Search

It is important to develop or validate remote sensing methods to explore agricultural management and food self-sufficiency in the agricultural areas of the Qinghai–Tibet Plateau under the influence of global change, ecological protection, and socio-economic development. Studies on the use of remote sensing to monitor crop planting on the Qinghai-Tibetan Plateau are limited, with inconclusive results. Therefore, in this study, we analyzed Sentinel-2A/B images and field survey data in Hualong, China (located in Hehuang Valley, Qinghai-Tibetan Plateau) for winter wheat identification and verification at different spatial scales based on the time series of the normalized difference phenology index (NDPI) and dynamic time warping (DTW) algorithm. The characteristic phenological period and the corresponding DTW threshold were optimized using remote sensing data extracted for winter wheat. The results showed that NDPI corresponding to the jointing-heading stage, grouting-harvesting stage, and jointing-harvesting stage with DTW could identify winter wheat regardless of whether the spatial scale was a single quadrat, a combination of two quadrats, or the entire study area. The NDPI corresponding to the jointing-heading stage (corresponding DTW threshold T = 0.158) could generate a relatively rational winter wheat map; the NDPI corresponding to the time series of the grouting-harvesting stage (combined with DTW threshold T = 0.195) could detect a planting area with relatively high accuracy when supported by cultivated land, which matches the statistical reporting of the winter wheat area data. Similarly, with the support of cultivated land data, the planted area could be identified early based on the phenological characteristics of winter wheat before overwintering; however, the extraction scheme needs to be optimized further.

HighlightsCorrelation coefficient between ET0 calculated by the PM and the HE, = 0.9 at 95% stations.Simple spline interpolation method more applicable than kriging interpolation for ET0 in mainland China.Linear regression correction model for the HE suggested by the FAO was further validated in large scale.Abstract. Reference crop evapotranspiration (ET0) is an important parameter employed in irrigation engineering. The Hargreaves ET0 equation (HE) is a simplified calculation method recommended by the Food and Agriculture Organization (FAO) of the United Nations and is used to determine ET0. However, the HE is empirical, and thus, the FAO suggests that linear regression correction be conducted when applying the HE in new areas. To discuss the validity of applying the HE linear regression correction model combined with spatial interpolation methods to the Chinese mainland, this study used meteorological data released by the China National Meteorological Data Service Center and considered the ET0-PM calculated by the Penman-Monteith equation to be the true value. With respect to conducting a linear regression correction with the HE, a regression analysis of the multi-year average of mean monthly daily ET0-PM and ET0-HE calculated by the HE showed that the values were significantly correlated, and correlation coefficients and R2 of 95% of stations exceeded 0.9 and 0.8, respectively. This indicates that the HE linear regression correction model recommended by the FAO is applicable for use in mainland China. The accuracy of ET0-HE corrected by the linear regression correction coefficient combined with simple tension spline interpolation was the optimal and stable at validation stations, whereas the accuracy of ET0-HE corrected by the linear regression correction coefficient combined with simple ordinary kriging interpolation contrasts with the accuracy from existing studies. This study can be used as a reference in large-scale correction research using ET0-HE, and it lays the foundation for research and development relating to spatiotemporal ET0 data products. Keywords: Coefficient modification, Linear regression model, Reference crop evapotranspiration, Spatial interpolation.

Rapid urbanization and continuous loss of rural labor force has resulted in abandonment of large areas of farmland in some regions of China. Remote sensing technology can indirectly help detect abandoned farmland size and quantity, which is of great significance for farmland protection and food security. This study took Qingyun and Wudi counties in Shandong Province as a study area and used CART decision tree classification to compile land use maps of 1990–2017 based on Landsat and HJ-1A data. We developed rules to identify abandoned farmland, and explored its spatial distribution, duration, and reclamation. CART accuracy exceeded 85% from 1990–2017. The maximum abandoned farmland area was 5503.86 ha during 1992–2017, with the maximum rate being 5.37%. Farmland abandonment rate was the highest during 1996–1998, and abandonment trend decreased year by year after 2006. Maximum abandonment duration was 15 years (1992–2017), mostly within 4 years and only a few exceeded 10 years. From 1993–2017, the maximum reclaimed abandoned farmland was 2022.3 ha, and the minimum ~20 ha. The maximum reclamation rate was 67.44%m, with annual average rate being 31.83%. This study will help analyze farmland abandonment driving forces in the study area and also provide references to identify abandoned farmland in other areas.

The standardized precipitation evapotranspiration index (SPEI) of the Tibetan Plateau was calculated using the CRU4.03 gridded dataset from 1901 to 2018 in this paper. Then, based on the SPEI data, drought on the Qinghai-Tibet Plateau was studied in terms of its spatial and temporal distributions and its changing characteristics over the last 100 years. The results revealed that the precipitation in the southeastern part of the Qinghai-Tibet Plateau has been steadily rising over the last 100 years, in conjunction with only minor temperature shifts. In the northwestern part of the plateau, precipitation has decreased significantly, accompanied by a significant increase in temperature. The drought on the Tibetan Plateau showed a clear gradual increase in aridity from southeast to northwest over the last hundred years. The SPEI also showed distinct seasonal patterns, steadily increasing in spring and summer and decreasing significantly in autumn and winter. In addition, each season had its own spatial characteristics. The northeastern part of the plateau, except the Qaidam Basin, showed a significant aridity trend in all seasons. A wet trend prevailed in the southeastern and southern areas. Drought on the Tibetan Plateau exhibits apparent cyclical oscillations with a main period of 54 years and has different cyclical characteristics in different seasons.

In China, the traditional method of determining actual grain yield is expensive, labor-intensive and inefficient. Using satellite imaging data, this work designed a remote-sensing-aided grain yield sampling and measuring method by studying the corn produced in Shunyi, Beijing, in 2015, based on counting plants and kernels. The aim of the research is to transform the traditional practice of field surveying, harvesting and weighing, into counting plants and kernels, culminating in building a regression model based on the relationship of the calculated sample yield and the corresponding normalized vegetation difference index (NDVI) for realizing the spatial distribution of the crop yield. The results show that the yield monitoring results based on counting plants and kernels and the validation error of NDVI regression model are all within a reasonable range. The scheme designed in this research is practically feasible, because it is simple and easy to operate; therefore, it is worthy of promotion and further work.