National-scale 10-m maps of cropland use intensity in China during 2018-2023.

The amount of actively cultivated land in China is increasingly threatened by rapid urbanization and rural population aging. Quantifying the extent and changes of active cropland and cropping intensity is crucial to global food security. However, national-scale datasets for smallholder agriculture are limited in spatiotemporal continuity, resolution, and precision. In this paper, we present updated annual Cropland Use Intensity maps in China (China-CUI10m) with descriptions of the extent of fallow/abandoned, actively cropped fields and cropping intensity at a 10-m resolution in recent six years (2018-2023). The dataset is produced by robust algorithms with no requirements for regional adjustments or intensive training samples, which take full advantage of the Sentinel-1 (S1) SAR and Sentinel-2 (S2) MSI time series. The China-CUI10m maps have achieved high accuracy when compared to ground truth data (Overall accuracy = 90.88%) and statistical data (R2 > 0.94). This paper provides the recent trends in cropland abandonment and agricultural intensification in China, which contributes to facilitating geographic-targeted cropland use control policies towards sustainable intensification of smallholder agricultural systems in developing countries.

Scene-based dual domain non-uniformity correction algorithm for stripe and optics-caused fixed pattern noise removal.

Non-uniformity is a long-standing problem that significantly degrades infrared images through fixed pattern noise (FPN). Existing scene-based algorithms for non-uniformity correction (NUC) effectively eliminate stripe FPN assuming consistent inter-frame non-uniformity. However, they are ineffective in handling spatially continuous optical FPN. In this paper, a scene-based dual domain correction approach is proposed to address the non-uniformity problem by simultaneously removing stripe and optics-caused FPN. We achieve this through gain correction in the frequency domain and offset correction in the spatial domain. To remove stripes, we approximate the desired image using a guided filter and iteratively update the bias correction parameters frame by frame. For optics-caused noise removal, we separate low frequency noise from the scene using Fourier transform and update the gain correction parameters accordingly. To mitigate ghost artifacts, a combined strategy is introduced to adaptively adjusts learning rates and weights during the updating stage. Comprehensive evaluations demonstrate that our proposed approach outperforms compared methods in both real and simulated non-uniformity infrared videos.

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.

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.

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.