The precise and rapid estimation of grassland biomass is an important scientific issue in grassland ecosystem research. In this study, based on a field survey of 1205 sites together with biomass data of the Xilingol grassland for the years 2005–2012 and the “accumulated” MODIS productivity starting from the beginning of growing season, we built regression models to estimate the aboveground biomass of the Xilingol grassland during the growing season, then further analyzed the overall condition of the grassland and the spatial and temporal distribution of the aboveground biomass. The results are summarized as follows: (1) The unitary linear model based on the field survey data and “accumulated” MODIS productivity data is the optimum model for estimating the aboveground biomass of the Xilingol grassland during the growing period, with the model accuracy reaching 69%; (2) The average aboveground biomass in the Xilingol grassland for the years 2005–2012 was estimated to be 14.35 Tg, and the average aboveground biomass density was estimated to be 71.32 g∙m⁻²; (3) The overall variation in the aboveground biomass showed a decreasing trend from the eastern meadow grassland to the western desert grassland; (4) There were obvious fluctuations in the aboveground biomass of the Xilingol grassland for the years 2005–2012, ranging from 10.56–17.54 Tg. Additionally, several differences in the interannual changes in aboveground biomass were observed among the various types of grassland. Large variations occurred in the temperate meadow-steppe and the typical grassland; whereas there was little change in the temperate desert-steppe and temperate steppe-desert.

This study analyzes the changes in drought patterns in China’s major crop-growing areas over the past 60 years. The analysis was done using both weather station data and Tropical Rainfall Measuring Mission (TRMM) Microwave Imager (TMI) rainfall data to calculate the Standardized Precipitation Index (SPI). The results showed that the occurrences of extreme drought were the most serious in recent years in the Southwest China and Sichuan crop-growing areas. The Yangtze River (MLRY) and South China crop-growing areas experienced extreme droughts during 1960–1980, whereas the Northeast China and Huang–Huai–Hai crop-growing areas experienced extreme droughts around 2003. The analysis showed that the SPIs calculated by TRMM data at time scales of one, three, and six months were reliable for monitoring drought in the study regions, but for 12 months, the SPIs calculated by gauge and TRMM data showed less consistency. The analysis of the spatial distribution of droughts over the past 15 years using TMI rainfall data revealed that more than 60% of the area experienced extreme drought in 2011 over the MLRY region and in 1998 over the Huang–Huai–Hai region. The frequency of different intensity droughts presented significant spatial heterogeneity in each crop-growing region.

Grassland productivity is the most direct indicator of grassland’s ecological status; thus, precise and rapid estimation of grassland productivity has an important significance for evaluating regional ecological carrying capacity, utilizing and developing natural resources reasonably. In this study, based on Moderate Resolution Imaging Spectroradiometer remote-sensing data, meteorological data, and ground measured data, the light utilization efficiency parameter of Carnegie–Ames–Stanford Approach model was optimized, and the optimized model was used to estimate net primary productivity (NPP) and analyse its spatio-temporal distribution of Xilingol grassland between 2005 and 2014. In addition, we investigated the NPP response mechanism to climate factors from two aspects, i.e. the accumulation amount and time of NPP. The results indicated that (i) Maximum light utilization efficiency was calculated to be 0.539 g C MJ⁻¹ for Xilingol grassland according to the least error criterion. (ii) Validation against NPP observations from 30 sites showed good performance of the optimized model in the research area, with an overall coefficient of determination (R²) of 0.72 and mean relative estimation error of 0.29. (iii) The annual mean NPP of Xilingol grassland was 161.23 g C m^–2 year^–1, with a decreasing trend from the northeast to the southwest. (iv) NPP presented increasing trend with a rate of 3.89 g C m^–2 year^–1 overall during 20052014, and the largest increase in NPP was found in temperate meadow-steppe. However, desert ecosystems showed the largest increasing rate after NPP standardization. (v) Precipitation was the main factor for driving carbon accumulation at the inter-annual scale, and the time to maximum NPP was significantly correlated with spring precipitation (R² = 0.637), NPP peaked early as the spring precipitation increasing.

Monitoring the cotton budding rate is important for growers so that they can replant cotton in a timely fashion at locations at which cotton density is sparse. In this study, a true-color camera was mounted on an unmanned aerial vehicle (UAV) and used to collect images of young cotton plants to estimate the germination of cotton plants. The collected images were preprocessed by stitching them together to obtain the single orthomosaic image. The support-vector machine method and maximum likelihood classification method were conducted to identify the cotton plants in the image. The accuracy evaluation indicated the overall accuracy of the classification for SVM is 96.65% with the Kappa coefficient of 93.99%, while for maximum likelihood classification, the accuracy is 87.85% with a Kappa coefficient of 80.67%. A method based on the morphological characteristics of cotton plants was proposed to identify and count the overlapping cotton plants in this study. The analysis showed that the method can improve the detection accuracy by 6.3% when compared to without it. The validation based on visual interpretation indicated that the method presented an accuracy of 91.13%. The study showed that the minimal resolution of no less than 1.2 cm/pixel in practice for image collection is necessary in order to recognize cotton plants accurately.

Energy is a key factor regulated by the neuroendocrine system. The objective of this study was to compare growth performance, serum biochemical indices, carcass traits, meat quality, and nutrient composition of lambs fed restricted metabolizable energy (ME) intakes to mimic the seasonal changes of the natural grasslands of northwest China. Nineteen male Dorper × Small Tailed Han lambs were assigned to treatments, control (CON) fed at 1.0 MJ/W^0.75 × d⁻¹ throughout the 180 d study, and restricted ME (RES) fed by sequentially restricting ME every 30 d (0.56, 0.84, 1.0, 0.84, 0.56, and 0.28 MJ/W^0.75 × d⁻¹). Lambs were harvested at the end of the study. Lambs fed the CON diet demonstrated higher (P < 0.05) average daily gain (ADG) compared with RES-fed lambs (158.79 and 57.01 g/d, for CON and RES, respectively), although ADG were lower in each period with the last period being a negative ADG for RES-fed lambs resulting in a loss of body weight. Both treatments demonstrated lower ADG in the last 3 study periods compared with the first 2 periods, which may be explained by cold stress (<−3 °C) increasing the lamb’s energy requirement. Feeding lambs a restricted ME diet resulted in lower (P < 0.05) blood serum glucose, triglycerides, total protein, calcium, phosphorus, immunoglobulin G, immunoglobulin M, and immunoglobulin A concentrations compared with CON-fed lambs, but both treatments were similar (P > 0.05) for cholesterol and high-density lipoprotein-cholesterol. Feeding lambs a restricted ME diet increased (P < 0.05) meat pH, while reducing the cooked meat rate compared with CON-fed lambs. Thus, the carcass yield, rib eye muscle area, and shear force were lower (P < 0.05) for RES-fed lambs compared with CON-fed lambs. The meat water concentration was higher (P < 0.05), whereas the concentrations of dry matter, crude fat, crude protein, and carbohydrate were lower (P < 0.05) for lambs fed a RES ME diet compared to CON-fed lambs. These results demonstrate that sequentially restricting ME intake, as natural grasslands might experience during seasonal forage quality and quantity changes, resulted in a reduction of body weight because lambs were mobilizing body reserves as a source of ME. The reduction in concentrations of key serum nutrients, as well as carcass traits and meat composition, demonstrate the body is responding to the meet the lambs ME requirements, which has implications for both meat quality and the animal’s physiological functions.

A practical algorithm was proposed to retrieve land surface temperature (LST) from Visible Infrared Imager Radiometer Suite (VIIRS) data in mid-latitude regions. The key parameter transmittance is generally computed from water vapor content, while water vapor channel is absent in VIIRS data. In order to overcome this shortcoming, the water vapor content was obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) data in this study. The analyses on the estimation errors of vapor content and emissivity indicate that when the water vapor errors are within the range of ±0.5 , the mean retrieval error of the present algorithm is 0.634 K; while the land surface emissivity errors range from −0.005 to +0.005, the mean retrieval error is less than 1.0 K. Validation with the standard atmospheric simulation shows the average LST retrieval error for the twenty-three land types is 0.734 K, with a standard deviation value of 0.575 K. The comparison between the ground station LST data indicates the retrieval mean accuracy is −0.395 K, and the standard deviation value is 1.490 K in the regions with vegetation and water cover. Besides, the retrieval results of the test data have also been compared with the results measured by the National Oceanic and Atmospheric Administration (NOAA) VIIRS LST products, and the results indicate that 82.63% of the difference values are within the range of −1 to 1 K, and 17.37% of the difference values are within the range of ±2 to ±1 K. In a conclusion, with the advantages of multi-sensors taken fully exploited, more accurate results can be achieved in the retrieval of land surface temperature.