Your search found 45 records
1 Gao, B. C. 1996. NDWI: A normalized difference water index for remote sensing of vegetation liquid water from space. Remote Sensing of Environment, 58:257-266.
(Location: IWMI-HQ Call no: P 7652 Record No: H039398)
2 Abdi-Kadirovna, I. A. 2007. Detection of spatial distribution of soil salinity using remote sensing and GIS. Thesis submitted to the Sub-Department of Water Resources, Wageningen University, The Netherlands, in partial fulfillment of the degree of Master of Science, under the TEMPUS Project. 39p.
Soil salinity ; Assessment ; Remote sensing ; GIS ; Irrigated farming ; Cotton ; Vegetation index ; Climate / Central Asia / Turkmenistan / Kyrgyzstan / Uzbekistan / Aral Sea Basin
(Location: IWMI HQ Call no: D 631.7.2 G770 ABD Record No: H040660)
3 Gamage, M. S. D. Nilantha; Ahmad, Mobin-ud-Din; Karimi, Poolad. 2007. Estimating cropped area and yield using time series of MODIS imagery based vegetation index in Gamasiab Sub-Basin of Karkheh River Basin, Iran. Sri Lanka Journal of Geo-Informatics, 4: 39-55.
Crop yield ; Wheat ; Vegetation index ; Remote sensing ; Models ; Time series ; River basins / Iran / Karkheh River Basin / Gamasiab Sub Basin
(Location: IWMI HQ Call no: IWMI 631.7.1 G690 GAM Record No: H041435)
4 Biradar, C. M.; Thenkabail, Prasad S.; Platonov, Alexander; Xiao, X.; Geerken, R.; Noojipady, P.; Turral, H.; Vithanage, Jagath. 2008. Water productivity mapping methods using remote sensing. Journal of Applied Remote Sensing, 2(1):023544. 22p. (Published online only)
Water productivity ; Mapping ; Remote sensing ; Vegetation index ; Evapotranspiration ; Wheat ; Rice ; Cotton ; Irrigated farming / Central Asia / Syr Darya River Basin
(Location: IWMI HQ Call no: e-copy only Record No: H041669)
The goal of this paper was to develop methods and protocols for water productivity mapping (WPM) using remote sensing data at multiple resolutions and scales in conjunction with field-plot data. The methods and protocols involved three broad categories: (a) Crop Productivity Mapping (CPM) (kg/m2); (b) Water Use (evapotranspiration) Mapping (WUM)(m3/m2); and (c) Water Productivity Mapping (WPM) (kg/m3). First, the CPMs were determined using remote sensing by: (i) Mapping crop types; (ii) modeling crop yield; and (iii) extrapolating models to larger areas. Second, WUM were derived using the Simplified Surface Energy Balance (SSEB) model. Finally, WPMs were produced by dividing CPMs and WUMs. The paper used data from Quickbird 2.44m, Indian Remote Sensing (IRS) Resoursesat-1 23.5m, Landsat-7 30m, and Moderate Resolution Imaging Spectroradiometer (MODIS) 250m and 500m, to demonstrate the methods for mapping water productivity (WP). In terms of physical water productivity (kilogram of yield produced per unit of water delivered), wheat crop had highest water productivity of 0.60 kg/m3 (WP), followed by rice with 0.5 kg/m3, and cotton with 0.42 kg/m3. In terms of economic value (dollar per unit of water delivered), cotton ranked highest at $ 0.5/m3 followed by wheat with $ 0.33/m3 and rice at $ 0.10/m3. The study successfully delineated the areas of low and high WP. An overwhelming proportion (50+%) of the irrigated areas were under low WP for all crops with nly about 10% area in high WP.
5 Gadisso, B. E. 2007. Drought assessment for the Nile Basin using meteosat second generation data with special emphasis on the Upper Blue Nile region. MSc thesis. Enschede, Netherlands: International Institute for Geo-information Science and Earth Observation (ITC). 76p.
Drought ; Monitoring ; Remote sensing ; Vegetation index ; Water supply ; River basins ; Yields ; Data processing ; Data analysis ; Maps / Ethiopia / Nile River Basin / Upper Blue Nile
(Location: IWMI HQ Call no: 632.12 G136 GAD Record No: H043881)
http://www.itc.nl/library/papers_2007/msc/wrem/gadisso.pdf
https://vlibrary.iwmi.org/pdf/H043881.pdf
https://vlibrary.iwmi.org/pdf/H043881.pdf
(1.78 MB) (1.77MB)
6 Chemin, Yann. 2012. Wavelet-based spatio-temporal fusion of observed rainfall with NDVI in Sri Lanka. Paper presented at the 33rd Asian Conference on Remote Sensing, Pattaya, Thailand, 26-30 November 2012. 11p.
Rain ; Water management ; Time series analysis ; Remote sensing ; Vegetation index ; Meteorological stations / Sri Lanka / Hingurakgoda
(Location: IWMI HQ Call no: e-copy only Record No: H045585)
http://www.academia.edu/1953778/Wavelet-based_spatio-temporal_fusion_of_observed_rainfall_with_NDVI_in_Sri_Lanka
https://vlibrary.iwmi.org/pdf/H045585.pdf
https://vlibrary.iwmi.org/pdf/H045585.pdf
(0.38 MB)
Availability of rainfall time-series is limited in many parts of the World, and the continuity of such records is variable. This research endeavors to extend actual daily rainfall observations to ungauged areas, taking into account events of rainfall as well as cumulative total daily rainfall, over a period of 11 years. Results show that rainfall events histograms can be reconstructed, and that total cumulative rainfall is estimated with 85% accuracy, using a surrounding network of rain gauges at 30-50 Km of distance from the point of study. This research can strengthen various types of research and applications such as ungauged basins research, regional climate modeling, food security early warning systems, agricultural insurance systems, etc.
7 Amarnath, Giriraj; Rajah, Ameer. 2013. Manual of the Training on Flood Inundation Mapping and Modeling: Case Study of Bangladesh, held at the Bangladesh Space Research and Remote Sensing Organization, Dhaka, Bangladesh, 12 - 16 May 2013. Colombo, Sri Lanka: International Water Management Institute (IWMI). 119p.
Training materials ; Remote sensing ; Flooding ; Mapping ; Vegetation index ; Models ; Case studies ; Satellite surveys ; Calibration ; Data analysis ; Surface water ; Land use ; Computer software / Bangladesh
(Location: IWMI HQ Call no: e-copy only Record No: H045843)
(5.14 MB)
8 Khamala, E. 2017. Review of the available remote sensing tools, products, methodologies and data to improve crop production forecasts. Rome, Italy: FAO. 94p.
Remote sensing ; Crop production ; Yield forecasting ; Crop modelling ; Early warning systems ; Drought ; Rain ; Global observing systems ; GIS ; Satellite observation ; Satellite imagery ; Microwave radiation ; Maps ; Statistical data ; Agricultural statistics ; Vegetation index ; Indicators ; National organizations ; Agencies / Africa South of Sahara / Kenya / Senegal / Zimbabwe
(Location: IWMI HQ Call no: e-copy only Record No: H048227)
(1.80 MB) (1.80 MB)
9 Al Zayed, I. S.; Elagib, N. A. 2017. Implications of non-sustainable agricultural water policies for the water-food nexus in large-scale irrigation systems: a remote sensing approach. Advances in Water Resources, 110:408-422. [doi: https://doi.org/10.1016/j.advwatres.2017.07.010]
Water policy ; Sustainable agriculture ; Irrigation systems ; Large scale systems ; Remote sensing ; Satellite imagery ; Rain ; Irrigation water ; Water management ; Food security ; Crop production ; Water use efficiency ; Irrigation efficiency ; Indicators ; Vegetation index ; Evapotranspiration ; Energy balance ; Models ; Monitoring ; Performance evaluation / Sudan / Gezira Irrigation Scheme
(Location: IWMI HQ Call no: e-copy only Record No: H048431)
(5.79 MB)
This study proposes a novel monitoring tool based on Satellite Remote Sensing (SRS) data to examine the status of water distribution and Water Use Efficiency (WUE) under changing water policies in large-scale and complex irrigation schemes. The aim is to improve our understanding of the water-food nexus in such schemes. With a special reference to the Gezira Irrigation Scheme (GeIS) in Sudan during the period 2000–2014, the tool devised herein is well suited for cases where validation data are absent. First, it introduces an index, referred to as the Crop Water Consumption Index (CWCI), to assess the efficiency of water policies. The index is defined as the ratio of actual evapotranspiration (ETa) over agricultural areas to total ETa for the whole scheme where ETa is estimated using the Simplified Surface Energy Balance model (SSEB). Second, the tool uses integrated Normalized Difference Vegetation Index (iNDVI), as a proxy for crop productivity, and ETa to assess the WUE. Third, the tool uses SSEB ETa and NDVI in an attempt to detect wastage of water. Four key results emerged from this research as follows: 1) the WUE has not improved despite the changing agricultural and water policies, 2) the seasonal ETa can be used to detect the drier areas of GeIS, i.e. areas with poor irrigation water supply, 3) the decreasing trends of CWCI, slope of iNDVI-ETa linear regression and iNDVI are indicative of inefficient utilization of irrigation water in the scheme, and 4) it is possible to use SSEB ETa and NDVI to identify channels with spillover problems and detect wastage of rainwater that is not used as a source for irrigation. In conclusion, the innovative tool developed herein has provided important information on the efficiency of a large-scale irrigation scheme to help rationalize laborious water management processes and increase productivity.
10 Nhamo, Luxon; van Dijk, R.; Magidi, J.; Wiberg, David; Tshikolomo, K. 2018. Improving the accuracy of remotely sensed irrigated areas using post-classification enhancement through UAV [Unmanned Aerial Vehicle] capability. Remote Sensing, 10(5):1-12. (Special issue: Remote Sensing for Crop Water Management). [doi: https://doi.org/10.3390/rs10050712]
Irrigated sites ; Remote sensing ; Unmanned aerial vehicles ; Land use mapping ; Land cover mapping ; Satellite imagery ; Landsat ; Farmland ; Vegetation index ; Crops / South Africa / Limpopo Province / Venda / Gazankulu
(Location: IWMI HQ Call no: e-copy only Record No: H048752)
(2.23 MB) (2.23 MB)
Although advances in remote sensing have enhanced mapping and monitoring of irrigated areas, producing accurate cropping information through satellite image classification remains elusive due to the complexity of landscapes, changes in reflectance of different land-covers, the remote sensing data selected, and image processing methods used, among others. This study extracted agricultural fields in the former homelands of Venda and Gazankulu in Limpopo Province, South Africa. Landsat 8 imageries for 2015 were used, applying the maximum likelihood supervised classifier to delineate the agricultural fields. The normalized difference vegetation index (NDVI) applied on Landsat imageries on the mapped fields during the dry season (July to August) was used to identify irrigated areas, because years of satellite data analysis suggest that healthy crop conditions during dry seasons are only possible with irrigation. Ground truth points totaling 137 were collected during fieldwork for pre-processing and accuracy assessment. An accuracy of 96% was achieved on the mapped agricultural fields, yet the irrigated area map produced an initial accuracy of only 71%. This study explains and improves the 29% error margin from the irrigated areas. Accuracy was enhanced through post-classification correction (PCC) using 74 post-classification points randomly selected from the 2015 irrigated area map. High resolution aerial photographs of the 74 sample fields were acquired by an unmanned aerial vehicle (UAV) to give a clearer picture of the irrigated fields. The analysis shows that mapped irrigated fields that presented anomalies included abandoned croplands that had green invasive alien species or abandoned fruit plantations that had high NDVI values. The PCC analysis improved irrigated area mapping accuracy from 71% to 95%.
11 Lloyd, B. J.; Dennison, P. E. 2018. Evaluating the response of conventional and water harvesting farms to environmental variables using remote sensing. Agriculture, Ecosystems and Environment, 262:11-17. [doi: https://doi.org/10.1016/j.agee.2018.04.009]
Water harvesting ; Farming systems ; Conventional farming ; Remote sensing ; Satellite imagery ; Climate change ; Environmental factors ; Vegetation index ; Crop yield ; Models / Africa South of Sahara / Burkina Faso
(Location: IWMI HQ Call no: e-copy only Record No: H048826)
(0.97 MB)
The majority of people in Sub-Saharan Africa (SSA) live in rural communities and practice subsistence farming. Variations in climate and other environmental factors affect the stability of local food production. This instability makes the adoption of efficient farming techniques critical in helping farmers achieve food, income, and livelihood security. Agricultural water conservation techniques called water harvesting are being implemented to increase crop yields in SSA. These techniques have been shown to increase water productivity, nutrients, and organic matter in the soil. This paper uses high-resolution imagery to identify and differentiate between farms using conventional and water-harvesting farm methods. An ordinary least-squares regression model was used to correlate seasonal maximum normalized difference vegetation index (NDVI) values with environmental factors for the different farming methods. The results suggest that water harvesting farm techniques have higher crop yields and are less dependent on precipitation than conventional farming methods. The methodology presented in this paper can be used to map use of water harvesting over large areas and monitor associated differences in productivity.
12 Amarnath, Giriraj; Pani, Peejush; Alahacoon, Niranga; Chockalingam, J.; Mondal, S.; Matheswaran, K.; Sikka, Alok; Rao, K. V.; Smakhtin, Vladimir. 2019. Development of a system for drought monitoring and assessment in South Asia. In Mapedza, Everisto; Tsegai, D.; Bruntrup, M.; McLeman, R. (Eds.). Drought challenges: policy options for developing countries. Amsterdam, Netherlands: Elsevier. pp.133-163. (Current Directions in Water Scarcity Research Volume 2) [doi: https://doi.org/10.1016/B978-0-12-814820-4.00010-9]
Drought ; Monitoring ; Assessment ; Temperature ; Rain ; Precipitation ; Satellite observation ; Weather forecasting ; Land use ; Land cover ; Remote sensing ; Vegetation index ; Agriculture ; Crop yield / South Asia / India / Sri Lanka / Pakistan
(Location: IWMI HQ Call no: IWMI Record No: H049369)
(15.10 MB)
13 Mpandeli, S.; Nhamo, Luxon; Moeletsi, M.; Masupha, T.; Magidi, J.; Tshikolomo, K.; Liphadzi, S.; Naidoo, D.; Mabhaudhi, T. 2019. Assessing climate change and adaptive capacity at local scale using observed and remotely sensed data. Weather and Climate Extremes, 26:100240. [doi: https://doi.org/10.1016/j.wace.2019.100240]
Climate change adaptation ; Assessment ; Remote sensing ; Drought ; Rain ; Temperature ; Water stress ; Resilience ; Risk reduction ; Strategies ; Smallholders ; Farmers ; Agricultural production ; Heat stress ; Vegetation index / South Africa / Limpopo / Capricorn
(Location: IWMI HQ Call no: e-copy only Record No: H049413)
https://www.sciencedirect.com/science/article/pii/S2212094719301380/pdfft?md5=07c6303aa103fe96c44be00ac162f087&pid=1-s2.0-S2212094719301380-main.pdf
https://vlibrary.iwmi.org/pdf/H049413.pdf
https://vlibrary.iwmi.org/pdf/H049413.pdf
(4.02 MB) (4.02 MB)
Climate variability and change impacts are manifesting through declining rainfall totals and increasing frequency and intensity of droughts, floods and heatwaves. These environmental changes are affecting mostly rural populations in developing countries due to low adaptive capacity and high reliance on natural systems for their livelihoods. While broad adaptation strategies exist, there is need to contextualise them to local scale. This paper assessed rainfall, temperature and water stress trends over time in Capricorn District, South Africa, using Standardized Precipitation Index, Thermal Heat Index, and Normalised Difference Vegetation Index (NDVI) as a proxy of water stress. Observed rainfall and temperature data from 1960 to 2015 was used to assess climatic variations, and NDVI was used to assess water stress from 2000 to 2019. Results show a marked increase in drought frequency and intensity, decreasing rainfall totals accompanied by increasing temperatures, and increasing water stress during the summer season. Long-term climatic changes are a basis to develop tailor-made adaptation strategies. Eighty-one percent of the cropped area in Capricorn District is rainfed and under smallholder farming, exposing the district to climate change risks. As the intensity of climate change varies both in space and time, adaptation strategies also vary depending on exposure and intensity. A combination of observed and remotely sensed climatic data is vital in developing tailor-made adaptation strategies.
14 Pocas, I.; Calera, A.; Campos, I.; Cunha, M. 2020. Remote sensing for estimating and mapping single and basal crop coefficientes: a review on spectral vegetation indices approaches. Agricultural Water Management, 233:106081. [doi: https://doi.org/10.1016/j.agwat.2020.106081]
Remote sensing ; Crops ; Water requirements ; Evapotranspiration ; Vegetation index ; Irrigation management ; Soil water balance ; Soil moisture ; Earth observation satellites ; Landsat ; Geographical information systems ; Monitoring ; Water stress ; Mapping ; Models
(Location: IWMI HQ Call no: e-copy only Record No: H049654)
(0.77 MB)
The advances achieved during the last 30 years demonstrate the aptitude of the remote sensing-based vegetation indices (VI) for the assessment of crop evapotranspiration (ETc) and irrigation requirements in a simple, robust and operative manner. The foundation of these methodologies is the well-established relationship between the VIs and the basal crop coefficient (Kcb), resulting from the ability of VIs to measure the radiation absorbed by the vegetation, as the main driver of the evapotranspiration process. In addition, VIs have been related with single crop coefficient (Kc), assuming constant rates of soil evaporation. The direct relationship between VIs and ET is conceptually incorrect due to the effect of the atmospheric demand on this relationship. The rising number of Earth Observation Satellites potentiates a data increase to feed the VI-based methodologies for estimating and mapping either the Kc or Kcb, with improved temporal coverage and spatial resolution. The development of operative platforms, including satellite constellations like Sentinels and drones, usable for the assessment of Kcb through VIs, opens new possibilities and challenges. This work analyzes some of the questions that remain inconclusive at scientific and operational level, including: (i) the diversity of the Kcb-VI relationships defined for different crops, (ii) the integration of Kcb-VI relationships in more complex models such as soil water balance, and (iii) the operational application of Kcb-VI relationships using virtual constellations of space and aerial platforms that allow combining data from two or more sensors.
15 Song, P.; Zheng, X.; Li, Y.; Zhang, K.; Huang, J.; Li, H.; Zhang, H.; Liu, L.; Wei, C.; Mansaray, L. R.; Wang, D.; Wang, X. 2020. Estimating reed loss caused by locusta migratoria manilensis using UAV [Unmanned Aerial Vehicle] -based hyperspectral data. Science of the Total Environment, 719:137519. [doi: https://doi.org/10.1016/j.scitotenv.2020.137519]
Crop losses ; Estimation ; Locusta migratoria ; Unmanned aerial vehicles ; Monitoring ; Forecasting ; Models ; Satellite observation ; Remote sensing ; Vegetation index / China / Kenli / Dongying / Shandong
(Location: IWMI HQ Call no: e-copy only Record No: H049853)
(3.89 MB)
Locusta migratoria manilensis has caused major damage to vegetation and crops. Quantitative evaluation studies of vegetation loss estimation from locust damage have seldom been found in traditional satellite-remote-sensing-based research due to insufficient temporal-spatial resolution available from most current satellite-based observations. We used remote sensing data acquired from an unmanned aerial vehicle (UAV) over a simulated Locusta migratoria manilensis damage experiment on a reed (Phragmites australis) canopy in Kenli District, China during July 2017. The experiment was conducted on 72 reed plots, and included three damage duration treatments with each treatment including six locust density levels. To establish the appropriate loss estimation models after locust damage, a hyperspectral imager was mounted on a UAV to collect reed canopy spectra. Loss components of six vegetation indices (RVI, NDVI, SAVI, MSAVI, GNDVI, and IPVI) and two “red edge” parameters (Dr and SDr) were used for constructing the loss estimation models. Results showed that: (1) Among the six selected vegetation indices, loss components of NDVI, MSAVI, and GNDVI were more sensitive to the variation of dry weight loss of reed green leaves and produced smaller estimation errors during the model test process, with RMSEs ranging from 8.8 to 9.1 g/m;. (2) Corresponding model test results based on loss components of the two selected red edge parameters yielded RMSEs of 27.5 g/m2 and 26.1 g/m2 for Dr and SDr respectively, suggesting an inferior performance of red edge parameters compared with vegetation indices for reed loss estimation. These results demonstrate the great potential of UAV-based loss estimation models for evaluating and quantifying degree of locust damage in an efficient and quantitative manner. The methodology has promise for being transferred to satellite remote sensing data in the future for better monitoring of locust damage of larger geographical areas.
16 Bezerra, F. G. S.; Aguiar, A. P. D.; Alvala, R. C. S.; Giarolla, A.; Bezerra, K. R. A.; Lima, P. V. P. S.; do Nascimentod, F. R.; Arai, E. 2020. Analysis of areas undergoing desertification, using EVI2 [Enhanced Vegetation Index 2] multi-temporal data based on MODIS [Moderate Resolution Imaging Spectroradiometer] imagery as indicator. Ecological Indicators, 117:106579. (Online first) [doi: https://doi.org/10.1016/j.ecolind.2020.106579]
Desertification ; Land degradation ; Satellite imagery ; Remote sensing ; Vegetation Index ; Indicators ; Semiarid zones ; Land use ; Land cover ; Monitoring ; Moderate resolution imaging spectroradiometer / Brazil
(Location: IWMI HQ Call no: e-copy only Record No: H049860)
(9.64 MB)
Desertification is a global problem that impacts a significative part of the Earth's surface, which cause a large environmental and social losses in several regions of the world. The Brazilian semiarid region, located mainly in the northeast part of the country, includes areas of moderate to very high susceptibility to desertification. In order to contribute to a comprehension of the dimensions of desertification in the Brazilian semiarid region, this paper aimed to develop a potential indicator for the evaluation and monitoring of this area, considering an appropriate temporal and spatial scales. For this objective, satellite data were used for the identification and monitoring of sub-areas potentially undergoing degradation/desertification. Thus multitemporal series of Enhanced Vegetation Index 2 (EVI2) covering the period between 2000 and 2016 was used, which were calculated from data provided by the MODIS sensor carried aboard the Terra satellite. Besides, previous samples were selected for the calibration and validation of the methodology. The results show an increase of areas potentially undergoing degradation/desertification, covering an area equal to 167,814.24 km2 at the end of the period analyzed (around 16.7% of the study area). Approximately 23.63% of the total degraded area comprises both the Very High Degradation Trajectory and High Degradation Trajectory. The proposed methodology contributed to the determination of the degree of the degradation through the determination of Degradation Trajectories, which differentiates it from the ones proposed in other studies; however, it is emphasized that this approach must be analyzed in association with additional information, such as trends and climatic scenarios of land use and land cover, as well as retrospective analyses of the landscape, soil erosion, field recognition, socioeconomic conditions, among others.
17 Nhamo, Luxon; Magidi, J.; Nyamugama, A.; Clulow, A. D.; Sibanda, M.; Chimonyo, V. G. P.; Mabhaudhi, T. 2020. Prospects of improving agricultural and water productivity through unmanned aerial vehicles. Agriculture, 10(7):256. [doi: https://doi.org/10.3390/agriculture10070256]
Agricultural productivity ; Water productivity ; Unmanned aerial vehicles ; Water management ; Plant health ; Crop yield ; Monitoring ; Vegetation index ; Remote sensing ; Evapotranspiration ; Water stress ; Irrigation scheduling ; Mapping ; Smallholders ; Farmers ; Models ; Disaster risk reduction ; Resilience ; Satellite imagery ; Cost benefit analysis
(Location: IWMI HQ Call no: e-copy only Record No: H049892)
(1.05 MB) (1.05 MB)
Unmanned Aerial Vehicles (UAVs) are an alternative to costly and time-consuming traditional methods to improve agricultural water management and crop productivity through the acquisition, processing, and analyses of high-resolution spatial and temporal crop data at field scale. UAVs mounted with multispectral and thermal cameras facilitate the monitoring of crops throughout the crop growing cycle, allowing for timely detection and intervention in case of any anomalies. The use of UAVs in smallholder agriculture is poised to ensure food security at household level and improve agricultural water management in developing countries. This review synthesises the use of UAVs in smallholder agriculture in the smallholder agriculture sector in developing countries. The review highlights the role of UAV derived normalised difference vegetation index (NDVI) in assessing crop health, evapotranspiration, water stress and disaster risk reduction. The focus is to provide more accurate statistics on irrigated areas, crop water requirements and to improve water productivity and crop yield. UAVs facilitate access to agro-meteorological information at field scale and in near real-time, important information for irrigation scheduling and other on-field decision-making. The technology improves smallholder agriculture by facilitating access to information on crop biophysical parameters in near real-time for improved preparedness and operational decision-making. Coupled with accurate meteorological data, the technology allows for precise estimations of crop water requirements and crop evapotranspiration at high spatial resolution. Timely access to crop health information helps inform operational decisions at the farm level, and thus, enhancing rural livelihoods and wellbeing.
18 Ouattara, B.; Forkuor, G.; Zoungrana, B. J. B.; Dimobe, K.; Danumah, J.; Saley, B.; Tondoh, J. E. 2020. Crops monitoring and yield estimation using sentinel products in semi-arid smallholder irrigation schemes. International Journal of Remote Sensing, 41(17):6527-6549. [doi: https://doi.org/10.1080/01431161.2020.1739355]
Crop yield ; Monitoring ; Forecasting ; Estimation ; Agricultural productivity ; Irrigation schemes ; Semiarid zones ; Smallholders ; Land use ; Land cover ; Satellite imagery ; Vegetation index ; Tomatoes ; Onions ; Beans / West Africa / Burkina Faso / Lake Bam
(Location: IWMI HQ Call no: e-copy only Record No: H049984)
(3.14 MB)
The use of earth observation data for crop mapping and monitoring in West Africa has concentrated on rainfed systems due to its pre-dominance in the sub-region. However, irrigated systems, though of limited extent, provide critical livelihood support to many. Accurate statistics on irrigated crops are, thus, needed for effective management and decision making. This study explored the use of Sentinel 1 (S-1) and Sentinel 2 (S-2) data to map the extent and yield of irrigated crops in an informal irrigation scheme in Burkina Faso. Random Forest classification and regression were used together with an extensive field data comprising 842 polygons. Four irrigated crops (tomoto, onion, green bean and other) were classified while the yield of tomatoes was modelled through regression analysis. Apart from spectral bands, derivatives (e.g. biophysical parameters and vegetation indices) from S-2 were used. Different data configuration of S-1, S-2 and their derivatives were tested to ascertain optimal temporal windows for accurate irrigated crop mapping and yield estimation. Results of the crop classification revealed a greater overall accuracy (76.3%) for S-2 compared to S-1 (69.4%), with S-2 biophysical parameters (especially the fraction of absorbed photosynthetic active radiation i.e fAPAR) being prominent. For yield prediction, however, S-1 VV polarization came up as the most prominent predictor in the regression analysis (R2adj= 0.63), while the addition of S-2 fAPAR marginally improved the fit (R2adj= 0.64). Tomato yield in the study area was found to range from 1 to 16 kg m-2, although about 83% of the area have yields of less than 10 kg m-2. Our study revealed that early season images (acquired in December) perform better in classifying irrigated crop compared to mid or late season. On the other hand, the use of early to mid-season (December to February) images for yield modelling produced reasonable prediction accuracy. This indicates the possibility of using S-1 and S-2 data to predict crop yield prior to harvest season for efficient planning and food security attainment.
19 Filgueiras, R.; Almeida, T. S.; Mantovani, E. C.; Dias, S. H. B.; Fernandes-Filho, E. I.; da Cunha, F. F.; Venancio, L. P. 2020. Soil water content and actual evapotranspiration predictions using regression algorithms and remote sensing data. Agricultural Water Management, 241:106346. [doi: https://doi.org/10.1016/j.agwat.2020.106346]
Soil water content ; Evapotranspiration ; Forecasting ; Remote sensing ; Irrigation management ; Decision making ; Vegetation index ; Water management ; Regression analysis ; Models ; Moderate resolution imaging spectroradiometer ; Machine learning / Brazil / Bahia
(Location: IWMI HQ Call no: e-copy only Record No: H049989)
(4.55 MB)
The application of technology and the development of data analysis, such as remote sensing and regression algorithms, are an easy and inexpensive way to estimate parameters related to water management, such as actual evapotranspiration (ETa) and soil water content (SWC). Therefore, the objective of this study was to predict the water management parameters with vegetation indices (VIs) and regression algorithms to enable irrigation management in a totally remote manner. The study was carried out in commercial maize areas irrigated by central pivots in the western part of the state of Bahia, Brazil. The MOD09GQ product was used to generate input data for the training models and to understand the phenology variations in the crops. The prediction of the dependent variables was tested using six regression algorithms, and the best algorithm was selected based on five statistical metrics. Among the regression models tested, the three that best fit the ETa and SWC data were RF (random forest), cubist (cubist regression), and GBM (gradient boosting machine), with slight superiority of cubist for the ETa and RF for the SWC. The fitted models for ETa and SWC showed the potential of VIs in providing information for irrigated agriculture and reinforcing the ability of regression algorithms in modelling the SWC and ETa variables. The findings make it possible to monitor irrigation efficiently with only the red and near infrared wavelengths, a fact that is considered the main contribution of this research to the practical and scientific communities.
20 Mwinuka, P. R.; Mbilinyi, B. P.; Mbungu, W. B.; Mourice, S. K.; Mahoo, H. F.; Schmitter, Petra. 2021. The feasibility of hand-held thermal and UAV-based multispectral imaging for canopy water status assessment and yield prediction of irrigated African eggplant (Solanum aethopicum L). Agricultural Water Management, 245:106584. [doi: https://doi.org/10.1016/j.agwat.2020.106584]
Water stress ; Eggplants ; Canopy ; Water requirements ; Crop yield ; Forecasting ; Infrared imagery ; Multispectral imagery ; Unmanned aerial vehicles ; Remote sensing ; Irrigated farming ; Irrigation water ; Performance evaluation ; Moisture content ; Vegetation index ; Plant developmental stages ; Temperature / Africa / United Republic of Tanzania / Rudewa Watershed
(Location: IWMI HQ Call no: e-copy only Record No: H050054)
https://www.sciencedirect.com/science/article/pii/S0378377420321314/pdfft?md5=25877087dd8e72a2377978976c8abc33&pid=1-s2.0-S0378377420321314-main.pdf
https://vlibrary.iwmi.org/pdf/H050054.pdf
https://vlibrary.iwmi.org/pdf/H050054.pdf
(6.03 MB) (6.03 MB)
This study was conducted to evaluate the feasibility of a mobile phone-based thermal and UAV-based multispectral imaging to assess the irrigation performance of African eggplant. The study used a randomized block design (RBD) with sub-plots being irrigated at 100% (I100), 80% (I80) and 60% (I60) of the calculated crop water requirements using drip. The leaf moisture content was monitored at different soil moisture conditions at early, vegetative and full vegetative stages. The results showed that, the crop water stress index (CWSI) derived from the mobile phone-based thermal images is sensitive to leaf moisture content (LMC) in I80 and I60 at all vegetative stages. The UAV-derived Normalized Difference Vegetation Index (NDVI) and Optimized Soil Adjusted Vegetation Index (OSAVI) correlated with LMC at the vegetative and full vegetative stages for all three irrigation treatments. In cases where eggplant is irrigated under normal conditions, the use of NDVI or OSAVI at full vegetative stages will be able to predict eggplant yields. In cases where, eggplant is grown under deficit irrigation, CWSI can be used at vegetative or full vegetative stages next to NDVI or OSAVI depending on available resources.
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