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The enhancement of cultivated land quality is an important measure to improve the production capability and ensure the food safety. At the construction course of cultivated land quality informatization, novel methods and techniques are promising to improve the current working pattern as well as raise the efficiency of cultivated land quality management. Irrigation guarantee capability is an important element of cultivated land quality monitoring, evaluation and construction. Evapotranspiration (ET) derived from remote sensing can be used for rapid monitoring of irrigation guarantee capability of cultivated land at the regional scale. In order to improve the acquisition efficiency of the regional cultivated land quality monitoring, a new evaluation method was proposed for irrigation capability evaluation of cultivated land based on Moderate Resolution Imaging Spectroradiometer (MODIS) ET products. First, in order to reduce the representative errors of meteorological data, the relationship between the monthly potential evapotranspiration (PET) parameter derived from MODIS evaporation products and the reference crop evapotranspiration (ET0) calculated from observed meteorological data with the Penman-Monteith (P-M) formula was analyzed. The continuous ET0 in space was obtained from the regression model. And then, water requirements for crop were obtained by the map of crop type from the classification of Landsat TM data and space-continuous ET0. Furthermore, the annual water requirement was calculated according to the difference of the water requirement for crop and the rainfall which was effectively consumed by crop. Additionally, based on the regional water balance principle, the actual quantity of irrigation was obtained from the difference of actual evaporation derived from ET products of MODIS and effective rainfall. The evaluation index of irrigation guarantee capability is defined as the ratio of the annual irrigation requirement and the actual quantity of irrigation, and used for the monitoring and evaluation of cultivated land irrigation guarantee capability. Finally, application and analysis were conducted in Hengshui City, Hebei Province, China. The R2 of the regression model between the PET and ET0 from meteorological stations reached 0.89. And the calculated results of the evaluation index of irrigation guarantee capability from 2005 to 2012 accurately reflected temporal-spatial variations for the cultivated land irrigation capability. Meanwhile, the comprehensive evaluation of irrigation capability, which was from the supervised classification results based on actual survey, had a good agreement with the classification results of irrigation probability in the gradation on agriculture land quality which had been updated in 2012. The area with no grade difference was up to 37% of the total cells, 38% of the study area had one-grade difference, and the area with two-grade difference was 19% of the study area. The area with three-grade difference only accounted for 6%. The grade difference of more than 75% of the cultivated land was less than one grade. Errors were mainly concentrated in the cultivated land around the city, because the MOD16A2 product with 1-km resolution was inadequate to accurately represent the information of that complex planting structure of cultivated land, and this problem would be solved with the development and improvement of remote sensing earth observation systems with higher resolution. The evaluation index of irrigation capability proposed in this paper has the advantage of clear physical meaning and easy data accessing. The experiment results indicate that the method can meet the needs of the regional cultivated land quality monitoring and evaluation. [ABSTRACT FROM AUTHOR]

The results of cultivated land quality gradation are made of the results of county, provincial, national levels. The results at the provincial level are integrated by the results at the county level. Provincial integration is an essential link in cultivated land quality grading work. The traditional provincial integration methods excessively depend on the provincial integration base map, which makes the integration efficiency low. Moreover, the quality of integration work completely depends on the quality of integration base map. Based on these defects, this paper comes up with a method that uses the grid instead of the integration base map. In provincial integration work, we need to build traceability relation between county units and provincial units. It requires all county units can find provincial units and all provincial units have their corresponding county units. Moreover, there is a principle we have to abide by: the ownership, the land type and the grade should be coincident between the county units and corresponding provincial units. That is "ownership-land type-grade" coincident principle. The purpose of this method is to make spatial distribution of provincial cultivated land quality is similar to that of the county when abiding by this principle. First we need to classify the county results according to "ownership-land type-grade". Then we determine the corresponding grid category according to the area dominant principle. Through the spatial nearest neighboring method, we find the grid unit closest to the unit at the county level to build "Grid-County grading unit" relations. But maybe there are some categories which can't be found in the provincial grids. Based this situation, the barycenter of the missing category was firstly calculated, then an uncultivated area grid closest to the barycenter was searched out, and lastly this grid was marked as the missing category. The grid coding method proposed in this paper stipulates that grid code is formed by county's administrative code, the grid rank, quality gradation and land class code. The length of the code is 21. At last, this paper uses the mean center and standard deviational ellipse to test the similarity of the spatial distribution of cultivated land before and after integration. This paper takes Daxing district, Bejing as a study area. By "ownership-land type-grade", there are 8 categories in Daxing. According to the distribution of cultivated land in Daxing district, 688 valid cultivated land grids were finally determined. Using the method in this article, we completed the integration at the provincial level for Daxing. After test, it showed that the difference of mean center between provincial grid results and county results is only 405.6 m, and the difference of distribution direction is only 0.34°, which means that this method can ensure the consistency of spatial distribution of cultivated land at different levels. Compared with the method which depends on the integration base map, the method based on grid doesn't make base map, reduces workload and improves work efficiency. This method orientates the provincial cultivated land into each grid and codes for these grids. Through grid code, we can quickly search cultivated land quality in the corresponding position, which is very convenient for both management and application. [ABSTRACT FROM AUTHOR]

Since the numbers and capacities of dairy cattle farms are increasing with every passing day, computer aided studies for the management of these farms become more important and are widely used in daily life. In this study, we assessed the potential of Xtion as a point cloud data acquisition system for the body measurements of Holstein cows in the context of dairy cow conformation. For this purpose, we selected Xtion for point cloud data acquisition and chose a dairy cow model and a dairy cow as the experimental subjects. In an indoor environment, three dimensional laser scanners with high precision were employed to get point cloud data of the cow model as comparison data, and Xtion was used for acquiring point cloud data at different distances away from the cow model as test data. After aligning the comparison data and test data by using commercial point cloud processing software, statistical errors between comparison data and test data including positive maximum distance, negative maximum distance, positive average distance, negative average distance, standard deviation, and RMS error were calculated by the professional point cloud software in order to quantitatively analyze the precision and density of point cloud data from Xtion at different distances. The test results indicate that the errors almost increased linearly with increasing of the distance between Xtion and subject. The average errors were less than ±5mm on the condition that the distance from Xtion to the subject were between 0.6m and 1.2m. The average errors were less than ±10mm on the condition that the distance between Xtion and the subject were between 1.2m and 1.8m. Maximum errors were always less than ±20mm. The density of point clouds decreased exponentially with increasing of the distance from Xtion to the subject to such an extent that body measurements were impossible to get on account of missing the mark points on the body. These results imply that the appropriate distance between Xtion and a cow is between 0.6m and 1.2m. Under the dairy cow breeding environment, body measurements including chest width, thurl width, rear leg side view, front nipple length, rear udder width, and rear udder height of cows were first determined manually, by direct measurement. Then we used Xtion to acquire point cloud data at a distance of less than 1.2 meters from the cow. Visualization of the point cloud data and interactive measurement of the cow body based on point cloud data from Xtion were performed by Meshlab so as to qualitatively analyze the sunlight and surface materials’ influence on the quality of point cloud data acquired from Xtion under dairy farm conditions. The results indicate that Xtion failed to give normal operation under the sunlight due to the presence of the IR rays in the sunlight. Point cloud acquisition from Xtion may have some small holes due to the material and appearance of a live cow body. The relative errors of comparing the body measurements obtained by Meshlab with the manual measurements were less than 10%, which indicates that Xtion is appropriate for body measurements of Holstein cows. [ABSTRACT FROM AUTHOR]

Along with the development of the starch and grain-alcohol industries, biomass energy production has recently become important. Cassava is an important biomass energy plant. In this paper, geographic information system (GIS) and remote sensing (RS) are used, along with a knowledge of the growing environment needed for cassava and the farmland and ecology protection policy of China, to evaluate the cassava growing potential of Wuming County. The processing of spatial data is done first. Then, the evaluation principles are defined according to the spatial data and the required growth conditions. The evaluation data are obtained by spatial data analysis according with the evaluation principles. Lastly, the cassava planting potential results are verified by referencing these to cassava planting statistical data for 2005 provided by Nanning City Government of Guangxi province. This paper provides the science basis for Wuming County to develop local grain-alcohol and biomass energy industries. [ABSTRACT FROM AUTHOR]

In the era of big data, the explosive growth of Earth observation data and the rapid advancement in cloud computing technology make the global-oriented spatiotemporal data simulation possible. These dual developments also provide advantageous conditions for discrete global grid systems (DGGS). DGGS are designed to portray real-world phenomena by providing a spatiotemporal unified framework on a standard discrete geospatial data structure and theoretical support to address the challenges from big data storage, processing, and analysis to visualization and data sharing. In this paper, the trinity of big Earth observation data (BEOD), cloud computing, and DGGS is proposed, and based on this trinity theory, we explore the opportunities and challenges to handle BEOD from two aspects, namely, information technology and unified data framework. Our focus is on how cloud computing and DGGS can provide an excellent solution to enable big Earth observation data. Firstly, we describe the current status and data characteristics of Earth observation data, which indicate the arrival of the era of big data in the Earth observation domain. Subsequently, we review the cloud computing technology and DGGS framework, especially the works and contributions made in the field of BEOD, including spatial cloud computing, mainstream big data platform, DGGS standards, data models, and applications. From the aforementioned views of the general introduction, the research opportunities and challenges are enumerated and discussed, including EO data management, data fusion, and grid encoding, which are concerned with analysis models and processing performance of big Earth observation data with discrete global grid systems in the cloud environment. [ABSTRACT FROM AUTHOR]

The greenhouse is the fastest growing food production approach and has become the symbol of protected agriculture with the development of agricultural modernization. Previous studies have verified the effectiveness of remote sensing techniques for mono-temporal greenhouse mapping. In practice, long-term monitoring of greenhouse from remote sensing data is vital for the sustainable management of protected agriculture and existing studies have been limited in understanding its spatiotemporal dynamics. This study aimed to generate multi-temporal greenhouse maps in a typical protected agricultural region (Shouguang region, north China) from 1990 to 2018 using Landsat imagery and the Google Earth Engine and quantify its spatiotemporal dynamics that occur as a consequence of the development of protected agriculture in the study area. The multi-temporal greenhouse maps were produced using random forest supervised classification at seven-time intervals, and the overall accuracy of the results greater than 90%. The total area of greenhouses in the study area expanded by 1061.94 km 2 from 1990 to 2018, with the largest growth occurring in 1995–2010. And a large number of increased greenhouses occurred in 10–35 km northwest and 0–5 km primary roads buffer zones. Differential change trajectories between the total area and number of patches of greenhouses were revealed using global change metrics. Results of five landscape metrics showed that various landscape patterns occurred in both spatial and temporal aspects. According to the value of landscape expansion index in each period, the growth mode of greenhouses was from outlying to edge-expansion and then gradually changed to infilling. Spatial heterogeneity, which measured by Shannon's entropy, of the increased greenhouses was different between the global and local levels. These results demonstrated the advantage of utilizing Landsat imagery and Google Earth Engine for monitoring the development of greenhouses in a long-term period and provided a more intuitive perspective to understand the process of this special agricultural production approach than relevant social science studies. [ABSTRACT FROM AUTHOR]

Continuous and accurate estimates of crop canopy leaf area index (LAI) and chlorophyll content are of great importance for crop growth monitoring. These estimates can be useful for precision agricultural management and agricultural planning. Our objectives were to investigate the joint retrieval of corn canopy LAI and chlorophyll content using filtered reflectances from Sentinel-2 and MODIS data acquired during the corn growing season, which, being generally hot and rainy, results in few cloud-free Sentinel-2 images. In addition, the retrieved time series of LAI and chlorophyll content results were used to monitor the corn growth behavior in the study area. Our results showed that: (1) the joint retrieval of LAI and chlorophyll content using the proposed joint probability distribution method improved the estimation accuracy of both corn canopy LAI and chlorophyll content. Corn canopy LAI and chlorophyll content were retrieved jointly and accurately using the PROSAIL model with fused Kalman filtered (KF) reflectance images. The relation between retrieved and field measured LAI and chlorophyll content of four corn-growing stages had a coefficient of determination (R2) of about 0.6, and root mean square errors (RMSEs) ranges of mainly 0.1–0.2 and 0.0–0.3, respectively. (2) Kalman filtering is a good way to produce continuous high-resolution reflectance images by synthesizing Sentinel-2 and MODIS reflectances. The correlation between fused KF and Sentinel-2 reflectances had an R2 value of 0.98 and RMSE of 0.0133, and the correlation between KF and field-measured reflectances had an R2 value of 0.8598 and RMSE of 0.0404. (3) The derived continuous KF reflectances captured the crop behavior well. Our analysis showed that the LAI increased from day of year (DOY) 181 (trefoil stage) to DOY 236 (filling stage), and then increased continuously until harvest, while the chlorophyll content first also increased from DOY 181 to DOY 236, and then remained stable until harvest. These results revealed that the jointly retrieved continuous LAI and chlorophyll content could be used to monitor corn growth conditions. [ABSTRACT FROM AUTHOR]

Arable land quality (ALQ) data are a foundational resource for national food security. With the rapid development of spatial information technologies, the annual acquisition and update of ALQ data covering the country have become more accurate and faster. ALQ data are mainly vector-based spatial big data in the ESRI (Environmental Systems Research Institute) shapefile format. Although the shapefile is the most common GIS vector data format, unfortunately, the usage of ALQ data is very constrained due to its massive size and the limited capabilities of traditional applications. To tackle the above issues, this paper introduces LandQv2, which is a MapReduce-based parallel processing system for ALQ big data. The core content of LandQv2 is composed of four key technologies including data preprocessing, the distributed R-tree index, the spatial range query, and the map tile pyramid model-based visualization. According to the functions in LandQv2, firstly, ALQ big data are transformed by a MapReduce-based parallel algorithm from the ESRI Shapefile format to the GeoCSV file format in HDFS (Hadoop Distributed File System), and then, the spatial coding-based partition and R-tree index are executed for the spatial range query operation. In addition, the visualization of ALQ big data with a GIS (Geographic Information System) web API (Application Programming Interface) uses the MapReduce program to generate a single image or pyramid tiles for big data display. Finally, a set of experiments running on a live system deployed on a cluster of machines shows the efficiency and scalability of the proposed system. All of these functions supported by LandQv2 are integrated into SpatialHadoop, and it is also able to efficiently support any other distributed spatial big data systems. [ABSTRACT FROM AUTHOR]

The advent of the era of big remote sensing data has transformed traditional data management and analysis models, among which visualization analysis has gradually become an effective method, and map tiles for remote sensing data have always played an important role. However, in high-latitude regions, especially in polar regions, the deformation caused by map projection still exists, which lowers the accuracy of global or large-scale visual analysis, as well as the execution efficiency of big data. To solve the above problems, this paper proposes an algorithm called HexTile, which uses a hexagonal discrete global grid system (DGGS) model to effectively avoid problems caused by map projection and ensure global consistency. At the same time, the algorithm was implemented based on the Spark platform, which also has advantages in efficiency. Based on the DGGS model, hierarchical hexagon map tile construction and a visualization algorithm were designed, including hexagonal slicing, merging, and stitching. The above algorithms were parallelized in Spark to improve the big data execution efficiency. Experiments were carried out with Landsat-8, and the results show that the HexTile algorithm can not only guarantee the quality of global data, but also give full play to the advantages of the cluster in terms of efficiency. Additionally, the visualization was conducted with Cesium and OpenLayers to validate the integration and completeness of hexagon tiles. The scheme proposed in this paper could provide a reference for spatiotemporal big data visualization technology. [ABSTRACT FROM AUTHOR]

• A bibliometric and content analysis was conducted to comprehensively review and analyze deep learning research in cropland field identification. • The paper discusses the challenges of deep learning-based research on cropland field identification, providing readers with insights and directions for future research in the field • This study fills the gap by providing a systematically summarized review of this research area. The cropland field (CF) is the basic unit of agricultural production and a key element of precision agriculture. High-precision delineations of CF boundaries provide a reliable data foundation for field labor and mechanized operations. In recent years, with the dual advancements in remote sensing satellite technology and artificial intelligence, enabling the extraction of CF information on a wide scale and with high precision, research on CF identification based on deep learning (DL) has emerged as a highly esteemed direction in this field. To comprehend the developmental trends within this field, this study employs bibliometric and content analysis methods to comprehensively review and analyze DL research in the field of CF identification from various perspectives. Initially, 93 relevant literature pieces were retrieved and screened from two databases, the Web of Science Core Collection and the Chinese Science Citation Database, for review. The previous studies underwent quantitative analysis using bibliometric software across five dimensions: publication year, literature type and publication journal, country, author, and keyword. Subsequently, we analyze the current status and trends of employing DL in the field of CF identification from four perspectives: remote sensing data sources, DL models, types of CF extraction results, and sample datasets. Simultaneously, we combed through current publicly available sample datasets and data products that can be referenced to produce sample datasets for CFs. Finally, the challenges and future research focus of DL-based CF identification research are discussed. This paper provides both qualitative and quantitative analyses of research on DL-based CF identification, elucidating the current status, development trends, challenges, and future research focuses. [ABSTRACT FROM AUTHOR]

• We proposed a delineating field parcel model (TSANet) based on satellite image time series. • TSANet learn the relevance of spatial-spectral-temporal feature representation. • TSANet is robust across space and time. • TSANet performs better than pervious methods. • The time series data from the main growing period has a greater impact. Satellite image time series (SITS), such as Sentinel-2 imagery, plays a crucial role in the delineation of agricultural fields by reducing the impacts of ambiguities due to the spatial arrangement of field boundaries. Existing delineate field parcel models rely extensively on spatial features derived from single-date imagery. However, several studies have exploited the potential of SITS to effectively tackle the complexities associated with the intrinsic consistency between agricultural fields and their boundaries. This paper proposes a novel Two-Stream Attention convolutional neural network (TSANet) to capture the subtle difference between agricultural fields from SITS. Specifically, a field temporal semantic stream is introduced to adaptively leverage the significance of spatial-spectral-temporal feature representation associated with the location of agricultural parcels, especially where transitions in crop types take place. Considering the consistency between field parcels and their boundaries, we developed a field boundary prediction stream to enhance the extraction of edge features, particularly for the extraction of small and irregular agricultural parcels. Moreover, a field parcel refining block is employed to further enhance the geometric accuracy of agricultural fields. We conducted experiments on Sentinel-2 images from the Netherlands. Results showed that our approach produced a better layout of agricultural fields, with an average F1-score of 0.91 than the existing 3D-UNet, U-TEA, and BiConvLSTM. In addition, through the analysis of both quantitative and qualitative results, the stronger robustness of the model compared to other algorithms has been verified by temporal transfer and large-scale spatial prediction. We compared the difference between SITS and the corresponding composite images, which further verified the influence of temporal variation on the proposed approach. This paper provides a general guide for delineating agricultural parcels using SITS. [ABSTRACT FROM AUTHOR]

Abstract: Regional crop yield prediction is a significant component of national food security assessment and food policy making. The crop growth model based on field scale is limited when it is extrapolated to regional scale to estimate crop yield due to the uncertainty of the input parameters. The data assimilation method which combines crop growth model and remotely sensed data has been proven to be the most effective method in regional yield estimation. The methods based on cost function are powerless with crop dynamic growth simulation and state variable dynamic update. However, sequence assimilation method has more advantages to overcome these problems, this paper presents a method of assimilation of time series HJ-1 A/B Normalized Difference Vegetation Index (NDVI) into the coupled model (e.g. WOrld FOod STudies (WOFOST) crop growth model and A two layer Canopy Reflectance Model (ACRM) radiative transfer mode) for winter wheat yield estimates using Ensemble Kalman Filter (EnKF) at the regional scale. The WOFOST model was selected as the crop growth model and calibrated and validated by the field measured data in order to accurately simulate the state variables and the growing process of winter wheat. The theoretically optimal time series LAI profile was obtained with the EnKF algorithm to reduce the errors which existed in both time series HJ-1 CCD NDVI and WOFOST–ACRM model. Finally, the winter wheat yield at the county level was estimated based on the optimized WOFOST model running on the wheat planting pixel. The experiment illustrates that in the potential mode, the EnKF algorithm has significantly improved the regional winter wheat yield estimates ( , RMSE=775 kg/ha) over the WOFOST simulation without assimilation ( , RMSE=2168 kg/ha) at county level compared to the official statistical yield data. Meanwhile, in the water-limited mode the results showed a high correlation ( , RMSE=3005 kg/ha) with statistical data. In general, our results indicate that EnKF is a reliable optimization method for assimilating remotely sensed data into the crop growth model for predicting regional winter wheat yield. [Copyright &y& Elsevier]

Abstract: To recreate human movements in a virtual environment in real time, we propose a new method for real-time tracking of 3D virtual full-body motion using a depth-sensing camera. The method uses natural interaction and a non-contact mode. The 3D virtual environment was constructed using a 3D graphics engine and human joint data were calculated using images acquired from a Prime Sense depth-sensing camera. Then skeletal data for the human model in a skinned mesh animation were separated by improving the mesh modules using a 3D graphics engine. Finally, motion data from the depth sensor were combined with joint data for the human model to yield full-body control of a virtual human (VH). Experimental results show that the proposed method can drive VH full-body movements in real time based on motion-sensing data. The method was applied in virtual driving training for agricultural machinery. Trainees can become familiar with the basic operations required for driving agricultural machinery using full-body motion instead of a mouse and keyboard. The training system is inexpensive and has high safety and a strong sense of immersion. [Copyright &y& Elsevier]

Abstract: One of the main purposes of spatio-temporal modeling is to replay and reproduce states at different historical moments. The spatio-temporal data integration is the key to building the model because it exerts a direct influence on the mode and efficiency of querying data in the database. In fact, in the previous study, the author has proposed a time-based spatio-temporal data integration method and proved that the efficiency of querying historical data with the integration method is higher than that with the widely used integration method regarding time as an attribute from the theoretical level. In the study, from the application level, the author, using real cadastral parcel alternation data and the numerical fitting method, makes a contrast analysis of these two methods in the query efficiency based on two situations of historical data query respectively and real query time data, and also further verifies the conclusion from the theoretical study. [Copyright &y& Elsevier]

Highlights • A fully automatic pose normalization framework is proposed. • The proposed pose normalization pipeline provide a solution for PLF. • A point clouds processing software for livestock body measurement can be downloaded. Abstract The shape of a livestock is a vital indicator of its health and value, whether for breeding or growth. Cutting-edge remote sensing technology provides an efficient and affordable way for acquiring point clouds of livestock, so that automated procedures of obtaining body measurements need to be established. A novel pose normalization method for 3D point clouds of livestock with similar forms of cows or pigs based on its bilateral symmetry properties is proposed in order to increase the degree of measuring automation. The proposed algorithm is combined in a hybrid scheme, which serves as the pose normalization procedure in an automatic body measurement system for livestock. Our extensive experiments on both synthetic and real world point clouds data show that the proposed approach has potential for generalizing well across livestock species and handles noise successfully in test data. In addition, the proposed pose normalization scheme outperforms current standard approach Principal Component Analysis (PCA) and state-of-the-art pose normalization method for pigs. [ABSTRACT FROM AUTHOR]

Body measurement plays an important role in animal breeding and production. In this paper, we develop a novel portable and automatic measurement system for pig body size. Firstly, we utilize two depth cameras to capture the point clouds of the scene with a pig from two viewpoints and implement the registration of the obtained point clouds. Secondly, we resort to Random Sample Consensus (RANSAC) to remove the background point cloud and extract the foreground pig point cloud with a Euclidean clustering. Finally, body measurement is conducted via pose normalization and morphological constraints on pig cloud. We evaluate the proposed system on 20 sets of the scenes with a pig in a commercial pig farm. Experimental results show that the pig object extraction algorithm achieves good performance. The average relative errors for body width, hip width, and body height are 10.30%, 5.87% and 7.01% respectively, which demonstrates the efficacy of the proposed system. [ABSTRACT FROM AUTHOR]

As increasing number of studies for shape measurement purposes in livestock farming by using consumer depth cameras, many software have been developed in order to measure livestock conformation. However, many of these softwares were designed only for specific livestock or body part of specific livestock with very limited body measurements. To be more flexible and general compared to the current software provided in the literature, an interactive software LSSA_CAU is developed to estimate body measurements of livestock based on 3d point clouds data. Livestock with similar forms of cows or pigs and standing with her head forward is assumed for designing algorithm used in LSSA_CAU. This software provides a set of tools for loading, rendering, segmenting, pose normalizing, measuring point clouds data of whole body surface of livestock in a semiautomatic manner. In order to validate the software, both synthetic and real world point clouds data of livestock were processed by using the LSSA_CAU. Our experiments show that the proposed software generalizes well across livestock species and supports customized body measurements. An updated LSSA_CAU version can be downloaded freely from https://github.com/LiveStockShapeAnalysis to livestock industry and research. [ABSTRACT FROM AUTHOR]

Increasing drought poses a big threat to food security over recent decades, highlighting the need for effective tools and adequate information for drought monitoring and mitigation. This study analyzed the performance of five climate-based drought indices and soil moisture measurements for monitoring winter wheat drought threat in China. We employed the Standardized Precipitation Index (SPI), the Standardized Precipitation Evapotranspiration Index (SPEI), the Palmer Drought Severity Index (PDSI), the Palmer Z index and the self-calibrated Palmer Drought Severity Index (scPDSI). On average, soil moisture at 50-cm depth correlated better with winter wheat yield during October-December of the previous year of harvest compared to soil moisture at 10-cm and 20-cm depths. Moreover, the 3-layer soil moisture and reference evapotranspiration (ETo) correlated weakly (Pearson’s r < 0.3) and even negatively with winter wheat yield. The SPI and SPEI at shorter (1–5 months) timescales during September-December in the previous year of harvest showed higher correlations with winter wheat yield. The SPEI trend in March-June has a significant positive influence on trend in winter wheat yield ( r > 0.40, p < 0.05). The climate-based drought indices can facilitate crop drought monitoring in water-limited regions due to the wide-availability of climatic data, particularly in the light of uncertainties arising from the crop model. Among the investigated indices, results revealed that the SPEI is advantageous for drought monitoring over the study area due to its multiscalarity and effective characterization of agricultural droughts. [ABSTRACT FROM AUTHOR]

To predict regional-scale winter wheat yield, we developed a crop model and data assimilation framework that assimilated leaf area index (LAI) derived from Landsat TM and MODIS data into the WOFOST crop growth model. We measured LAI during seven phenological phases in two agricultural cities in China’s Hebei Province. To reduce cloud contamination, we applied Savitzky–Golay (S–G) filtering to the MODIS LAI products to obtain a filtered LAI. We then regressed field-measured LAI on Landsat TM vegetation indices to derive multi-temporal TM LAIs. We developed a nonlinear method to adjust LAI by accounting for the scale mismatch between the remotely sensed data and the model’s state variables. The TM LAI and scale-adjusted LAI datasets were assimilated into the WOFOST model to allow evaluation of the yield estimation accuracy. We constructed a four-dimensional variational data assimilation (4DVar) cost function to account for the observations and model errors during key phenological stages. We used the shuffled complex evolution–University of Arizona algorithm to minimize the 4DVar cost function between the remotely sensed and modeled LAI and to optimize two important WOFOST parameters. Finally, we simulated winter wheat yield in a 1-km grid for cells with at least 50% of their area occupied by winter wheat using the optimized WOFOST, and aggregated the results at a regional scale. The scale adjustment substantially improved the accuracy of regional wheat yield predictions ( R 2 = 0.48; RMSE = 151.92 kg ha −1 ) compared with the unassimilated results ( R 2 = 0.23; RMSE = 373.6 kg ha −1 ) and the TM LAI results ( R 2 = 0.27; RMSE = 191.6 kg ha −1 ). Thus, the assimilation performance depends strongly on the LAI retrieval accuracy and the scaling correction. Our research provides a scheme to employ remotely sensed data, ground-measured data, and a crop growth model to improve regional crop yield estimates. [ABSTRACT FROM AUTHOR]

Crop residues are effective for the prevention of soil erosion. The crop residue cover (CRC) can be mapped by remote sensing. Different morphologies of crop residue will affect the spectral reflectance, reducing the accuracy of CRC estimation by multispectral data. However, the influence of residue morphology is not fully considered on the accuracy of CRC mapping using satellite images. In addition, the spectral indices are easily saturated and less sensitive to high-density areas of crop residue. This study selected four maize planting sites to obtain hyperspectral reflectance and unmanned aerial vehicle (UAV) images. The effects of CRC and residue morphology on spectral reflectance were analyzed, and a new Residue Adjust Normalized Difference Residue Index (RANDRI) was proposed. UAV images were used to extract ground CRC data for training a CRC prediction model based on Sentinel-2 MSI data. Finally, the piecewise prediction model based on different residue indices was used to map CRC. The study results highlighted a linear relationship between the reflectance intersection of shortwave infrared 2 reflectance and red edge 3 of Sentinel-2 MSI with different residual morphologies, called the residue line. The model accuracy of RANDRI optimized by residual line parameters was better than that of the Normalized Difference Residue Index and Soil Adjust Normalized Difference Residue Index (SANDRI) in the high-density area of crop residue. RANDRI can weaken the influence of residue morphologies on modeling accuracy. The CRC spatial distribution by the piecewise SANDRI+RANDRI model was more consistent with CRC measured than that of the RANDRI models individually. The determination coefficient of the piecewise model was 0.82, and the relative error was 10.66%. The piecewise model can effectively improve the anti-saturation ability of the spectral indices. We suggest a rapid and accurate approach for monitoring the CRC and provide a more suitable CRC mapping strategy for high-density areas of crop residue using multispectral remote sensing data. • Crop residue cover was mapped using remote sensing. • The effects of crop residue cover and residue morphology on spectral reflectance were analyzed. • Novel crop residue cover prediction models were suggested and analyzed. [ABSTRACT FROM AUTHOR]

Evaluation of arable land ecosystem services capacity and arable land-use intensity (ALUI) is important for recognising key regional factors that impact arable land attributes changes, which is crucial for planning sustainable patterns of arable land use. The chronic lack of coordination between these two types of evaluation studies has made it hard to provide enough information for developing arable land-use management and control policies. Here, we generated a 1-km-grid map of arable land potential yield and county-level arable land productive-capacity. The impact of land-use and land-cover change on county-level total arable land productive-capacity during 1990–2010 had been estimated. Then we determined the aggregation–distribution characteristics of four indexes (i.e. average arable land potential yield, average ALUI, total arable land area and arable land productive-capacity reserves) at the county-level by the k-means algorithm to assess the regional coordination between arable land productive-capacity protection and arable land use. The results show that during 1990–2010, land-use change led to arable land productive-capacity decreases in 2007 of China's 2733 counties (nearly 73.5% of the total counties' count). Most of these counties are in central and southern China, and their corresponding arable land productive-capacity decrement rates are generally < 6.15%. Counties with decrement rates > 6.15% are mainly in the Yangtze and Pearl River delta regions. The geographical detector shows that county-level arable land-area change is a primary factor that drives county-level arable land productive-capacity increase. Its determinant power can be quantified as 74.154%. In contrast, its determinant power to county-level arable land productive-capacity decrease is only 38.542%, which demonstrates that occupy high-capacity arable land and supplement low-capacity arable land have a greater role in causing reduction of county arable land productive-capacity. Total arable land productive-capacity and use intensity show only slight determinant power to county-level arable land productive-capacity decrease. It indicates that insufficient attention has been paid to the protection of arable land productive-capacity and the farmers' willingness in the implementation of China requisition-compensation balance policy. The aggregation–distribution characteristics show that arable land productive-capacity protection is in coordination with arable land use for most of the provinces in China. Arable land with high or medium potential yield tends to be used at high or medium intensity. A lack of coordination is most evident in the insufficient ALUI, particularly in seven of the thirteen major grain-producing provinces. Other evidence of weak coordination is in the low potential yield versus high farming conditions and willingness, where unsuited planting modes should be prevented. Lastly, challenges for exploring sustainable arable land use path have been discussed. This study is greatly instructive for recognising interrelations between natural conditions and arable land-use patterns and for exploring shortcomings that impede regionally sustainable arable land use. • Impact of LUCC on county-level arable land productivity during 1990–2010 is estimated. • Difference in determinant power to arable land productivity decrease and increase is detected. • Regional coordination between arable land productivity and use intensity is assessed. • Correlation between arable land potential yield and its distance to urban is assessed. • Generate county-level normalised total arable land productive capacity dataset. [ABSTRACT FROM AUTHOR]