Your search found 41 records
1 Tarpanelli, A.; Amarnath, Giriraj; Brocca, L.; Massari, C.; Moramarco, T. 2017. Discharge estimation and forecasting by MODIS and altimetry data in Niger-Benue River. Remote Sensing of Environment, 195:96-106. [doi: https://doi.org/10.1016/j.rse.2017.04.015]
Weather forecasting ; Flooding ; Rivers ; Discharges ; Estimation ; Satellite imagery ; Moderate Resolution Imaging Spectroradiometer ; Models ; Radar ; Remote sensing ; Water levels ; Downstream / Nigeria / Niger River / Benue River / Lokoja / Makurdi
(Location: IWMI HQ Call no: e-copy only Record No: H048996)
(3.09 MB)
Flooding is one of the most devastating natural hazards in the world and its forecast is essential in flood risk reduction and disaster response decision. The lack of adequate monitoring networks, especially in developing countries prevents near real-time flood prediction that could help to reduce the loss of lives and economic damages. In the last few years, increasing availability of multi-satellite sensors induced to develop new techniques for retrieving river discharge and especially in supporting discharge nowcasting and forecasting activities. Recently, the potential of radar altimetry to estimate water levels and discharge in ungauged river sites with good accuracy has been demonstrated. However, the considerable benefit derived from this technique is attenuated by the low revisit time of the satellite (10 or 35 days, depending on the satellite mission) causing delays on the predicting operations. For this reason, sensors with a higher temporal resolution such as the MODerate resolution Imaging Spectroradiometer (MODIS), working in visible/Infra-Red bands, can support flood forecasting.
In this study, we performed the forecast of river discharge by using MODIS and we compared it with the radar altimetry and in-situ data along the Niger-Benue River in Nigeria to develop an operational flood forecasting scheme that could help in rapid emergency response and decision making processes. In the first step, four MODIS products (daily and, 8-day from the TERRA and AQUA satellites) at two gauged sites were used for discharge estimation. Secondly, the capability of remote sensing sensors to forecast discharge a few days (~4 days) in advance at a downstream section using MODIS is analyzed and also compared with the one obtained by the use of radar altimetry by ENVISAT and Jason-2.
The results confirmed the capability of the MODIS data to estimate river discharge with performance indices N0.97 and 0.95 in terms of coefficient of correlation and Nash Sutcliffe efficiency. In particular, RMSE does not exceed 1300 m3 /s and the fractional RMSE ranges between 0.15 and 0.23. For the forecasting exercise, both altimetry and MODIS provide satisfactory results with positive coefficient of persistence considering 4 days of lead time (N0.34). Although altimetry was found to be more accurate in the forecasting of river discharge (RMSE ~350 m3 /s), the much higher temporal resolution of MODIS guarantees a continuity that is more suitable to address operational activities.
In this study, we performed the forecast of river discharge by using MODIS and we compared it with the radar altimetry and in-situ data along the Niger-Benue River in Nigeria to develop an operational flood forecasting scheme that could help in rapid emergency response and decision making processes. In the first step, four MODIS products (daily and, 8-day from the TERRA and AQUA satellites) at two gauged sites were used for discharge estimation. Secondly, the capability of remote sensing sensors to forecast discharge a few days (~4 days) in advance at a downstream section using MODIS is analyzed and also compared with the one obtained by the use of radar altimetry by ENVISAT and Jason-2.
The results confirmed the capability of the MODIS data to estimate river discharge with performance indices N0.97 and 0.95 in terms of coefficient of correlation and Nash Sutcliffe efficiency. In particular, RMSE does not exceed 1300 m3 /s and the fractional RMSE ranges between 0.15 and 0.23. For the forecasting exercise, both altimetry and MODIS provide satisfactory results with positive coefficient of persistence considering 4 days of lead time (N0.34). Although altimetry was found to be more accurate in the forecasting of river discharge (RMSE ~350 m3 /s), the much higher temporal resolution of MODIS guarantees a continuity that is more suitable to address operational activities.
2 Tarpanelli, A.; Santi, E.; Tourian, M. J.; Filippucci, P.; Amarnath, Giriraj; Brocca, L. 2019. Daily river discharge estimates by merging satellite optical sensors and radar altimetry through artificial neural network. IEEE Transactions on Geoscience and Remote Sensing, 57(1):329-341. [doi: https://doi.org/10.1109/TGRS.2018.2854625]
Rivers ; Discharges ; Estimation ; Water levels ; Remote sensing ; Satellite imagery ; Landsat ; Moderate Resolution Imaging Spectroradiometer ; Neural networks ; Radar ; Performance indexes ; Time series analysis ; Case studies / Nigeria / Italy / Niger River / Benue River / Po River / Lokoja / Pontelagoscuro
(Location: IWMI HQ Call no: e-copy only Record No: H048997)
(2.81 MB)
Thanks to the large number of satellites, the multimission approach is becoming a viable method to integrate measurements and intensify the number of samples in space and time for monitoring the earth system. In this paper, we merged data from different satellite missions, optical sensors, and altimetry, for estimating daily river discharge through the application of the artificial neural network (ANN) technique. ANN was selected among other retrieval techniques because it offers an easy but effective way of combining input data from different sources into the same retrieval algorithm. The network is trained in a calibration period and validated in an independent period against in situ observations of river discharge for two gauging sites: Lokoja along the Niger River and Pontelagoscuro along the Po River. For optical sensors, we found that the temporal resolution is more important than the spatial resolution for obtaining accurate discharge estimates. Our results show that Landsat fails in the estimation of extreme events by missing most of the peak values due to its long revisit time (14–16 days). Better performances are obtained from Moderate Resolution Imaging Spectroradiometer (MODIS) and Medium Resolution Imaging Spectrometer. Radar altimetry provides results in between MODIS-TERRA and MODIS-AQUA at Lokoja, whereas it outperforms all single optical sensors at Pontelagoscuro. The multimission approach, involving optical sensors and altimetry, is found the most reliable tool to estimate river discharge with a relative root-mean-square error of 0.12% and 0.27% and Nash-Sutcliffe coefficient of 0.98 and 0.83 for the Niger and Po rivers, respectively.
3 Busetto, L.; Zwart, S. J.; Boschetti, M. 2019. Analysing spatial-temporal changes in rice cultivation practices in the Senegal River Valley using MODIS time-series and the phenorice algorithm. International Journal of Applied Earth Observation and Geoinformation, 75:15-28. [doi: https://doi.org/10.1016/j.jag.2018.09.016]
Agricultural practices ; Rice ; Intensive cropping ; Time series analysis ; Satellite observation ; Monitoring ; Rivers ; Irrigated farming ; Estimation ; Phenology ; Moderate resolution imaging spectroradiometer / West Africa / Senegal River Valley
(Location: IWMI HQ Call no: e-copy only Record No: H049456)
(4.87 MB)
In this study we used the PhenoRice algorithm to track recent variations of rice cultivation practices along the Senegal River Valley. Time series of MODIS imagery with 250 m spatial resolution and a nominal 8-days frequency were used as input for the algorithm to map the spatial and temporal variations of rice cultivated area and of several important phenological metrics (e.g., crop establishment and harvesting dates, length of season) for the 2003–2016 period in both the dry and the wet rice cultivation seasons. Comparison between PhenoRice results and ancillary and field data available for the Senegal part of the study area showed that the algorithm is able to track the interannual variations of rice cultivated area, despite the total detected rice area being consistently underestimated. PhenoRice estimates of crop establishment and harvesting dates resulted accurate when compared with field observations available for two sub-regions for a period of 10 years, and thus allow assessing interannual variability and tracking changes in agronomic practices. An analysis of interannual trends of rice growing practices based on PhenoRice results highlighted a clear shift of rice cultivation from the wet to the dry season starting approximately from 2008. The shift was found to be particularly evident in the delta part of the SRV. Additionally, a statistically significant trend was revealed starting 2006 towards a longer dry season (r2 = 0.81; Slope = 1.24 days y-1) and a shorter wet season (r2 = 0.65; Slope = 0.53 days y-1). These findings are in agreement with expert knowledge of changes ongoing in the area. In particular the shorter wet season is attributed to shortage of labor and equipment leading to a delay in completion of harvesting operations in the dry season, which led to the adoption of short-duration rice varieties by farmers in the wet season to avoid risk of yield losses due to climatic constraints. Aforementioned results highlight the usefulness of the PhenoRice algorithm for providing insights about recent variations in rice cultivation practices over large areas in developing countries, where high-quality up to date information about changes in agricultural practices are often lacking.
4 Senay, G. B.; Kagone, S.; Velpuri, Naga M. 2020. Operational global actual evapotranspiration: development, evaluation, and dissemination. Sensors, 20(7):1915. (Special issue: Advances in Remote Sensors for Earth Observation and Modeling of Earth Processes) [doi: https://doi.org/10.3390/s20071915]
Evapotranspiration ; Evaluation ; Water balance ; Energy balance ; Drought ; Monitoring ; Models ; Moderate Resolution Imaging Spectroradiometer ; Remote sensing ; Satellite observation ; River basins ; Precipitation ; Estimation ; Land cover
(Location: IWMI HQ Call no: e-copy only Record No: H049657)
(3.92 MB) (3.92 MB)
Satellite-based actual evapotranspiration (ETa) is becoming increasingly reliable and available for various water management and agricultural applications from water budget studies to crop performance monitoring. The Operational Simplified Surface Energy Balance (SSEBop) model is currently used by the US Geological Survey (USGS) Famine Early Warning System Network (FEWS NET) to routinely produce and post multitemporal ETa and ETa anomalies online for drought monitoring and early warning purposes. Implementation of the global SSEBop using the Aqua satellite’s Moderate Resolution Imaging Spectroradiometer (MODIS) land surface temperature and global gridded weather datasets is presented. Evaluation of the SSEBop ETa data using 12 eddy covariance (EC) flux tower sites over six continents indicated reasonable performance in capturing seasonality with a correlation coefficient up to 0.87. However, the modeled ETa seemed to show regional biases whose natures and magnitudes require a comprehensive investigation using complete water budgets and more quality-controlled EC station datasets. While the absolute magnitude of SSEBop ETa would require a one-time bias correction for use in water budget studies to address local or regional conditions, the ETa anomalies can be used without further modifications for drought monitoring. All ETa products are freely available for download from the USGS FEWS NET website.
5 Wang, R.; Liu, Y. 2020. Recent declines in global water vapor from MODIS products: artifact or real trend? Remote Sensing of Environment, 247:111896. (Online first) [doi: https://doi.org/10.1016/j.rse.2020.111896]
Water vapour ; Moderate Resolution Imaging Spectroradiometer ; Models ; Evaluation ; Remote sensing ; Satellites ; Climate change ; Trends ; Observation
(Location: IWMI HQ Call no: e-copy only Record No: H049758)
(6.62 MB)
Atmospheric water vapor plays a key role in the global water and energy cycles. Accurate estimation of water vapor and consistent representation of its spatial-temporal variation are critical to climate analysis and model validation. This study used ground observational data from global radiosonde and sunphotometer networks to evaluate MODIS (MODerate-resolution Imaging Spectroradiometer) precipitable water vapor (PWV) products for 2000–2017. The products included the thermal-infrared (TIR) (Collection 6, C006) and its updated version (Collection 061, C061), and near-infrared (NIR) products (C061). Our results demonstrated that compared to its earlier version subject to sensor crosstalk problem, the C061_TIR data showed improved accuracy in terms of bias, standard deviation, mean absolute error, root mean square error, and coefficient of determination, regression slope and intercept. Among the PWV products, C061_NIR data achieved the best overall performance in accuracy evaluation. The C061_NIR revealed the PWV had a multi-year average of 2.50 ± 0.08 cm for the globe, 2.03 ± 0.06 cm for continents, and 2.70 ± 0.09 cm for oceans in 2000–2017. The PWV values yielded an increasing rate of 0.015 cm/year for the globe, 0.010 cm/year for continents, and 0.017 cm/year for oceans. Nearly 98.95% of the globe showed an increasing trend, 80.74% of statistical significance, mainly distributed within and around the tropical zones. The findings should be valuable for understanding of global water and energy cycles.
6 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.
7 Zhao, W.; Duan, S.-B. 2020. Reconstruction of daytime land surface temperatures under cloud-covered conditions using integrated MODIS [Moderate Resolution Imaging Spectroradiometer]/terra land products and MSG [Meteosat Second Generation] geostationary satellite data. Remote Sensing of Environment, 247:111931. (Online first) [doi: https://doi.org/10.1016/j.rse.2020.111931]
Land cover ; Air temperature ; Satellite observation ; Geostationary satellite ; Moderate resolution imaging spectroradiometer ; Clouds ; Solar radiation ; Vegetation ; Regression analysis ; Models / Europe
(Location: IWMI HQ Call no: e-copy only Record No: H049904)
(5.29 MB)
There is considerable demand for satellite observations that can support spatiotemporally continuous mapping of land surface temperature (LST) because of its strong relationships with many surface processes. However, the frequent occurrence of cloud cover induces a large blank area in current thermal infrared-based LST products. To effectively fill this blank area, a new method for reconstructing the cloud-covered LSTs of Terra Moderate Resolution Imaging Spectroradiometer (MODIS) daytime observations is described using random forest (RF) regression approach. The high temporal resolution of the Meteosat Second Generation (MSG) LST product assisted in identifying the temporal variations in cloud cover. The cumulative downward shortwave radiation flux (DSSF) was estimated as the solar radiation factor for each MODIS pixel based on the MSG DSSF product to represent the impact from cloud cover on incident solar radiation. The RF approach was used to fit an LST linking model based on the datasets collected from clear-sky pixels that depicted the complicated relationship between LST and the predictor variables, including the surface vegetation index (the normalized difference vegetation index and the enhanced vegetation index), normalized difference water index, solar radiation factor, surface albedo, surface elevation, surface slope, and latitude. The fitted model was then used to reconstruct the LSTs of cloud-covered pixels. The proposed method was applied to the Terra/MODIS daytime LST product for four days in 2015, spanning different seasons in southwestern Europe. A visual inspection indicated that the reconstructed LSTs thoroughly captured the distribution of surface temperature associated with surface vegetation cover, solar radiation, and topography. The reconstructed LSTs showed similar spatial pattern according to the comparison with clear-sky LSTs from temporally adjacent days. In addition, evaluations against Global Land Data Assimilation System (GLDAS) NOAH 0.25° 3-h LST data and reference LST data derived based on in-situ air temperature measurements showed that the reconstructed LSTs presented a stable and reliable performance. The coefficients of determination derived with the GLDAS LST data were all above 0.59 on the four examined days. These results indicate that the proposed method has a strong potential for reconstructing LSTs under cloud-covered conditions and can also accurately depict the spatial patterns of LST.
8 Nouri, H.; Nagler, P.; Borujeni, S. C.; Munez, A. B.; Alaghmand, S.; Noori, B.; Galindo, A.; Didan, K. 2020. Effect of spatial resolution of satellite images on estimating the greenness and evapotranspiration of urban green spaces. Hydrological Processes, 34(15):3183-3199. [doi: https://doi.org/10.1002/hyp.13790]
Urban areas ; Evapotranspiration ; Satellite imagery ; Remote sensing ; Landsat ; Moderate resolution imaging spectroradiometer ; Soil water balance ; Estimation ; Normalized difference vegetation index ; Sustainability / Australia / Adelaide
(Location: IWMI HQ Call no: e-copy only Record No: H049915)
https://onlinelibrary.wiley.com/doi/epdf/10.1002/hyp.13790
https://vlibrary.iwmi.org/pdf/H049915.pdf
https://vlibrary.iwmi.org/pdf/H049915.pdf
(4.14 MB) (4.14 MB)
Urban green spaces (UGS), like most managed land covers, are getting progressively affected by water scarcity and drought. Preserving, restoring and expanding UGS require sustainable management of green and blue water resources to fulfil evapotranspiration (ET) demand for green plant cover. The heterogeneity of UGS with high variation in their microclimates and irrigation practices builds up the complexity of ET estimation. In oversized UGS, areas too large to be measured with in situ ET methods, remote sensing (RS) approaches of ET measurement have the potential to estimate the actual ET. Often in situ approaches are not feasible or too expensive. We studied the effects of spatial resolution using different satellite images, with high-, medium- and coarse-spatial resolutions, on the greenness and ET of UGS using Vegetation Indices (VIs) and VI-based ET, over a 780-ha urban park in Adelaide, Australia. We validated ET with the ground-based ET method of Soil Water Balance. Three sets of imagery from WorldView2, Landsat and MODIS, and three VIs including the Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI) and Enhanced Vegetation Index 2 (EVI2), were used to assess long-term changes of VIs and ET calculated from the different imagery acquired for this study (2011–2018). We found high correspondence between ET-MODIS and ET-Landsat (R2 > 0.99 for all VIs). Landsat-VIs captured the seasonal changes of greenness better than MODIS-VIs. We used artificial neural network (ANN) to relate the RS-ET and ground data, and ET-MODIS (EVI2) showed the highest correlation (R2 = 0.95 and MSE =0.01 for validation). We found a strong relationship between RS-ET and in situ measurements, even though it was not explicable by simple regressions; black box models helped us to explore their correlation. The methodology used in this research makes a strong case for the value of remote sensing in estimating and managing ET of green spaces in water-limited cities.
9 Wei, Y.; Lu, M.; Wu, W.; Ru, Y. 2020. Multiple factors influence the consistency of cropland datasets in Africa. International Journal of Applied Earth Observation and Geoinformation, 89:102087. [doi: https://doi.org/10.1016/j.jag.2020.102087]
Farmland ; Datasets ; Land fragmentation ; Remote sensing ; Land cover mapping ; Moderate resolution imaging spectroradiometer ; Irrigated land ; Vegetation ; Precipitation ; Food security / Africa South of Sahara
(Location: IWMI HQ Call no: e-copy only Record No: H049971)
https://www.sciencedirect.com/science/article/pii/S0303243419310463/pdfft?md5=0684753fd3e8666ecb686aa90c95632d&pid=1-s2.0-S0303243419310463-main.pdf
https://vlibrary.iwmi.org/pdf/H049971.pdf
https://vlibrary.iwmi.org/pdf/H049971.pdf
(4.01 MB) (4.01 MB)
Accurate geo-information of cropland is critical for food security strategy development and grain production management, especially in Africa continent where most countries are food-insecure. Over the past decades, a series of African cropland maps have been derived from remotely-sensed data, existing comparison studies have shown that inconsistencies with statistics and discrepancies among these products are considerable. Yet, there is a knowledge gap about the factors that influence their consistency. The aim of this study is thus to estimate the consistency of five widely-used cropland datasets (MODIS Collection 5, GlobCover 2009, GlobeLand30, CCI-LC 2010, and Unified Cropland Layer) in Africa, and to explore the effects of several limiting factors (landscape fragmentation, climate and agricultural management) on spatial consistency. The results show that total cropland area for Africa derived from GlobeLand30 has the best fitness with FAO statistics, followed by MODIS Collection 5. GlobCover 2009, CCI-LC 2010, and Unified Cropland Layer have poor performances as indicated by larger deviations from statistics. In terms of spatial consistency, disagreement is about 37.9 % at continental scale, and the disparate proportion even exceeds 50 % in approximately 1/3 of the countries at national scale. We further found that there is a strong and significant correlation between spatial agreement and cropland fragmentation, suggesting that regions with higher landscape fragmentation generally have larger disparities. It is also noticed that places with better consistency are mainly distributed in regions with favorable natural environments and sufficient agricultural management such as well-developed irrigated technology. Proportions of complete agreement are thus located in favorable climate zones including Hot-summer Mediterranean climate (Csa), Subtropical highland climate (Cwb), and Temperate Mediterranean climate (Csb). The level of complete agreement keeps rising as the proportion of irrigated cropland increases. Spatial agreement among these datasets has the most significant relationship with cropland fragmentation, and a relatively small association with irrigation area, followed by climate conditions. These results can provide some insights into understanding how different factors influence the consistency of cropland datasets, and making an appropriate selection when using these datasets in different regions. We suggest that future cropland mapping activities should put more effort in those regions with significant disagreement in Sub-Saharan Africa.
10 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.
11 You, N.; Dong, J. 2020. Examining earliest identifiable timing of crops using all available sentinel 1/2 imagery and Google Earth Engine. ISPRS Journal of Photogrammetry and Remote Sensing, 161:109-123. [doi: https://doi.org/10.1016/j.isprsjprs.2020.01.001]
Crop production ; Satellite imagery ; Farmland ; Maize ; Soybeans ; Rice ; Vegetation ; Land cover ; Mapping ; Landsat ; Moderate resolution imaging spectroradiometer ; Forecasting / China / Heilongjiang
(Location: IWMI HQ Call no: e-copy only Record No: H049991)
(14.30 MB)
Timely and accurate information on crop planting areas is critical for estimating crop production, and earlier crop mapping can benefit decision-making related to crop insurance, land rental, supply-chain logistics, and food market. Previous efforts generally produce crop planting area maps after harvest and early season cropping information is rarely available. New opportunities emerge with rapid increase in satellite data acquisition and cloud computing platform such as Google Earth Engine (GEE) which can access and process a vast volume of multi-sensor images. Here we aimed to examine earliest identifiable timing (EIT) of major crops (rice, soybean, and corn) and generate early season crop maps independent of within-year field surveys in the Heilongjiang province, one most important province of grain production in China. The Random Forest classifiers were trained based on early season images and field samples in 2017, then were transferred (applied) to corresponding images in 2018 to obtain resultant maps. Six scenarios with different temporal intervals (10d, 15d, 20d, and 30d) and data integration (Sentinel-2 and Sentinel-1, a total of 16, 450 images) were compared to get the optimal crop maps. The results showed that the Sentinel-2 time series and 10-day composite outperformed in obtaining EITs and crop maps. We found various EITs for the three grain staples. Specifically, rice could be identified in the late transplanting stage (four months before harvest) with F1 score of 0.93, following by corn recognizable in the early heading stage (two months before harvest, with F1 score of 0.92) and soybean in the early pod setting stage (50 days before harvest, with F1 score of 0.91). The crop maps in the EITs based on the classifier transfer approach have comparable accuracies (overall accuracy = 0.91) comparing to the traditional post-season mapping approach based on current year’s all available images and samples (overall accuracy = 0.95). This study suggests the potential of growing fine resolution observations for timely monitoring of crop planting area within season, which provides valuable and timely information for different stakeholders and decision makers.
12 Kubitza, C.; Krishna, V. V.; Schulthess, U.; Jain, M. 2020. Estimating adoption and impacts of agricultural management practices in developing countries using satellite data. A scoping review. Agronomy for Sustainable Development, 40(3):16. [doi: https://doi.org/10.1007/s13593-020-0610-2]
Agricultural practices ; Developing countries ; Satellite imagery ; Landsat ; Radar ; Remote sensing ; Moderate resolution imaging spectroradiometer ; Intensive cropping ; Crop yield ; Tillage ; Technology ; Soil conservation ; Water conservation ; Smallholders ; Vegetation index ; Land cover ; Irrigation
(Location: IWMI HQ Call no: e-copy only Record No: H050034)
(0.86 MB)
Development and dissemination of sustainable practices are key to enhance agricultural productivity in developing countries and to curtail potential negative externalities. Rigorous adoption/impact evaluations provide valuable lessons to enhance the capacity of agricultural research-for-development (R4D) systems in this context. Conventional evaluation studies rely solely on farm-household surveys for data. Generation of survey data however requires considerable financial and human capital, and the process often misses several important explanatory variables, ignores the longer-term impacts, and suffers from measurement errors. Complementary data sources are explored to make the evaluations more robust and rigorous. Here we review 54 studies that used satellite data to estimate adoption and impact of agricultural practices in developing countries. Some evidence on successful application of satellite data in high-income countries is also provided. The main findings of the paper are threefold: (1) satellite data have been successfully used to detect agricultural practices, such as cropping intensity, tillage, crop residue cover, irrigation, and soil and water conservation; (2) only a few studies have estimated the yield impacts of agricultural practices, although the estimation of crop yields with satellite data is fairly developed; and (3) only a small number of studies have explored impact estimation beyond the biophysical sphere. Estimation of certain environmental impacts of agricultural practices is possible through satellite data, although only a few studies have carried it out. Not many have assessed the economic impacts of interventions. We conclude that satellite data analysis allows information access with little delay and over longer periods, provide a unique set of variables over wide geographies, and reduce measurement error in certain variables. However, more interdisciplinary research is necessary to speed up the uptake of this alternative data source in R4D evaluations.
13 Chen, Y.; Fang, G.; Hao, H.; Wang, X. 2020. Water use efficiency data from 2000 to 2019 in measuring progress towards SDGs in Central Asia. Big Earth Data, 14p. (Online first) [doi: https://doi.org/10.1080/20964471.2020.1851891]
Water use efficiency ; Sustainable Development Goals ; Agricultural water use ; Water resources ; Evapotranspiration ; Ecosystems ; Remote sensing ; Moderate resolution imaging spectroradiometer ; Datasets / Central Asia / Kazakhstan / Kyrgyzstan / Tajikistan / Turkmenistan / Uzbekistan
(Location: IWMI HQ Call no: e-copy only Record No: H050142)
https://www.tandfonline.com/doi/pdf/10.1080/20964471.2020.1851891?needAccess=true
https://vlibrary.iwmi.org/pdf/H050142.pdf
https://vlibrary.iwmi.org/pdf/H050142.pdf
(5.50 MB) (5.50 MB)
Central Asia, located in the hinterland of the Eurasian continent, is characterized with sparse rainfall, frequent droughts and low water use efficiency. Limited water resources have become a key factor restricting the sustainable development of this region. Accurately assessing the efficiency of water resources utilization is the first step to achieve the UN Sustainable Development Goals (SDGs) in Central Asia. However, since the collapse of the Soviet Union, the evaluation of water use efficiency is difficult due to low data availability and poor consistency. To fill this gap, this paper developed a Water Use Efficiency dataset (WUE) based on the Moderate Resolution Imaging Spectroradiometer (MODIS) Gross Primary Production (GPP) data and the MODIS evapotranspiration (ET) data. The WUE dataset ranges from 2000 to 2019 with a spatial resolution of 500 m. The agricultural WUE was then extracted based on the Global map of irrigated areas and MODIS land use map. As a complementary, the water use amount per GDP was estimated for each country. The present dataset could reflect changes in water use efficiency of agriculture and other sectors.
14 Qaiser, G.; Tariq, S.; Adnan, S.; Latif, M. 2021. Evaluation of a composite drought index to identify seasonal drought and its associated atmospheric dynamics in northern Punjab, Pakistan. Journal of Arid Environments, 185:104332. (Online first) [doi: https://doi.org/10.1016/j.jaridenv.2020.104332]
Drought ; Climate change ; Temperature ; Precipitation ; Monitoring ; Crop yield ; Normalized difference vegetation index ; Meteorological observations ; Moderate resolution imaging spectroradiometer / Pakistan / Punjab / Potwar Plateau / Islamabad / Attock / Chakwal / Jhelum / Rawalpindi
(Location: IWMI HQ Call no: e-copy only Record No: H050153)
(9.72 MB)
Drought is one of the most devastating climate extremes in terms of its spatial extent and intensity. Rainfed areas are extremely vulnerable to drought, but effective monitoring may lessen the impact of such events. This study developed a composite drought index (CDI) for monitoring and assessing seasonal droughts in rainfed areas of the Potwar Plateau of Pakistan, using remotely sensed and observed meteorological datasets. We identified four severe-to-extreme drought periods in the Rabi season (wheat; 2000–01, 2001–02, 2009–10, and 2011–12) and four such events in the Kharif season (maize; 2000–2002 and 2009). An intense agro-meteorological drought was experienced in 2000, which reduced the wheat and maize yields to -54.6% and -29.9%, respectively. Our analysis revealed that these conditions could be explained by the vertically integrated moisture flux divergence (MFD), moisture transport, and total precipitable water (TPW) anomalies. For example, the presence of a strong MFD anomaly over the study area was responsible for preventing moisture transport from the Arabian Sea and Bay of Bengal, resulting in dry conditions. The index developed here can effectively monitor seasonal droughts in rainfed areas, which may help inform strategies to lessen the impact of such events.
15 Ali, S.; Cheema, M. J. M.; Waqas, M. M.; Waseem, M.; Awan, Usman Khalid; Khaliq, T. 2020. Changes in snow cover dynamics over the Indus Basin: evidences from 2008 to 2018 MODIS NDSI trends analysis. Remote Sensing, 12(17):2782. (Special issue: Interactive Deep Learning for Hyperspectral Images) [doi: https://doi.org/10.3390/rs12172782]
Snow cover ; Estimation ; Mapping ; Trends ; River basins ; Catchment areas ; Temperature ; Clouds ; Landsat ; Satellite imagery ; Moderate resolution imaging spectroradiometer ; Uncertainty / Pakistan / Indus Basin / Himalayas / Chenab River Catchment / Jhelum River Catchment / Indus River Catchment / Eastern Rivers Catchment
(Location: IWMI HQ Call no: e-copy only Record No: H050209)
(4.20 MB) (4.20 MB)
The frozen water reserves on the Earth are not only very dynamic in their nature, but also have significant effects on hydrological response of complex and dynamic river basins. The Indus basin is one of the most complex river basins in the world and receives most of its share from the Asian Water Tower (Himalayas). In such a huge river basin with high-altitude mountains, the regular quantification of snow cover is a great challenge to researchers for the management of downstream ecosystems. In this study, Moderate Resolution Imaging Spectroradiometer (MODIS) daily (MOD09GA) and 8-day (MOD09A1) products were used for the spatiotemporal quantification of snow cover over the Indus basin and the western rivers’ catchments from 2008 to 2018. The high-resolution Landsat Enhanced Thematic Mapper Plus (ETM+) was used as a standard product with a minimum Normalized Difference Snow Index (NDSI) threshold (0.4) to delineate the snow cover for 120 scenes over the Indus basin on different days. All types of errors of commission/omission were masked out using water, sand, cloud, and forest masks at different spatiotemporal resolutions. The snow cover comparison of MODIS products with Landsat ETM+, in situ snow data and Google Earth imagery indicated that the minimum NDSI threshold of 0.34 fits well compared to the globally accepted threshold of 0.4 due to the coarser resolution of MODIS products. The intercomparison of the time series snow cover area of MODIS products indicated R2 values of 0.96, 0.95, 0.97, 0.96 and 0.98, for the Chenab, Jhelum, Indus and eastern rivers’ catchments and Indus basin, respectively. A linear least squares regression analysis of the snow cover area of the Indus basin indicated a declining trend of about 3358 and 2459 km2 per year for MOD09A1 and MOD09GA products, respectively. The results also revealed a decrease in snow cover area over all the parts of the Indus basin and its sub-catchments. Our results suggest that MODIS time series NDSI analysis is a useful technique to estimate snow cover over the mountainous areas of complex river basins.
16 Yan, Y.; Wu, C.; Wen, Y. 2021. Determining the impacts of climate change and urban expansion on net primary productivity using the spatio-temporal fusion of remote sensing data. Ecological Indicators, 127:107737. (Online first) [doi: https://doi.org/10.1016/j.ecolind.2021.107737]
Climate change ; Urbanization ; Remote sensing ; Net primary productivity ; Moderate resolution imaging spectroradiometer ; Normalized difference vegetation index ; Landsat ; Precipitation ; Fertilization ; Land use ; Land cover ; Ecosystems ; Grasslands ; Farmland ; Forests / China / Beijing
(Location: IWMI HQ Call no: e-copy only Record No: H050393)
https://www.sciencedirect.com/science/article/pii/S1470160X21004027/pdfft?md5=96d56d824ca51ab536802d836e7e164b&pid=1-s2.0-S1470160X21004027-main.pdf
https://vlibrary.iwmi.org/pdf/H050393.pdf
https://vlibrary.iwmi.org/pdf/H050393.pdf
(9.65 MB) (9.65 MB)
Climate change (CLC) and urban expansion (URE) have profoundly altered the terrestrial net primary productivity (NPP). Many studies have determined the effects of CLC and URE on the NPP. However, these studies were conducted at low resolutions (250–1000 m), making it difficult to detect many smaller new urban lands, and thus potentially underestimating the contribution of URE. To accurately determine the contributions of CLC and URE to the NPP, this study takes Beijing as an example and uses an Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) to fuse the spatial resolution of the Landsat Normalized Difference Vegetation Index (NDVI) and the temporal resolution of the Moderate Resolution Imaging Spectroradiometer (MODIS) NDVI to generate a new NDVI with a high spatio-temporal resolution. Compared with the Landsat NDVI, the NDVI fused by the ESTARFM is found to be reliable. The fused NDVI was then inputted into the Carnegie–Ames–Stanford Approach (CASA) model to generate the NPP with a high spatio-temporal resolution, namely, the 30-m NPP. Compared with the 250-m NPP generated by directly inputting the MODIS NDVI into the CASA model, the 30-m NPP as a new ecological indicator is more accurate than the 250-m NPP. Due to the high resolution of the 30-m NPP and its increased ability to detect more new urban lands, the total loss of the 30-m NPP caused by URE is much higher than that of the 250-m NPP. For the same reason, especially in rapidly urbanized areas, the contribution ratio of URE to the 30-m NPP is much higher than that to the 250-m NPP. Moreover, in natural vegetation cover areas, CLC, which is measured by the interannual changes in temperature, precipitation, and solar radiation, is the leading factor of the change in the NPP. However, within the urban areas, residual factors other than CLC and URE, such as the introduction of exotic high-productivity vegetation, irrigation, fertilization, and pest control, dominate the change in the NPP. The results of this study are expected to contribute to a deeper understanding of the influences of CLC and URE on terrestrial ecosystem carbon cycles and provide an important theoretical reference for urban planning.
17 White, E. Jr.; Kaplan, D. 2021. Identifying the effects of chronic saltwater intrusion in coastal floodplain swamps using remote sensing. Remote Sensing of Environment, 258:112385. [doi: https://doi.org/10.1016/j.rse.2021.112385]
Saltwater intrusion ; Coastal area ; Floodplains ; Wetlands ; Swamps ; Sea level ; Remote sensing ; Moderate resolution imaging spectroradiometer ; Vegetation index ; Freshwater ; Downstream ; Upstream ; Hydrology ; Ecosystem services / USA / Gulf of Mexico / Texas / Louisiana / Florida
(Location: IWMI HQ Call no: e-copy only Record No: H050452)
(3.38 MB)
Coastal floodplain swamps (CFS) are an important part of the coastal wetland mosaic, however they are threatened due to accelerated rates of sea level rise and saltwater intrusion (SWI). While remote sensing-based detection of wholesale coastal ecosystem shifts (i.e., from forest to marsh) are relatively straightforward, assessments of chronic, low-level SWI into CFS using remote sensing have yet to be developed and can provide a critical early-warning signal of ecosystem deterioration. In this study, we developed nine ecologically-based hypotheses to test whether remote sensing data could be used to reliably detect the presence of CFS experiencing SWI. Hypotheses were motivated by field- and literature-based understanding of the phenological and vegetative dynamics of CFS experiencing SWI relative to unimpacted, control systems. Hypotheses were organized into two primary groups: those that analyzed differences in summary measures (e.g., median and distribution) between SWI-impacted and unimpacted control sites and those that examined timeseries trends (e.g., sign and magnitude of slope). The enhanced vegetation index (EVI) was used as a proxy for production/biomass and was generated using MODIS surface reflectance data spanning 2000 to 2018. Experimental sites (n = 8) were selected from an existing network of long-term monitoring sites and included 4 pairs of impacted/non-impacted CFS across the northern Gulf of Mexico from Texas to Florida. The four best-supported hypotheses (81% across all sties) all used summary statistics, indicating that there were significant differences in the EVI of CFS experiencing chronic, low-level SWI compared to controls. These hypotheses were tested using data across a large and diverse region, supporting their implementation by researchers and managers seeking to identify CFS undergoing the first phases of SWI. In contrast, hypotheses that assessed CFS change over time were poorly supported, likely due to the slow and variable pace of ecological change, relatively short remote sensing data record, and/or specific site histories. Overall, these results show that remote sensing data can be used to identify differences in CFS vegetation associated with long-term, low-level SWI, but further methodological advancements are needed to reliably detect the temporal transition process.
18 Roushangar, K.; Ghasempour, R.; Kirca, V. S. O.; Demirel, M. C. 2021. Hybrid point and interval prediction approaches for drought modeling using ground-based and remote sensing data. Hydrology Research, 21p. (Online first) [doi: https://doi.org/10.2166/nh.2021.028]
Drought ; Models ; Forecasting ; Remote sensing ; Precipitation ; Artificial intelligence ; Soil moisture ; Vegetation ; Uncertainty ; Moderate resolution imaging spectroradiometer / Iran Islamic Republic / Azerbaijan
(Location: IWMI HQ Call no: e-copy only Record No: H050464)
https://iwaponline.com/hr/article-pdf/doi/10.2166/nh.2021.028/905169/nh2021028.pdf
https://vlibrary.iwmi.org/pdf/H050464.pdf
https://vlibrary.iwmi.org/pdf/H050464.pdf
(1.38 MB) (1.38 MB)
Drought as a severe natural disaster has devastating effects on the environment; therefore, reliable drought prediction is an important issue. In the current study, based on lower upper bound estimation, hybrid models including data preprocessing, permutation entropy, and artificial intelligence (AI) methods were used for point and interval predictions of short- to long-term series of Standardized Precipitation Index in the Northwest of Iran. Ground-based and remote sensing precipitation data were used covering the period of 1983–2017. In the modeling process, first, the data processing capability via variational mode decomposition (VMD), ensemble empirical mode decomposition, and permutation entropy (PE) was investigated in drought point prediction. Then, interval prediction was applied for tolerating increased uncertainty and providing more details for practical operation decisions. The simulation results demonstrated that the proposed integrated models could achieve significantly better performance compared to single models. Hybrid PE models increased the modeling accuracy up to 40 and 55%. Finally, the efficiency of developed models was verified for Normalized Difference Vegetation Index prediction. Results demonstrated that the proposed methodology based on remote sensing data and VMD–PE–AI approaches could be successfully used for drought modeling, especially in limited or non-gauged areas.
19 Liu, Z.; Liu, Y.; Wang, J. 2021. A global analysis of agricultural productivity and water resource consumption changes over cropland expansion regions. Agriculture, Ecosystems and Environment, 321:107630. [doi: https://doi.org/10.1016/j.agee.2021.107630]
Agricultural productivity ; Water resources ; Water use ; Farmland ; Spatial analysis ; Ecosystems ; Land use change ; Land cover ; Grasslands ; Precipitation ; Moderate resolution imaging spectroradiometer
(Location: IWMI HQ Call no: e-copy only Record No: H050681)
(7.26 MB)
Cropland expansion often occurs on grasslands and partial forests. However, there is little quantified understanding of how cropland expansion affected the agricultural productivity and water resource consumption globally. In this study, we used spatially explicit satellite-based data, including land use maps, net primary productivity and evapotranspiration from 2001 to 2018, and the space-for-time substitution technique to investigate the relationships between cropland expansion and agricultural productivity and water resource consumption. Results showed that global cropland area presented a significant net increasing trend with 1.9 × 104 km2/a (p < 0.01) since 2000. Net increased cropland area over the Northern Hemisphere and the Southern Hemisphere occupied 27.1% and 72.9% of global total net increase, respectively. Large-area cropland expansion mainly focused on Eastern Asia, Southern Asia, Eastern Europe, Southern America, and Northern America. Particularly, cropland expansion in the Southern America deserved the greatest attention. At the global scale, new expanded croplands caused average NPP decrease and average ET decrease compared to original ecosystems, but performances were evident differences in subregions. Cropland expansion in the Southern America evidently decreased NPP and ET compared to other places. In contrast, new expanded croplands in most subregions of Asia and Northern America performed higher the agriculture productivity, while the increases were done at the expense of more water resource consumption. Although cropland expansion only slightly decreased NPP compared to original ecosystems globally, new expanded croplands often occurred in water-limited or temperature-limited areas according to precipitation and temperature gradations. This study suggests that cropland expansion should more consider sustainable land use and development, and reduce the risks of cropland expansion on natural ecosystems as much as possible.
20 Koppa, Nisha; Amarnath, Giriraj. 2021. Geospatial assessment of flood-tolerant rice varieties to guide climate adaptation strategies in India. Climate, 9(10):151. (Special issue: Climate Change and Food Insecurity) [doi: https://doi.org/10.3390/cli9100151]
Flooding tolerance ; Rice ; Seeds ; Climate change adaptation ; Strategies ; Remote sensing ; Geographical information systems ; Spatial data ; Assessment ; Disaster risk management ; Rainfed farming ; Agricultural production ; Land use ; Farmers ; Livelihoods ; Moderate Resolution Imaging Spectroradiometer / India
(Location: IWMI HQ Call no: e-copy only Record No: H050735)
(3.18 MB) (3.18 MB)
Rice is the most important food crop. With the largest rain-fed lowland area in the world, flooding is considered as the most important abiotic stress to rice production in India. With climate change, it is expected that the frequency and severity of the floods will increase over the years. These changes will have a severe impact on the rain-fed agriculture production and livelihoods of millions of farmers in the flood affected region. There are numerous flood risk adaptation and mitigation options available for rain-fed agriculture in India. Procuring, maintaining and distributing the newly developed submergence-tolerant rice variety called Swarna-Sub1 could play an important role in minimizing the effect of flood on rice production. This paper assesses the quantity and cost of a flood-tolerant rice seed variety- Swarna-Sub1, that would be required during the main cropping season of rice i.e., kharif at a district level for 17 major Indian states. The need for SS1 seeds for rice production was assessed by developing a geospatial framework using remote sensing to map the suitability of SS1, to help stakeholders prepare better in managing the flood risks. Results indicate that districts of Bihar, West Bengal and Uttar Pradesh will require the highest amount of SS1 seeds for flood adaptation strategies. The total estimated seed requirement for these 17 states would cost around 370 crores INR, less than 0.01 percent of Indian central government’s budget allocation for agriculture sector.
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