scholarly journals GCI30: a global dataset of 30-m cropping intensity using multisource remote sensing imagery

2021 ◽  
Author(s):  
Miao Zhang ◽  
Bingfang Wu ◽  
Hongwei Zeng ◽  
Guojin He ◽  
Chong Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Surface ◽  
Agricultural Land ◽  
Cropping Systems ◽  
Agricultural System ◽  
Joint Control ◽  
Optical Remote Sensing ◽  
Multiple Cropping ◽  
Efficient Manner ◽  
Cropping Intensity

Abstract. The global distribution of cropping intensity (CI) is essential to our understanding of agricultural land use management on Earth. Optical remote sensing has revolutionized our ability to map CI over large areas in a repeated and cost-efficient manner. Previous studies have mainly focused on investigating the spatiotemporal patterns of CI ranging from regions to the entire globe with the use of coarse-resolution data, which are inadequate for characterizing farming practices within heterogeneous landscapes. To fill this knowledge gap, in this study, we utilized multiple satellite data to develop a global, spatially continuous CI map dataset at 30-m resolution (GCI30). Accuracy assessments indicated that GCI30 exhibited high agreement with visually interpreted validation samples and in situ observations from the PhenoCam network. We carried out both statistical and spatial comparisons of GCI30 with existing global CI estimates. Based on GCI30, we estimated that the global average annual CI during 2016–2018 was 1.05, which is close to the mean (1.04) and median (1.13) CI values of the existing six estimates, although the spatial resolution and temporal coverage vary significantly among products. A spatial comparison with two other satellite based land surface phenology products further suggested that GCI30 was not only capable of capturing the overall pattern of global CI but also provided many spatial details. GCI30 indicated that single cropping was the primary agricultural system on Earth, accounting for 81.57 % (12.28 million km2) of the world’s cropland extent. Multiple-cropping systems, on the other hand, were commonly observed in South America and Asia. We found large variations across countries and agroecological zones, reflecting the joint control of natural and anthropogenic drivers on regulating cropping practices. As the first global coverage, fine-resolution CI product, GCI30 can facilitate ongoing efforts to achieve sustainable development goals (SDGs) by improving food production while minimizing environmental impacts. The data are available on Harvard Dataverse: https://doi.org/10.7910/DVN/86M4PO (Zhang et al, 2020).

scholarly journals GCI30: a global dataset of 30 m cropping intensity using multisource remote sensing imagery

2021 ◽  
Vol 13 (10) ◽  
pp. 4799-4817
Author(s):  
Miao Zhang ◽  
Bingfang Wu ◽  
Hongwei Zeng ◽  
Guojin He ◽  
Chong Liu ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Land Surface ◽  
Agricultural Land ◽  
Agricultural Land Use ◽  
Agricultural System ◽  
Joint Control ◽  
Optical Remote Sensing ◽  
Multiple Cropping ◽  
Cropping Intensity

Abstract. The global distribution of cropping intensity (CI) is essential to our understanding of agricultural land use management on Earth. Optical remote sensing has revolutionized our ability to map CI over large areas in a repeated and cost-efficient manner. Previous studies have mainly focused on investigating the spatiotemporal patterns of CI ranging from regions to the entire globe with the use of coarse-resolution data, which are inadequate for characterizing farming practices within heterogeneous landscapes. To fill this knowledge gap, in this study, we utilized multiple satellite data to develop a global, spatially continuous CI map dataset at 30 m resolution (GCI30). Accuracy assessments indicated that GCI30 exhibited high agreement with visually interpreted validation samples and in situ observations from the PhenoCam network. We carried out both statistical and spatial comparisons of GCI30 with six existing global CI estimates. Based on GCI30, we estimated that the global average annual CI during 2016–2018 was 1.05, which is close to the mean (1.09) and median (1.07) CI values of the existing six global CI estimates, although the spatial resolution and temporal coverage vary significantly among products. A spatial comparison with two satellite-based land surface phenology products further suggested that GCI30 was not only capable of capturing the overall pattern of global CI but also provided many spatial details. GCI30 indicated that single cropping was the primary agricultural system on Earth, accounting for 81.57 % (12.28×106 km2) of the world's cropland extent. Multiple-cropping systems, on the other hand, were commonly observed in South America and Asia. We found large variations across countries and agroecological zones, reflecting the joint control of natural and anthropogenic drivers on regulating cropping practices. As the first global-coverage, fine-resolution CI product, GCI30 is expected to fill the data gap for promoting sustainable agriculture by depicting worldwide diversity of agricultural land use intensity. The GCI30 dataset is available on Harvard Dataverse: https://doi.org/10.7910/DVN/86M4PO (Zhang et al., 2020).

Impact of LULC Changes on LST in Rajshahi District of Bangladesh: A Remote Sensing Approach

2020 ◽  
Vol 3 (1) ◽  
pp. 11-23 ◽  
Author(s):  
Abdulla Al Kafy ◽  
Abdullah Al-Faisal ◽  
Mohammad Mahmudul Hasan ◽  
Md. Soumik Sikdar ◽  
Mohammad Hasib Hasan Khan ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Surface Temperature ◽  
Land Surface ◽  
Agricultural Land ◽  
Global Climate ◽  
Urban Expansion ◽  
Water Bodies ◽  
Landsat 8 ◽  
Lulc Changes ◽  
The Impact

Urbanization has been contributing more in global climate warming, with more than 50% of the population living in cities. Rapid population growth and change in land use / land cover (LULC) are closely linked. The transformation of LULC due to rapid urban expansion significantly affects the functions of biodiversity and ecosystems, as well as local and regional climates. Improper planning and uncontrolled management of LULC changes profoundly contribute to the rise of urban land surface temperature (LST). This study evaluates the impact of LULC changes on LST for 1997, 2007 and 2017 in the Rajshahi district (Bangladesh) using multi-temporal and multi-spectral Landsat 8 OLI and Landsat 5 TM satellite data sets. The analysis of LULC changes exposed a remarkable increase in the built-up areas and a significant decrease in the vegetation and agricultural land. The built-up area was increased almost double in last 20 years in the study area. The distribution of changes in LST shows that built-up areas recorded the highest temperature followed by bare land, vegetation and agricultural land and water bodies. The LULC-LST profiles also revealed the highest temperature in built-up areas and the lowest temperature in water bodies. In the last 20 years, LST was increased about 13ºC. The study demonstrates decrease in vegetation cover and increase in non-evaporating surfaces with significantly increases the surface temperature in the study area. Remote-sensing techniques were found one of the suitable techniques for rapid analysis of urban expansions and to identify the impact of urbanization on LST.

scholarly journals Multiple cropping systems of the world and the potential for increasing cropping intensity

2020 ◽  
Vol 64 ◽  
pp. 102131 ◽  
Author(s):  
Katharina Waha ◽  
Jan Philipp Dietrich ◽  
Felix T. Portmann ◽  
Stefan Siebert ◽  
Philip K. Thornton ◽  
...  
Keyword(s):  
Cropping Systems ◽  
Multiple Cropping ◽  
Cropping Intensity ◽  
The World

scholarly journals Improved representation of agricultural land use and crop management for large-scale hydrological impact simulation in Africa using SWAT+

2022 ◽  
Vol 26 (1) ◽  
pp. 71-89
Author(s):  
Albert Nkwasa ◽  
Celray James Chawanda ◽  
Jonas Jägermeyr ◽  
Ann van Griensven
Keyword(s):  
Remote Sensing ◽  
Management Practices ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Hydrological Models ◽  
Multiple Cropping ◽  
Crop Phenology ◽  
Cropping Season ◽  
Cropping Seasons ◽  
Global Datasets

Abstract. To date, most regional and global hydrological models either ignore the representation of cropland or consider crop cultivation in a simplistic way or in abstract terms without any management practices. Yet, the water balance of cultivated areas is strongly influenced by applied management practices (e.g. planting, irrigation, fertilization, and harvesting). The SWAT+ (Soil and Water Assessment Tool) model represents agricultural land by default in a generic way, where the start of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and subtropical regions such as sub-Saharan Africa, where crop growth dynamics are mainly controlled by rainfall rather than temperature. In this study, we present an approach on how to incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a regional SWAT+ model for northeastern Africa. We evaluate the influence of the crop phenology representation on simulations of leaf area index (LAI) and evapotranspiration (ET) using LAI remote sensing data from Copernicus Global Land Service (CGLS) and WaPOR (Water Productivity through Open access of Remotely sensed derived data) ET data, respectively. Results show that a representation of crop phenology using global datasets leads to improved temporal patterns of LAI and ET simulations, especially for regions with a single cropping cycle. However, for regions with multiple cropping seasons, global phenology datasets need to be complemented with local data or remote sensing data to capture additional cropping seasons. In addition, the improvement of the cropping season also helps to improve soil erosion estimates, as the timing of crop cover controls erosion rates in the model. With more realistic growing seasons, soil erosion is largely reduced for most agricultural hydrologic response units (HRUs), which can be considered as a move towards substantial improvements over previous estimates. We conclude that regional and global hydrological models can benefit from improved representations of crop phenology and the associated management practices. Future work regarding the incorporation of multiple cropping seasons in global phenology data is needed to better represent cropping cycles in areas where they occur using regional to global hydrological models.

scholarly journals FIRST RESULTS OF THE LEM BENCHMARK DATABASE FOR AGRICULTURAL APPLICATIONS

2020 ◽  
Vol XLIII-B5-2020 ◽  
pp. 251-256
Author(s):  
I. D. Sanches ◽  
R. Q. Feitosa ◽  
B. Montibeller ◽  
P. M. Achanccaray Diaz ◽  
A. J. B. Luiz ◽  
...  
Keyword(s):  
Remote Sensing ◽  
Management Practices ◽  
Cropping Systems ◽  
Remote Sensing Data ◽  
Landsat 8 ◽  
Multiple Cropping ◽  
Tropical Regions ◽  
Benchmark Database ◽  
First Results ◽  
Agricultural Applications

Abstract. Applying remote sensing technology to map and monitor agriculture and its impacts can greatly contribute for the proper development of this activity, promoting efficient food, fiber and energy production. For that, not only remote sensing images are needed, but also ground truth information, which is a key factor for the development and improvement of methodologies using remote sensing data. While a variety of images are current available, inclusive cost-free images, field reference data is scarcer. For agricultural applications, especially in tropical regions such as Brazil, where the agriculture is very dynamic and diverse (recent agricultural frontiers, crop rotations, multiple cropping systems, several management practices, etc.), and cultivated over a vast territory, this task is not trivial. One way of boosting the researches in agricultural remote sensing is to stimulate people to share their data, and to foster different groups to use the same dataset, so distinct methods can be properly compared. In this context, our group created the LEM Benchmark Database (a project funded by the ISPRS Scientific Initiative project - 2017) from the Luiz Eduardo Magalhães (LEM) municipality, Bahia State, Brazil. The database contains a set of pre-processed multitemporal satellite images (Landsat-8/OLI, Sentinel-2/MSI and SAR band-C Sentinel-1) and shapefiles of agricultural fields with their correspondent monthly land use classes, covering the period of one Brazilian crop year (2017–2018). In this paper we present the first results obtained with this database.

scholarly journals Pemetaan Wilayah Lahan Basah Berdasarkan Data Penginderaan Jauh Optik Dan Radar

2020 ◽  
Vol 4 (2) ◽  
pp. 48-61
Author(s):  
Rian Nurtyawan ◽  
Ervan Muktamar Hendarna
Keyword(s):  
Remote Sensing ◽  
Soil Moisture ◽  
Surface Temperature ◽  
Land Surface Temperature ◽  
Land Surface ◽  
Satellite Image ◽  
Soil Surface ◽  
Landsat 8 ◽  
Optical Remote Sensing ◽  
Radar Sensing

ABSTRAKPada umumnya lahan basah dikelola menjadi area pertanian ataupun perkebunan. Fungsi lahan basah memiliki fungsi ekologis seperti pengendali banjir, pencegah intrusi air laut, erosi, pencemaran, dan pengendali iklim global. Data pengindraan jauh yang digunakan pengelolaan lahan basah yaitu pengindraan jauh optik dan radar. Tujuan dari penelitian ini adalah mengeksplorasi korelasi potensial dari data optik dan radar untuk mengamati dinamika pada kawasan lahan basah tersebut dan melakukan pemetaan. Metode yang digunakan pada pengindraan jauh optik yaitu LST (Land Surface Temperature) berdasarkan Citra Satelit Landsat-8 dan metode yang digunakan pada pengindraan jauh radar yaitu estimasi kelembaban tanah berdasarkan Citra Satelit Sentinel-1A. Hasil pengamatan dinamika dan pemetaan pada wilayah Kabupaten Bandung Raya memiliki nilai kelembaban tanah tertinggi pada Bulan Mei dengan nilai kelembapan tanah tanah rata-rata sebesar 20,9 % pada polarisasi VH. Suhu permukaan tanah terendah terjadi pada bulan Mei dengan nilai suhu rata-rata sebesar 19.5 °C. Kolerasi antara nilai kelembapan tanah tanah dan suhu permukaan tanah pada wilayah Kabupaten Bandung Raya berdasarkan metode koefisien determinasi sebesar R2=0.705 didapatkan bahwa semakin tinggi nilai kelembapan tanah tanah maka nilai suhu permukaan tanah akan semakin rendah.Kata kunci: Kawasan lahan basah, Pengindraan Jauh Optik, Pengindraan Jauh Radar, Pengamatan Dinamika, Pemetaan. ABSTRACTIn general wetlands managed become an area of agriculture or plantations. The extent of wetland that has been used can be damaged if it is not managed properly and integrated.. The purpose of this research is to explore the potential correlations between several parameters of optical and radar data to observe the dynamics of wetlands area and mapping the wetlands area. The methodology that was used in optical remote sensing is LST (Land Surface Temperature) based on Landsat-8 Satellite Image and the method used in remote radar sensing is estimation of soil moisture based on Sentinel-1A Satellite Image. The result of the observation in the area and mapping the dynamics in Bandung Raya District had the highest soil moisture values in May with 27% of soil water level in VH polarization and 78.1% in VV polarization and the lowest value in each month is 11.8% and the highest soil surface temperature in August with a value 37.9 ° C and the minimum value 19 ° C..Keywords: Wetland Area, Optical Remote Sensing, Remote Radar Sensing, Dynamics Observation, Mapping.

scholarly journals Identification and Classification of Complex Agricultural Croplands Using Multi-Temporal ALOS-2/PALSAR-2 Data: A Case Study in Central Java, Indonesia

2018 ◽  
Vol 10 (2) ◽  
pp. 58
Author(s):  
Prima Rizky Mirelva ◽  
Ryota Nagasawa
Keyword(s):  
Remote Sensing ◽  
Paddy Field ◽  
Agricultural Land ◽  
National Income ◽  
Landsat 8 ◽  
Observation Data ◽  
Optical Remote Sensing ◽  
Cropping Pattern ◽  
Central Java ◽  
Multi Temporal

The agriculture sector makes a significant contribution to the Indonesian economy and has become one of the sources of national income. Therefore, precise agricultural mapping is very important to national and regional administrations. Satellite remote sensing provides the most effective tool for identifying a wide expanse of agriculture croplands. However, cloud coverage in tropical regions limits the use of optical remote sensing. SAR is an active remote sensing technique, which offers completely cloud-free observation data. The multi-temporal ALOS-2/PALSAR-2 data were used in this study, complemented by optical multi-temporal remote sensing data, that is, Landsat 8 OLI for classifying complex agricultural croplands. The study area, located in the Klaten Regency, Central Java Province, with 112 km2 coverage, was selected because of its dynamic cropping pattern and complex agricultural land use types. In this study, the RGB composite of HH, HV and HV-HH, derived from ALOS-2/PALSAR-2 polarizations, was found to be effective at separating two types of paddy field cropping pattern: all-year paddy (paddy-I) and paddy upland fields (paddy-II). The multi-temporal Landsat 8 data were also found to be useful for observing the cropping pattern. Moreover, the classification accuracy, which was as high as 85.02% in terms of overall accuracy, with a kappa coefficient of 0.824, from multi-temporal ALOS-2/PALSAR-2 data, was obtained. These results show that multi-temporal ALOS-2/PALSAR-2 data are capable of discriminating between two different paddy field cropping types, as well as beneficial for discriminating between the cropping stage and cropping pattern information for several other land uses.

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