Large-scale agricultural expansion can influence near-surface climate by altering surface energy fluxes, water, and albedo. It is less clear whether such effects extend through the full troposphere and how such effects vary in time. Here we use a novel dataset documenting the massive land use and land cover change due to agricultural expansion in Northeast China from 1982 to 2010 to assess how such expansion has influenced climate over the full troposphere. Confronting our land classification and climate data with a number of statistical approaches (linear regression, correlation analysis, Granger-causality), we find that cropland significantly increased by ∼28% over the near 30 year period in Northeast China-an average rate of nearly a percentage per year. This massive 30 year agricultural expansion is tightly associated with near-surface cooling identified in station data during the late growing season (August to September). Assuming no cropland expansion over the 30 year period, surface temperature would have increased by 0.93 °C ± 0.4 °C. Furthermore, the fingerprint of cropland-associated cooling extends upward into the atmospheric column, influencing the vertical structure of the regional troposphere and potentially its circulation. For every 10 percentage points increase in cropland fraction over Northeast China, regional full-troposphere temperature and geopotential height significantly decrease by 0.2 °C–0.6 °C and 20 m–80 m, respectively. These observed relationships are remarkably coherent across datasets, methodological choices, atmospheric levels, and theory, suggesting that the observational effects we identify are robust and imply the possibility of detectable land use change effects on regional circulation, with potential consequences for the East Asian monsoon.

County-level economic statistics estimation using remotely sensed data, such as nighttime light data, has various advantages over traditional methods. However, uncertainties in remotely sensed data, such as the saturation problem of the Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) NSL (nighttime stable lights) data, may influence the accuracy of this remote sensing-based method, and thus hinder its use. This study proposes a simple method to address the saturation phenomenon of nighttime light data using the GDP growth rate. Compared with other methods, the NSL data statistics obtained using the new method reflect the development of economics more accurately. We use this method to calibrate the DMSP-OLS NSL data from 1992 to 2013 to obtain the NSL density data for each county and linearly regress them with economic statistics from 2004 to 2013. Regression results show that lighting data is highly correlated with economic data. We then use the light data to further estimate the county-level GDP, and find that the estimated GDP is consistent with the authoritative GDP statistics. Our approach provides a reliable way to capture county-level economic development in different regions.

Land use and land cover (LULC) data are a central component of most land-atmosphere interaction studies, but there are two common and highly problematic scale mismatches between LULC and climate data. First, in the spatial domain, researchers rarely consider the impact of scaling up fine-scale LULC data to match coarse-scale climate datasets. Second, in the temporal domain, climate data typically have sub-daily, daily, monthly, or annual resolution, but LULC datasets often have much coarser (e.g., decadal) resolution. We first explored the effect of three spatial scaling methods on correlations among LULC data and a land surface climatic variable, latent heat flux in China. Scaling by a fractional method preserved significant correlations among LULC data and latent heat flux at all three studied scales (0.5°, 1.0°, and 2.5°), whereas nearest-neighbor and majority-aggregation methods caused these correlations to diminish and even become statistically non-significant at coarser spatial scales (i.e., 2.5°). In the temporal domain, we identified fractional changes in croplands, forests, and grasslands in China using a recently developed and annually resolved time series of LULC maps from 1982 to 2012. Relative to common LULC change (LULCC) analyses conducted over two-time steps or several time periods, this annually resolved, 31-year time series of LULC maps enables robust interpretation of LULCC. Specifically, the annual resolution of these data enabled us to more precisely observe three key and statistically significant LULCC trends and transitions that could have consequential effects on land-atmosphere interaction: (1) decreasing grasslands to increasing croplands in the Northeast China plain and the Yellow river basin, (2) decreasing croplands to increasing forests in the Yangtze river basin, and (3) decreasing grasslands to increasing forests in Southwest China. Our study not only demonstrates the importance of using a fractional spatial rescaling method, but also illustrates the value of annually resolved LULC time series for detecting significant trends and transitions in LULCC, thus potentially facilitating a more robust use of remotely sensed data in land-atmosphere interaction studies.

The distribution of C3 and C4 vegetation plays an important role in the global carbon cycle and climate change. Knowledge of the distribution of C3 and C4 vegetation at a high spatial resolution over local or regional scales helps us to understand their ecological functions and climate dependencies. In this study, we classified C3 and C4 vegetation at a high resolution for spatially heterogeneous landscapes. First, we generated a high spatial and temporal land surface reflectance dataset by blending MODIS (Moderate Resolution Imaging Spectroradiometer) and ETM+ (Enhanced Thematic Mapper Plus) data. The blended data exhibited a high correlation (R² = 0.88) with the satellite derived ETM+ data. The time-series NDVI (Normalized Difference Vegetation Index) data were then generated using the blended high spatio-temporal resolution data to capture the phenological differences between the C3 and C4 vegetation. The time-series NDVI revealed that the C3 vegetation turns green earlier in spring than the C4 vegetation, and senesces later in autumn than the C4 vegetation. C4 vegetation has a higher NDVI value than the C3 vegetation during summer time. Based on the distinguished characteristics, the time-series NDVI was used to extract the C3 and C4 classification features. Five features were selected from the 18 classification features according to the ground investigation data, and subsequently used for the C3 and C4 classification. The overall accuracy of the C3 and C4 vegetation classification was 85.75% with a kappa of 0.725 in our study area.

Evaluating vegetation phenology is crucial for a better understanding of the effects of climate change on the terrestrial ecosystem. The scientific community has used various vegetation index data sets from different sensors to quantify vegetation phenology from regional to global scales. The normalized difference vegetation index (NDVI) related to photosynthetic activities is the most widely used index. Recently, a number of published articles have used the Medium Resolution Imaging Spectrometer (MERIS) terrestrial chlorophyll index (MTCI) to measure vegetation phenology. MTCI can closely represent the red-edge position (REP). Unlike NDVI, MTCI is more sensitive to high values of chlorophyll content. However, the consistency of vegetation phenological metrics derived from MTCI and NDVI needs to be further explored. This study compared two phenological metrics, i.e. onset of greenness (OG) and end of senescence (ES), extracted from MERIS MTCI data and Advanced Very High Resolution Radiometer (AVHRR) Global Inventory Modeling and Mapping Studies (GIMMS) first generation NDVI (NDVIg) data, which has the longest time records, at nine regions in China from 2003 to 2006. The results showed that the differences of OG and ES vary between different vegetation types, regions, and years, although both NDVI and MTCI time series capture the growth patterns well for most vegetation types. Compared to ES, the OG estimates are more consistent. NDVI yields in general later ES estimates than MTCI.