This must be compared with devices that have similar functions enabled. The specification includes the limitations imposed by the characteristics of the analog output buffer. Reference control power is high. Note Avoid using P2[4] for digital signaling when using an analog resource that depends on the Analog Reference.
Some coupling of the digital signal may appear on the AGND. A maximum of 36 x 1, block endurance cycles is allowed. This may be balanced between operations on 36x1 blocks of 1, maximum cycles each, 36x2 blocks of maximum cycles each, or 36x4 blocks of maximum cycles each to limit the total number of cycles to 36x1, and that no single block ever sees more than 1, cycles.
For the full industrial range, the user must employ a temperature sensor user module FlashTemp and feed the result to the temperature argument before writing.
See Figure 5 on Internal Main Oscillator page See Figure 5 on F24M Nominal 0 24 Maximum Frequency of signal on row input or row output. Refer to Figure 10 on page Using factory trim values. Refer to Figure 9 on page See the individual user module data sheets for information on maximum frequencies for user modules.
This is at frequencies above the corner frequency defined by the on-chip 8. Figure At high frequencies, increased power level reduces the noise spectrum level. With the CPU clock divider set to 1, the external clock must adhere to the maximum frequency and duty cycle requirements. If the frequency of the external clock is greater than 12 MHz, the CPU clock divider must be set to 2 or greater.
In this case, the CPU clock divider ensures that the fifty percent duty cycle requirement is met. After this period, the first clock pulse is generated. This specification assumes that SysClk is operating at 24 MHz, nominal. Important Note Emulation tools may require a larger area on the target PCB than the chip's footprint.
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Support Forum. Comparatively, the proportions of triple and continuous cropping were quite small, with their distributions mainly limited to Southeast Asia. These regions are commonly characterized by warm and humid climates, except for the Nile River basin, in which irrigation has been commonly used to support intensive farming practices Zohaib and Choi, The red line indicates the standard deviation SD.
We combined Australia and Oceania New Zealand, Melanesia, Micronesia, and Polynesia due to the rarity of cropland on these two continents. Globally, South America exhibited the most intensified cropping level, followed by Asia and Europe. Specifically, the average TNCC values were 3. South America and Asia also possessed the largest standard deviations of TNCC, indicating the inherent diversity of agricultural activities within these two continents as weather conditions directly affect cropping practices Iizumi and Ramankutty, For example, in Asia, triple- and continuous-cropping systems were distributed in Southeast Asia, including Indonesia, Malaysia, southern Thailand, and the Mekong River Delta in Vietnam.
Double cropping was concentrated in the North China Plain, Ganga River basin and southern China, while the rest of Asia was dominated by a single-cropping pattern, covering central Asia, northeastern Asia, and southern India.
In contrast, low to moderate CI levels were typically found in high-latitude countries, such as Canada, Russia, and Mongolia. In addition to the latitude gradient, we found that the diversity of cropland management played a critical role in shaping the CI pattern. For example, some high-latitude European countries Germany, Poland, Belarus, etc. Rainfed agricultural practices lead to fewer cropping cycles in the Middle East and North African countries, except for Egypt, where most croplands are irrigated Wu et al.
Taking climate conditions into account, the heterogeneity of global CI becomes even more prominent among different AEZs. Arid regions, which cover vast areas in Africa, Australia, and Central Asia, are associated with fewer cropping cycles due to a lack of water for irrigation Chiew et al. In contrast, intensive farming is widely distributed in humid and low-latitude areas such as South China and the Mekong Delta. Overall, countries and AEZs with intensive farming are more subject to internal variability, as reflected by higher standard deviations Fig.
Globally, there are 14 countries and 7 AEZs exhibiting standard deviations greater than 1. Regions with low CI averages but high CI standard deviations were observed only on the western coast of South America and Queensland to Victoria in Australia, where partial irrigation in the former Xie et al. The high standard deviations in Australia and Oceania mainly resulted from the high within-country and zonal heterogeneity, which may encompass aspects including the exceptionally variable climate with the prevalence of floods and droughts King et al.
In addition to these regular drivers, the political situation may cause CI spatiotemporal diversity. Notably, for instance, we found an unusually high standard deviation in Afghanistan, which was caused by both crop failure during the emergence to early development stages due to adverse weather conditions Rousta et al. Due to the differences in methods, input data, and spatial resolution of the existing products containing CI metrics as listed in Table 2 and GCI30, the statistical average CI at global scales varied among the products.
Statistically, our CI is in a remarkably high agreement level with estimates based on the existing six estimates mean CI: 1. The minimum CI among the seven studies was estimated to be 0. The CI of other existing products listed in Table 2 ranges from 1.
The main reason is that statistics-based CI could not exclude the fallow land area as the agriculture statistics usually lack statistical information on fallow land, while fallow land could be identified using remote sensing Zhang et al. On the one hand, the actual harvest frequency estimated by Wu et al.
Each pixel of cropland was assigned to either a single-cropping or double-cropping category, and fallow pixels were not considered, which will result in a higher CI Wu et al.
On the other hand, GCI30 systematically underestimates the cropping intensity when the harvest window is narrow between two growing seasons as a valid phenology season should include both green-up and green-down segments based on the GCI30 algorithm C.
Figure 9 illustrated the differences in statistical annual CI at the country scale between GCI30 and four reference datasets. Statistical values of CI at the national scale are available in Table S4. Spatially, positive CI difference values were commonly found in Southeast Asia, the Indian subcontinent, and some parts of Europe. There were also discrepancies when these two phenology products were used as the baselines.
However, the opposite tendency was observed using VIP4, which exhibited vast negative pixel distributions in Europe and the North China Plain. In general, substantial variations were detected through these spatially explicit maps. Specifically, three out of the four subregions had at least one-fifth of the pixels featuring negative CI differences.
Finally, in South Asia, the positive and negative pixel percentages were almost equal, i. In fact, the reliability of these two land surface phenology products, especially VIP4, is affected by several factors, including a coarse spatial resolution, temporal mismatch, and algorithm structure differences when compared to GCI Given the CI distribution with a fine spatial resolution, GCI30 is associated with reduced uncertainties caused by the mixed-pixel effect.
In addition to the improvement of mapping accuracy, GCI30 has the potential to monitor landscape-scale cropping practices on fragmented land parcels by smallholders, which comprise over half of the rural populations in developing nations that are most vulnerable to food security and environmental challenges Morton et al.
Compared with the generalizable crop phenophase pattern, the GCI30 algorithm is not only efficient in mapping the CI distribution across various AEZs but is also flexible enough to be improved with updated data inputs.
For example, the Harmonized Landsat and Sentinel-2 surface reflectance dataset Claverie et al. The successful production of GCI30 on the GEE platform illustrates a paradigm of mapping farming practices that is globally consistent and locally relevant using state-of-the-art cloud computing resources Lewis et al. It inspires future global fine-scale agricultural research that was previously not applicable. A large number of natural factors and anthropogenic drivers are related to CI at the planetary scale.
The errors in GCI30 could be related to the uncertainties in input data and limitations of the algorithm. The reliability of the cropland extent is a major factor constraining CI mapping performance. Despite the high overall accuracy of the generated cropland extent, classification errors still exist, especially in some regions of Africa and Asia where small cropland patches are mixed with other land covers Gong et al.
Although we follow the definition of cropland to select a subset of classes of a layer that best fit in the definition for each of the land-cover and cropland products, the inconsistency among the 10 land cover or specific cropland layer products still exists.
However, the GCI30 product excluded the greenhouse pixels as CI of greenhouse crops is detected as zero cropping monitored by remote sensing. The second concern is the perennial woody crops such as orchards and vineyards from NLCD. As the NLCD data were only used for the Alaska region, they will have very limited impact on the integrated global cropland layer and accordingly a minor effect on GCI On the other hand, as no single product has yet been shown to be consistently accurate in representing cropland distribution, our approach by integrating a different dataset is still better than relying on a single source of land cover or cropland layer Fritz et al.
The GCI30 algorithm depends heavily on crop phenological information derived from the time series of vegetation indices. In particular, the presence of clouds in the early and mid agricultural growing season is preventing optical remote sensing satellites from accurate agricultural applications including cropping cycle detection Whitcraft et al.
Lower data qualities were observed in the Amazon, western Africa, South and Southeast Asia, and South and Southwest China than other regions due to the high cloudy frequency Fig.
Although the cloud frequency is relatively low in western Russia and central Europe compared with the above cloud-prone regions Whitcraft et al. We further evaluated the uncertainty in the GCI30 at the global scale. In general, the places with high uncertainty coincided with the cloud-prone regions, which might be a resultant of high invalid satellite observations Fig. S2, S3. The fragmented agricultural fields and complex farming practices in the regions including western Africa, South and Southeast Asia, and the East African Highlands Fritz et al.
In Argentina, the cropland field size is large, and the cloud presence is less frequent. However, large bias of cropping cycles and high uncertainties were commonly observed Figs. Rice paddies are fundamentally different from non-flooded croplands, which affects CI mapping performance. While promising, its application was limited due to the lack of a specific rice paddy layer.
Therefore, more improvements can be included, such as integrating synthetic-aperture radar SAR data time series for more accurate flood signal detection Singha et al. Additionally, it is noteworthy that the GCI30 product provides insight only into the current actual cropping intensity; however, it is not linked to the potential cropping cycles. To assess the CI gaps between potential and actual situations, climate models could be introduced to simulate the potential cropping cycles under long-term average weather conditions.
The proposed method can be readily applied to other years to retrieve long-term CI maps, which will fill in the knowledge gaps of decades-long changes in cropping practices and interannual variations Iizumi and Ramankutty, Such information is key to improving our understanding of the CI response to climate in a more granular manner. To be precise, the spatial resolution of the product is 0.
The valid values for the first layer are 1, 2, and 3, representing single cropping, double cropping, or triple cropping, respectively. The continuous-cropping type or the number of cropping cycles larger than three per year is assigned as in the above-mentioned two layers. Based on the phenophase-based mapping framework, GCI30 identified CI by enumerating the transition points between growing and non-growing periods.
Accuracy assessments and intercomparisons with existing land surface phenology products suggested that GCI30 was reliable across different climate zones and cropping systems. National- and AEZ-level statistics demonstrated the joint influence of natural and anthropogenic drivers in controlling CI spatial patterns in most areas of the world.
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