Remote Sensing Based Detection of Crop Phenology for Agricultural Zones in China Using a New Threshold Method

You, Xu; Meng, Jun; Zhang, Miao; Dong, Ting

doi:10.3390/rs5073190

Cited by 87 publications

(51 citation statements)

References 36 publications

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“…In addition to its importance for modeling, regional-to-global land cover and land use information is essential for studies focused on interactions between humans and natural systems [14][15][16][17]. Cropping intensity, which we define here as the number of cropping cycles per year, is an important dimension of land use that is strongly influences water demand and agricultural production [18][19][20][21], but has received relatively little attention.…”

Section: Introductionmentioning

confidence: 99%

Mapping Crop Cycles in China Using MODIS-EVI Time Series

et al. 2014

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Abstract:As the Earth's population continues to grow and demand for food increases, the need for improved and timely information related to the properties and dynamics of global agricultural systems is becoming increasingly important. Global land cover maps derived from satellite data provide indispensable information regarding the geographic distribution and areal extent of global croplands. However, land use information, such as cropping intensity (defined here as the number of cropping cycles per year), is not routinely available over large areas because mapping this information from remote sensing is challenging. In this study, we present a simple but efficient algorithm for automated mapping of cropping intensity based on data from NASA's (NASA: The National Aeronautics and Space Administration) MODerate Resolution Imaging Spectroradiometer (MODIS). The proposed algorithm first applies an adaptive Savitzky-Golay filter to smooth Enhanced Vegetation Index (EVI) time series derived from MODIS surface reflectance data. It then uses an iterative moving-window methodology to identify cropping cycles from the smoothed EVI time series. Comparison of results from our algorithm with OPEN ACCESSRemote Sens. 2014, 6 2474 national survey data at both the provincial and prefectural level in China show that the algorithm provides estimates of gross sown area that agree well with inventory data. Accuracy assessment comparing visually interpreted time series with algorithm results for a random sample of agricultural areas in China indicates an overall accuracy of 91.0% for three classes defined based on the number of cycles observed in EVI time series. The algorithm therefore appears to provide a straightforward and efficient method for mapping cropping intensity from MODIS time series data.

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Section: Introductionmentioning

confidence: 99%

Mapping Crop Cycles in China Using MODIS-EVI Time Series

et al. 2014

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“…The static threshold method is limited in that it does not always apply to the entire region. In contrast, the dynamic threshold extracts the vegetation phenology by using the varying VI or LAI value obtained from various threshold algorithms [11,15,26,[28][29][30]. Although the advantage of evaluating vegetation phenology using the dynamic threshold is obvious, the accuracy of the retrieved results strongly depends on the threshold algorithms.…”

Section: Introductionmentioning

confidence: 99%

“…To date, these phenology retrieval methods have focused on detecting the start and end of the vegetation growing season [5,11,15,[26][27][28][29][30][31]. The associated retrieval results are inconsistent [21].…”

Section: Introductionmentioning

confidence: 99%

“…So far, three main types of approaches can help people identify the vegetation phenophases [8,9], which are phenological observations on the ground for individual plants or tree stands [4,10], derivative phenophases based on remote senesing data [2,6,8,[11][12][13][14][15][16][17], and plant phenology models based on climate factors (e.g., temperature, light and soil moisture, etc.) [18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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A New Equation for Deriving Vegetation Phenophase from Time Series of Leaf Area Index (LAI) Data

et al. 2014

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Accurately modeling the land surface phenology based on satellite data is very important to the study of vegetation ecological dynamics and the related ecosystem process. In this study, we developed a Sigmoid curve (S-curve) function by integrating an asymmetric Gaussian function and a logistic function to fit the leaf area index (LAI) curve. We applied the resulting asymptotic lines and the curvature extrema to derive the vegetation phenophases of germination, green-up, maturity, senescence, defoliation and dormancy. The new proposed S-curve function has been tested in a specific area (Shangdong Province, China), characterized by a specific pattern in leaf area index (LAI) time course due to the dominant presence of crops. The function has not yet received any global testing. The identified phenophases were validated against measurement stations in Shandong Province. (i) From the site-scale comparison, we find that the detected phenophases using the S-curve (SC) algorithm are more consistent with the observations than using the logistic (LC) algorithm and the asymmetric Gaussian (AG) algorithm, especially for the germination and dormancy. The phenological recognition rates (PRRs) of the SC algorithm are obviously higher than those of two other algorithms. The S-curve function fits the LAI curve much better than the logistic function and asymmetric Gaussian function; (ii) The retrieval results OPEN ACCESSRemote Sens. 2014, 6 5651 of the SC algorithm are reliable and in close proximity to the green-up observed data whether using the AVHRR LAI or the improved MODIS LAI. Three inversion algorithms shows the retrieval results based on AVHRR LAI are all later than based on improved MODIS LAI. The bias statistics reveal that the retrieval results based on the AVHRR LAI datasets are more reasonable than based on the improved MODIS LAI datasets. Overall, the S-curve algorithm has the advantage of deriving vegetation phenophases across time and space as compared to the LC algorithm and the AG algorithm. With the SC algorithm, the vegetation phenophases can be extracted more effectively.

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“…Remote sensing enables low-cost monitoring of vegetative multi-temporal dynamics over the entire region and therefore is a powerful tool to examine phenological changes on a regional scale [38,39].…”

Section: Estimation Of the Wheat Growing Period Based On Satellite Datamentioning

confidence: 99%

Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data

et al. 2016

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Abstract:Irrigation is crucial to agriculture in arid and semi-arid areas and significantly contributes to crop development, food diversity and the sustainability of agro-ecosystems. For a specific crop, the separation of its irrigated and rainfed areas is difficult, because their phenology is similar and therefore less distinguishable, especially when there are phenology shifts due to various factors, such as elevation and latitude. In this study, we present a simple, but robust method to map irrigated and rainfed wheat areas in a semi-arid region of China. We used the Normalized Difference Vegetation Index (NDVI) at a 30ˆ30 m spatial resolution derived from the Chinese HJ-1A/B (HuanJing(HJ) means environment in Chinese) satellite to create a time series spanning the whole growth period of wheat from September 2010 to July 2011. The maximum NDVI and time-integrated NDVI (TIN) that usually exhibit significant differences between irrigated and rainfed wheat were selected to establish a classification model using a support vector machine (SVM) algorithm. The overall accuracy of the Google-Earth testing samples was 96.0%, indicating that the classification results are accurate. The estimated irrigated-to-rainfed ratio was 4.4:5.6, close to the estimates provided by the agricultural sector in Shanxi Province. Our results illustrate that the SVM classification model can effectively avoid empirical thresholds in supervised classification and realistically capture the magnitude and spatial patterns of rainfed and irrigated wheat areas. The approach in this study can be applied to map irrigated/rainfed areas in other regions when field observational data are available.

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Remote Sensing Based Detection of Crop Phenology for Agricultural Zones in China Using a New Threshold Method

Cited by 87 publications

References 36 publications

Mapping Crop Cycles in China Using MODIS-EVI Time Series

Mapping Crop Cycles in China Using MODIS-EVI Time Series

A New Equation for Deriving Vegetation Phenophase from Time Series of Leaf Area Index (LAI) Data

Mapping Irrigated and Rainfed Wheat Areas Using Multi-Temporal Satellite Data

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