Multiple cropping provides China with a very important system of intensive cultivation, and can effectively enhance the efficiency of farmland use while improving regional food production and security. A multiple cropping index (MCI), which represents the intensity of multiple cropping and reflects the effects of climate change on agricultural production and cropping systems, often serves as a useful parameter. Therefore, monitoring the dynamic changes in the MCI of farmland over a large area using remote sensing data is essential. For this purpose, nearly 30 years of MCIs related to dry land in the North China Plain (NCP) were efficiently extracted from remotely sensed leaf area index (LAI) data from the Global LAnd Surface Satellite (GLASS). Next, the characteristics of the spatial-temporal change in MCI were analyzed. First, 2162 typical arable sample sites were selected based on a gridded spatial sampling strategy, and then the LAI information was extracted from the samples. Second, the Savizky-Golay filter was used to smooth the LAI time-series data of the samples, and then the MCIs of the samples were obtained using a second-order difference algorithm. Finally, the geo-statistical Kriging method was employed to map the spatial distribution of the MCIs and to obtain a time-series dataset of the MCIs of dry land over the NCP. The results showed that all of the MCIs in the NCP showed an increasing trend over the entire study period and increased most rapidly from 1982 to 2002. Spatially, MCIs decreased from south to north; also, high MCIs were mainly concentrated in the relatively flat areas. In addition, the partial spatial changes of MCIs had clear geographical characteristics, with the largest change in Henan Province.

China is a large grain producer and consumer. Thus, obtaining information about the cropping intensity (CI) in cultivated land, as well as understanding the intensified utilization of cultivated land, is important to ensuring an increased grain production and food security for China. This study aims to detect and map the changes in CI over a period of 36 years across China’s core grain-producing area—the North China Plain (NCP)— using remotely sensed leaf area index (LAI) time series data acquired by the Global LAnd Surface Satellite (GLASS) products. We first selected 2132 sample points that consisted entirely, or almost entirely, of cultivated cropland from all pixels; the biennial LAI curves for the sample points were then extracted; the Savitzky–Golay filter and second-order difference algorithm were then applied to reconstruct the biennial LAI curves and obtain the number of peaks in these curves. In addition, the multiple cropping index (MCI) was calculated to represent the CI. Finally, the spatial distribution of the CI of cultivated land on the NCP was mapped from 1982 to 2018 using a geo-statistical kriging approach. Spatially, the results indicate that the CI of cultivated land over the NCP exhibits a distinct spatial pattern that conforms to “high in the south, low in the north”. The single cropping system (SCS) mainly occurred in the higher latitude area ranging from 37.04°N to 42.54°N, and the double cropping system (DCS) mainly existed in the lower latitude area between 31.95°N and 39.97°N. Temporally, the CI increased over the study period, but there were some large fluctuations in CI from 1982 to 1998 and it maintained relatively stable since 2000. Across the NCP, 68.14% of cultivated land experienced a significant increase in CI during the 36-year period, while only 3.87% showed a significant decrease. We also found that, between 1982 and 2018, the northern boundary of the area for DCS underwent a significant westward expansion and northward movement. Our results show a good degree of consistency with statistical data and previous research and also help to improve the reliability of satellite-based identification of CI using low spatial resolution LAI products. The results provide important information that can be used for analyzing and evaluating the rational utilization of cultivated land resources; thus, ensuring food security and realizing agricultural sustainability not only for the NCP, but for China as a whole. These results also highlight the value of satellite remote sensing to the long-term monitoring of cropping intensity at large scales.

Quantifying the phenological variations of Populus euphratica Olivier (P. euphratica) resulting from climate change is vital for desert ecosystems. There has previously been great progress in the influence of climate change on vegetation phenology, but knowledge of the variations in P. euphratica phenology is lacking in extremely arid areas. In this study, a modified method was proposed to explore P. euphratica phenology and its response to climate change using 18-year Global Land Surface Satellite (GLASS) leaf area index (LAI) time series data (2000–2017) in the upper Tarim River basin. The start of the growing season (SOS), length of the growing season (LOS), and end of the growing season (EOS) were obtained with the dynamic threshold method from the reconstructed growth time series curve by using the Savitzky–Golay filtering method. The grey relational analysis (GRA) method was utilized to analyze the influence between the phenology and the key climatic periods and factors. Importantly, we also revealed the positive and negative effects between interannual climate factors and P. euphratica phenology using the canonical correlation analysis (CCA) method, and the interaction between the SOS in spring and EOS in autumn. The results revealed that trends of P. euphratica phenology (i.e., SOS, EOS, and LOS) were not significant during the period from 2000–2017. The spring temperature and sunshine duration (SD) controlled the SOS, and the EOS was mainly affected by the temperature and SD from June–November, although the impacts of average relative humidity (RH) and precipitation (PR) on the SOS and EOS cannot be overlooked. Global warming may lead to SOS advance and EOS delay, and the increase in SD and PR may lead to earlier SOS and later EOS. Runoff was found to be a more key factor for controlling P. euphratica phenology than PR in this region.

In recent years, ozone pollution has been increasing in some parts of the world. In this study, we used the Beijing–Tianjin–Tangshan (BJ-TJ-TS) urban agglomeration region as a case study and used satellite remotely sensed inversion data and hourly ground monitoring observations of surface ozone concentrations, meteorological data, and other factors from 2016 to 2019 to explore the spatiotemporal dynamic characteristics of surface ozone concentration and its pollution levels. We also investigated their coupling relationships with meteorological factors, including temperature, pressure, relative humidity, wind velocity, and sunshine duration, in order to support the development of effective control measures for regional ozone pollution. The results revealed that the surface ozone concentration throughout the BJ-TJ-TS region from 2016 to 2019 exhibited an overall pattern of high values in the northwest and low values in the southeast, as well as an obvious difference between built-up and non-built-up areas (especially in Beijing). Meanwhile, a notable increasing trend of ozone levels was discovered in the BJ and TJ areas from 2016 to 2019, whereas this upward trend was not evident in the TS area. In all three areas, the highest monthly average ozone values occurred in the summer month of June, while the lowest monthly average levels occurred in the winter month of December. Their diurnal variation values reached a maximum value at approximately 3:00–4:00 p.m. and a minimum value at approximately 7:00 a.m. It is clear that high temperature, long sunshine duration, low atmospheric pressure, and weak wind velocity conditions, as well as certain relative humidity levels, readily led to high-concentration ozone pollution. Meanwhile, the daily average values of the five meteorological factors on days with Grade I and Grade II ozone pollution displayed different characteristics.

Land use and land cover changes (LUCC) affects the atmospheric environment directly or indirectly. Therefore, understanding the atmospheric response to LUCC is of great significance to maintain and improve the ecological environment. In this study, based on fine particulate matter (PM_2.5) and LC products, we first compared the differences of PM_2.5 between urban and surrounding areas, and then further investigated the variations of PM_2.5 in different and land cover (LC) using Mann-Kendall (MK) test and Sen’s trend analysis approach at the district-level in India during 1998–2015. The results showed that the numbers of districts where the differences of PM_2.5 (DPM_2.5) between urban and the surrounding areas were greater than zero were increasing during 1998–2015. There is an upward trendency of annual mean PM_2.5. The annual mean PM_2.5 was higher than 40 μg/m³ in 58% of India’s areas where there were mainly located in the Ganges plains of northern India with cropland (L01) and urban areas (L07). The annual mean PM_2.5 was less than 10 μg/m³ were mainly found in north-western India with permanent ice and snow (L10), accounting for 10% of India’s area. There are significant positive trends of PM_2.5 concentration in 90% of cropland (L01) and 88% of urban area (L07) and the average slope were 0.83 μg/m³ and 0.82 μg/m³ respectively, which were higher than those in the rest of LC. This research serves as the basis of reference for the equitable allocation of land resources and restructuring of land use and land cover patterns in urban areas of India that severely affected by air pollution.

Specialty crop industries generate heterogeneous knowledge across cultivation, pest and disease management, post-harvest handling, quality control, and marketing, yet this knowledge is often dispersed across technical documents, web resources, relational datasets, and expert experience. Taking the jujube (Ziziphus jujuba Mill.) industry as a case study, this study develops a reproducible ontology-constrained knowledge-engineering framework. A lifecycle-oriented domain ontology and a curated knowledge graph prototype were constructed from verified ontology-aligned records, while unstructured texts were used to evaluate prospective information-extraction components. Four BERT-based named entity recognition models were compared under leakage-controlled five-fold cross-validation, and versioned ontology-constrained trigger rules were evaluated under a gold-entity setting. The final ontology contained 457 RDF triples, 38 classes, 13 object properties, and 71 data properties, and logical consistency checks detected no inconsistent named classes. BERT-Softmax obtained the highest mean NER F1-score (0.328 ± 0.146) in this small corpus, but the fold variability does not support a general claim of superiority over CRF-based architectures. Under gold entities, the refined trigger rules achieved an F1-score of 0.683 ± 0.142; in a stricter diagnostic using automatically predicted fine-grained entities, end-to-end relation extraction declined to 0.035 ± 0.054. On a 93-question benchmark generated from verified graph triples, the rule-graph QA engine achieved a token-level F1-score of 0.973 and a tail-set exact accuracy of 98.9%, primarily reflecting relation mapping and graph retrieval correctness. A separate 20-question human-authored challenge audit found three partially supported questions and no fully supported question, illustrating the boundary beyond graph-grounded retrieval. The results provide case-specific, exploratory evidence that explicit ontology constraints can support reproducible knowledge organization and verifiable retrieval under limited domain data; they do not establish deployment-level or unrestricted agricultural QA performance.