Spatial scale transformation-based estimation model for fresh grass yield: a case study of the Xilingol Grassland, Inner Mongolia, China.

Estimating the grass yield of a grassland area is of vital theoretical and practical significance for determining grazing capacity and maintaining ecological balance. Due to the spatial inconsistency between sampling and remote sensing data, improving the accuracy of fresh grass yield (FGY) estimation based on remote sensing is difficult. Using vegetation coverage at different spatial scales, this paper proposes a spatial scale transformation (SST)-based estimation model for FGY adopting normalized difference vegetation index (NDVI) as its estimation factor, using the grassland in Xilingol League, Inner Mongolia, as the study area. Results showed that the SST-based FGY estimation model was able to greatly improve estimation precision; the relative estimation error (REE) of the estimation models constructed using linear with intercept zero (linear-0) and power functions were 18.16% and 18.35%, respectively. The estimation models constructed using linear-0 and power functions were employed to estimate the grass yield of the grassland in Xilingol League, and the total FGYs estimated were 8.777 × 1010 kg and 8.583 × 1010 kg, respectively. The two models obtained roughly the same estimates, but there were significant differences between them in the spatial distributions of FGY per unit. Taking net primary productivity (NPP) as an example, the effectiveness of other remote sensing data as estimation factors was further verified, and the results showed that SST-based estimation for FGY also effectively improved the estimation accuracy of grass yield.

Evaluation of urban expansion and the impacts on vegetation in Chinese Loess Plateau: a multi-scale study.

Vegetation degradation caused by rapid urban expansion is a pressing global challenge. Focusing on the Chinese Loess Plateau (CLP), we use satellite observations from 2000 to 2017 to evaluate the spatiotemporal pattern of urban expansion, and its imprint on vegetation across old urban, new urban, urban, non-urban areas as well as the entire urbanization intensity (UI) gradient (from 0 to 100%). We found a massive increase of urban impervious surface area (UISA) in the CLP from 2000 to 2017, and an uneven expansion of UISA at different urban agglomerations and cities. Less green were found in urban and new urban areas, while old urban and non-urban areas generally showed an improved greening pattern. In addition, the annual maximum EVI (EVImax) differences between urban and non-urban areas were - 0.0995 on average from 2000 to 2017. The Guanzhong Plain urban agglomeration (GPUA) witnessed the most significant EVImax differences (- 0.120), and the Ningxia Yanhuang urban agglomeration (NYUA) witnessed the lowest EVImax differences (- 0.012). The EVImax showed significantly decreased trends along the entire spectrum of urbanization gradient for 97.4% (38 of 39) cities and five urban agglomerations. The most significant decrease was found in the GUPA (slope = - 0.0197/10a, p < 0.01), while the smallest drop was found in the NYUA (slope = - 0.011/10a, p < 0.01). This study offered a fundamental support for understanding the vegetation variation along the urban-rural gradient, which may help stakeholders to make better ecological management policies for urban vegetation in ecologically fragile areas.

Spatiotemporal dynamics of ecosystem water use efficiency over the Chinese Loess Plateau base on long-time satellite data.

Ecosystem water use efficiency (eWUE), defined as the ratio between carbon gains and water loss from the system, has been recognized as an important characteristic of carbon and water balances. The long-lasting "Grain for Green" Program (GFGP) initiated in 1999 in China's Loess Plateau (CLP) is a large-scale ecological program in the world, which aims to improve the CLP's ecosystem resilience by enhancing vegetation cover and productivity. Understanding how the GFGP can affect eWUE is imperative to ensuring sustainable water resources and to promoting sustainable management strategies. In this study, we evaluated the spatiotemporal variability of growing-season eWUE and examined its response to both climate change and vegetation coverage from 1982 to 2017. Our results indicate that growing-season eWUE, gross primary productivity (GPP), and evapotranspiration (ET) in CLP area increased significantly from 1982 to 2017. Specifically, eWUE, GPP, and ET increased more rapidly after China established the program. The most significant growth area of eWUE was found in main areas conducting GFGP project, including the Loess hilly and gully area (LHGA). Spatially, eWUE, GPP, and ET in the growing season increased from northwest to southeast, and higher eWUE was found in areas with high vegetation cover. The spatial and temporal variability of eWUE was related to vegetation cover (expressed as leaf area index, LAI) and climatic variability. Significant positive correlations were observed between growing-season LAI, temperature, and eWUE, because the LAI and temperature have a greater effect on photosynthesis than ET. Our results suggested that the GFGP was the main driving force that causes the spatial-temporal variability of eWUE in CLP.

Spatial heterogeneity of changes in cropland ecosystem water use efficiency and responses to drought in China.

Understanding cropland ecosystem water use efficiency (eWUE) responses to drought is important for sustainable water resource management and food security. Today in China, the spatiotemporal patterns of eWUE and responses to drought across different cropland classes remain poorly quantified. In this study, we characterized the spatial temporal variability in cropland eWUE and response to drought in China from 1982 to 2017 using the satellite-retrieved evapotranspiration (ET), gross primary production (GPP), and self-calibrating Palmer Drought Severity Index (scPDSI), in conjunction with the Global Food Security-support Analysis Data product for Crop Dominance (GFSAD1KCD) data. Results indicated that (1) mean annual cropland eWUE had a spatial range from 0 to 9.94 g C kg-1 H2O, with higher values (2.06 g C kg-1 H2O) in class 4 (rainfed: wheat, rice, and soybeans dominant), whereas the lowest eWUE (1.58 g C kg-1 H2O) occurred in class 2 (irrigated mixed crop 1: wheat, rice, barley, and soybeans). (2) Annual eWUE, GPP, and ET values for croplands in China increased significantly between 1982 and 2017. Class 1 (irrigated wheat and rice) had the highest trend of 0.011 g C kg-1 H2O yr-1, and class 6 (rainfed: corn and soybeans) had the lowest of 0.0007 g C kg-1 H2O yr-1. Apart from class 4, annual GPP and ET were enhanced in most cropland classes from 1982 to 2017 (p<0.01). (3) Rainfed croplands generally had higher eWUE, GPP, and ET values than irrigated croplands. Except for rainfed cropland eWUE, all other cropland variables increased significantly (p<0.001) from 1982 to 2017. (4) Correlation analysis found that the 19.66% (15.62%) of cropland had significant negative (positive) correlations between eWUE and current-year scPDSI. The legacy effects of drought on cropland eWUE indicated that previous and current-year drought impacts on cropland eWUE were in the same direction. Our results provide insights into variability in cropland eWUE and its response to drought in China, where there is a growing demand for agricultural water resource management.

Evaluating the cumulative and time-lag effects of drought on grassland vegetation: A case study in the Chinese Loess Plateau.

The increased frequency of drought events in recent years is known to be responsible for significantly altering plant biodiversity in many of Earth's ecosystems, though the specifics of vegetation-drought interactions, especially the cumulative and time-lag responses, remains unclear. This study aimed to quantitatively investigate how grassland vegetation over the Chinese Loess Plateau (CLP) reacts to drought, specifically the observed cumulative and time-lag effects which are caused, using a combination of the Normalized Difference Vegetation Index (NDVI) and a multiple time-scale drought index (Standardized Precipitation and Evapotranspiration Index, SPEI). Our results revealed that while drought conditions have widespread cumulative impacts on grass growth in the CLP, the time lag effect of drought covered about half of the total area of the CLP. The cumulative effect of drought on grass was found to take place over various time scales, ranging from 5 to 10 months, while the time lag effect occurred within 2-3 months. The different response time of vegetation growth to the cumulative effect of drought in the CLP was found to be highly related to different water conditions. The accumulated months and mean rmax-cum both had a significant negative correlation with the mean annual SPEI (R2 = 0.90, P < 0.001; R2 = 0.70, P < 0.001, respectively). The lagged months and mean rmax-lag were also found to be negatively correlated with the mean annual SPEI (R2 = 0.547, P < 0.05; R2 = 0.785, P < 0.01, respectively).