Search

Wind‐induced crop lodging can reduce agricultural production and impact food security. However, a systematic evaluation of large‐scale crop lodging and its drivers is lacking, mostly due to the limited observations available. Such knowledge gaps hinder the application of effective management practices to mitigate yield losses. Here, we quantify maize‐lodging induced by three consecutive typhoons in Northeast China in 2020 using direct crop canopy height observations from the Global Ecosystem Dynamics Investigations (GEDI) instrument and optical satellite imagery. We show that the canopy water‐related vegetation index provides a robust wall‐to‐wall mapping of crop lodging, supported by changes in GEDI canopy height. Our spatially contiguous lodging maps show that lodging intensity is mainly due to typhoon intensity (64.34%), with maize plantation fraction (11.94%) and phenology (7.94%) as secondary factors. The shelterbelt forest may partly alleviate the lodging risk, but its effect requires further investigation. Considering the recent expansion of maize cultivation and the poleward shift of landfalling typhoons alongside climate change, the lodging risk may increase for maize and further threaten China's food security. Plain Language Summary: Wind‐induced crop lodging can substantially reduce agricultural production and jeopardize regional and global food security. However, large‐scale characterizations of typhoon‐induced crop lodging are limited, hindering the understanding of its overall impacts and driving mechanisms. We show for the first time that the novel space‐borne LiDAR and optical satellite data can provide a consistent picture of crop damage caused by typhoons at the regional scale, mainly due to typhoon intensity and maize planting fraction. Future overlap between increased typhoons and maize expansion may further threaten China's food security. Our insights also apply to other major maize‐producing regions affected by landfalling typhoons or hurricanes, such as the Midwestern US, and help improve crop lodging monitoring systems and mitigation practices. Key Points: Global Ecosystem Dynamics Investigation footprints can well reveal changes in maize height caused by typhoons in Northeast ChinaThe land surface water index provides a robust wall‐to‐wall mapping of crop lodgingMaize lodging intensity is mainly due to typhoon intensity (64.34%), followed by maize plantation fraction (11.94%) and phenology (7.94%) [ABSTRACT FROM AUTHOR]

Biophysical processes and people's livelihoods largely depend on ecosystem productivity; thus, land degradation is harmful for local sustainable development. Most previous assessments of land degradation used a comprehensive index system or trend analysis without localized benchmarks; thus, they were not fully objective. Here, we assessed the land degradation in the Heihe River Basin (HRB), China using the local net primary production (NPP) scaling (LNS) method. We improved the method by dividing land capability classes (LCC) using remote sensing data, and determining the reference NPP for each LCC to represent the productivity under optimal environmental conditions. To assess the extent, severity and trend of land degradation, we obtained LNS percent values which was defined as the ratio of present NPP to reference NPP. We categorized our identified land degradation hotspots, determined their causes and proposed potential strategies for land conservation. Our results showed that nearly one quarter of the area was degraded land, mainly located in the upper stream. The average NPP was 35.75% lower than the reference NPP, with a reduction amount of 0.82 GgC yr−1. Only the lower stream showed a significant decreasing trend in LNS percent values. The areas with a positive trend in LNS percent values were primarily observed in the middle stream croplands. We found the degradation condition deteriorated first and recovered later in the HRB. Land use change contributed more than climate change to the local NPP of the degraded areas. Our research provides a more general framework for assessing land productivity reduction induced by anthropogenic and natural processes in the drylands. [ABSTRACT FROM AUTHOR]

Understanding the changes in aridity is crucial because of the substantial impacts it can have on human society. However, realistic aridity changes and their underlying climatic mechanisms demonstrate large uncertainties in China. Here we adopted the aridity index (AI) to assess the aridity changes in China, and we quantified the relative contributions of various climatic drivers (the precipitation, net radiation, wind speed, relative humidity, and air temperature) to AI changes based on the station data during 1961–2016. Our results showed that the arid/semi‐arid northwest China and Tibet Plateau experienced wetting trends, while western sub‐humid north and northeast China and western humid south China exhibited drying trends during 1961–2016. The dominant climatic drivers involved in the changing AI trends changed from arid to humid regions. In arid northwest China, increasing precipitation and decreasing wind speeds contributed the most to the wetting trends (0.024 and 0.017 in 56 years, respectively). In the semi‐arid Tibet Plateau, increasing precipitation dominated the wetting trends (0.045 in 56 years). However, in sub‐humid/humid north and northeast China and south China, decreasing wind speed and net radiation dominated the annual AI changes (0.061 and 0.092 in 56 years, respectively). In addition, AI of northwest China and the Tibet Plateau exhibited a significant abrupt change in 1987 and exhibited wetter states afterwards, which could be caused by the abrupt increase in precipitation in 1987. This study could improve current understanding of the aridity changes in China and help adapt its potential impacts. This study investigated the aridity changes and their underlying climatic mechanisms in China using station data during 1961–2016. Results showed that the "arid regions getting drier; humid regions getting wetter" is not applicable to China. The dominant climatic driver for aridity changes varies from arid to humid regions. Precipitation dominates the wetting trends in arid/semi‐arid northwest China and the Tibet Plateau, whereas wind speed and net radiation dominate aridity changes in sub‐humid/humid north and northeast China and south China. [ABSTRACT FROM AUTHOR]

Climate variability and climate extremes are important climatic determinants of plant growth, species distribution and net primary productivity. A comprehensively quantitative analysis of the sensitivity and resilience of ecosystems to climate variability is vital to identify which regions and species are most in danger in response to future climate change. Here, we proposed an empirical approach to assess the relative sensitivity and resilience of ecosystems to short-term climate variability in the Heihe River Basin (HRB) which is the second largest inland river basin in the northern China and contains ecosystems of semi-arid, arid and hyper-arid types. Based on the monthly time series of normalized difference vegetation index (NDVI), land surface temperatures (LST) and the ratio of actual evapotranspiration to potential evapotranspiration (AET/PET) derived from MODIS sensor from 2000 to 2013, we developed a multiple linear regressive and autoregressive model to determine the sensitivity of NDVI anomalies to climate variability indicated by monthly LST anomalies (temperature variability) and monthly AET/PET anomalies (water variability). We included 1 month time lag of NDVI anomalies in order to reflect ecosystem resilience. We found that the sensitivity and resilience to climate variability were different in the upper, middle and lower reaches of the HRB. Temperature variability dominantly controlled vegetation anomalies in the upper reach, but water variability was the dominant climatic factor in the middle and lower reaches. The different responses of semi-arid to hyper-arid ecosystems to climate variability depended much on the distinct climatic conditions and diverse vegetation types. Ecosystems in drier condition tended to show higher sensitivity to water variability, and ecosystems with colder climate were likely to be more sensitive to temperature variability. The most sensitive vegetation type to water variability and temperature variability in the HRB was crop and meadow, respectively. Grass had been proved to have the lowest resilience. Our research on the sensitivity and resilience of semi-arid to hyper-arid ecosystems is helpful for formulating and implementing adaptation and mitigation strategies in response to climate change. [ABSTRACT FROM AUTHOR]

Unprecedented human-induced land cover changes happened in China after the Reform and Opening-up in 1978, matching with the era of Landsat satellite series. However, it is still unknown whether Landsat data can effectively support retrospective analysis of land cover changes in China over the past four decades. Here, for the first time, we conduct a systematic investigation on the availability of Landsat data in China, targeting its application for retrospective and continuous monitoring of land cover changes. The latter is significant to assess impact of land cover changes, and consequences of past land policy and management interventions. The total and valid observations (excluding clouds, cloud shadows, and terrain shadows) from Landsat 5/7/8 from 1984 to 2017 were quantified at pixel scale, based on the cloud computing platform Google Earth Engine (GEE). The results show higher intensity of Landsat observation in the northern part of China as compared to the southern part. The study provides an overall picture of Landsat observations suitable for satellite-based annual land cover monitoring over the entire country. We uncover that two sub-regions of China (i.e., Northeast China-Inner Mongolia-Northwest China, and North China Plain) have sufficient valid observations for retrospective analysis of land cover over 30 years (1987–2017) at an annual interval; whereas the Middle-Lower Yangtze Plain (MLYP) and Xinjiang (XJ) have sufficient observations for annual analyses for the periods 1989–2017 and 2004–2017, respectively. Retrospective analysis of land cover is possible only at a two-year time interval in South China (SC) for the years 1988–2017, Xinjiang (XJ) for the period 1992–2003, and the Tibetan Plateau (TP) during 2004–2017. For the latter geographic regions, land cover dynamics can be analyzed only at a three-year interval prior to 2004. Our retrospective analysis suggest that Landsat-based analysis of land cover dynamics at an annual interval for the whole country is not feasible; instead, national monitoring at two- or three-year intervals could be achievable. This study provides a preliminary assessment of data availability, targeting future continuous land cover monitoring in China; and the code is released to the public to facilitate similar data inventory in other regions of the world. [ABSTRACT FROM AUTHOR]