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Wheat vegetative growth, defined as the stage from green-up date to heading date (STAGE), is a crucial phrase, which largely affects crop yield. Previous some studies mainly focused on site-based changes of STAGE. However, there are few studies concerning large-scale changes of STAGE, which limits our understanding of regional changes of STAGE in response to climate change. Here, we introduced robust satellite-based phenological algorithms as well as third-generation global inventory modeling and mapping studies normalized difference vegetation index for the period of 1982–2015 to spatially derive winter wheat green-up date, heading date, and STAGE over croplands of the North China Plain (NCP). Changes of STAGE and their response to climate change were then investigated. Results showed that a strong predicted ability of introduced heading date algorithm was observed with r of 0.88 ( p R 2 = 0.11, p = 0.06). Stage of 23.4% cropland pixels performed significantly lengthened trends ( p r = 0.59, p < 0.01). This study highlights important roles of remote sensing data and satellite-based phenological algorithms for regional crop growth monitoring and management.

Poverty restricts rural sustainable development and urban&ndash, rural integration. Agricultural development is an engine of rural economic development and poverty elimination. Agricultural structure adjustment and rural poverty alleviation in Yulin City from the agro-pastoral transition zone of Northern China were studied using statistical data. Our results showed that the rural poverty ratio in Yulin was 7.70% in 2017, with clearly higher trends in southeast regions and lower trends in northeast regions. Northern Yulin had a lower proportion of primary industry to gross domestic production and ratio of agriculture to animal husbandry than southern Yulin. The agricultural structure variability index and agricultural specialization index have changed little, while the actual growth rate of the rural per capita net income has dramatically declined in the last decade. The rural poverty ratio was positively correlated with proportion of primary industry and ratio of agriculture to animal husbandry. These results suggest that a reduced proportion of primary industry and animal husbandry development will promote rural poverty alleviation and future rural revitalization.

Advances in the timing of spring green-up date are a typical response of vegetation global change. Long-term observations of plant phenology have been used to track vegetation response to climate change. Normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI) have been the most commonly used indicators in reconstructing spring green-up dates from remote sensing over the past several decades. The Moderate Resolution Imaging Spectroradiometer (MODIS) based phenology time series from NDVI and EVI with an enhanced TIMESAT algorithm are the two operational phenology products developed recently with sun-sensor geometry corrected reflectance. However, a comprehensive intercomparison and evaluation of these two spring green-up datasets using intensive ground observations have not been conducted, limiting their applications in regional interpretation of land surface phenology. Therefore, we used 455 ground observations from USA National Phenology Network (NPN) and 106 observations from 21 AmeriFlux sites to assess and validate the spring green-up dates for United States for 2000–2013. Our results indicate that the spring green-up dates from NDVI and EVI showed a good agreement in eastern United States, while substantial differences (70 days) were found in western and southern areas. Ground observations generally had a significant correlation (p < 0.01) with the two MODIS spring green-up dates, especially for deciduous broadleaf forests with root mean square error (RMSE) of 12 days and 16 days when compared with USA-NPN and AmeriFlux observations, respectively. Spring green-up dates from EVI overall showed a better relationship and a lower RMSE in reference to both USA-NPN and AmeriFlux observations than NDVI-based spring green-up dates. Our results highlight the importance of a rigorous validation of remote sensing products to better understand their limitations in operational applications.

Non-photosynthetic components within the canopy (e.g., dry leaves and stem) contribute little to photosynthesis and therefore, remote sensing of gross primary production (GPP) could be improved by the removal of these components. A scaled enhanced vegetation index (EVI), which is usually regarded as a linear function of EVI, was found to have the strongest relationship with chlorophyll level fraction of absorbed photosynthetically active radiation (FPARchl) and can help improve GPP estimation in croplands compared to canopy level FPAR (FPARcanopy). However, the application of the FPARchl theory to other plant functional types (PFTs) and the underlying reasons remain largely unknown. In this study, based on standard MODIS algorithm we comprehensively assessed the performances of FPARcanopy, scaled EVI (FPARchl1), normalized difference vegetation index (NDVI), scaled NDVI (FPARchl2) and EVI as proxies of FPAR for estimating GPP at four forest and six non-forest sites (e.g., grasslands, croplands and wetlands) from ChinaFLUX, representing a wide range of ecosystems with different canopy structures and eco-climatic zones. Our results showed that the scaled EVI (FPARchl1) as FPAR effectively improved the accuracy of estimated GPP for the entire PFTs. FPARchl1 substantially improved forest GPP estimations with higher coefficient of determination (R2), lower root mean square error (RMSE) and lower bias. In comparison, for non-forest PFTs, the improvement in R2 between estimated GPP based on FPARchl1 (GPPchl1) and flux tower GPP was less evident than those between flux GPP and GPP estimations from FPARcanopy (GPPcanopy), FPARchl2, NDVI and EVI. The temperature and water attenuation scalars played important roles in reducing the difference of various GPP and indirectly reducing the impact of different FPARs on GPP in non-forest PFTs. Even so, FPARchl1 is an ecologically more meaningful parameter since FPARchl1 and flux tower GPP dropped to zero more synchronously in both forest and non-forest sites. In particular, we found that the improvement of GPPchl1 relative to GPPcanopy was positively correlated with the maximum leaf area index (LAI), implying the importance of site characteristic in regulating the strength of the improvement. This is encouraging for remote sensing of GPP for which vegetation parameter retrieval has often been found to be less successful at high LAI due to saturations in reflective and scattering domains. Our results demonstrate the significance of accurate and ecologically meaningful FPAR parameterization for improving our current capability in GPP modeling.

Sustainable land use, targeted poverty alleviation (TPA) and integrated urban/town development are key issues for the poverty-stricken counties of China. This paper explored the spatio-temporal pattern and driving forces of construction land change in Longzhou, a typical poverty-stricken border county in southwest China, based on high-resolution remote sensing images and field investigations. The results showed that, from 2011 to 2016, the construction land in Longzhou increased by 10.08%. The largest increase shown by urban built-up areas (37.74%) followed by rural residential (25.48%), industrial and mining (20.96%), and transportation areas (15.49%) as well by tourist facility areas (0.33%). The accelerated urban development, rural housing construction, and construction of industrial parks and transportation facilities are main factors driving construction land expansion, while the implementation of TPA strategy and the booming of border trade activities are important policy drivers of the construction land expansion. Some of the major implications for improving poverty-alleviation-oriented land use policies were further discussed. The authors argue that land use policy innovation could play a key role in breaking the Matthew effect (poor get poorer, rich get richer) of China's uneven regional development and generating and accelerating the transformation development of poverty-stricken counties. It is fundamental to meet the demand of construction land for TPA, meanwhile raise the intensive level of land utilization. The authors suggest that the government need to further improve and implement preferential land use policies for poverty-stricken counties from the provincial level, appropriately increase the quotas of construction land, and effectively strengthen the rational, efficient and intensive use of the quotas of construction land, and provide solid land policy support for poverty alleviation and thus achieve the Sustainable Development Goals with higher quality.

Substantial efforts have been made to identify crop types by region, but few studies have been able to classify crops in early season, particularly in regions with heterogeneous cropping patterns. This is because image time series with both high spatial and temporal resolution contain a number of irregular time series, which cannot be identified by most existing classifiers. In this study, we firstly proposed an improved artificial immune network (IAIN), and tried to identify major crops in Hengshui, China at early season using IAIN classifier and short image time series. A time series of 15-day composited images was generated from 10 m spatial resolution Sentinel-1 and Sentinel-2 data. Near-infrared (NIR) band and normalized difference vegetation index (NDVI) were selected as optimal bands by pair-wise Jeffries–Matusita distances and Gini importance scores calculated from the random forest algorithm. When using IAIN to identify irregular time series, overall accuracy of winter wheat and summer crops were 99% and 98.55%, respectively. We then used the IAIN classifier and NIR and NDVI time series to identify major crops in the study region. Results showed that winter wheat could be identified 20 days before harvest, as both the producer’s accuracy (PA) and user’s accuracy (UA) values were higher than 95% when an April 1–May 15 time series was used. The PA and UA of cotton and spring maize were higher than 95% with image time series longer than April 1–August 15. As spring maize and cotton mature in late August and September–October, respectively, these two crops can be accurately mapped 4–6 weeks before harvest. In addition, summer maize could be accurately identified after August 15, more than one month before harvest. This study shows the potential of IAIN classifier for dealing with irregular time series and Sentinel-1 and Sentinel-2 image time series at early-season crop type mapping, which is useful for crop management.

The fraction of photosynthetically active radiation (FPAR) as a key biophysical variable plays an important role in gross primary production (GPP) modeling. In this study, we employed six different FPAR derived from MODIS FPAR, leaf area index (LAI), normalized difference vegetation index (NDVI) and enhanced vegetation index (EVI), respectively, which were widely used for modeling GPP in previous studies. Flux GPP was collected from ChinaFLUX, covering four forest sites and four non-forest sites. Correlation analysis was used to evaluate the potential of different FPAR to model GPP. Our results showed that FPAR derived from EVI had the stronger correlation with flux GPP than MOD15 FPAR and FPAR derived from LAI, but FPAR derived from NDVI was not observed. This study highlights the potential of FPAR derived from EVI in GPP modeling.

The photochemical reflectance index (PRI) has been suggested as an indicator of light use efficiency (LUE) for various plant functional types at both leaf and canopy scales. However, the application of PRI for the estimation of LUE has been always contaminated by canopy structures and directional effects associated with changes in illumination and viewing angles. The most important variable that controls the structural associated characteristics is the leaf area index (LAI), which is a major determinant of the absorbed photosynthetic active radiation (APAR) by a canopy. Using ground measured PRI and LUE over three growing seasons on winter wheat in China during 2005–2007, we found that canopy LAI overall explained 66% (p < 0.001) variance of PRI, indicating the essential of removing influences of external structural factors on PRI. Suggested by this, we defined the structural-related signal in PRI (sPRI) as a function of LAI and consequently, the residual PRI (rPRI=PRI-sPRI) would be independent on canopy structural characteristics. Our results showed that a mixed non-linear model using rPRI and variety and sampling time as fixed and random effects, respectively, had an improved accuracy in the estimation of LUE over PRI with increased R2 for both the overall dataset and data of each year. This finding is also supported when validated at flux tower site that the MODIS-derived daily rPRI can provide improved modeling of LUE (R2=0.49, p

Rapid urbanization has occurred in northwestern China, threatening the sustainability of its fragile dryland ecosystems. A lack of precise urban land-cover information has limited our understanding on the urbanization in the dryland. Here, we examined urban land-cover changes from 2000 to 2014 in 21 major cities that comprise over 50% of the developed land in arid China, using Landsat Enhanced Thematic Mapper Plus and Operational Land Imager data, and a hybrid classification method. The 15-m resolution urban land-cover products (including impervious surfaces, vegetation, bare soil, and water bodies) had an overall accuracy of 90.37%. Based on these new land use products, we found the urbanization in arid China was characterized by the dramatic expansion of impervious surface (+13.23%) and reduction of bare soil (−13.41%), while the proportions of vegetation (+0.27%) and water (−0.10%) remained stable. The observed dynamic equilibrium of vegetated ratio implies an increasing harmonization of urbanization and greening, which was particularly important for the sustainability of fragile urban ecosystems in arid regions. From an economic perspective, gross domestic product and population were significantly correlated with impervious surfaces, and oasis cities displayed a stronger ability to attract new residents than desert cities.

Monitoring the spring green-up date (GUD) has grown in importance for crop management and food security. However, most satellite-based GUD models are associated with a high degree of uncertainty when applied to croplands. In this study, we introduced an improved GUD algorithm to extract GUD data for 32 years (1982–2013) for the winter wheat croplands on the North China Plain (NCP), using the third-generation normalized difference vegetation index form Global Inventory Modeling and Mapping Studies (GIMMS3g NDVI). The spatial and temporal variations in GUD with the effects of the pre-season climate and soil moisture conditions on GUD were comprehensively investigated. Our results showed that a higher correlation coefficient (r = 0.44, p