable information from millions of pictures remains challenging. This paper targets the detection of specific crop types from crowdsourced road view photos. A first large, public, multiclass road view crop photo dataset named iCrop was established for the development of crop type detection with deep learning. Five state-of-the-art deep convolutional neural networks including InceptionV4, DenseNet121, ResNet50, MobileNetV2, and ShuffleNetV2 were employed to compare the baseline performance. ResNet50 outperformed the others according to the overall accuracy (87.9%), and ShuffleNetV2 outperformed the others according to the efficiency (13 FPS). The decision fusion schemes major voting was used to further improve crop identification accuracy. The results clearly demonstrate the superior accuracy of the proposed decision fusion over the other non-fusion-based methods in crop type detection of imbalanced road view photos dataset. The voting method achieved higher mean accuracy (90.6-91.1%) and can be leveraged to classify crop type in crowdsourced road view photos.

Tree water-use efficiency and growth dynamics in response to climatic and environmental changes in a temperate forest in Beijing, China.

Understanding the responses of local forests to the gradually rising atmospheric CO2 concentrations (ca) and changing environment is critical for appropriate management activities. This work used tree ring width measures (i.e. basal areal increment, BAI) and carbon (C) and nitrogen (N) signals to explore the intrinsic water use efficiency (iWUE) and tree growth dynamics of three major tree species (Pinus massoniana, P.tabuliformis and Larix gmelinii) in the Miyun Reservae Basin (MRB) of Beijing. The results indicate that ca was a primary contributor to tree growths, especially at the remote site where rising ca accounted for 92% and 74% of BAI changes for P. tabuliformis and L. gmelinii, respectively. N deposition was found to have a positive effect on BAI at this site. The controlling effect of ca on tree growth at the close-to-city site was smaller (52% and 44% of the contributions for P. tabuliformis and P. massoniana, respectively), while the negative influences of N deposition on BAI tends to be intensified. iWUE showed consistent increase during the entire growth period at all sites. Quadratic relationships between iWUE and BAI were observed, which indicated that the rising ca stimulated photosynthesis, contributing to the initial BAI and iWUE increase. However, the intensified water stress resulting from reduced precipitation and increased temperature led to a reduction in tree stomatal conductance causing the subsequent increase in iWUE but decrease in BAI. Of the site- and species-related responses of tree growth to ca, climatic and environmental changes in the MRB, the site-related variation dominated. The non-linear relationship between BAI and ca combined with the quadratic relationship between BAI and iWUE indicate a decreased ability of forests to capture atmospheric CO2 once the CO2 tipping point has passed.

Hidden Loss of Wetlands in China.

To counter their widespread loss, global aspirations are for no net loss of remaining wetlands [1]. We examine whether this goal alone is sufficient for managing China's wetlands, for they constitute 10% of the world's total. Analyzing wetland changes between 2000 and 2015 using 30-m-resolution satellite images, we show that China's wetlands expanded by 27,614 km2 but lost 26,066 km2-a net increase of 1,548 km2 (or 0.4%). This net change hides considerable complexities in the types of wetlands created and destroyed. The area of open water surface increased by 9,110 km2, but natural wetlands-henceforth "marshes"-decreased by 7,562 km2. Of the expanded wetlands, restoration policies contributed 24.5% and dam construction contributed 20.8%. Climate change accounted for 23.6% but is likely to involve a transient increase due to melting glaciers. Of the lost wetlands, agricultural and urban expansion contributed 47.7% and 13.8%, respectively. The increase in wetlands from conservation efforts (6,765 km2) did not offset human-caused wetland losses (16,032 km2). The wetland changes may harm wildlife. The wetland loss in east China threatens bird migration across eastern Asia [2]. Open water from dam construction flooded the original habitats of threatened terrestrial species and affected aquatic species by fragmenting wetland habitats [3]. Thus, the "no net loss" target measures total changes without considering changes in composition and the corresponding ecological functions. It may result in "paper offsets" and should be used carefully as a target for wetland conservation.

Variation in actual evapotranspiration following changes in climate and vegetation cover during an ecological restoration period (2000-2015) in the Loess Plateau, China.

The spatial distribution of water resources largely influences Earth ecosystems and human civilization. Being a major component of the global water cycle, evapotranspiration (ET) serves as an indicator of the availability of water resources. Understanding the actual ET (ETa) variation mechanism at different spatial and temporal scales can improve management of water use within the sustainable development limits. In this study, remote sensing derived ETa data were used to study water resource fluctuations in the Loess Plateau, China. This region covers diverse climate types from humid to arid and experienced large changes in vegetation cover during a revegetation project between 2000 and 2015. The relations between spatiotemporal variation of ETa, climate factors and vegetation change were explored using statistical methods. The results show that cropland, forestland and grassland take the largest percentage of total ETa. Total ETa exhibited a marginally increasing trend (p < 0.1) during 2000-2010 and no trend during 2011-2015. Windspeed and vegetation cover index highly influenced ETa, followed by atmospheric pressure, air humidity, precipitation, bright sunshine duration and temperature. Temperature has little effect on ETa throughout the Loess Plateau. The monitoring of water resources based upon water balance between precipitation, ETa and river flow changes shows that water consumption deficit is consistent with vegetation changes: it was large during 2000-2010 when vegetation increased rapidly and decreased after 2010. These results could help to develop different water saving strategies across the Loess Plateau and build a better monitoring system of water resources.

Altered trends in carbon uptake in China's terrestrial ecosystems under the enhanced summer monsoon and warming hiatus.

The carbon budgets in terrestrial ecosystems in China are strongly coupled with climate changes. Over the past decade, China has experienced dramatic climate changes characterized by enhanced summer monsoon and decelerated warming. However, the changes in the trends of terrestrial net ecosystem production (NEP) in China under climate changes are not well documented. Here, we used three ecosystem models to simulate the spatiotemporal variations in China's NEP during 1982-2010 and quantify the contribution of the strengthened summer monsoon and warming hiatus to the NEP variations in four distinct climatic regions of the country. Our results revealed a decadal-scale shift in NEP from a downtrend of -5.95 Tg C/yr2 (reduced sink) during 1982-2000 to an uptrend of 14.22 Tg C/yr2 (enhanced sink) during 2000-10. This shift was essentially induced by the strengthened summer monsoon, which stimulated carbon uptake, and the warming hiatus, which lessened the decrease in the NEP trend. Compared to the contribution of 56.3% by the climate effect, atmospheric CO2 concentration and nitrogen deposition had relatively small contributions (8.6 and 11.3%, respectively) to the shift. In conclusion, within the context of the global-warming hiatus, the strengthening of the summer monsoon is a critical climate factor that enhances carbon uptake in China due to the asymmetric response of photosynthesis and respiration. Our study not only revealed the shift in ecosystem carbon sequestration in China in recent decades, but also provides some insight for understanding ecosystem carbon dynamics in other monsoonal areas.

Carbon pools in China's terrestrial ecosystems: New estimates based on an intensive field survey.

China's terrestrial ecosystems have functioned as important carbon sinks. However, previous estimates of carbon budgets have included large uncertainties owing to the limitations of sample size, multiple data sources, and inconsistent methodologies. In this study, we conducted an intensive field campaign involving 14,371 field plots to investigate all sectors of carbon stocks in China's forests, shrublands, grasslands, and croplands to better estimate the regional and national carbon pools and to explore the biogeographical patterns and potential drivers of these pools. The total carbon pool in these four ecosystems was 79.24 ± 2.42 Pg C, of which 82.9% was stored in soil (to a depth of 1 m), 16.5% in biomass, and 0.60% in litter. Forests, shrublands, grasslands, and croplands contained 30.83 ± 1.57 Pg C, 6.69 ± 0.32 Pg C, 25.40 ± 1.49 Pg C, and 16.32 ± 0.41 Pg C, respectively. When all terrestrial ecosystems are taken into account, the country's total carbon pool is 89.27 ± 1.05 Pg C. The carbon density of the forests, shrublands, and grasslands exhibited a strong correlation with climate: it decreased with increasing temperature but increased with increasing precipitation. Our analysis also suggests a significant sequestration potential of 1.9-3.4 Pg C in forest biomass in the next 10-20 years assuming no removals, mainly because of forest growth. Our results update the estimates of carbon pools in China's terrestrial ecosystems based on direct field measurements, and these estimates are essential to the validation and parameterization of carbon models in China and globally.

Effects of national ecological restoration projects on carbon sequestration in China from 2001 to 2010.

The long-term stressful utilization of forests and grasslands has led to ecosystem degradation and C loss. Since the late 1970s China has launched six key national ecological restoration projects to protect its environment and restore degraded ecosystems. Here, we conducted a large-scale field investigation and a literature survey of biomass and soil C in China's forest, shrubland, and grassland ecosystems across the regions where the six projects were implemented (∼16% of the country's land area). We investigated the changes in the C stocks of these ecosystems to evaluate the contributions of the projects to the country's C sink between 2001 and 2010. Over this decade, we estimated that the total annual C sink in the project region was 132 Tg C per y (1 Tg = 1012 g), over half of which (74 Tg C per y, 56%) was attributed to the implementation of the projects. Our results demonstrate that these restoration projects have substantially contributed to CO2 mitigation in China.

A Refined Crop Drought Monitoring Method Based on the Chinese GF-1 Wide Field View Data.

In this study, modified perpendicular drought index (MPDI) models based on the red-near infrared spectral space are established for the first time through the analysis of the spectral characteristics of GF-1 wide field view (WFV) data, with a high spatial resolution of 16 m and the highest frequency as high as once every 4 days. GF-1 data was from the Chinese-made, new-generation high-resolution GF-1 remote sensing satellites. Soil-type spatial data are introduced for simulating soil lines in different soil types for reducing errors of using same soil line. Multiple vegetation indices are employed to analyze the response to the MPDI models. Relative soil moisture content (RSMC) and precipitation data acquired at selected stations are used to optimize the drought models, and the best one is the Two-band enhanced vegetation index (EVI2)-based MPDI model. The crop area that was statistically significantly affected by drought from a local governmental department, and used for validation. High correlations and small differences in drought-affected crop area was detected between the field observation data from the local governmental department and the EVI2-based MPDI results. The percentage of bias is between −21.8% and 14.7% in five sub-areas, with an accuracy above 95% when evaluating the performance via the data for the whole study region. Generally the proposed EVI2-based MPDI for GF-1 WFV data has great potential for reliably monitoring crop drought at a relatively high frequency and spatial scale. Currently there is almost no drought model based on GF-1 data, a full exploitation of the advantages of GF-1 satellite data and further improvement of the capacity to observe ground surface objects can provide high temporal and spatial resolution data source for refined monitoring of crop droughts.