Satellite based calculation of spatially distributed crop water requirements for cotton and wheat cultivation in Fergana Valley, Uzbekistan

Conrad, Christopher; Rahmann, Maren; Machwitz, Miriam; Stulina, Galina; Paeth, Heiko; Dech, Stefan

doi:10.1016/j.gloplacha.2013.08.002

Cited by 52 publications

(30 citation statements)

References 38 publications

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“…RF is also relatively insensitive to the number of input data and multicollinearity of the data [41]. RF has been successfully applied in several regression and classification problems of EO data and generally achieves good results [17,38,40,[42][43][44][45][46][47][48].…”

Section: Random Forest (Rf) Classificationmentioning

confidence: 99%

First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe

2016

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Abstract:The study presents the preliminary results of two classification exercises assessing the capabilities of pre-operational (August 2015) Sentinel-2 (S2) data for mapping crop types and tree species. In the first case study, an S2 image was used to map six summer crop species in Lower Austria as well as winter crops/bare soil. Crop type maps are needed to account for crop-specific water use and for agricultural statistics. Crop type information is also useful to parametrize crop growth models for yield estimation, as well as for the retrieval of vegetation biophysical variables using radiative transfer models. The second case study aimed to map seven different deciduous and coniferous tree species in Germany. Detailed information about tree species distribution is important for forest management and to assess potential impacts of climate change. In our S2 data assessment, crop and tree species maps were produced at 10 m spatial resolution by combining the ten S2 spectral channels with 10 and 20 m pixel size. A supervised Random Forest classifier (RF) was deployed and trained with appropriate ground truth. In both case studies, S2 data confirmed its expected capabilities to produce reliable land cover maps. Cross-validated overall accuracies ranged between 65% (tree species) and 76% (crop types). The study confirmed the high value of the red-edge and shortwave infrared (SWIR) bands for vegetation mapping. Also, the blue band was important in both study sites. The S2-bands in the near infrared were amongst the least important channels. The object based image analysis (OBIA) and the classical pixel-based classification achieved comparable results, mainly for the cropland. As only single date acquisitions were available for this study, the full potential of S2 data could not be assessed. In the future, the two twin S2 satellites will offer global coverage every five days and therefore permit to concurrently exploit unprecedented spectral and temporal information with high spatial resolution.

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Section: Random Forest (Rf) Classificationmentioning

confidence: 99%

First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe

2016

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“…Moreover, the crop maps are the foundation for crop modeling, irrigation water distributions and land water management, which are important for decision makers [11][12][13][14][15]. However, most of the previous studies relied on the field reference data in the mapping year to train the classifiers [16,17].…”

Section: Introductionmentioning

confidence: 99%

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

Wang

Zhan

et al. 2016

IJGI

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Most methods used for crop classification rely on the ground-reference data of the same year, which leads to considerable financial and labor cost. In this study, we presented a method that can avoid the requirements of a large number of ground-reference data in the classification year. Firstly, we extracted the Normalized Difference Vegetation Index (NDVI) time series profiles of the dominant crops from MODIS data using the historical ground-reference data in multiple years (2006, 2007, 2009 and 2010). Artificial Antibody Network (ABNet) was then employed to build reference NDVI time series for each crop based on the historical NDVI profiles. Afterwards, images of Landsat and HJ were combined to obtain 30 m image time series with 15-day acquisition frequency in 2011. Next, the reference NDVI time series were transformed to Landsat/HJ NDVI time series using their linear model. Finally, the transformed reference NDVI profiles were used to identify the crop types in 2011 at 30 m spatial resolution. The result showed that the dominant crops could be identified with overall accuracy of 87.13% and 83.48% in Bole and Manas, respectively. In addition, the reference NDVI profiles generated from multiple years could achieve better classification accuracy than that from single year (such as only 2007). This is mainly because the reference knowledge from multiple years contains more growing conditions of the same crop. Generally, this approach showed potential to identify crops without using large number of ground-reference data at 30 m resolution.

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“…It was developed by Food and Agriculture Organization (FAO) (FAO 2009). CROPWAT has been widely used in assessing and predicting CWR, deficit irrigation scheduling, and cropping patterns in many countries of the world (George et al 2000;Nazeer 2009;Wang and Feng 2012;Conrad et al 2013;Chowdhury et al 2013). The results of these studies indicated that the CROPWAT model is a reliable tool in understanding CWR, irrigation planning, and manage irrigation scheduling.…”

Section: Introductionmentioning

confidence: 99%

Modeling water requirements of major crops and their responses to climate change in the North China Plain

2015

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The North China Plain (NCP) is one of the most important food production bases in China. However, its agriculture water resources are threatened by climate change. In this paper, the CROPWAT model is used to evaluate crop water requirement (CWR), crop green water requirement (CGWR), and crop blue water requirement (CBWR) for main crops in NCP (winter wheat, summer maize, cotton, millet, and soybean) with a spatial resolution of 5 arc-minute from 1961 to 2010. Their responses to future climate changes are investigated. The results show that the mean annual total CWR of the main crops during growing periods amounted to 114.68 km 3 a -1 in the past 50 years. More than 72 % of CWR to support NCP crop production is green water. The spatial distributions of CWR, CGWR, and CBWR are closely related to the planting areas and irrigation availability. Summer maize, millet, and soybean are high CGWR crops with proportions of above 84 %, while the lowest CGWR proportion is in winter wheat, 58.89 %. For climate change impacts in future, holding the crop planting system and irrigation conditions unchanged, it is projected that the total CWR in 2030s will require approximately 8.75-11.25 km 3 a -1 additional water. Results show that the CWR increase in 2030s is mainly due to the increase in temperature. Under the projected temperature in 2030s and the current rainfall scenario, total CWR, CGWR, and CBWR increments were 8.58, 1.76, and 6.82 km 3 a -1 , respectively. Nearly 80 % of the CWR increment is from the increase in CBWR. Therefore, agricultural water shortage crisis will further aggravate under future climate change scenarios in NCP, and effective water-saving measures must be taken to mitigate the negative effects of climate change.

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Satellite based calculation of spatially distributed crop water requirements for cotton and wheat cultivation in Fergana Valley, Uzbekistan

Cited by 52 publications

References 38 publications

First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe

First Experience with Sentinel-2 Data for Crop and Tree Species Classifications in Central Europe

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

Modeling water requirements of major crops and their responses to climate change in the North China Plain

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