Maps of cropping patterns in China during 2015-2021.

Multiple cropping is a widespread approach for intensifying crop production through rotations of diverse crops. Maps of cropping intensity with crop descriptions are important for supporting sustainable agricultural management. As the most populated country, China ranked first in global cereal production and the percentages of multiple-cropped land are twice of the global average. However, there are no reliable updated national-scale maps of cropping patterns in China. Here we present the first recent annual 500-m MODIS-based national maps of multiple cropping systems in China using phenology-based mapping algorithms with pixel purity-based thresholds, which provide information on cropping intensity with descriptions of three staple crops (maize, paddy rice, and wheat). The produced cropping patterns maps achieved an overall accuracy of 89% based on ground truth data, and a good agreement with the statistical data (R2 ≥ 0.89). The China Cropping Pattern maps (ChinaCP) are available for public download online. Cropping patterns maps in China and other countries with finer resolutions can be produced based on Sentinel-2 Multispectral Instrument (MSI) images using the shared code.

Dense canopies browning overshadowed by global greening dominant in sparse canopies.

Greening, an increase in photosynthetically active plant biomass, has been widely reported as period-related and region-specific. We hypothesized that vegetation trends were highly density-dependent with intensified browning in dense canopies and increased greening in sparse canopies. We exploited this insight by estimating vegetation trends in peak growth from dense to sparse canopies graded from 1 to 20 using the non-parametric Mann-Kendall trend test based on the 500 m 8-day composite MODIS Near Infrared Reflectance of terrestrial vegetation (NIRv) time series datasets in the past two decades (2001-2019) at the global scale. We found that global greening increased by 1.42% per grade with strong fit before grade 15 (R2 = 0.95): net browning (11% browning vs 9% greening) exhibited in high-density canopies (NIRv > 0.39) in contrast to 32% greening in low-density canopies (NIRv ≈ 0.15). While the density-dependent greening was evidenced across different biomes and ecosystems, the steepest gradient (changes per grade) in cropland highlighted the increasingly intensified agricultural activities globally. Greening gradients declined in the dryland, but enhanced in the High-latitude ecosystems driven by warming, especially in the shrubland. Density-dependent vegetation trends were accounted for by the disproportionately impacts from climate changes and the unequal contributions of Land Cover Changes (LCC) among dense and sparse canopies. Vegetation trends and greening gradients could be extensively facilitated by Wetting or Decreasing solar Radiation (WDR), especially in sparse grassland and shrubland. Browning was dominant in dense canopies, which was further aggravated by Drying and Increasing solar Radiation (DIR), especially woody vegetation. This study implied the widespread degradation or mortality of highly productive vegetation hidden among global greening dominant in open ecosystems, which might be further exacerbated by the predicted increasing drought under global warming.

Assessing the contemporary status of Nebraska's eastern saline wetlands by using a machine learning algorithm on the Google Earth Engine cloud computing platform.

Nebraska's eastern saline wetlands are globally unique and highly vulnerable inland salt marsh ecosystems. This research aims to evaluate the status of the saline wetlands in eastern Nebraska to discover the conditions of saline wetland hydrology, hydrophytes, and hydraulic soil. The research adopts machine learning and Google Earth Engine to classify Sentinel-2 imagery for water and vegetation classification and the National Agriculture Imagery Program imagery for salinity conditions. Six machine learning models are applied in water, soil, and vegetation detection in the study area. The optimal model (linear kernel SVM) generates an overall accuracy of 99.95% for water classification. For saline vegetation classification, the optimal model is the gradient tree boost with an overall accuracy of 94.07%. The overall accuracy values of saline soil classification using the optimal model (linear kernel SVM) varied among different years. The results of this study show the possibility of an observation approach for continuously monitoring Nebraska's eastern saline wetlands. The water classification results show that the saline wetlands in this area all have a similar temporal water cover pattern within each year. For saline vegetation, the peak season in this area is between June and July. The years 2019 (19.00%) and 2018 (17.69%) had higher saline vegetation cover rates than 2017 (10.54%). The saline soil classification shows that the saline soil area is highly variable in response to changes in the water and vegetation conditions. The research findings provide solid scientific evidence for conservation decision-making in these saline wetland areas.

Climate change impacts the subsurface transport of atrazine and estrone originating from agricultural production activities.

Climate change will impact soil properties such as soil moisture, organic carbon and temperature and changes in these properties will influence the sorption, biodegradation and leaching of trace organic contaminants to groundwater. In this study, we conducted a modeling case study to evaluate atrazine and estrone transport in the subsurface under current and future climate conditions at a field site in central Nebraska. According to the modeling results, in the future, enhanced evapotranspiration and increased average air temperature may cause drier soil conditions, which consequently reduces the biodegradation of atrazine and estrone in the water phase. On the other hand, greater transpiration rates lead to greater root solute uptake which may decrease the concentration of atrazine and estrone in the soil profile. Another consequence of future climate is that the infiltration and leaching rates for both atrazine and estrone may be lower under future climate scenarios. Reduced infiltration of trace organic compounds may indicate that lower trace organic concentrations in groundwater may occur under future climate scenarios.

Three-dimensional modeling of nitrate-N transport in vadose zone: Roles of soil heterogeneity and groundwater flux.

Contamination of groundwater from nitrogen fertilizers in agricultural lands is an important environmental and water quality management issue. It is well recognized that in agriculturally intensive areas, fertilizers and pesticides may leach through the vadose zone and eventually reach groundwater. While numerical models are commonly used to simulate fate and transport of agricultural contaminants, few models have considered a controlled field work to investigate the influence of soil heterogeneity and groundwater flow on nitrate-N distribution in both root zone and deep vadose zone. In this work, a numerical model was developed to simulate nitrate-N transport and transformation beneath a center pivot-irrigated corn field on Nebraska Management System Evaluation area over a three-year period. The model was based on a realistic three-dimensional sediment lithology, as well as carefully controlled irrigation and fertilizer application plans. In parallel, a homogeneous soil domain, containing the major sediment type of the site (i.e. sandy loam), was developed to conduct the same water flow and nitrate-N leaching simulations. Simulated nitrate-N concentrations were compared with the monitored nitrate-N concentrations in 10 multi-level sampling wells over a three-year period. Although soil heterogeneity was mainly observed from top soil to 3 m below the surface, heterogeneity controlled the spatial distribution of nitrate-N concentration. Soil heterogeneity, however, has minimal impact on the total mass of nitrate-N in the domain. In the deeper saturated zone, short-term variations of nitrate-N concentration correlated with the groundwater level fluctuations.

Automatic and adaptive paddy rice mapping using Landsat images: Case study in Songnen Plain in Northeast China.

Spatiotemporal explicit information on paddy rice distribution is essential for ensuring food security and sustainable environmental management. Paddy rice mapping algorithm through the Combined Consideration of Vegetation phenology and Surface water variations (CCVS) has been efficiently applied based on the 8day composites time series datasets. However, the great challenge for phenology-based algorithms introduced by unpromising data availability in middle/high spatial resolution imagery, such as frequent cloud cover and coarse temporal resolution, remained unsolved. This study addressed this challenge through developing an automatic and Adaptive paddy Rice Mapping Method (ARMM) based on the cloud frequency and spectral separability. The proposed ARMM method was tested on the Landsat 8 Operational Land Imager (OLI) image (path/row 118/028) in the Songnen Plain in Northeast China in 2015. First, the whole study region was automatically and adaptively subdivided into undisturbed and disturbed regions through a per-pixel strategy based on Landsat image data availability during key phenological stage. Second, image objects were extracted from approximately cloud-free images in disturbed and undisturbed regions, respectively. Third, phenological metrics and other feature images from individual or multiple images were developed. Finally, a flexible automatic paddy rice mapping strategy was implemented. For undisturbed region, an object-oriented CCVS method was utilized to take the full advantages of phenology-based method. For disturbed region, Random Forest (RF) classifier was exploited using training data from CCVS-derived results in undisturbed region and feature images adaptively selected with full considerations of spectral separability and the spatiotemporal coverage. The ARMM method was verified by 473 reference sites, with an overall accuracy of 95.77% and kappa index of 0.9107. This study provided an efficient strategy to accommodate the challenges of phenology-based approaches through transferring knowledge in parts of a satellite scene with finer time series to targets (other parts) with deficit data availability.

Greater phenological sensitivity on the higher Tibetan Plateau: new insights from weekly 5 km EVI2 datasets.

Plateau vegetation is considered to be highly sensitive to climate change, especially at higher altitudes. Although the Tibetan Plateau has experienced intensive warming over the past few decades, there is much contradictory evidence regarding its phenological variations and the impact of climatic change. In this study, we explored vegetation phenology through the inflexion point-based method with the weekly 0.05° EVI2 datasets from 1982 to 2010. We observed complex spatiotemporal variations in vegetation phenology on the higher Tibetan Plateau from three aspects. From a spatial aspect, the altitudinal gradients of phenological dates, as well as their directions, varied among different altitudes over the past three decades. Compared with delaying with elevation at altitudes below 5000 m, the phenological parameters at altitudes above 5000 m significantly advanced with increasing altitudes. At higher altitudes, much stronger altitudinal gradients (slope) of phenological dates were observed in the 2000s than in the 1980s and 1990s, i.e., 2.19, 3.47, and 3.68 days' advance for start, maximum, and end dates, respectively, compared to less than 1 day's change per 100 m increase in altitude. From a temporal dynamic aspect, when analyzed at different altitudinal bands, the dynamic trends in phenological dates were generally not significant except the advancing trends in the maximum dates at altitudes above 5000 m and the delaying trend in the end dates at altitudes of 4500-5000 m in the twenty-first century. Remarkable elevation dependency was also observed at the pixel level: increasing amplitudes of phenological dynamic trends were observed at higher altitudes when obtaining their minimum around 5000 m. These spatiotemporal variations of vegetation phenology were due to combined effects from both temperature and precipitation: more abundant rainfall and greater magnitudes of dynamic trends were observed in the average daily minimum temperature (slope = 0.08 °C/year) and annual precipitation (slope = 2.17 mm/year) at higher altitudes.

A comprehensively quantitative method of evaluating the impact of drought on crop yield using daily multi-scale SPEI and crop growth process model.

The quantitative evaluation of the impact of drought on crop yield is one of the most important aspects in agricultural water resource management. To assess the impact of drought on wheat yield, the Environmental Policy Integrated Climate (EPIC) crop growth model and daily Standardized Precipitation Evapotranspiration Index (SPEI), which is based on daily meteorological data, are adopted in the Huang Huai Hai Plain. The winter wheat crop yields are estimated at 28 stations, after calibrating the cultivar coefficients based on the experimental site data, and SPEI data was taken 11 times across the growth season from 1981 to 2010. The relationship between estimated yield and multi-scale SPEI were analyzed. The optimum time scale SPEI to monitor drought during the crop growth period was determined. The reference yield was determined by averaging the yields from numerous non-drought years. From this data, we propose a comprehensive quantitative method which can be used to predict the impact of drought on wheat yields by combining the daily multi-scale SPEI and crop growth process model. This method was tested in the Huang Huai Hai Plain. The results suggested that estimation of calibrated EPIC was a good predictor of crop yield in the Huang Huai Hai Plain, with lower RMSE (15.4 %) between estimated yield and observed yield at six agrometeorological stations. The soil moisture at planting time was affected by the precipitation and evapotranspiration during the previous 90 days (about 3 months) in the Huang Huai Hai Plain. SPEIG90 was adopted as the optimum time scale SPEI to identify the drought and non-drought years, and identified a drought year in 2000. The water deficit in the year 2000 was significant, and the rate of crop yield reduction did not completely correspond with the volume of water deficit. Our proposed comprehensive method which quantitatively evaluates the impact of drought on crop yield is reliable. The results of this study further our understanding why the adoption of counter measures against drought is important and direct farmers to choose drought-resistant crops.

Assessing Nebraska playa wetland inundation status during 1985-2015 using Landsat data and Google Earth Engine.

Playa wetlands in Nebraska provide globally important habitats for migratory waterfowl. Inundation condition is an important indicator of playa wetland functionality. However, there is a lack of long-term continuous monitoring records for playa wetlands. The objective of this study was to determine a suitable index for Landsat images to map the playa inundation status in March and April during 1985-2015. Four types of spectral indices-negative normalized vegetation index, Normalized Difference Water Index (NDWI), modified NDWI, and Tasseled Cap Wetness-Greenness Difference (TCWGD)-were evaluated to detect playa inundation conditions from Landsat images. The results indicate that the TCWGD is the most suitable index for distinguishing playa inundation status. By using Landsat images and Google Earth Engine, we mapped the spring inundation condition of Nebraska playas during 1985-2015. The results show that the total inundated areas were 176.79 km2 in spring migratory season, representing 18.92% of the total area of playa wetlands. There were 9898 wetlands inundated at least once in either March or April during the past 30 years, representing 29.41% of a total of 33,659 historical wetlands. After comparing the historical hydric soil footprints and the inundated areas, the results indicate that the hydrological conditions of the majority of playas in Nebraska have changed. The inundated wetlands are candidates for protection and/or partial restoration, and the un-inundated wetlands need more attention for wetland restoration. Wetlands in areas enrolled in conservation easements had a significantly high level of playa inundation status than non-conserved wetlands during spring migratory seasons in the past decades.These conservation easements only count for 4.29% of the total footprint areas, but they have contributed 20.82% of the inundation areas in Nebraska during the past 30 years.

Use RUSLE2 model to assess the impact of soil erosion on playa inundation and hydrophyte conditions in the Rainwater Basin, Nebraska.

Playas in the Rainwater Basin region in Nebraska are globally important wetlands that are continuously threatened by culturally accelerated sedimentation. Using annual habitat survey data and wetland vegetation inventories, inundation and hydrophyte community distributions were evaluated for properties under different types of conservation status. Annual soil erosion rates from surrounding watersheds were calculated to estimate sediment accumulated rates using the Revised Universal Soil Loss Equation 2 (RUSLE2). The slope-length component of the RUSLE2 was derived from 2009 light detection and ranging (LiDAR) data after the methods described by Van Remortel (Computers & Geosciences 30:1043-1053, 2004). Wetlands enrolled in conservation programs were inundated more and were dominated to a greater degree by hydrophytes than wetlands not enrolled in these programs. The mean estimated soil erosion rate at the Rainwater Basin landscape level was 4.67 tons/ha/year, and the mean estimated sediment accumulation depth for public watersheds was estimated as 0.19 cm/year. Without appropriate conservation actions, the current inundated acres and wetland acres growing hydrophytes would be further reduced by sediment accumulation. The results illustrated the importance of conservation programs to protect wetlands.

Rice cropping density and intensity lessened in southeast China during the twenty-first century.

Accurate and updated time series maps of paddy rice distribution and planting intensity will greatly improve our knowledge. Unfortunately, spatiotemporal explicit information on rice fields is relatively limited, and considerable uncertainties still exist as regards to its inter-annual variations in China. In this study, an improved rice mapping methodology was proposed through combined consideration of vegetation phenology and surface moisture variations from different seasonal rice. This method was applied to southeast China based on 500 m 8 day composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhance Vegetation Indices with two bands (EVI2) during the period 2001-2013. Its efficiency was validated with 763 ground survey sites, with an overall accuracy of 95.02 % and the kappa index of 0.9217. Spatiotemporal analysis indicated that rice cropping density and intensity lessened in southeast China during the period 2001-2013. Particularly, the paddy rice-planted areas reduced by 30.09 %, changing from 231,005 to 161,484 km(2). Among them, the planted areas of double rice decreased by 49.34 %, changing from 34,215 to 17,335 km(2). Therefore, averaged rice cropping intensity in southeast China decreased from 1.148 to 1.107. The primary dynamic patterns were from single rice or a rotation of rice plus other crops to non-rice (93,386 km(2)) and double rice to non-double rice (24,132 km(2)). When analyzed at provincial and altitudinal gradient levels, it was obvious that areas with greater rice cropping density or intensity were associated with more remarkable reductions. Graphical abstract The left graph shows that the rice cropping density lessened in Hubei, Hunan, Guangdong, Jiangxi, Anhui, Jiangsu, Henan provinces and other three provincial-level administrative units (Zhejiang, Fujian and Shanghai) from 2001 to 2013. The middle graph indicates the movement of gravity center as well as the variations in the total planted areas of single rice, rice plus others and double rice. The right graph denotes that the rice cropping intensity decreased in each provincial-level administrative unit from 2001 to 2013.

A new approach for crop identification with wavelet variance and JM distance.

This paper develops a new crop mapping method through combined utilization of both time and frequency information based on wavelet variance and Jeffries-Matusita (JM) distance (CIWJ for short). A two-dimensional wavelet spectrum was obtained from datasets of daily continuous vegetation indices through a continuous wavelet transform using the Mexican hat and the Morlet mother wavelets. The time-average wavelet variance (TAWV) and the scale-average wavelet variance (SAWV) were then calculated based on the wavelet spectrum of the Mexican hat and the Morlet wavelet, respectively. The class separability based on the JM distance was evaluated to discriminate the proper period or scale range applied. Finally, a procedure for criteria quantification was developed using the TAWV and SAWV as the major metrics, and the similarity between unclassified pixels and established land use/cover types was calculated. The proposed CIWJ method was applied to the middle Hexi Corridor in northwest China using 250-m 8-day composite moderate-resolution imaging spectroradiometer (MODIS) enhanced vegetation index (EVI) time series datasets in 2012. The CIWJ method was shown to be efficient in crop field mapping, with an overall accuracy of 83.6 % and kappa coefficient of 0.7009, assessed with 30 m Chinese Environmental Disaster Reduction Satellite (HJ-1)-derived data. Compared with methods utilizing information on either frequency or time, the CIWJ method demonstrates tremendous potential for efficient crop mapping and for further applications. This method could be applied to either coarse or high spatial resolution images for agricultural crop identification, as well as other more general or specific land use classifications.

Characterizing spatiotemporal non-stationarity in vegetation dynamics in China using MODIS EVI dataset.

This paper evaluated the spatiotemporal non-stationarity in the vegetation dynamic based on 1-km resolution 16-day composite Moderate Resolution Imaging Spectroradiometer (MODIS) Enhanced Vegetation Index (EVI) datasets in China during 2001-2011 through a wavelet transform method. First, it revealed from selected pixels that agricultural crops, natural forests, and meadows were characterized by their distinct intra-annual temporal variation patterns in different climate regions. The amplitude of intra-annual variability generally increased with latitude. Second, parameters calculated using a per-pixel strategy indicated that the natural forests had the strongest variation pattern from seasonal to semiannual scales, and the multiple-cropping croplands typically showed almost equal variances distributed at monthly, seasonal, and semiannual scales. Third, spatiotemporal non-stationarity induced from cloud cover was also evaluated. It revealed that the EVI temporal profiles were significantly distorted with regular summer cloud cover in tropical and subtropical regions. Nevertheless, no significant differences were observed from those statistical parameters related to the interannual and interannual components between the de-clouded and the original MODIS EVI datasets across the whole country. Finally, 12 vegetation zones were proposed based on spatiotemporal variability, as indicated by the magnitude of interannual and intra-annual dynamic components, normalized wavelet variances of detailed components from monthly to semiannual scale, and proportion of cloud cover in summer. This paper provides insightful solutions for addressing spatiotemporal non-stationarity by evaluating the magnitude and frequency of vegetation variability using monthly, seasonal, semiannual to interannual scales across the whole study area.

Identifying potential conflict associated with oil and gas exploration in Texas state coastal waters: A multicriteria spatial analysis.

Recent interest in expanding offshore oil production within waters of the United States has been met with opposition by groups concerned with recreational, environmental, and aesthetic values associated with the coastal zone. Although the proposition of new oil platforms off the coast has generated conflict over how coastal resources should be utilized, little research has been conducted on where these user conflicts might be most intense and which sites might be most suitable for locating oil production facilities in light of the multiple, and often times, competing interests. In this article, we develop a multiple-criteria spatial decision support tool that identifies the potential degree of conflict associated with oil and gas production activities for existing lease tracts in the coastal margin of Texas. We use geographic information systems to measure and map a range of potentially competing representative values impacted by establishing energy extraction infrastructure and then spatially identify which leased tracts are the least contentious sites for oil and gas production in Texas state waters. Visual and statistical results indicate that oil and gas lease blocks within the study area vary in their potential to generate conflict among multiple stakeholders.

[Crop productivity model and its application].

Within agricultural ecosystems the interaction between crop and surroundings is quite complicated. In order to know the feedbacks of crop to surroundings in agricultural ecosystem, a research on crop productivity model is becoming important. This paper discussed the backgrounds of the emergency of crop productivity model, thinking that the crop productivity model was a means which could be used to simulate growth process by mathematical ways and computer technology, and analyzed the physiology of crop growth and the correlation between crop and surroundings. At the same time, it discussed four phases of crop productivity model developing, including infancy, youth, adolescent and maturity. Secondly, the paper summarized the crop productivity model's function of agricultural eco-environment protection from three aspects, including scientific research, management of crop, and analysis of agricultural decision, and discussed the disadvantages of crop productivity model. The disadvantages embodied in three facets: 1) though the simple crop productivity model could be used easily in practice, its regional adaptiability was too weak; 2) complicated crop productivity model had too much parameters to obtain, which made it difficult to use in practice; 3) the inconsistency of basic data format in different research areas also made the model's regional adaptability too weak. It's indicated in this paper that the establishment of universal and uniform basic data format is favorable to the extension and application of crop productivity model in other regions. Geographic information system (GIS) was emerged in 1960, which has powerful functions of obtaining, depositing, managing and analyzing spatial data, and is useful to solve the problem of weakly regional adaptability. Meanwhile, friendly interface is favorable to the extension in common users. Finally, the paper discussed the combination of crop productivity model and geographic information system, summarized some researches on friendly interface of crop productivity model, and pointed out that the establishment of universal crop productivity model interface is the focus of the future.