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The aboveground biomass (AGB) of a forest is an important indicator of the forest's terrestrial carbon storage and its relation to climate change. Due to the advantage of extensive spatial coverage and low cost, coarse-resolution remote sensing data is the main data source for wall-to-wall mapping of forest AGB at the regional scale. Despite this, improving the accuracy and efficiency of forest AGB estimation is a major challenge. In this study, two optical imageries, Moderate Resolution Imaging Spectroradiometer (MODIS) 500 m imagery and Fengyun-3C Visible and Infrared Radiometer (FY-3C VIRR) 1000 m imagery, were used and compared for forest AGB estimation in Yunnan Province, southwest China. One parametric approach, multiple linear regression (MLR), and two nonparametric approaches, k-nearest neighbor (KNN) and random forest (RF), were applied for the two imagery datasets, respectively. We evaluated the performance of the combination of remote sensing data and modeling approaches by comparing the accuracies and also explored the potential of FY-3C imagery data in forest AGB estimation at the regional scale as it was used for this purpose for the first time. We found that the machine learning models KNN and RF provided better results than MLR. From the three approaches for both MODIS and FY-3C imagery, RF performed best with R2 values of 0.84 and 0.81 and RMSE of 23.18 and 23.43, respectively. Estimation of forest AGB based on MODIS was marginally better than the estimation based on FY-3C. FY-3C imagery could therefore be an additional optical remote sensing data source of coarse spatial resolution, comparable to MODIS data which has been widely used for regional forest AGB estimation. Indices related to forest canopy moisture levels from both types of imagery were sensitive to forest AGB. The RF model and MODIS imagery were then applied to map the spatial variation of forest AGB of Yunnan Province. As a result of our study, we determined that Yunnan Province has a total forest AGB of 2123.22 Mt, with a mean value of 58.05 t/ha for forestland in 2016. [ABSTRACT FROM AUTHOR]

This study attempts to develop an effective algorithm to directly derive near surface air temperature from EOS/MODIS data. From a theoretical viewpoint, the split-window algorithm for retrieving near surface air temperature was developed based on the radiative transfer equation, which includes the calculation of atmospheric thermal radiance, the linearization of Planck functions, the transformation from effective atmospheric mean temperature to near surface air temperature and other derivation processes. Considering that the coefficients of the theoretical algorithm are highly dependent on the atmospheric profile, which is difficult to acquire in practical applications, a semi-empirical split-window algorithm is generated on the basis of the theoretical algorithm to improve the practicality. The semi-empirical algorithm was applied and validated in the Jing-Jin-Ji (JJJ) Region and the Jiang-Zhe-Hu-Wan (JZHW) Region in China. Results indicate that the algorithm achieves an MAE of 2.11 °C in the JJJ Region and an MAE of 2.22 °C in the JZHW Region. The semi-empirical split-window algorithm also shows better stability than linear regression and machine learning methods when being applied to other data periods. Due to its accuracy and simplicity, the semi-empirical split-window algorithm is a novel method for retrieving near surface air temperature from MODIS thermal bands. [ABSTRACT FROM AUTHOR]

A hybrid inversion method was developed to estimate the leaf chlorophyll content (LCC) and canopy chlorophyll content (CCC) of crops. Fifty hyperspectral vegetation indices (VIs), such as the photochemical reflectance index (PRI) and canopy chlorophyll index (CCI), were compared to identify the appropriate VIs for crop LCC and CCC inversion. The hybrid inversion models were then generated from different modelling methods, including the curve-fitting and least squares support vector regression (LS-SVR) and random forest regression (RFR) algorithms, by using simulated Compact High Resolution Imaging Spectrometer (CHRIS) datasets that were generated by a radiative transfer model. Finally, the remote-sensing mapping of a CHRIS image was completed to test the inversion accuracy. The results showed that the remote-sensing mapping of the CHRIS image yielded an accuracy ofR2= 0.77 and normalized root mean squared error (NRMSE) = 17.34% for the CCC inversion, and an accuracy of onlyR2= 0.33 and NRMSE = 26.03% for LCC inversion, which indicates that the remote-sensing technique was more appropriate for obtaining chlorophyll content at the canopy scale (CCC) than at the leaf scale (LCC). The estimated results of various VIs and algorithms suggested that the PRI and CCI were the optimal VIs for LCC and CCC inversion, respectively, and RFR was the optimal method for modelling. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is one of the most important variables measured by satellite remote sensing. Public domain data are available from the newly operational Landsat-8 Thermal Infrared Sensor (TIRS). This paper presents an adjustment of the split window algorithm (SWA) for TIRS that uses atmospheric transmittance and land surface emissivity (LSE) as inputs. Various alternatives for estimating these SWA inputs are reviewed, and a sensitivity analysis of the SWA to misestimating the input parameters is performed. The accuracy of the current development was assessed using simulated Modtran data. The root mean square error (RMSE) of the simulated LST was calculated as 0.93 °C. This SWA development is leading to progress in the determination of LST by Landsat-8 TIRS. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is an essential parameter widely used in environmental studies. The Medium Resolution Spectral Imager II (MERSI-II) boarded on the second generation Chinese polar-orbiting meteorological satellite, Fengyun-3D (FY-3D), provides a new opportunity for LST retrieval at a spatial resolution of 250 m that is higher than that of the already widely used Moderate Resolution Imaging Spectrometer (MODIS) LST data of 1000 m. However, there is no operational LST product from FY-3D MERSI-II data available for free access. Therefore, in this study, we developed an improved two-factor split-window algorithm (TFSWA) of LST retrieval from this data source as it has two thermal-infrared (TIR) bands. The essential coefficients of the TFSWA algorithm have been carefully and precisely estimated for the FY-3D MERSI-II TIR thermal bands. A new approach for estimating land surface emissivity has been developed using the ASTER Global Emissivity Database (ASTER GED) and the International Geosphere-Biosphere Program (IGBP) data. A model to estimate the atmospheric water vapor content (AWVC) from the three atmospheric water vapor absorption bands (bands 16, 17, and 18) has been developed as AWVC has been recognized as the most important factor determining the variation of AT. Using MODTRAN 5.2, the equations for the AT estimate from the retrieved AWVC were established. In addition, the AT of the pixels at the far edge of FY-3D MERSI-II data may be strongly affected by the increase of the optical path. Viewing zenith angle (VZA) correction equations were proposed in the study to correct this effect on AT estimation. Field data from four stations were applied to validate the improved TFSWA in the study. Cross-validation with MODIS LST (MYD11) was also conducted to evaluate the improved TFSWA. The cross-validation result indicates that the FY-3D MERSI-II LST from the improved TFSWA are comparable with MODIS LST while the correlation coefficients between FY-3D MERSI-II LST and MODIS LST over the Mid-East China region are in the range of 0.84~0.98 for different seasons and land cover types. Validation with 318 field LST samples indicates that the average MAE and R2 of the scenes at the four stations are about 1.97 K and 0.98, respectively. Thus, it could be concluded that the improved TFSWA developed in the study can be a good algorithm for LST retrieval from FY-3D MERSI-II data with acceptable accuracy. [ABSTRACT FROM AUTHOR]

Spatially distributed air temperature is desired for various scientific studies, including climatalogical, hydrological, agricultural, environmental and ecological studies. In this study, empirical models with regard to land cover and spatial scale were introduced and compared to estimate air temperature from satellite-derived land surface temperature and other environmental parameters. Aqua MODIS (Moderate Resolution Imaging Spectroradiometer) data and meteorological data obtained throughout 2005 in the Yangtze River Delta were adopted to develop statistical algorithms of air temperature. Four empirical regression models with different forms and different independent variables resulted in errors ranging from 2.20°C to 2.34°C. Considering the different relationships between air temperature and land surface temperature for different land types, these four models were evaluated and the most proper equation for each land-cover type was determined. The model containing these selected equations gave a slightly improved mean absolute error (MAE) of 2.18°C. Then the spatial scale effect of this empirical model was analysed with observed air temperature and spatially averaged land surface characteristics. The result shows that the estimation error of air temperature tends to be lower as spatial window size increases, suggesting that the model performances are improved by spatially averaging land surface characteristics. Comprehensively considering the accuracy and computational demand, 5 × 5 pixel size is the most favourable window size for estimating air temperature. The validation of the empirical model at 5 × 5 pixel size shows that it achieves an MAE of 1.98°C and an R 2 of 0.9215. This satisfactory result indicates that this approach is proper for estimating air temperature, and spatial window size is an important factor that should be considered when calculating air temperature. It is expected that better accuracy will be achieved if the different weights of pixels at different distances can be set according to high-density micro-meteorological data. [ABSTRACT FROM AUTHOR]

We map temporal-spatial variation of vegetation cover in the grassland of Hulun Buir in northern China by analyzing remotely sensed data from MODIS. Results from our study indicate that vegetation cover in the grassland steadily decreased during 2000–09, with relatively high variations in both spatial and temporal dimensions. Average vegetation fractional cover (PV) was 0.56 for this period, with a maximum of 0.64 in 2002 and a minimum of 0.43 in 2007 when a massive drought occurred. In the early 2000s, the cover was approximately ∼0.60, dropping to ∼0.52 in 2007–09. The percentage of both dense (0.50 ≤ PV < 0.70) and very dense (PV ≥ 0.70) categories of grass gradually decreased by 6.58% during 2000–09, while the area of grasslands following into the bare surface (PV < 0.15) and very sparse (0.15 ≤ PV < 0.25) categories expanded up to 3.84 × 106 square kilometers over these ten years. The western part of the grassland where temperate steppe prevails had lower vegetation cover than the eastern part where lowland meadow and mountain meadow dominate. This variation of vegetation helps to understand ecosystem degradation on the grassland discussed in several studies though precipitation shortage and intensive grazing activities are the driving forces for the vegetation variation. Therefore, monitoring of vegetation change on the grassland is imperative for making sound environmental policies. [ABSTRACT FROM PUBLISHER]

Abstract: Timely diagnosis of crop diseases in fields is critical for precision on-farm disease management. Remote sensing technology can be used as an effective and inexpensive method to identify diseased plants in a field scale. However, due to the diversity of crops and their associated diseases, application of the technology to agriculture is still in research stage, which needs to be elaborately investigated for algorithm development and standard image processing procedures. In this paper, we examined the applicability of broadband high spatial-resolution ADAR (Airborne Data Acquisition and Registration) remote sensing data to detect rice sheath blight and developed an approach to further explore the applicability. Based on the field symptom measurements, a comprehensive field disease index (DI) was constructed to measure infection severity of the disease and to relate to image sampled infections. In addition to direct band digital number (DN) values, band ratio indices and standard difference indices were used to examine possible correlations between field and image data. The results indicated that the broadband remote sensing imagery has the capability to detect the disease. Some image indices such as RI14, SDI14 and SDI24 worked better than others. A correlation coefficient above 0.62 indicated that these indices would be valuable to use for identification of the rice disease. In the validation analysis, we obtained a small root mean square error (RMS = 9.1), confirming the applicability of the developed method. Although the results were encouraging, it was difficult to discriminate healthy plants from light infection ones when DI&#60;20 because of their spectral similarities. Hence, it was clear that identification accuracy increases when infection reaches medium-to-severe levels (DI>35). This phenomenon illustrated that remote sensing images with higher spectral resolution (more bands and narrower bandwidth) were required in order to further examine the capability of separating the light diseased plants from healthy plants. [Copyright &y& Elsevier]

Large-scale farming of agricultural crops requires on-time detection of diseases for pest management. Hyperspectral remote sensing data taken from low-altitude flights usually have high spectral and spatial resolutions, which can be very useful in detecting stress in green vegetation. In this study, we used late blight in tomatoes to illustrate the capability of applying hyperspectral remote sensing to monitor crop disease in the field scale and to develop the methodologies for the purpose. A series of field experiments was conducted to collect the canopy spectral reflectance of tomato plants in a diseased tomato field in Salinas Valley of California. The disease severity varied from stage 1 (the light symptom), to stage 4 (the sever damage). The economic damage of the crop caused by the disease is around the disease stage 3. An airborne visible infrared imaging spectrometer (AVIRIS) image with 224 bands within the wavelength range of 0.4–2.5 μm was acquired during the growing season when the field data were collected. The spectral reflectance of the field samples indicated that the near infrared (NIR) region, especially 0.7–1.3 μm, was much more valuable than the visible range to detect crop disease. The difference of spectral reflectance in visible range between health plants and the infected ones at stage 3 was only 1.19%, while the difference in the NIR region was high, 10%. We developed an approach including the minimum noise fraction (MNF) transformation, multi-dimensional visualization, pure pixels endmember selection and spectral angle mapping (SAM) to process the hyperspectral image for identification of diseased tomato plants. The results of MNF transformation indicated that the first 28 eigenimages contain useful information for classification of the pixels and the rest were mainly noise-dominated due to their low eigenvalues that had few signals. Therefore, the 28 signal eigenimages were used to generate a multi-dimensional visualization space for endmember spectra selection and SAM. Classification with the SAM technique of plants’ spectra showed that the late blight diseased tomatoes at stage 3 or above could be separated from the healthy plants while the less infected plants (at stage 1 or 2) were difficult to separate from the healthy plants. The results of the image analysis were consistent with the field spectra. The mapped disease distribution at stage 3 or above from the image showed an accurate conformation of late blight occurrence in the field. This result not only confirmed the capability of hyperspectral remote sensing in detecting crop disease for precision disease management in the real world, but also demonstrated that the spectra-based classification approach is an applicable method to crop disease identification. [Copyright &y& Elsevier]

Surface energy balance model is an essential approach for heat flux and evaporation estimation in applied meteorology and hydrology. Due to the complexity of soil–air interface system, the model has been simplified for different purposes in many researches. A complete model with full description of its complex factor relationships and its numerical solution has not been yet implemented in practical use. This paper presents a complete surface energy balance model with its inner relations cited from different researches. The model couples soil temperature change simultaneously with soil moisture movement, which makes the solution of the model uneasy. A detailed methodology of numerical approximation to the complete model is presented in the study for practical use. Soil heat and latent heat fluxes in the model are determined according to both soil temperature change and soil moisture movement, which are described as two differential equations. Crank–Nicolson implicit method is used to expand the differential equations into two sets of simultaneous linear equations, which are then solved by applying Gauss''s elimination method. Latent heat flux is determined at the balance when evaporation from the surface is equal to the soil water loss. And surface temperature is estimated as the heat fluxes of the surface reaching the status of balance. The iterative computation of Newton–Raphson method is used to approximate latent heat flux and surface temperature from the balances. Based on this complexity of the model''s relationships, a detailed computation procedure of the model is proposed. The methodology has been validated through application to south Israeli desert for heat flux and surface temperature estimation. A good matching of the simulated soil temperature to the measured one proves the validity of the model and method used for its numerical solution. [Copyright &y& Elsevier]

A thermal anomaly was observed on the remote sensing images in the sand dunes across the Israel–Egypt border. The Israeli side with more vegetation cover has higher (>2°C) land surface temperature (LST) during the day than the Egyptian side where bare sand prevails. This anomaly is very obvious at about noon in the hot dry summer season. A micrometeorological model has been established in terms of surface energy balance for simulating the surface temperature change and heat flux variation of the region. The purpose of this modeling is to understand the mechanism leading to the occurrence of the thermal anomaly and to reveal the key factors controlling the surface temperature change. The characteristic of the model lies in its coupling soil temperature changing simultaneously with soil moisture movement, described as two differential equations. The methodology for the numerical solution of the model has been developed. The required meteorological data and soil parameters were measured at the study region in the hot summer season for the simulation. Two typical and the most important surface patterns of the region are considered: biogenic crust representing the Israeli side and the bare sand representing the Egyptian side. Results from simulation indicate that surface albedo contributes most to the surface temperature difference between the two typical surfaces, which is followed by sub-soil properties (mainly soil moisture) difference. Biogenic crust has lower surface albedo than bare sand. Consequently, it absorbs much more incident sky radiation. During the hot summer season, the region is very dry and vegetation is in dormancy. The canopy of most shrubs reduces to minimum. Even though the Israeli side has more vegetation, the evapo-transpiration contributed by the vegetation is still very small (<7%) in comparison with the net radiation. This small latent heat flux has little effect on the surface energy balance process in the arid environment. LST change in the desert region is mainly controlled by the amount of incident solar energy absorbed by the ground as soil heat. Therefore, the anomalous LST change and thermal variation on both sides of the region can still be explained as the direct result of the obvious albedo difference on both sides. This albedo difference is mainly caused by the sharp contrast of surface composition especially different biogenic crust and bare sand cover rates on both sides. The overgrazing and other anthropogenic stresses are the direct reasons leading to the bare sand (high albedo) prevalent on the Egyptian side while the conservation policy helps the development of biogenic crust and vegetation (low albedo) on the Israeli side. [Copyright &y& Elsevier]

The extensive requirement of landsurface temperature (LST) for environmental studies and management activities of the Earth's resources has made the remote sensing of LST an important academic topic during the last two decades. Many studies have been devoted to establishing the methodology for the retrieval of LST from channels 4 and 5 of Advanced Very High Resolution Radiometer (AVHRR) data. Various split-window algorithms have been reviewed and compared in the literature to understand their differences. Different algorithms differ in both their forms and the calculation of their coefficients. The most popular form of split-window algorithm is T[sub s]=T[sub 4]+A(T[sub 4]-T [sub 5])+B , where T[sub s] is land surface temperature, T[sub 4] and T[sub 5] are brightness temperatures of AVHRR channels 4 and 5, A and B are coefficients in relation to atmospheric effects, viewing angle and ground emissivity. For the actual determination of the coefficients, no matter the complexity of their calculation formulae in various algorithms, only two ways are practically applicable, due tothe unavailability of many required data on atmospheric conditions and ground emissivities in situ satellite pass. Ground data measurements can be used to calibrate the brightness temperature obtained by remote sensing into the actual LST through regression analysis on a sample representing the studied region. The other way is standard atmospheric profile simulationusing computer software such as LOWTRAN7. Ground emissivity has a considerable effect on the accuracy of retrieving LST from remote sensing data. Generally, it is rational to assume an emissivity of 0.96 for most ground surfaces. However, the difference of ground emissivity between channels 4 and 5 also has a significant impact on the accuracy of LST retrieval. By combining the data of AVHRR channels 3, 4 and 5, the difference can be directly calculated from remote sensing data. Therefore, much more study is required on how to accurately determine the coefficients of split-window algorithms in the application of remote sensing to examine LST change and distribution in the real world. [ABSTRACT FROM AUTHOR]

The atmosphere has substantial effects on optical remote sensing imagery of the Earth's surface from space. These effects come through the functioning of atmospheric particles on the radiometric transfer from the Earth's surface through the atmosphere to the sensor in space. Precipitable water vapor (PWV), CO2, ozone, and aerosol in the atmosphere are very important among the particles through their functioning. This study presented an algorithm to retrieve total PWV from the Chinese second-generation polar-orbiting meteorological satellite FengYun 3D Medium Resolution Spectral Imager 2 (FY-3D MERSI-2) data, which have three near-infrared (NIR) water vapor absorbing channels, i.e., channel 16, 17, and 18. The algorithm was improved from the radiance ratio technique initially developed for Moderate-Resolution Imaging Spectroradiometer (MODIS) data. MODTRAN 5 was used to simulate the process of radiant transfer from the ground surfaces to the sensor at various atmospheric conditions for estimation of the coefficients of ratio technique, which was achieved through statistical regression analysis between the simulated radiance and transmittance values for FY-3D MERSI-2 NIR channels. The algorithm was then constructed as a linear combination of the three-water vapor absorbing channels of FY-3D MERSI-2. Measurements from two ground-based reference datasets were used to validate the algorithm: the sun photometer measurements of Aerosol Robotic Network (AERONET) and the microwave radiometer measurements of Energy's Atmospheric Radiation Measurement Program (ARMP). The validation results showed that the algorithm performs very well when compared with the ground-based reference datasets. The estimated PWV values come with root mean square error (RMSE) of 0.28 g/cm2 for the ARMP and 0.26 g/cm2 for the AERONET datasets, with bias of 0.072 g/cm2 and 0.096 g/cm2 for the two reference datasets, respectively. The accuracy of the proposed algorithm revealed a better consistency with ground-based reference datasets. Thus, the proposed algorithm could be used as an alternative to retrieve PWV from FY-3D MERSI-2 data for various remote sensing applications such as agricultural monitoring, climate change, hydrologic cycle, and so on at various regional and global scales. [ABSTRACT FROM AUTHOR]

The efficient management of commercial orchards strongly requires accurate information on plant growing status for the implementation of necessary farming activities such as irrigation, fertilization, and pest control. Crown planar area and plant number are two very important parameters directly relating to fruit growth conditions and the final productivity of an orchard. In this study, in order to propose a novel and effective method to extract the crown planar area and number of mature and young papayas based on visible light images obtained from a DJ Phantom 4 RTK, we compared different vegetation indices (NGRDI, RGBVI, and VDVI), filter types (high- and low-pass filters), and filter convolution kernel sizes (3–51 pixels). Then, Otsu's method was used to segment the crown planar area of the papayas, and the mean–standard deviation threshold (MSDT) method was used to identify the number of plants. Finally, the extraction accuracy of the crown planar area and number of mature and young papayas was validated. The results show that VDVI had the highest capability to separate the papayas from other ground objects. The best filter convolution kernel size was 23 pixels for the low-pass filter extraction of crown planar areas in mature and young plants. As to the plant number identification, segmentation could be set to the threshold with the highest F-score, i.e., the deviation coefficient n = 0 for single young papaya plants, n = 1 for single mature ones, and n = 1.4 for crown-connecting mature ones. Verification indicated that the average accuracy of crown planar area extraction was 93.71% for both young and mature papaya orchards and 95.54% for extracting the number of papaya plants. This set of methods can provide a reference for information extraction regarding papaya and other fruit trees with a similar crown morphology. [ABSTRACT FROM AUTHOR]

The precise estimation of sugarcane yield at the field scale is urgently required for harvest planning and policy-oriented management. Sugarcane yield estimation from satellite remote sensing is available, but satellite image acquisition is affected by adverse weather conditions, which limits the applicability at the field scale. Secondly, existing approaches from remote sensing data using vegetation parameters such as NDVI (Normalized Difference Vegetation Index) and LAI (Leaf Area Index) have several limitations. In the case of sugarcane, crop yield is actually the weight of crop stalks in a unit of acreage. However, NDVI's over-saturation during the vigorous growth period of crops results in significant limitations for sugarcane yield estimation using NDVI. A new sugarcane yield estimation is explored in this paper, which employs allometric variables indicating stalk magnitude (especially stalk height and density) rather than vegetation parameters indicating the leaf quantity of the crop. In this paper, UAV images with RGB bands were processed to create mosaic images of sugarcane fields and estimate allometric variables. Allometric equations were established using field sampling data to estimate sugarcane stalk height, diameter, and weight. Additionally, a stalk density estimation model at the pixel scale of the plot was created using visible light vegetation indices from the UAV images and ground survey data. The optimal stalk density estimation model was applied to estimate the number of plants at the pixel scale of the plot in this study. Then, the retrieved height, diameter, and density of sugarcane in the fields were combined with stalk weight data to create a model for estimating the sugarcane yield per plot. A separate dataset was used to validate the accuracy of the yield estimation. It was found that the approach presented in this study provided very accurate estimates of sugarcane yield. The average yield in the field was 93.83 Mg ha−1, slightly higher than the sampling yield. The root mean square error of the estimation was 6.63 Mg ha−1, which was 5.18% higher than the actual sampling yield. This study offers an alternative approach for precise sugarcane yield estimation at the field scale. [ABSTRACT FROM AUTHOR]

Cloud-free remote sensing images are required for many applications, such as land cover classification, land surface temperature retrieval and agricultural-drought monitoring. Cloud cover in remote sensing images can be pervasive, dynamic and often unavoidable. Current techniques of cloud removal for the VNIR (visible and near-infrared) bands still encounters the problem of pixel values estimated for the cloudy area incomparable and inconsistent with the cloud-free region in the target image. In this paper, we proposed an efficient approach to remove thick clouds and their shadows in VNIR bands using multi-temporal images with good maintenance of DN (digital number) value consistency. We constructed the spectral similarity between the target image and reference one for DN value estimation of the cloudy pixels. The information reconstruction was done with 10 neighboring cloud-free pair-pixels with the highest similarity over a small window centering the cloudy pixel between target and reference images. Four Landsat5 TM images around Nanjing city of Jiangsu Province in Eastern China were used to validate the approach over four representative surface patterns (mountain, plain, water and city) for diverse sizes of cloud cover. Comparison with the conventional approaches indicates high accuracy of the approach in cloud removal for the VNIR bands. The approach was applied to the Landsat8 OLI (Operational Land Imager) image on 29 April 2016 in Nanjing area using two reference images. Very good consistency was achieved in the resulted images, which confirms that the proposed approach could be served as an alternative for cloud removal in the VNIR bands using multi-temporal images. [ABSTRACT FROM AUTHOR]

Irrigation is one of the key agricultural management practices of crop cultivation in the world. Irrigation practice is traceable on satellite images. Most irrigated area mapping methods were developed based on time series of NDVI or backscatter coefficient within the growing season. However, it has been found that winter irrigation out of growing season is also dominating in north China. This kind of irrigation aims to increase the soil moisture for coping with spring drought and reduce the wind erosion in spring. This study developed a remote sensing-based classification approach to identify irrigated fields out of growing season with Radom Forest algorithm. Four spectral bands and all Normalized Difference Vegetation Index (NDVI) like indices computed from any two of these four bands for each of the seven scenes of GF-1 satellite data were used as the input features in the building of separated RF models and in applying the built models for the classification. The results showed that the mean of the highest out-of-bag accuracies for seven RF models was 94.9% and the mean of the averaged out-of-bag accuracies in the plateau for seven RF models was 94.1%; the overall accuracy for all seven classified outputs was in the range of 86.8–92.5%, Kappa in the range of 84.0–91.0% and F1-Score in the range of 82.1–90.1%. These results showed that the classification was neither overperformed nor underperformed as the accuracies of all classified images were lower than the model ones. This study also found that irrigation started to be applied as early as in November and irrigated fields were increased and suspended in December and January due to freezing conditions. The newly irrigated fields were found again in March and April when the temperature rose above zero degrees. The area of irrigated fields in the study area were increasing over time with sizes of 98.6, 166.9, 208.0, 292.8, 538.0, 623.1, 653.8 km2 from December to April, accounting for 6.1%, 10.4%, 12.9%, 18.2%, 33.4%, 38.7%, and 40.6% of the total irrigatable land in the study area, respectively. The results showed that the method developed in this study performed well. This study found on the satellite images that 40.6% of irrigatable fields were already irrigated before the sowing season and the irrigation authorities were supposed to improve their water supply capacity in the whole year with this information. This study may complement the traditional consideration of retrieving irrigation maps only in growing season with remote sensing images for a large area. [ABSTRACT FROM AUTHOR]

The daily highest air temperature (Tmax) is a key parameter for global and regional high temperature analysis which is very difficult to obtain in areas where there are no meteorological observation stations. This study proposes an estimation framework for obtaining high-precision Tmax. Firstly, we build a near-surface air temperature diurnal variation model to estimate Tmax with a spatial resolution of 0.1 ∘ for China from 1979 to 2018 based on multi-source data. Then, in order to further improve the estimation accuracy, we divided China into six regions according to climate conditions and topography and established calibration models for different regions. The analysis shows that the mean absolute error (MAE) of the dataset (10.5281/zenodo.6322881, Wang et al., 2021) after correction with the calibration models is about 1.07 ∘ C and the root mean square error (RMSE) is about 1.52 ∘ C, which is higher than that before correction to nearly 1 ∘ C. The spatial–temporal variations analysis of Tmax in China indicated that the annual and seasonal mean Tmax in most areas of China showed an increasing trend. In summer and autumn, the Tmax in northeast China increased the fastest among the six regions, which was 0.4 ∘ C per 10 years and 0.39 ∘ C per 10 years, respectively. The number of summer days and warm days showed an increasing trend in all regions while the number of icing days and cold days showed a decreasing trend. The abnormal temperature changes mainly occurred in El Niño years or La Niña years. We found that the influence of the Indian Ocean basin warming (IOBW) on air temperature in China was generally greater than those of the North Atlantic Oscillation and the NINO3.4 area sea surface temperature after making analysis of ocean climate modal indices with air temperature. In general, this Tmax dataset and analysis are of great significance to the study of climate change in China, especially for environmental protection. [ABSTRACT FROM AUTHOR]

Satellite-based soil moisture products are suitable for large-scale regional monitoring due to the accessibility. Five soil moisture products including SMAP, ESA CCI, and AMSR2 (ascending, descending, and average) were selected in the continental United States (US) from 2016 to 2021. To evaluate the performance of the products and assess their applicability, ISMN (International Soil Moisture Network) data were used as the in situ measurement. PBIAS (Percentage of BIAS), R (Pearson correlation coefficient), RMSE (Root Mean Square Error), ubRMSE (unbiased RMSE), MAE (Mean Absolute Error), and MBE (Mean Bias Error) were selected for evaluation. The performance of five products over six observation networks and various land cover types was compared, and the differences were analyzed at monthly, seasonal, and annual scales. The results show that SMAP had the smallest deviation with the ISMN data because PBIAS was around −0.13, and MBE was around −0.02 m3/m3. ESA CCI performed the best in almost all aspects; its R reached around 0.7, and RMSE was only around 0.07 m3/m3 at the three time scales. The performance of the AMSR2 products varied greatly across the time scales, and increasing errors and deviations showed from 2016 to 2020. The PBO_H2O and USCRN networks could reflect soil moisture characteristics in the continental US, while iRON performed poorly. The evaluation of the networks was closely related to spatial distributions. All products performed better over grasslands and shrublands with R, which was greater than 0.52, and ubRMSE was around 0.1 m3/m3, while products performed worse over forests, where PBIAS was less than −0.62, and RMSE was greater than 0.2 m3/m3, except for ESA CCI. From the boxplot, SMAP was close to the ISMN data with differences less than 0.004 m3/m3 between the median and lower quartiles. [ABSTRACT FROM AUTHOR]

Since the estimate of moisture stress coefficients (MSC) in the current Carnegie-Ames-Stanford-Approach (CASA) model still requires considerable inputs from ground meteorological data and many soil parameters, here we present a modified CASA model by introducing the land-surface water index (LSWI) and scaled precipitation to model the vegetation net primary productivity (NPP) in the arid and semiarid climate of the Mongolian Plateau. The field-observed NPP data and a previously proposed model (the Yu-CASA model) were used to evaluate the performance of our LSWI-based CASA model. The results show that the NPP predicted by both the LSWI-based CASA model and the Yu-CASA model showed good agreement with the observed NPP in the grassland ecosystems in the study area, with coefficients of determination of 0.717 and 0.714, respectively. The LSWI-based CASA model also performed comparably with the Yu-CASA model at both biome and per-pixel scales when keeping other inputs unchanged, with a difference of approximately 16 g C in the growing-season total NPP and an average value of 2.3 g C bias for each month. This indicates that, unlike an earlier method that estimated MSC based entirely on climatic variables or a soil moisture model, the method proposed here simplifies the model structure, reduces the need for ground measurements, and can provide results comparable with those from earlier models. The LSWI-based CASA model is potentially an alternative method for modelling NPP for a wide range of vegetation types in the Mongolian Plateau. [ABSTRACT FROM AUTHOR]

Near-surface air temperature (Ta) is an important physical parameter that reflects climate change. Many methods are used to obtain the daily maximum (Tmax⁡), minimum (Tmin⁡), and average (Tavg) temperature, but are affected by multiple factors. To obtain daily Ta data (Tmax⁡ , Tmin⁡ , and Tavg) with high spatio-temporal resolution in China, we fully analyzed the advantages and disadvantages of various existing data. Different Ta reconstruction models were constructed for different weather conditions, and the data accuracy was improved by building correction equations for different regions. Finally, a dataset of daily temperature (Tmax⁡ , Tmin⁡ , and Tavg) in China from 1979 to 2018 was obtained with a spatial resolution of 0.1 ∘. For Tmax⁡ , validation using in situ data shows that the root mean square error (RMSE) ranges from 0.86 to 1.78 ∘ , the mean absolute error (MAE) varies from 0.63 to 1.40 ∘ , and the Pearson coefficient (R2) ranges from 0.96 to 0.99. For Tmin⁡ , the RMSE ranges from 0.78 to 2.09 ∘ , the MAE varies from 0.58 to 1.61 ∘ , and the R2 ranges from 0.95 to 0.99. For Tavg , the RMSE ranges from 0.35 to 1.00 ∘ , the MAE varies from 0.27 to 0.68 ∘ , and the R2 ranges from 0.99 to 1.00. Furthermore, various evaluation indicators were used to analyze the temporal and spatial variation trends of Ta , and the Tavg increase was more than 0.03 ∘ C yr -1 , which is consistent with the general global warming trend. In summary, this dataset has high spatial resolution and high accuracy, which compensates for the temperature values (Tmax⁡ , Tmin⁡ , and Tavg) previously missing at high spatial resolution and provides key parameters for the study of climate change, especially high-temperature drought and low-temperature chilling damage. The dataset is publicly available at 10.5281/zenodo.5502275 (Fang et al., 2021a). [ABSTRACT FROM AUTHOR]

Eddy covariance data are widely used for the investigation of surface–air interactions. Although numerous datasets exist in public depositories for land ecosystems, few research groups have released eddy covariance data collected over lakes. In this paper, we describe a dataset from the Lake Taihu eddy flux network, a network consisting of seven lake sites and one land site. Lake Taihu is the third-largest freshwater lake (area of 2400 km 2) in China, under the influence of subtropical climate. The dataset spans the period from June 2010 to December 2018. Data variables are saved as half-hourly averages and include micrometeorology (air temperature, humidity, wind speed, wind direction, rainfall, and water or soil temperature profile), the four components of surface radiation balance, friction velocity, and sensible and latent heat fluxes. Except for rainfall and wind direction, all other variables are gap-filled, with each data point marked by a quality flag. Several areas of research can potentially benefit from the publication of this dataset, including evaluation of mesoscale weather forecast models, development of lake–air flux parameterizations, investigation of climatic controls on lake evaporation, validation of remote-sensing surface data products and global synthesis on lake–air interactions. The dataset is publicly available at https://yncenter.sites.yale.edu/data-access (last access: 24 October 2020) and from the Harvard Dataverse (10.7910/DVN/HEWCWM; Zhang et al., 2020). [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a key variable for high temperature and drought monitoring and climate and ecological environment research. Due to the sparse distribution of ground observation stations, thermal infrared remote sensing technology has become an important means of quickly obtaining ground temperature over large areas. However, there are many missing and low-quality values in satellite-based LST data because clouds cover more than 60 % of the global surface every day. This article presents a unique LST dataset with a monthly temporal resolution for China from 2003 to 2017 that makes full use of the advantages of MODIS data and meteorological station data to overcome the defects of cloud influence via a reconstruction model. We specifically describe the reconstruction model, which uses a combination of MODIS daily data, monthly data and meteorological station data to reconstruct the LST in areas with cloud coverage and for grid cells with elevated LST error, and the data performance is then further improved by establishing a regression analysis model. The validation indicates that the new LST dataset is highly consistent with in situ observations. For the six natural subregions with different climatic conditions in China, verification using ground observation data shows that the root mean square error (RMSE) ranges from 1.24 to 1.58 ∘ C, the mean absolute error (MAE) varies from 1.23 to 1.37 ∘ C and the Pearson coefficient (R2) ranges from 0.93 to 0.99. The new dataset adequately captures the spatiotemporal variations in LST at annual, seasonal and monthly scales. From 2003 to 2017, the overall annual mean LST in China showed a weak increase. Moreover, the positive trend was remarkably unevenly distributed across China. The most significant warming occurred in the central and western areas of the Inner Mongolia Plateau in the Northwest Region, and the average annual temperature change is greater than 0.1 K (R>0.71 , P<0.05), and a strong negative trend was observed in some parts of the Northeast Region and South China Region. Seasonally, there was significant warming in western China in winter, which was most pronounced in December. The reconstructed dataset exhibits significant improvements and can be used for the spatiotemporal evaluation of LST in high-temperature and drought-monitoring studies. The data are available through Zenodo at 10.5281/zenodo.3528024 (Zhao et al., 2019). [ABSTRACT FROM AUTHOR]

Eddy covariance data are widely used for the investigation of surface-air interactions. Although numerical datasets exist in public depositories for upland ecosystems, few research groups have released eddy covariance data collected over lakes. In this paper, we describe a dataset from the Lake Taihu Eddy Flux Network, a network consisting of seven lake sites and one land site. Lake Taihu is the third largest freshwater lake (area 2,400 km2) in China, under the influence of subtropical climate. The dataset spans the period from June 2010 to December 2018. Data variables are recorded at half-hourly intervals and include micrometeorology (air temperature, humidity, wind speed, wind direction, rainfall, and water/soil temperature profile), the four components of surface radiation balance, friction velocity, and sensible and latent heat fluxes. Except for rainfall and wind direction, all other variables are gap-filled, with each datapoint marked by a quality flag. Several areas of research can potentially benefit from the publication of this dataset, including evaluation of mesoscale weather forecast models, development of lake-air flux parameterizations, investigation of climatic controls on lake evaporation, validation of remote sensing surface data products, and global synthesis on lake-air interactions. The dataset is publicly available at https://yncenter.sites.yale.edu/data-access and from Harvard Dataverse (doi: 10.7910/DVN/HEWCWM) [ABSTRACT FROM AUTHOR]

• NPP, phenology and summer drought in the Mongolian Plateau were quantified. • Summer drought was significantly intensified from 1982 to 2011. • Summer drought played a significant role in determining annual NPP variations. • Phenology had contribution to NPP in spring and autumn, but not to the annual NPP. • Proportion of summer NPP to annual NPP is important for phenology-NPP relationship. Vegetation phenology has long been recognized as an effective indicator of ecosystem function and plays a significant role in the dynamics of plant productivity. Using the 30-year NDVI and meteorological data (1982–2011), we quantified the spatiotemporal dynamics of net primary productivity (NPP), start (SOS), end (EOS) and length (LOS) of growing season and summer drought index as standardized precipitation index (SPI) for the Mongolian Plateau. The independent and interactive contributions of phenological changes and summer drought on annual NPP were analyzed to explore the potential regulatory mechanisms of phenology on plant productivity. Results showed that NPP, SOS, EOS and LOS averaged at 265.4 g C/m2, 123, 272 and 149 days, respectively, on the plateau and appeared stable during 1982–2011, but with high spatial variations. However, the summer droughts were significantly intensified from 1982 to 2011 (R2=0.21, P=0.01), with more pronounced drought during 1999–2011. More importantly, summer droughts played a very significant role in determining annual NPP dynamics (R2=0.47, P<0.001) due to the highest proportion of summer NPP to annual NPP (70%). The SOS and EOS had stronger contributions to NPP in the spring (R2=0.26, P=0.004 for April) and autumn (R2=0.44, P<0.001 for September; and R2=0.25, P=0.005 for October) than that to the annual NPP. Due to the stronger influences of EOS on NPP compared to SOS and the larger proportion of autumn NPP (15%) to annual NPP compared to spring (13%), we emphasize the importance of future studies on the climatic extremes (e.g., droughts) during the spring and autumn. [ABSTRACT FROM AUTHOR]

• QCOC/FeBG cryogel possess rapid hemostatic ability for non-compressible bleeding. • FeBG as an active substance can promote erythrocyte aggregation by ion dissolution. • QCOC/FeBG composite cryogel possess ultrafast water absorption performance. • QCOC/FeBG cryogel has excellent hemostatic properties in liver bleeding model. Traditional hemostatic materials have limited hemostatic effect on non-compressible wound, that is likely to develop into coagulopathy and leads to high mortality. As a powerful hemostatic material, in addition to having the function of forming solid physical barriers and effectively activating coagulation factors to rapidly control hemostasis, it should also has the ability to bypass the coagulation pathway to handle the bleeding of coagulopathic patients. In this work, we designed a QCOC composite cryogel by gelation of quaternized chitosan (QCS) and hydroxyethyl cellulose oxide (OHEC) at low temperature. Meanwhile, iron-doped bioactive glass (FeBG) as an active substance was introduced into the QCOC composite cryogel to promote erythrocyte aggregation by ion dissolution and activate intrinsic and extrinsic pathways. QCOC/FeBG composite cryogel had interconnected macroporous structure, ultrafast water absorption performance (reaching water absorption saturation in 4 s) and good shape memory properties (restoring to its original shape in 1 s). Additionally, the composite cryogel showed excellent antibacterial properties against S. aureus and E. coli as well as good cell biocompatibility. Furthermore, the QCOC/FeBG composite cryogel showed excellent hemostatic performance in the model of liver non-compressible hemorrhage with coagulation disorders, and the hemostasis time and blood loss were 60.87 ± 2.09 s and 366.38 ± 32.69 mg respectively, which were much lower than commercial gelatin sponge (196.62 ± 19.02 s and 1439.37 ± 165.27 mg). These results proved that QCOC/FeBG composite cryogel has excellent hemostatic properties, antibacterial properties, portability and usability, indicating a great potential as a timely hemostatic material in daily civil and military hemostasis. [ABSTRACT FROM AUTHOR]

Preeclampsia is a serious complication of pregnancy and leads to maternal hypertension and proteinuria. It remains a major health problem for mothers and babies across the world due to high maternal and fetal morbidity and mortality. Accumulated data have implicated the critical role of microRNA in preeclampsia. However, to date, the role of miR‐548c‐5p in preeclampsia remains vaguely understood. In this study, we first elucidate the role of miR‐548c‐5p and its underlying molecular mechanism in preeclampsia. Compared with healthy controls, miR‐548c‐5p was obviously downregulated in serum exosomes and placental mononuclear cells in patients with preeclampsia. Nonetheless, PTPRO was significantly upregulated and negatively associated with miR‐548c‐5p in placental mononuclear cells in patients with preeclampsia. PTPRO was a target of miR‐548c‐5p. PTPRO was downregulated in the miR‐548c‐5p‐overexpressed macrophages. In addition, miR‐548c‐5p could inhibit the proliferation and activation of LPS‐stimulated macrophages, as evidenced by decreased levels of inflammatory cytokines (IL‐12 and TNF‐α) and less nuclear translocation of pNF‐κB in pTHP1 cells. MiR‐548c‐5p acts as an anti‐inflammatory factor in preeclampsia. The axis of miR‐548c‐5p/PTPRO/NF‐κB may provide novel targets for the diagnosis and treatment of preeclampsia. MiR‐548c‐5p was obviously downregulated in preeclampsia. MiR‐548c‐5p inhibited the proliferation and inflammation of pTHP1 macrophages dependent on the NF‐κB signaling pathway. [ABSTRACT FROM AUTHOR]

Water-use efficiency (WUE), defined as the ratio of net primary productivity (NPP) to evapotranspiration (ET), is an important indicator to represent the trade-off pattern between vegetation productivity and water consumption. Its dynamics under climate change are important to ecohydrology and ecosystem management, especially in the drylands. In this study, we modified and used a late version of Boreal Ecosystem Productivity Simulator (BEPS), to quantify the WUE in the typical dryland ecosystems, Temperate Eurasian Steppe (TES). The Aridity Index (AI) was used to specify the terrestrial water availability condition. The regional results showed that during the period of 1999–2008, the WUE has a clear decreasing trend in the spatial distribution from arid to humid areas. The highest annual average WUE was in dry and semi-humid sub-region (DSH) with 0.88 gC mm-1 and the lowest was in arid sub-region (AR) with 0.22 gC mm-1. A two-stage pattern of WUE was found in TES. That is, WUE would enhance with lower aridity stress, but decline under the humid environment. Over 65% of the region exhibited increasing WUE. This enhancement, however, could not indicate that the grasslands were getting better because the NPP even slightly decreased. It was mainly attributed to the reduction of ET over 70% of the region, which is closely related to the rainfall decrease. The results also suggested a similar negative spatial correlation between the WUE and the mean annual precipitation (MAP) at the driest and the most humid ends. This regional pattern reflected the different roles of water in regulating the terrestrial ecosystems under different aridity levels. This study could facilitate the understanding of the interactions between terrestrial carbon and water cycles, and thus contribute to a sustainable management of nature resources in the dryland ecosystems. [ABSTRACT FROM AUTHOR]

Based on the vegetation map of Mongolia, Global Inventory Monitoring and Modelling Studies (GIMMS) normalized difference vegetation index (NDVI) (1982-2006), theModerate Resolution Imaging Spectroradiometer (MODIS) NDVI (2000-2010), and temperature and precipitation data derived from 60 meteorological stations, this study has thoroughly examined vegetation dynamics inMongolia and their responses to regional climate change at biome scale. To ensure continuity and consistency between the two NDVI datasets, the MODIS NDVI was first calibrated to the GIMMS NDVI based on the overlapping period of 2000-2006. Good calibration results with R² values of 0.86-0.98 between the two NDVI datasets were obtained and can detect subtle trends in the long-term vegetation dynamics of Mongolia. The results indicated that for various biomes, although NDVI changes during 1982-2010 showed great variation, vegetation greening for all biomes in Mongolia seem to have stalled or even decreased since 1991-1994, particularly for meadow steppe (0.0015 year-1), typical steppe (-0.0010 year-1), and desert steppe (-0.0008 year-1), which is an apparent turning point (TP) of the vegetation growth trend inMongolia. A pronounced drying trend (from -4.399mmyear-1 in meadow steppe since 1990 to -2.445mmyear-1 in alpine steppe since 1993) occurred between 1990 and 1994, and persistently warming temperatures (0.015 °Cyear-1 in alpine steppe to 0.070 °C year-1 in forest and meadow steppe) until recently have likely played a major role in this NDVI trend reversal. However, the NDVI TP varied by biome, month, and climate and was not coupled exactly with climatic variables. The impact on climate of both same-time and lagged-time temperature and precipitation effects also varied strongly across biomes and months. On the whole, climate-related vegetation decline and associated potential desertification trends will likely be among the major sources of ecological pressure for each biome inMongolia, which could intensify environmental problems like sandstorms in other East Asian regions. [ABSTRACT FROM AUTHOR]

Desertification is treated as an important and critical environmental hazard. In the face of increasingly serious grassland desertification, China has made great efforts to combat desertification and a series of key national ecological projects has been launched. However, accurate, timely, and effective monitoring and assessment are required to determine whether these projects work well. The Horqin sandy land represents the largest area of sandy land in China. In this article, the Naiman and Ongniud Banners were studied because these contain the main desertified grassland in Horqin. Next, a desertification classification and grading system was designed for the Horqin sandy land after conducting fieldwork. Based on spectral mixture analysis (SMA) and decision-tree methods, we interpreted Landsat Thematic Mapper/Enhanced Thematic Mapper Plus/Operational Land Imager (TM/ETM+/OLI) images of the study area from four years: 1985, 1992, 2001, and 2013. Overall, the following results were obtained. The total area of desertified grassland in the Naiman and Ongniud Banners increased from 5979 km2in 1985 to 9195 km2in 1992 (an increase of 53.79%) and then decreased to 7828 km2in 2001 and to 6023 km2in 2013. The changes in the areas of desertified grassland with various degrees of desertification displayed the same trends as that of the total desertified grassland area. The severely desertified grassland expanded from 1872 km2in 1985 to 3723 km2in 1992 before shrinking to 2189 km2in 2013. The annual rates of expansion of desertified grassland during the periods 1985–1992, 1992–2001, and 2001–2013 were 7.68%, −1.65%, and −1.92%, respectively, and the corresponding expansion rates of severely desertified grassland were 14.12%, −3.48%, and −1.19%, respectively. Both the desertified grasslands and the areas with various degrees of desertification displayed significant expansion during the period 1985–1992. Since 1992, this trend has reduced. During the study period, the changes in temperature and precipitation did not benefit the reversal of grassland sandy desertification. Furthermore, the growing population and expansion of livestock production and farming inhibited such reversal. However, the results presented in this article suggest that a reversal in grassland sandy desertification has been occurring since 1992. The results indicate that ecological engineering measures have helped reverse desertification and have promoted the restoration of grassland vegetation. [ABSTRACT FROM PUBLISHER]

Leaf area index (LAI) is an important indicator of crop growth. In this paper, a hybrid inversion method was developed to estimate the LAI values of crops. Based on PROSAIL simulation datasets, 43 hyperspectral vegetation indices (VIs), including the optimized soil-adjusted vegetation index (OSVAI) and modified triangular vegetation index (MTVI2), were analyzed to identify optimal VIs for estimating LAI values. Hybrid inversion models were then built to determine the relationships between optimal simulated VIs and the LAI values (generated by the PROSAIL model) using modeling methods, including curve fitting, artificial neural network (ANN) and random forest regression (RFR) algorithms. Finally, remote sensing mapping of a Compact High Resolution Imaging Spectrometer (CHRIS) image was completed using the inversion model to verify the LAI estimation accuracy. The remote sensing mapping of the CHRIS image yielded an accuracy of R 2 = 0.928 and RMSE = 0.485 for OSAVI and R 2 = 0.910 and RMSE = 0.554 for MTVI2, demonstrating the feasibility of high-accuracy estimation of crop LAI using hyperspectral VIs and a hybrid inversion method. The estimation results of various VIs suggested that the identification of the appropriate VIs is critical to improve the inversion accuracy. In addition, to obtain the appropriate VIs, the factors must be evaluated with respect to two aspects, i.e., the sensitivity to target parameters and the insensitivity to interference. In this study, OSVAI and MTVI2 were sensitive to LAI and relatively insensitive to the effects of interference factors, such as chlorophyll, soil background, sky scattered light and observed geometry. Therefore, these indices could be primarily used as VIs for an LAI estimation. The inversion results of different datasets demonstrated that prior information is critical for improving the inversion accuracy and identifying the optimal VIs. Additionally, based on the comparison of the curve fitting, ANN, and RFR algorithms, RFR was an optimal method for modeling in this study, as indicated by the higher R 2 and lower RMSE values for different datasets and various VIs. [ABSTRACT FROM AUTHOR]

Grassland is not only an important landscape of the Beijing–Tianjin sandstorm source control project area, but also a significant object of the Beijing–Tianjin sandstorm source control project. By taking the situation in 2000 as the base of comparison and using the established grassland vegetation growth model, the monitoring and evaluation of grassland vegetation dynamic variation in the project area from 2000 to 2010 was conducted based on MODIS 16 days NDVI data. The conclusions are as follows: (1) The comparative result of average growth between each year from 2001 to 2010 and the base year was on the good side in general; the grassland growth was good both in the early and later periods of grassland growth peak season than in the first years of the project implementation, indicating that the implementation of the Beijing–Tianjin sandstorm source project has significantly improved the growth conditions of grassland vegetation; (2) With regard to the annual dynamic variation of grassland growth, the area proportions of the grasslands, of which the average grassland growth was on the good side, fluctuated and increased slightly with the time changes in general. The area proportions of the grasslands, of which the average grassland growth was on the bad side, fluctuated and decreased in general. The area proportions of the grasslands with normal growth showed an increasing overall trend; (3) From the regional perceptive on four zones, including the northern arid grassland desertification control zone, Hunshandake sandy land control zone, the farming-pastoral area of desertified land control zone, and the water conservation zone of Yanshan hills and mountains, except that the grassland growth in the farming-pastoral area of desertified land control zone was bad, the average growth of other three zones was good each year from 2001 to 2010 compared with the base year. (4) In respect of space, the regions with big grassland growth variation in the project area were concentrated in the western and eastern sections of the northern arid grassland desertification control zone and the western section of Hunshandake sandy land control zone. The grassland growth variation in the water conservation zone of Yanshan hills and mountains and the farming-pastoral area of desertified land control zone were relatively stable. On one hand, the conclusions of this paper can evaluate the effectiveness of the project control, on the other hand, it can also provide scientific basics to grassland management departments, facilitate the rational utilization of grassland, and preserve the regional ecological balance. [ABSTRACT FROM AUTHOR]

Ecological security is very important for regional development and society stability, and it has become a hot topic currently. The concept of ecological security can be understood from two aspects, namely the broader sense and the narrower sense. In a broader sense, ecological security includes natural, economic, and social ecological security, while in a narrow sense, it means the security of natural and half natural ecological system, including ecological system and environment security. As special public resources, the quantity and quality of cultivated land are closely related to national food security and social stability, and has great significance in the maintenance of national security. In this study, taking the cultivated land in eastern low hills and coastal region of Qinhuangdao, China as the research area, a cultivated land landscape ecological security evaluation model was built based on the principles of landscape ecology and ecological security. The model contained five landscape pattern indexes including fragmentation, isolation, fractal dimension and shape factor, which could reflect production efficiency, stability and resilience of cultivated land. With three TM(ETM+) remote sensing images in 1990, 2000, and 2010 as well as GIS techniques, the landscape ecological security of cultivated land in the research area was comprehensively evaluated based on each town unit, and the evolution of landscape ecological security pattern and gravity center was analyzed using center of gravity method. At last, the driving factors of landscape ecological security evolution were discussed. The results showed that the landscape ecological security of cultivated land in the research area had been declining all the time with the value decreasing from 0.63 in 1993 to 0.5 in 2000. The fragmentation of cultivated land in low mountainous and hilly areas was more serious, so its landscape ecological security was lower than coastal area. The landscape ecological security was divided into three levels according to Natural Break grading result of three years including I level when ecological security was equal or less than 0.3, I level when ecological security was greater than 0.3 and equal or less than 0.65, and III level when ecological security was equal or greater than 0.65. In addition, this study demonstrated that the pattern of landscape ecological security changed obviously. From 1993 to 2000, the I level area had no noticeable change, but the II and III level area were converted to each another. From 2000 to 2010, the I and II level area were expanded, which were converted from the III level area. In the past 20 years, the gravity center of landscape ecological security of cultivated land had transferred to the adjacent regions of Qinhuangdao city. It indicated that the landscape ecological security of coastal area had been increased obviously. The reduction of rainfall and runoff, with the rapid increase in economy and population, and governmental policy decision all had a profound effect on the landscape ecological security variation of cultivated land. This study applied landscape pattern index to describe the landscape structure of cultivated land in ecological security study. The studies on ecological security evaluation of cultivated land are conductive to protection of fragile ecological environment and local sustainable development of cultivated land resources. [ABSTRACT FROM AUTHOR]

Abstract: State and local governments in China have implemented a series of grassland protection policies to address the problem of grassland degradation. In 2003, Ningxia was the first province to implement a province-wide grazing ban. The effect of this ban is contentious at all levels of government and has become a topic of public concern. Grassland desertification is the most direct indicator of the effect of the grazing ban. We selected 14 counties and cities in north-central Ningxia as the study area. A desertification classification and grading system for Ningxia's grassland was then designed based on fieldwork and expert review. Using the Spectral Mixture Analysis (SMA) and decision-tree methods, we interpreted Landsat TM/ETM+ images of the study area during four years: 1993, 2000, 2006 and 2011. The following results were obtained: from 1993 to 2011, the area of desertified grassland in north-central Ningxia decreased gradually from 8702km2 in 1993 to 7485km2 in 2011, a decrease of 13.98%; the degree of desertification gradually decreased from 3573km2 of severely desertified grassland in 1993 to 1450km2 in 2011, a decrease of 59.41%; desertified grassland vegetation was restored rapidly during 2000–2006 and 2006–2011, reducing the total area of desertified grassland annually by 1.87 and 0.61%, respectively; finally, the area of severely desertified grassland decreased annually by 5.78 and 6.28% during 2000–2006 and 2006–2011, respectively. These results show that the region-wide grazing ban, together with other ecological engineering measures, has helped reverse desertification and promote the restoration of grassland vegetation. [Copyright &y& Elsevier]

Agricultural landscapes are characterized by diversity and complexity, which makes crop mapping at a regional scale a top priority for different purposes such as administrative decisions and farming management. Project 32194 of the Dragon 4 Program was implemented to meet the requirements of crop mapping, with the specific objective to develop suitable approaches for precise crop mapping with combined uses of European and Chinese high- and medium-resolution satellite images. Two sub-projects were involved in the project. The first was to focus on the use of time series high-resolution satellite data, including Sentinel-2 (S2, European satellite data) and Gaofen-1 (GF-1, Chinese satellite data), due to their similar spectral bands for Earth observation, while the second was to focus on medium-resolution data sources, i.e., the European Project for On-Board Autonomy–Vegetation (PROBA-V) and Chinese Fengyun-3 Medium Resolution Spectral Imager (FY-3 MERSI) satellite data, also due to their similar spectral channels. The approach of the European Space Agency (ESA) Sent2Agri project for crop mapping was adapted in the first sub-project and applied to the Yellow River irrigated district (YERID) of Ningxia in northwest China in order to assess its ability to accurately identify crop types in China. The goal of the second sub-project was to explore the potential of both European and Chinese medium-resolution satellite data for crop assessment in a large area. Methods to handle the data and retrieve the required information for the precise crop mapping were developed in the study, including the adaptation of the ESA approach to GF-1 data and the application of algorithms for classification. A scheme for the validation of the crop mapping was developed in the study. The results of implementing the scheme to the YERID in Ningxia indicated that the overall accuracies of crop mapping with S2 and GF-1 can be high, up to 94–97%, and the mapping had an accuracy of 88% with the PROBA-V and FY3B-MERSI data. The very high accuracy suggests the possibility of precise crop mapping with the combined use of time series high- and medium-resolution satellite data when suitable approaches are chosen to handle the data for the classification of crop types. [ABSTRACT FROM AUTHOR]

Most algorithms for land surface temperature (LST) retrieval depend on acquiring prior knowledge. To overcome this drawback, we propose a novel LST retrieval method based on model-data-knowledge-driven and deep learning, called the MDK-DL method. Based on the expert knowledge and radiation transfer model, we deduce LST retrieval mechanism and determine the best combination of the thermal infrared (TIR) bands of the sensor. Then, we use the radiation transfer model simulation and reliable satellite-ground data to establish a training and test database, and finally use the deep learning neural network for optimal computation. Three typical high-, medium- and low-spatial-resolution TIR remote sensing datasets (from Gaofen, the Moderate Resolution Imaging Spectroradiometer (MODIS), and Fengyun) are used for theoretical simulation and application analysis. The simulation shows that the minimum mean absolute error (MAE) is less than 0.1 K (standard deviation: 0.04 K; correlation coefficient: 1.000) at a small viewing direction (<7.5°) and less than 0.8 K at a large viewing direction (<65°). The in situ validation shows that the minimum MAE obtained by the optimal band combination is approximately 1 K (root mean square error (RMSE) = 1.12 K; coefficient of determination (R2) = 0.902). The retrieval accuracy is improved by increasing the number of TIR bands in the atmospheric window, and adding accurate atmospheric water vapor information produces better results. In general, four TIR bands in the atmospheric window bands are sufficient to retrieve the LST with high accuracy. Likewise, three TIR bands plus atmospheric water vapor information are sufficient for the retrieval requirements. All analyses indicate that our method is feasible and reliably accurate and can also be used to help design the instrument band to retrieve the LST with high precision. [Display omitted] • A novel LST retrieval method is proposed based on model-data-knowledge-driven and DL. • Abduction is introduced from artificial intelligence to retrieve LST. • The DL-NN algorithm make good use of the relationship among geophysical parameters. • DL-NN can be used to solve and optimize complex LST retrieval calculations. [ABSTRACT FROM AUTHOR]

Abstract: Due to rapid economic development, high levels of potentially harmful elements are continuously being released into the suburban soils of the Nanjing area, China. Conventional methods for investigating soil potentially harmful element contamination based on raster sampling and chemical analysis are time consuming and relatively expensive. Reflectance spectroscopy within the visible-near-infrared region has been widely used to predict soil constituents due to its rapidity, convenience and accuracy. The objective of this study was to examine the possibility of using soil reflectance spectra as a rapid method to simultaneously assess contaminant metals (Ni, Cr, Cu, Hg, Pb, Zn) and As in the suburban soils of the Nanjing area. One hundred and twenty soil samples were collected for chemical analyses and spectral measurements. Prediction of contaminant elements was achieved by a partial least-square regression (PLSR) approach. According to their relationships with Fe, the seven contaminant elements could be categorized into two groups. The results showed that the prediction accuracy for Group II (Ni, Cr, Cu and Hg) was higher than that for Group I (Pb, Zn and As). This finding was consistent with the fact that the correlation coefficients between Group II and Fe were higher than that between Group I and Fe. It was concluded that element-sorption by spectrally active Fe oxides was the major mechanism by which to predict spectrally featureless contaminant elements. This conclusion was strengthened by the fact that the PLSR regression coefficients, which revealed the most important wavelengths for prediction, were attributed to absorption features of Fe oxides. Future study with real remote sensing data and field measurements are strongly recommended. [Copyright &y& Elsevier]

Crop type classification with satellite imageries is widely applied in support of crop production management and food security strategy. The abundant supply of these satellite data is accelerating and blooming the application of crop classification as satellite data at 10 m to 30 m spatial resolution have been made accessible easily, widely and free of charge, including optical sensors, the wide field of viewer (WFV) onboard the GaoFen (GF, high resolution in English) series from China, the MultiSpectral Instrument (MSI) onboard Sentinel 2 (S2) from Europe and the Operational Land Imager (OLI) onboard Landsat 8 (L8) from USA, thanks to the implementation of the open data policy. There are more options in using the satellite data as these three data sources are available. This paper explored the different capability of these three data sources for the crop type mapping in the same area and within the same growing season. The study was executed in a flat and irrigated area in Northwest China. Nine types of crop were classified using these three kinds of time series of data sources in 2017 and 2018, respectively. The same suites of the training samples and validation samples were applied for each of the data sources. Random Forest (RF) was used as the classifier for the crop type classification. The confusion error matrix with the OA, Kappa and F1-score was used to evaluate the accuracy of the classifications. The result shows that GF-1 relatively has the lowest accuracy as a consequence of the limited spectral bands, but the accuracy is at 93–94%, which is still excellent and acceptable for crop type classification. S2 achieved the highest accuracy of 96–98%, with 10 available bands for the crop type classification at either 10 m or 20 m. The accuracy of 97–98% for L8 is in the middle but the difference is small in comparison with S2. Any of these satellite data may be used for the crop type classification within the growing season, with a very good accuracy if the training datasets were well tuned. [ABSTRACT FROM AUTHOR]

It is very important to understand the temporal and spatial variations of land surface temperature (LST) in Africa to determine the effects of temperature on agricultural production. Although thermal infrared remote sensing technology can quickly obtain surface temperature information, it is greatly affected by clouds and rainfall. To obtain a complete and continuous dataset on the spatiotemporal variations in LST in Africa, a reconstruction model based on the moderate resolution imaging spectroradiometer (MODIS) LST time series and ground station data was built to refactor the LST dataset (2003–2017). The first step in the reconstruction model is to filter low-quality LST pixels contaminated by clouds and then fill the pixels using observation data from ground weather stations. Then, the missing pixels are interpolated using the inverse distance weighting (IDW) method. The evaluation shows that the accuracy between reconstructed LST and ground station data is high (root mean square er–ror (RMSE) = 0.84 °C, mean absolute error (MAE) = 0.75 °C and correlation coefficient (R) = 0.91). The spatiotemporal analysis of the LST indicates that the change in the annual average LST from 2003–2017 was weak and the warming trend in Africa was remarkably uneven. Geographically, "the warming is more pronounced in the north and the west than in the south and the east". The most significant warming occurred near the equatorial region in South Africa (slope > 0.05, R > 0.61, p < 0.05) and the central (slope = 0.08, R = 0.89, p < 0.05) regions, and a nonsignificant decreasing trend occurred in Botswana. Additionally, the mid-north region (north of Chad, north of Niger and south of Algeria) became colder (slope > −0.07, R = 0.9, p < 0.05), with a nonsignificant trend. Seasonally, significant warming was more pronounced in winter, mostly in the west, especially in Mauritania (slope > 0.09, R > 0.9, p < 0.5). The response of the different types of surface to the surface temperature has shown variability at different times, which provides important information to understand the effects of temperature changes on crop yields, which is critical for the planning of agricultural farming systems in Africa. [ABSTRACT FROM AUTHOR]

Socio-economical conditions profoundly influence terrestrial ecosystems, especially the agroecosystems. However, the effects of large-scale "top-down" socio-economical changes on patterns of vegetation production and C sequestration in pastures remain largely unclear. The contrasting institutional and policy shifts in 1990s over the two sub-region of Temperate Eurasian Steppe (TES), i.e. , the Kazakh Steppe (KS) and the Mongol Steppe (MS), provide a unique opportunity to illustrate the human and natural interactions. Combining multiple information from remote sensing, land model, climate and inventory data, this study investigated how the regional trend and inter-annual variability (IAV) of leaf area index (LAI), net primary productivity (NPP), net ecosystem productivity (NEP) were associated with different institutional and policy shifts. From 1997 to 2016, climate is the primary control factor to the IAV of the ecosystem indexes (EIs, i.e. , LAI, NPP, NEP) at a regional view. Highly contrasting impacts of human appropriation indexes (HAIs, i.e. , livestock number and agricultural GDP) to the EIs were found for the two sub-region. The effect of HAIs on EIs was weak in the MS, but significant negative correlations between HAIs and EIs were found in the KS. Further decomposition into administrative divisions showed that the swift rise of human appropriation in China was accompanied with increases in grassland NPP and NEP, owing to the policy shift to sustainable management. But the institutional shift to market-driven economy and increasing human appropriation generally acted as a negative factor to EIs in various countries over the KS, especially in Uzbekistan and Turkmenistan. Regional evidences revealed the importance of large-scale socio-economic shifts in shaping the pattern of important ecosystem properties of grasslands and emphasized the importance of sustainability development in managing pasture systems. • Policy shifts significantly affect the human-nature interactions in pasture systems. • The "Top-down" sustainability development strongly promote the grasslands recovery. • Sharp shift to market-driven economy caused grassland ecosystem services reduction. • Human impact on ecosystem services can be larger than climate in agroecosystems. [ABSTRACT FROM AUTHOR]

The thermal infrared (TIR) data from the Medium Resolution Spectral Imager II (MERSI-2) on the Chinese meteorological satellite FY-3D have high spatiotemporal resolution. Although the MERSI-2 land surface temperature (LST) products have good application prospects, there are some deviations in the TIR band radiance from MERSI-2. To accurately retrieve LSTs from MERSI-2, a method based on a cross-calibration model and split window (SW) algorithm is proposed. The method is divided into two parts: cross-calibration and LST retrieval. First, the MODTRAN program is used to simulate the radiation transfer process to obtain MERSI-2 and Moderate Resolution Imaging Spectroradiometer (MODIS) simulation data, establish a cross-calibration model, and then calculate the actual brightness temperature (BT) of the MERSI-2 image. Second, according to the characteristics of the near-infrared (NIR) bands, the atmospheric water vapor content (WVC) is retrieved, and the atmospheric transmittance is calculated. The land surface emissivity is estimated by the NDVI-based threshold method, which ensures that both parameters (transmittance and emissivity) can be acquired simultaneously. The validation shows the following: 1) The average accuracy of our algorithm is 0.42 K when using simulation data; 2) the relative error of our algorithm is 1.37 K when compared with the MODIS LST product (MYD11A1); 3) when compared with ground-measured data, the accuracy of our algorithm is 1.23 K. Sensitivity analysis shows that the SW algorithm is not sensitive to the two main parameters (WVC and emissivity), which also proves that the estimation of LST from MERSI-2 data is feasible. In general, our algorithm exhibits good accuracy and applicability, but it still requires further improvement. [ABSTRACT FROM AUTHOR]