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Vegetation phenology and its spatiotemporal driving factors are essential to reflect global climate change, the surface carbon cycle and regional ecology, and further quantitative studies on spatiotemporal heterogeneity and its two-way driving are needed. Based on MODIS phenology, meteorology, land cover and other data from 2001 to 2019, this paper analyzes the phenology change characteristics of the Yangtze River Delta from three dimensions: time, plane space and elevation. Then, the spatiotemporal heterogeneity of phenology and its driving factors are explored with random forest and geographic detector methods. The results show that (1) the advance of start of season (SOS) is insignificant—with 0.17 days per year; the end of season (EOS) shows a significant delay—0.48 days per year. The preseason temperature has a greater contribution to SOS, while preseason precipitation is main factor in determining EOS. (2) Spatial differences of the phenological index do not strictly obey the change rules of latitude at a provincial scale. The SOS of Jiangsu and Anhui is earlier than that of Zhejiang and Shanghai, and EOS shows an obvious double-clustering phenomenon. In addition, a divergent response of EOS with elevation grades is found; the most significant changes are observed at grades below 100 m. (3) Land cover (LC) type is a major factor of the spatial heterogeneity of phenology, and its change may also be one of the insignificant factors driving the interannual change of phenology. Furthermore, nighttime land surface temperature (NLST) has a relatively larger contribution to the spatial heterogeneity in non-core urban areas, but population density (PD) contributes little. These findings could provide a new perspective on phenology and its complex interactions between natural or anthropogenic factors. [ABSTRACT FROM AUTHOR]

Cropland phenology provides key information in managing agricultural practices and modelling crop yield. However, most of the existing phenological products have coarse spatial resolution ranging from 250 to 8000 m, which is not sufficient to capture the critical spatial details of cropland phenology at the landscape scale. Landsat imagery provides an unprecedented data source to generate 30-m spatial resolution phenological products. This paper explored the potential of utilizing multi-year Landsat enhanced vegetation index to derive annual phenological metrics of a double-season agricultural land from 1993 to 2009 in a sub-urban area of Shanghai, China. We used all available Landsat TM and ETM+ observations (538 scenes) and developed a Landsat double-cropping phenology (LDCP) algorithm. LDCP captures the temporal trajectory of multi-year enhanced vegetation index time series very well, with the degree of fitness ranging from 0.78 to 0.88 over the study regions. We found good agreements between derived annual phenological metrics and in situ observation, with root mean square error ranging from 8.74 to 18.04 days, indicating that the proposed LDCP is capable of detecting double-season cropland phenology. LDCP could reveal the spatial heterogeneity of cropland phenology at parcel scales. Phenology metrics were retrieved for approximately one-third and two-thirds of the 17 years for the first and second cropping cycles, respectively, depending on the number of good quality Landsat data. In addition, we found an advanced peak of season for both cropping cycles in 50–60% of the study area, and a delayed start of season for the second cropping cycle in 50–70% of the same area. The potential drivers of those trends might be climate warming and changes in agricultural practices. The derived cropland phenology can be used to help estimate historical crop yields at Landsat spatial resolution, providing insights on evaluating the effects of climate change on temporal variations of crop growth, and contributing to food security policy making. [ABSTRACT FROM AUTHOR]

In this work, we study ground deformation of ocean-reclaimed platforms as retrieved from interferometric synthetic aperture radar (InSAR) analyses. We investigate, in particular, the suitability and accuracy of some time-dependent models used to characterize and foresee the present and future evolution of ground deformation of the coastal lands. Previous investigations, carried out by the authors of this paper and other scholars, related to the zone of the ocean-reclaimed lands of Shanghai, have already shown that ocean-reclaimed lands are subject to subside (i.e., the ground is subject to settling down due to soil consolidation and compression), and the temporal evolution of that deformation follows a certain predictable model. Specifically, two time-gapped SAR datasets composed of the images collected by the ENVISAT ASAR (ENV) from 2007 to 2010 and the COSMO-SkyMed (CSK) sensors, available from 2013 to 2016, were used to generate long-term ground displacement time-series using a proper time-dependent geotechnical model. In this work, we use a third SAR data set consisting of Radarsat-2 (RST-2) acquisitions collected from 2012 to 2016 to further corroborate the validity of that model. As a result, we verified with the new RST-2 data, partially covering the gap between the ENV and CSK acquisitions, that the adopted model fits the data and that the model is suitable to perform future projections. Furthermore, we extended these analyses to the area of Pearl River Delta (PRD) and the city of Shenzhen, China. Our study aims to investigate the suitability of different time-dependent ground deformation models relying on the different geophysical conditions in the two areas of Shanghai and Shenzhen, China. To this aim, three sets of SAR data, collected by the ENV platform (from both ascending and descending orbits) and the Sentinel-1A (S1A) sensor (on ascending orbits), were used to obtain the ground displacement time-series of the Shenzhen city and its surrounding region. Multi-orbit InSAR data products were also combined to discriminate the up–down (subsidence) ground deformation time-series of the coherent points, which are then used to estimate the parameters of the models adopted to foresee the future evolution of the land-reclaimed ground consolidation procedure. The exploitation of the obtained geospatial data and products are helpful for the continuous monitoring of coastal environments and the evaluation of the socio-economical impacts of human activities and global climate change. [ABSTRACT FROM AUTHOR]