Using Moderate-Resolution Temporal NDVI Profiles for High-Resolution Crop Mapping in Years of Absent Ground Reference Data: A Case Study of Bole and Manas Counties in Xinjiang, China

Hao, Pengyu; Wang, Li; Zhan, Yulin; Niu, Zheng

doi:10.3390/ijgi5050067

Cited by 34 publications

(25 citation statements)

References 56 publications

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“…The confusion between cotton and grape in Bole was mainly caused by the large standard deviation of grape NDVI profiles (Fig. 11), which was consistent with the large variability of grape reference NDVI time series (Hao et al, 2016). In addition, the confusion between cotton and maize in both study regions was caused by the high similarity between the cotton and maize NDVI time series (Fig.…”

Section: Resultssupporting

confidence: 63%

Estimation of different data compositions for early-season crop type classification

Hao

Niu

et al. 2018

PeerJ

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Timely and accurate crop type distribution maps are an important inputs for crop yield estimation and production forecasting as multi-temporal images can observe phenological differences among crops. Therefore, time series remote sensing data are essential for crop type mapping, and image composition has commonly been used to improve the quality of the image time series. However, the optimal composition period is unclear as long composition periods (such as compositions lasting half a year) are less informative and short composition periods lead to information redundancy and missing pixels. In this study, we initially acquired daily 30 m Normalized Difference Vegetation Index (NDVI) time series by fusing MODIS, Landsat, Gaofen and Huanjing (HJ) NDVI, and then composited the NDVI time series using four strategies (daily, 8-day, 16-day, and 32-day). We used Random Forest to identify crop types and evaluated the classification performances of the NDVI time series generated from four composition strategies in two studies regions from Xinjiang, China. Results indicated that crop classification performance improved as crop separabilities and classification accuracies increased, and classification uncertainties dropped in the green-up stage of the crops. When using daily NDVI time series, overall accuracies saturated at 113-day and 116-day in Bole and Luntai, and the saturated overall accuracies (OAs) were 86.13% and 91.89%, respectively. Cotton could be identified 40∼60 days and 35∼45 days earlier than the harvest in Bole and Luntai when using daily, 8-day and 16-day composition NDVI time series since both producer’s accuracies (PAs) and user’s accuracies (UAs) were higher than 85%. Among the four compositions, the daily NDVI time series generated the highest classification accuracies. Although the 8-day, 16-day and 32-day compositions had similar saturated overall accuracies (around 85% in Bole and 83% in Luntai), the 8-day and 16-day compositions achieved these accuracies around 155-day in Bole and 133-day in Luntai, which were earlier than the 32-day composition (170-day in both Bole and Luntai). Therefore, when the daily NDVI time series cannot be acquired, the 16-day composition is recommended in this study.

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Section: Resultssupporting

confidence: 63%

Estimation of different data compositions for early-season crop type classification

Hao

Niu

et al. 2018

PeerJ

Self Cite

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“…Therefore, many scholars use the asymmetric double-sigmoid function method proposed by Soudani [18] to fit the crop curve for classification [19][20][21][22]. In addition, machine learning algorithms are gradually being applied to the fitting of spectral curves [23]. However, due to environmental factors such as moisture, light, temperature, crop varieties, the inter-annual variation of planting time, and regional differences, the growth and spectral curves of the same crops are inconsistent, which affects the accuracy of identifying crop types based on spectral similarity.…”

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confidence: 99%

Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China

Lin

Liu

et al. 2019

Sustainability

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Accurate, year-by-year crop distribution information is a key element in agricultural production regulation and global change governance. However, due to the high sampling costs and insufficient use of historical samples, a supervised classifying method for sampling every year is unsustainable for mapping crop types over time. Therefore, this paper proposes a method for the generation and screening of new samples for 2018 based on historical crop samples, and then it builds a crop mapping model for that current season. Pixels with the same crop type in the historical year (2013-2017) were extracted as potential samples, and their spectral features and spatial information in the current year (2018) were used to generate new samples based on clustering screening. The research result shows that when the clustering number is different, the number and structure of new generated sample also changes. The sample structure generated in Luobei County was not balanced, with the 'other crop' representing less than 3.97%, but the structure of southwest Hulin City was more balanced. Based on the newly generated samples and the ground reference data of classified year, the classification models were constructed. The average classification accuracies of Luobei County in 2018 based on new generated samples and field samples were 69.35% and 77.59%, respectively, while those of southwest Hulin City were 80.44% and 82.94%, respectively. Combined with historical samples and the spectral information of the current year, this study proposes a method to generate new samples. It can overcome the problem of crop samples only being collected in the field due to the difficulty of visual interpretation, effectively improve the use of historical data, and also provide a new idea for sustainable crop mapping in many regions lacking seasonal field samples.Sustainability 2019, 11, 5052 2 of 17 samples. In addition, in existing crop classification studies, seasonal samples are often only used for crop classification in the current season, and they are rarely used for the next season or even the next few seasons, resulting in a low utilization rate of historical data [1,6,8]. The difficulty of sampling and the low utilization of historical data have led to the high cost of year-by-year mapping. At the same time, after collecting data every year, it is necessary to carry out complex operations such as data preprocessing, and the final crop map is often obtained after the harvest, which leads to a lag of crop mapping and its guiding role for farming being greatly weakened. Therefore, it is difficult to meet the demand for crop mapping by sampling every year. The question of how to sustainably carry out crop classification based on historical data has become a research hotspot.There are three main methods for classifying crops based on historical data: One is to reuse the spectral curve, the second is to reuse the training model, and the third is to generate "training samples."Reusing spectral curves is a method for classification based on the fact t...

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“…Therefore, we opted for a multi-temporal decision tree approach able to accept as input parameters NDVI, time frame, elevation, clouds, and potential croplands. The four cereals farmed in Dudh Koshi have a similar seasonal NDVI dynamics from sowing to harvesting: a Gaussian shape with the peak almost in the middle of the growing period [10,11]. Thus, each crop was mapped by thresholding the NDVI values, according to their growing season as described in the introduction.…”

Section: Methodsmentioning

confidence: 99%

Analysis of changes in crop farming in the Dudh Koshi (Nepal) driven by climate changes

Gianinetto

Polinelli

Frassy

et al. 2017

Earth Resources and Environmental Remote Sensing/Gis Applications VIII

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Nepal is one of the poorest nations of the world and the Koshi Basin includes some of the poorest regions of this country. It's farming system is subsistence agriculture, mainly rainfed, with crop productivity among the lowest in South Asia. Nepal is also severely impacted by climate changes, such as retreat of glaciers, rise in temperature, erratic rainfalls and increase in frequency of extreme weather. This paper describes the spatio-temporal evolution of cultivated land in Dudh Koshi during the last four decades (1970s-2010s), by mapping the farming of its four main cereals in the districts of Solukhumbu, Okhaldunga and Kothang from space. The analysis of satellite time series showed a 10% of increment in farmland from 1970s to 1990s, and about 60% in the following twenty years. With a shift of cropping to higher altitudes. Data belonging to of the second twenty years are strongly correlated with the population growth observed in the same period (0.97

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Using Moderate-Resolution Temporal NDVI Profiles for High-Resolution Crop Mapping in Years of Absent Ground Reference Data: A Case Study of Bole and Manas Counties in Xinjiang, China

Cited by 34 publications

References 56 publications

Estimation of different data compositions for early-season crop type classification

Estimation of different data compositions for early-season crop type classification

Crop Mapping Based on Historical Samples and New Training Samples Generation in Heilongjiang Province, China

Analysis of changes in crop farming in the Dudh Koshi (Nepal) driven by climate changes

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