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 Improved Representation of Agricultural Land Use and Crop Management for Large Scale Hydrological Impact Simulation in Africa using SWAT+

2021 ◽  
Author(s):  
Albert Nkwasa ◽  
Celray James Chawanda ◽  
Jonas Jägermeyr ◽  
Ann van Griensven
Keyword(s):  
Management Practices ◽  
Agricultural Land ◽  
Swat Model ◽  
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, harvesting). The SWAT+ 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 sub-tropical regions such as the 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 Northeast 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 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 regional to global hydrological models.

Representation of crop phenology and associated management practices in the SWAT+ model using global datasets for large scale hydrological applications

2021 ◽  
Author(s):  
Albert Nkwasa ◽  
Celray James Chawanda ◽  
Ann van Griensven ◽  
Jonas Jägermeyr
Keyword(s):  
Remote Sensing ◽  
Management Practices ◽  
Swat Model ◽  
Remote Sensing Data ◽  
Hydrological Models ◽  
Multiple Cropping ◽  
Crop Phenology ◽  
Sensing Data ◽  
Cropping Seasons ◽  
Global Datasets

<p>Even though cropland cultivation covers over 40% of the planet’s ice free land surface, 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, harvesting). For instance, the SWAT+ model represents agricultural land by default in a generic way where the timing of the cropping season is driven by accumulated heat units. However, this approach does not work for tropical and sub-tropical regions such as the sub-Saharan Africa where crop growth dynamics are mainly controlled by rainfall rather than temperature.</p><p>In this study, we present an approach on how to reasonably incorporate crop phenology using decision tables and global datasets of rainfed and irrigated croplands with the associated cropping calendar and fertilizer applications in a SWAT+ model for North Eastern 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 derived from Proba-V satellite and WaPOR 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. 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 global hydrological models.</p>

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.

Study of Ancient Land Boundaries at Tauric Chersonesos Using Satellite Images

2019 ◽  
Vol 25 (1) ◽  
pp. 44-58 ◽  
Author(s):  
Edgar A. Terekhin ◽  
Tatiana N. Smekalova
Keyword(s):  
Remote Sensing ◽  
Information Systems ◽  
Geographic Information Systems ◽  
Satellite Images ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Field Surveys ◽  
The 1960S ◽  
Better Than ◽  
Made In

Abstract The near chora (agricultural land) of Tauric Chersonesos was investigated using multiyear remote sensing data and field surveys. The boundaries of the land plots were studied with GIS (Geographic Information Systems) technology and an analysis of satellite images. Reliable reconstruction of the borders has been done for 231 plots (from a total of about 380), which is approximately 53% of the Chersonesean chora. During the last 50 years, most of the ancient land plots have been destroyed by modern buildings, roads, or forests. However, in the 1960s, a significant part of the chora was still preserved. Changes in preservation with time were studied with the aid of satellite images that were made in 1966 and 2015. During that period, it was found that the number of plots with almost-complete preservation decreased from 47 to 0. Those land plots whose preservation was better than 50% dropped from 104 to 4. A temporal map shows this decline in preservation. It was found that the areas of land plots could be determined accurately with satellite images; compared to field surveys, this accuracy was about 99%.

scholarly journals REMOTE SENSING AND GIS FOR LAND COVER CLASSIFICATION AND CHANGE DETECTION IN GAZIPUR SADAR, BANGLADESH DURING 1973-2017

2018 ◽  
Vol 5 (2) ◽  
pp. 215
Author(s):  
Md Arafat Hassan ◽  
Rakibul Islam ◽  
Rehnuma Mahjabin
Keyword(s):  
Remote Sensing ◽  
Supervised Classification ◽  
Agricultural Land ◽  
Satellite Image ◽  
Remote Sensing Data ◽  
Remote Sensing And Gis ◽  
Vegetation Coverage ◽  
Accurate Information ◽  
Sal Forest ◽  
Shed Light

This paper has been developed to capture the land coverage change in Gazipur Sadar Upazila with the help of remote sensing data of 44 years from 1973 to 2017. After acquiring the study area image of 1973, 1991, 2006 and 2017 supervised classification method has been used to get the accurate information from the satellite image and the whole outcome has been transformed into measurable unit (sq km) and graphs. The accuracy of land coverage was ranged from 85% to 89%. The outcome says that the acceleration of economic growth and pressure of huge population took a heavy toll on the vegetation coverage which decreased -199.7%. People are destroying vegetation coverage for building up settlements and infrastructure. In the year 2017, the map shows that the built-up area increased 312.9% for industry, settlement and agricultural purpose. Moreover agricultural land also drops down from 42% to 32%.  The rapid rate of decreasing vegetation coverage and small amount of existing vegetation coverage only 57 sq km (in 2017) is a red alert for the region. The Sal forest and other special flora species of that region is valuable resource for environment. This paper shed light on the fact that it is urgent to protect vegetation coverage so it will help the authority to make good policies and use other techniques to save vegetation coverage.

scholarly journals Geoecological assessment of the state of postagrogenic lands of the Moscow village soviet council of the Republic of Khakassia on the basis of remote sensing data

2020 ◽  
Vol 149 ◽  
pp. 03006 ◽  
Author(s):  
Ekaterina V. Pavlova ◽  
Anastasiia I. Volkova ◽  
Ekaterina A. Demina
Keyword(s):  
Remote Sensing ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Point Of View ◽  
Economic Point ◽  
Fallow Lands ◽  
Village Council ◽  
Qualitative State ◽  
The Republic ◽  
Map Scale

Currently, the consequences which take place in Khakassia expansion of tree-shrub vegetation on fallow lands have not been properly assessed neither from an ecological nor economic point of view. Based on the analysis of the agricultural map scale 1: 100 000 decoding images Landsat 4–5, 7, 8 and Sentinel 1, and 2, as well as subsatellite ground researches were carried out the identification, the description and assessment of the qualitative state of postagrogenic lands of Khakassia exposed to the processes of overgrowth of tree-shrub vegetation. As an example, this article analyzes the processes of overgrowth of agricultural land on the example of the territory of the Moscow village council of Ust-Abakan district. A geoinformation project of spatial distribution of postagrogenic lands within the Moscow village council of Ust-Abakan district of Khakassia was developed. The results of the research showed that in the studied area in the structure of agricultural land 67204 hectares of land belongs to the fallows located at different stages of recovery of which 77 % exposed to overgrowth processes. The obtained data indicate the need for the formation of management decisions in the field of land use.

scholarly journals A Review of the Application of Remote Sensing Data for Abandoned Agricultural Land Identification with Focus on Central and Eastern Europe

2019 ◽  
Vol 11 (23) ◽  
pp. 2759 ◽  
Author(s):  
Tomáš Goga ◽  
Ján Feranec ◽  
Tomáš Bucha ◽  
Miloš Rusnák ◽  
Ivan Sačkov ◽  
...  
Keyword(s):  
Remote Sensing ◽  
World Literature ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Field Research ◽  
Radar Data ◽  
Expert Assessment ◽  
Biomass Estimation ◽  
Agricultural Abandonment ◽  
Expert Analysis

This study aims to analyze and assess studies published from 1992 to 2019 and listed in the Web of Science (WOS) and Current Contents (CC) databases, and to identify agricultural abandonment by application of remote sensing (RS) optical and microwave data. We selected 73 studies by applying structured queries in a field tag form and Boolean operators in the WOS portal and by expert analysis. An expert assessment yielded the topical picture concerning the definitions and criteria for the identification of abandoned agricultural land (AAL). The analysis also showed the absence of similar field research, which serves not only for validation, but also for understanding the process of agricultural abandonment. The benefit of the fusion of optical and radar data, which supports the application of Sentinel-1 and Sentinel-2 data, is also evident. Knowledge attained from the literary sources indicated that there exists, in the world literature, a well-covered problem of abandonment identification or biomass estimation, as well as missing works dealing with the assessment of the natural accretion of biomass in AAL.

scholarly journals DISTRIBUTION OF RESTORATION SUCCESSIONS IN TAIGA AND FOREST-STEPPE LANDSCAPES OF THE PERMSKY PRIKAMYE (ON THE EXAMPLE OF AGRICULTURAL LANDS)

2021 ◽  
Vol 31 (4) ◽  
pp. 416-424
Author(s):  
A.P. Belousova ◽  
N.N. Nazarov
Keyword(s):  
Remote Sensing ◽  
Soil Fertility ◽  
Forest Cover ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Forest Steppe ◽  
Landscape Changes ◽  
Agricultural Lands ◽  
Natural Factors ◽  
Agricultural Use

The research of forest cover development on agricultural lands in the Perm Prikamye was carried the example of taiga and forest-steppe types of landscapes. The Babkinsko-Yugovskoy and Irensko-Kungursky landscapes were select for research. Received information about the geosystem condition in different years using remote sensing data. All landscape changes were record during the formed stable snow cover. As a result, was divide into two classes - forested and treeless areas. Established, the main natural factors of land differentiation by an areas and a pace of withdrawal from agricultural use are the small contours of agricultural land and differences in soil fertility. The growth pace of forest geosystems within the forest-steppe landscape was 2.5 times higher than of the taiga. The research of the dynamics of forest cover showed that in the Perm Prikamye in the forest-steppe landscape substitution of anthropogenic geosystems with natural-anthropogenic ("wild") accompanied by the development of forest biogeocenosis, not steppe.

scholarly journals Assessment of Change in the Built-Up Index of Uyo Metropolis and Its Environs Using Remote Sensing

2019 ◽  
Vol 4 (6) ◽  
pp. 84-89 ◽  
Author(s):  
Aniekan Effiong Eyoh ◽  
Akwaowo Ekpa
Keyword(s):  
Remote Sensing ◽  
Land Use ◽  
Quantitative Analysis ◽  
Land Cover ◽  
Agricultural Land ◽  
Remote Sensing Data ◽  
Land Use Land Cover ◽  
Positive Growth ◽  
Bare Land ◽  
Erdas Imagine

The research was aim at assessing the change in the Built-up Index of Uyo metropolis and its environs from 1986 to 2018, using remote sensing data. To achieve this, a quantitative analysis of changes in land use/land cover within the study area was undertaken using remote sensing dataset of Landsat TM, ETM+ and OLI sensor images of 1986, 2000 and 2018 respectively. Supervised classification, using the maximum likelihood algorithm, was used to classify the study area into four major land use/land cover types; built-up land, bare land/agricultural land, primary swamp vegetation and secondary vegetation. Image processing was carried out using ERDAS IMAGINE and ArcGIS software. The Normalised Difference Built-up Index (NDBI) was calculated to obtain the built-up index for the study area in 1986, 2000 and 2018 as -0.20 to +0.45, -0.13 to +0.55 and -0.19 to +0.63 respectively. The result of the quantitative analysis of changes in land use/land cover indicated that Built-up Land had been on a constant and steady positive growth from 6.76% in 1986 to 11.29% in 2000 and 44.04% in 2018.

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