MODIS Imagery Research Articles

Geospatial modeling of land cover change in the Chocó-Darien global ecoregion of South America; One of most biodiverse and rainy areas in the world.

The tropical rain forests of northwest South America fall within the Chocó-Darien Global Ecoregion (CGE). The CGE is one of 25 global biodiversity hotspots prioritized for conservation due to its high biodiversity and endemism as well as threats due to deforestation. The analysis of land-use and land-cover (LULC) change within the CGE using remotely sensed imagery is challenging because this area is considered to be one of the rainiest places on the planet (hence high frequency of cloud cover). Furthermore, the availability of high-resolution remotely sensed data is low for developing countries before 2015. Using the Random Forest ensemble learning classification tree system, we developed annual LULC maps in the CGE from 2002 to 2015 using a time series of cloud-free MODIS vegetation index products. The MODIS imagery was processed through a Gaussian weighted filter to further correct for cloud pollution and matched to visual interpretations of land cover and land use from available high spatial resolution imagery (WorldView-2, Quick Bird, Ikonos and GeoEye-1). Validation of LULC maps resulted in a Kappa of 0.87 (Sd = 0.008). We detected a gradual replacement of forested areas with agriculture (mainly grassland planted to support livestock grazing), and secondary vegetation (agriculture reverting to forest) across the CGE. Forest loss was higher between 2010–2015 when compared to 2002–2010. LULC change trends, deforestation drivers, and reforestation transitions varied according to administrative organization (countries: Panamanian CGE, Colombian CGE, and Ecuadorian CGE).

Mapping fractional cropland covers in Brazil through integrating LSMA and SDI techniques applied to MODIS imagery

MODIS time-series imagery is promising for generating regional and global land cover products. For Brazil, however, accurate fractional cropland covers (FCC) information is difficult to obtain due to frequent cloud coverage and the mixing-pixel problem. To address these problems, this study developed an innovative approach to mapping the FCC of the Mato Grosso State, Brazil through integrating Linear Spectral Mixture Analysis (LSMA) and Seasonal Dynamic Index (SDI) models. With MOD13Q1 time-series EVI imagery, a SDI was developed to represent the phenology of croplands. Furthermore, fractional land covers (e.g., vegetation, soil, and low albedo components) were derived with the LSMA algorithms. A stepwise regression model was established to estimate the FCC at the regional scale. Finally, ground truth cropland cover information was extracted from Landsat TM imagery using a hybrid method. Results indicated that the combination of multiple feature variables produced better results when compared with individual variables. Through cross-validation and comparative analysis, the coefficient of determination (R2) between the reference and estimated FCCs reached 0.84 with a Root Mean Square Error (RMSE) of 0.13. This indicates that the proposed method effectively improved the accuracy of fractional cropland mapping. When compared to the traditional per-pixel “hard” classification, the sub-pixel level maps illustrated detailed cropland spatial distribution patterns. Keywords: fractional cropland covers (FCC), MODIS, enhanced vegetation index (EVI), subpixel mapping, remote sensing DOI: 10.25165/j.ijabe.20191201.4419 Citation: Zhu C M, Zhang X, Huang Q H. Mapping fractional cropland covers in Brazil through integrating LSMA and SDI techniques applied to MODIS imagery. Int J Agric & Biol Eng, 2019; 12(1): 192–200.

Weakly-coupled geo-statistical mapping of soil salinity to Stepwise Multiple Linear Regression of MODIS spectral image products

Integrating remote sensing and geo-statistical techniques are of expanding field of researches, for soil salinity mapping taking advantage of steadily improving technology for the remote and proximal sensing of land features at the terrain surface. The main objective of the research is to enhance the performance of soil salinity mapping by employing MODIS spectral products and results of laboratory soil analysis into a geo-statistical soil properties analysis. The study area is the Sarvestan region, located in the southeast of Shiraz, Iran. The research followed a stratified random cluster sampling approach for collecting 240 soil samples in 60 geo-referenced soil pits from top of bare soils (5–10 cm). The MODIS data sets used were acquired during soil sampling. Stepwise multiple linear regression (SLMR) was employed for selecting MODIS products, that carry the most information on soil factors. Geo-statistical methods including Ordinary Kriging (OK), Co-kriging (CK), and Regression Kriging (RK) were used in mapping soil properties. Statistical criteria were considered to validate the models developed by SMLR. RK and OK as they are the best in EC and pH prediction. RK presents a higher effectiveness for the soil variables than CK, and confirms the usefulness of coupling SMLR to geostatistical mapping processes. The results indicate that MODIS imageries improve the capability of geostatistical methods for soil salinity mapping. The results showed that the use of MODIS imageries increased the G-Values of RK by 13%, 5%, 3%, 7% and 1% on average for OM, SAR, Na, K, and Mg respectively. RK also showed a good competitiveness for the EC and pH, when compared with OK. The research presentes an integrated helpful and effective mapping tool, especially in areas where there is lack of intensive field data on soil properties.

Comparison of Methods to Estimate Lake-Surface-Water Temperature Using Landsat 7 ETM+ and MODIS Imagery: Case Study of a Large Shallow Subtropical Lake in Southern Brazil

Water temperature regulates many processes in lakes; therefore, evaluating it is essential to understand its ecological status and functioning, and to comprehend the impact of climate change. Although few studies assessed the accuracy of individual sensors in estimating lake-surface-water temperature (LSWT), comparative analysis considering different sensors is still needed. This study evaluated the performance of two thermal sensors, MODIS and Landsat 7 ETM+, and used Landsat methods to estimate the SWT of a large subtropical lake. MODIS products MOD11 LST and MOD28 SST were used for comparison. For the Landsat images, the radiative transfer equation (RTE), using NASA’s Atmospheric Correction Parameter Calculator (AtmCorr) parameters, was compared with the single-channel algorithm in different approaches. Our results showed that MOD11 obtained the highest accuracy (RMSE of 1.05 ° C), and is the recommended product for LSWT studies. For Landsat-derived SWT, AtmCorr obtained the highest accuracy (RMSE of 1.07 ° C) and is the recommended method for small lakes. Sensitivity analysis showed that Landsat-derived LSWT using the RTE is very sensitive to atmospheric parameters and emissivity. A discussion of the main error sources was conducted. We recommend that similar tests be applied for Landsat imagery on different lakes, further studies on algorithms to correct the cool-skin effect in inland waters, and tests of different emissivity values to verify if it can compensate for this effect, in an effort to improve the accuracy of these estimates.

Application of Multi-periodic Harmonic Model for Classification of Multi-temporal Satellite Data: MODIS and GOCI Imagery

A multi-temporal approach using remotely sensed time series data obtained over multiple years is a very useful method for monitoring land covers and land-cover changes. While spectral-based methods at any particular time limits the application utility due to instability of the quality of data obtained at that time, the approach based on the temporal profile can produce more accurate results since data is analyzed from a long-term perspective rather than on one point in time. In this study, a multi-temporal approach applying a multi-periodic harmonic model is proposed for classification of remotely sensed data. A harmonic model characterizes the seasonal variation of a time series by four parameters: average level, frequency, phase, and amplitude. The availability of high-quality data is very important for multi-temporal analysis. An satellite image usually have many unobserved data and bad-quality data due to the influence of observation environment and sensing system, which impede the analysis and might possibly produce inaccurate results. Harmonic analysis is also very useful for real-time data reconstruction. Multiperiodic harmonic model is applied to the reconstructed data to classify land covers and monitor land-cover change by tracking the temporal profiles. The proposed method is tested with the MODIS and GOCI NDVI time series over the Korean Peninsula for 5 years from 2012 to 2016. The results show that the multi-periodic harmonic model has a great potential for classification of land-cover types and monitoring of land-cover changes through characterizing annual temporal dynamics.

New inventory of dust sources in Central Asia derived from the daily MODIS imagery

This paper presents the first inventory of dust emission sources in Central Asia and western China (35-50°N, 50-100°E) derived from the twice daily MODIS imagery from 2003-2012. The high-resolution (1 km) dust enhancement product was generated and used to produce maps of dust point sources and gridded data sets of dust emission frequencies. The most active dust emissions were observed in the eastern part of the Tarim basin (Lop Nur salt lake) followed by the Aralkum. A high frequency of dust emissions was recorded in the regions which were not reported in literature to date: the upper Amudarya region in northern Afghanistan and the Pre-Aral region (from the Ustyurt Plateau to the Betpak Dala desert). Dust emissions were associated mainly with the fluvial features (dry river beds and lakes), agricultural activities and fire damage to vegetation. In the eastern and northern parts of the study region and in the Aralkum, dust emissions peaked in spring while in the western and southern parts, they peaked in summer. The Aralkum exhibited a consistent growth in the frequency and intensity of dust emissions and similar but weaker trends were observed in the Karakum and Kyzylkum.

Mapping fractional cropland covers in Brazil through integrating LSMA and SDI techniques applied to MODIS imagery

MODIS time-series imagery is promising for generating regional and global land cover products. For Brazil, however, accurate fractional cropland covers (FCC) information is difficult to obtain due to frequent cloud coverage and the mixing-pixel problem. To address these problems, this study developed an innovative approach to mapping the FCC of the Mato Grosso State, Brazil through integrating Linear Spectral Mixture Analysis (LSMA) and Seasonal Dynamic Index (SDI) models. With MOD13Q1 time-series EVI imagery, a SDI was developed to represent the phenology of croplands. Furthermore, fractional land covers (e.g., vegetation, soil, and low albedo components) were derived with the LSMA algorithms. A stepwise regression model was established to estimate the FCC at the regional scale. Finally, ground truth cropland cover information was extracted from Landsat TM imagery using a hybrid method. Results indicated that the combination of multiple feature variables produced better results when compared with individual variables. Through cross-validation and comparative analysis, the coefficient of determination (R2) between the reference and estimated FCCs reached 0.84 with a Root Mean Square Error (RMSE) of 0.13. This indicates that the proposed method effectively improved the accuracy of fractional cropland mapping. When compared to the traditional per-pixel “hard” classification, the sub-pixel level maps illustrated detailed cropland spatial distribution patterns. Keywords: fractional cropland covers (FCC), MODIS, enhanced vegetation index (EVI), subpixel mapping, remote sensing DOI: 10.25165/j.ijabe.20191201.4419 Citation: Zhu C M, Zhang X, Huang Q H. Mapping fractional cropland covers in Brazil through integrating LSMA and SDI techniques applied to MODIS imagery. Int J Agric & Biol Eng, 2019; 12(1): 192–200.

Comparison of satellite reflectance algorithms for estimating turbidity and cyanobacterial concentrations in productive freshwaters using hyperspectral aircraft imagery and dense coincident surface observations

We analyzed 37 satellite reflectance algorithms and 321 variants for five satellites for estimating turbidity in a freshwater inland lake in Ohio using coincident real hyperspectral aircraft imagery converted to relative reflectance and dense coincident surface observations. This study is part of an effort to develop simple proxies for turbidity and algal blooms and to evaluate their performance and portability between satellite imagers for regional operational turbidity and algal bloom monitoring. Turbidity algorithms were then applied to synthetic satellite images and compared to in situ measurements of turbidity, chlorophyll-a (Chl-a), total suspended solids (TSS) and phycocyanin as an indicator of cyanobacterial/blue green algal (BGA) abundance. Several turbidity algorithms worked well with real Compact Airborne Spectrographic Imager (CASI) and synthetic WorldView-2, Sentinel-2 and Sentinel-3/MERIS/OLCI imagery. A simple red band algorithm for MODIS imagery and a new fluorescence line height algorithm for Landsat-8 imagery had limited performance with regard to turbidity estimation. Blue-Green Algae/Phycocyanin (BGA/PC) and Chl-a algorithms were the most widely applicable algorithms for turbidity estimation because strong co-variance of turbidity, TSS, Chl-a, and BGA made them mutual proxies in this experiment.

Agro-ecological variability effects on an index-based insurance design for extreme events

Index-based insurance (IBI) is a risk transfer tool that is widely used in low-incomes countries, especially catered to smallholder farmers. A good correlation between the selected index (in this case Normalized Difference Vegetation Index -NDVI-) and the crop losses of the insured area is a key requirement for well-performing system. Usually, IBI application area is defined by political boundaries, despite that variation in soil, topography, and climate is more important. In this work, agro-ecological variability effects will be studied in the IBI context. The case study was the rice crop failure in Babahoyo canton (Ecuador), using NDVI as the crop damage indicator.In a previous study, zones of similar soil, topographic and climatic characteristics (AHZs) were defined using principal component analysis in Babahoyo canton. Two largest AHZs were found in this canton. They were f7 and f15, which account for 91% of the total rice crop area in Babahoyo. We sampled reflectance values of red, blue, near infra-red (NIR) and medium infra-red (MIR) bands over bare soil (1st of January) by observation points, within f7 and f15. The reflectance band values were used to study whether the difference between the two AHZs could be appreciated through remote sensing. Then, we did the same but for NDVI average of the rice crop cycle, i.e., 15th of January to 15th of May (NDVI_ave). It was done for confirming that the AHZs (f7 and f15) have an influence over rice crop development. The used product was MODIS imagery (2001–2017).We stratified the sampling through the AHZs, applying 30% density for ensuring the accuracy of the selected pixels. Statistically significant differences between the f7 and f15 zones were found in all the analysed reflectance bands and in NDVI_ave. This found supported the differences that the agro-ecological map pointed out in its database. At the same time, the NDVI_ave distribution for each AHZ presented a statistically significant difference of the accumulated probability below the threshold (0.4), which is confirmed as a reliable threshold for rice crop failure. This stratified sampling could improve the representativeness and accuracy of crop state assessments and reduce the basis risk.

A suite of macrophyte species distribution models for investigating hydrology-driven spatial changes in a large flood-pulsed tropical wetland

ABSTRACTSpatial ecological models of wetland systems are important tools for evaluating system response to changes in hydrological inputs, arising from anthropogenic manipulation or climate change. We present an approach using multiple species distribution models (SDMs) for floodplain macrophytes, based on empirical relationships with flood duration. 27 indicator species were identified for four floodplain classes in a seasonally pulsed distributary of the Okavango Delta, based on data from a field survey of 30 floodplain sites. Spatial hydrological history was constructed using a combination of Landsat and MODIS imagery, and ground-truthing. Relationships between duration and occurrence were used to construct general linear probability models (GLMs) of species along the hydroperiod gradient. The GLM parameters were then used to construct SDMs driven by a spatial hydrological model, which generates annual assessments of inundation duration in months in each 1km2 cell across the Delta. The modelled distribution fits were mostly robust (18 GLMs had p < 0.05). Tests of model performance against a validation set of five floodplain sites showed an average of 87% correct predictions of presence. The strong performance of the models and the ability to produce map-based and quantitative output indicate good potential for use in assessment of basin development scenarios.

Rainfall–runoff, flood inundation and sensitivity analysis of the 2014 Pakistan flood in the Jhelum and Chenab river basin

ABSTRACTThe major flood of 2014 in the two eastern, transboundary rivers, the Jhelum and Chenab in Punjab, Pakistan, was simulated using the two-dimensional rainfall–runoff model. The simulated hydrograph showed good agreement with the observed discharge at the model outlet and intervening barrages, with a Nash-Sutcliffe efficiency of 0.86 at the basin outlet. Further, simulated flood inundation extent showed good agreement with the MODIS imagery with a fit (%) of 0.87. For some affected areas that experienced short-duration flooding, local housing damage data confirmed the simulated results. Besides the rainfall–runoff and flood inundation modelling, parameter sensitivity analysis was undertaken to identify the influence of various river and floodplain parameters. The analysis showed that the river channel geometric parameters and the roughness coefficients exerted the primary influence over flood extent and peak flow.

Analysing spatial–temporal changes in rice cultivation practices in the Senegal River Valley using MODIS time-series and the PhenoRice algorithm

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.

Evaluating RADARSAT-2 for the Monitoring of Lake Ice Phenology Events in Mid-Latitudes

Lake ice is an important component in understanding the local climate as changes in temperature have an impact on the timing of key ice phenology events. In recent years, there has been a decline in the in-situ monitoring of lake ice events in Canada and microwave remote sensing imagery from synthetic aperture radar (SAR) is more widely used due to the high spatial resolution and response of backscatter to the freezing and melting of the ice surface. RADARSAT-2 imagery was used to develop a threshold-based method for determining lake ice events for mid-latitude lakes in Central Ontario from 2008 to 2017. Estimated lake ice phenology events are validated with ground-based observations and are compared against the Moderate Resolution Imaging Spectroradiometer (MODIS band 2). The threshold-based method was found to accurately identify 12 out of 17 freeze events and 13 out of 17 melt events from 2015–2017 when compared to ground-based observations. Mean absolute errors for freeze events ranged from 2.5 to 10.0 days when compared to MODIS imagery while the mean absolute error for water clear of ice (WCI) ranged from 1.5 to 7.1 days. The method is important for the study of mid-latitude lake ice due to its unique success in detecting multiple freeze and melting events throughout the ice season.

Variability in annual temperature cycle in the urban areas of the United States as revealed by MODIS imagery

Due to its large spatial coverage and frequent revisit, satellite-derived land surface temperature (LST) has been recently used to explore annual temperature cycle (ATC) variations at regional and global scales. However, variability in seasonality of LSTs has not been examined in detail, particularly in urban areas where elevated temperatures are normally observed. By assuming repetitive temperature cycles, this study aims to reveal differences in ATC parameters between urban and rural areas and the impacts of surface urban heat island (UHI) on the ATC range over the continental United States. To this end, urban areas of larger than 10 km2 (a total of 1856 urban polygons) in the continental United States were identified from the map of urban extents produced by Defense Meteorological Satellite Program/Operational Linescan System (DMSP/OLS) nighttime light data from 2012. The corresponding rural polygons of the same size were generated by using a buffer method. The ATC parameters were optimized using a sinusoidal function fitted with the 8-day MODIS LST composite data. Results showed that urban and rural areas exhibited a significant difference, with a p-value <0.01, in the ATC parameters, including mean annual surface temperature (MAST), yearly amplitude of surface temperature (YAST), and the revised phase shift parameter. The higher MAST and YAST, but lower phase shift values, were mainly associated with the urban areas. This finding indicated that urban areas contributed to changes in extreme temperatures (the minimum and maximum temperatures) as well as to an overall warming. The regression analysis suggested that surface UHI intensities were positively correlated with the differences in MAST (R2 = 0.9) and YAST (R2 = 0.5) between urban and rural areas, but negatively correlated (R2 = 0.2) with the differences in the revised phase shift parameter. In addition, highest surface UHIs (∼3 k) and largest differences in ATC parameters were observed in tropical regions, followed by temperature zones, continental (cold) zones, and arid zones. Overall, this study revealed that urbanization-induced land cover changes could influence urban systems by enhancing temperature variations. However, it should be cautioned that uncertainty in the analysis may arise from the characterization of surface UHIs for the selected ∼2000 cities with varying configuration and morphological dimensions.

Snow cover mapped daily at 30 meters resolution using a fusion of multi-temporal MODIS NDSI data and Landsat surface reflectance

Snow fall and melt events are complex meteorological phenomena that help chart the effects of climate change and impact many critical environmental processes including hydrologic and biogeographic systems. Daily snow maps, derived from MODIS imagery, provide managers and researchers with vital snow cover information, but only at spatial scales of 500 m or more. Finer resolution time series maps, however, retain large temporal gaps, particularly during recurrent cloud cover. This paper’s authors have developed the novel algorithm MODSAT-NDSI to harness the strengths of both coarse and finer spatial resolution imagery by fusing MODIS and Landsat normalized difference snow index (NDSI) data. Daily 30 m snow cover maps were thus generated for 2000 – 2017 with an overall accuracy of 90%, using 33 validation sites distributed throughout south-central British Columbia. Snow cover trends were analyzed across stratified elevation bands and land cover types, revealing that snow cover persists under lower elevation forests for an average of 23.5 d longer than in adjacent open areas during spring. We conclude that the MODSAT-NDSI approach captures temporal and spatial advantages of freely available snow cover datasets and can be modified to suit a variety of novel investigations relating to snow cover or other spectral indices.

Geospatial Patterns and Drivers of Forest Fire Occurrence in Romania

Timely and accurate spatial explicit forest fire risk assessment and mapping is essential for forest fire prevention and suppression preparedness, firefighting resources allocation, and efficient multi-level fire management policies. This paper describes the application and validation of an approach for forest fire risk analysis and fire risk zoning over Romania in order to identify areas at national scale where fires are most likely to occur and to threat existing values, resources and assets. A modeling approach based on logistic regression using historical fire observations has been developed based on environmental, socio-economic and demographic data availability at national scale. Ignitions were positively related to south-western slopes and occurred mostly in fuel type of xerophyte oaks as well as in areas of heterogeneous (natural/agricultural) landscape. In addition to the human variables the pattern of ignitions was also significantly related to slope and temperature of the driest quarter. The risk zones produced by the multiple logistic regression model presented satisfactory accuracy when compared with historical fire perimeters extracted from MODIS imagery. The findings of this study could be used by fire managers to implement prevention measures at forest areas with high fire risk. Furthermore, attention should be given to areas with high fire ignition probability, where the vulnerability and potential impact is higher.

ANALISA PERBANDINGAN BERDASARKAN IDENTIFIKASI AREA KEBAKARAN DENGAN MENGGUNAKAN CITRA LANDSAT-8 DAN CITRA MODIS (Studi Kasus : Kawasan Gunung Bromo)

Land and forest fire is a condition where the land and forest are hit by flame, causing land and forest destruction, economic losses, and environmental value. In the dry season, some of the regions in Indonesia often hit by flame. One example is a fire that occurred in the area of Mountain Bromo on October, 20th to 23rd 2014. These fires resulted in approximately 1.487 Ha of savanna forest got burned. Burned area can be identified by using remote sensing data, such as Landsat-8 and MODIS imagery. The method of identification used is the NDVI (Normalized Difference Vegetation Index) that utilizes near-infrared band and red band. Burned area is classified based on the value of the three threshold models. The use of multiple threshold models is done with the purpose of seeking the greatest accuracy value. Based on the results of burned area identification, obtained the greatest accuracy values of Landsat-8 is 48.394% from the μ - 1σ models and the wide of burned area is 1.354,5 Ha. While the greatest accuracy values of MODIS is 57,089% from the μ models and the wide of burned area is 1.005,209 Ha.

Multidimensional Arrays for Analysing Geoscientific Data

Geographic data is growing in size and variety, which calls for big data management tools and analysis methods. To efficiently integrate information from high dimensional data, this paper explicitly proposes array-based modeling. A large portion of Earth observations and model simulations are naturally arrays once digitalized. This paper discusses the challenges in using arrays such as the discretization of continuous spatiotemporal phenomena, irregular dimensions, regridding, high-dimensional data analysis, and large-scale data management. We define categories and applications of typical array operations, compare their implementation in open-source software, and demonstrate dimension reduction and array regridding in study cases using Landsat and MODIS imagery. It turns out that arrays are a convenient data structure for representing and analysing many spatiotemporal phenomena. Although the array model simplifies data organization, array properties like the meaning of grid cell values are rarely being made explicit in practice.

Evaluation of satellite retrievals of water quality parameters for Lake Victoria in East Africa

Lake Victoria in East Africa is a major ecosystem, whose size and importance has warranted the exploration of MODIS imagery to provide continuous and accurate water quality information. To this effect, two sea expeditions (in November 2014 and February 2015) were carried out to collect in situ lake surface temperature (LST), chlorophyll a (Chl) and Secchi disk depth (SDD) to compare with the corresponding satellite derived variables. Comparisons were made based on the following error matrices: coefficient of correlation (r), mean square error (MSE), root mean square error (RMSE), mean absolute deviation (MAD) and mean absolute percent error (MAPE). In general the results showed that satellite derived LST gives a good estimate of in situ LST. Conversely, the error matrices indicated that satellite derived Chl yielded more uncertainty when compared with in situ Chl. This could be because of the fact that the MODIS Chl product algorithm was designed for oceans and may not be appropriate for inland lakes. Satellite derived SDD compared fairly well with in situ SDD. From these results it is evident that with more in situ observations and improved algorithms, MODIS can be useful to improve how water quality is monitored on Lake Victoria.

Estimating net irrigation requirement of winter wheat using model- and satellite-based single and basal crop coefficients

Evapotranspiration (ET) is one of the key parameters in water and energy balance equation. According to FAO 56, crop evapotranspiration (ETc) is calculated from multiplying reference evapotranspiration (ET0) by crop coefficient (Kc). But, due to excessive simplification of Kc curve in the FAO approach, potential evapotranspiration (ETp) would be miscalculated. Therefore, accurate estimates of ETp entail improving Kc estimates. In this study, Kc curves of early- and late-planted winter wheat were obtained based on two main satellite-based methods: (1) ratio approach (2) vegetation indices (VIs) approach. In the ratio approach, basal crop coefficient (Kcb) and single crop coefficient (Kc) was directly calculated from the ratio of potential transpiration (Tp) to ET0 (using SWAP) and ETp to ET0 (using SWAP and the Priestly-Taylor equation), respectively. The VI approach makes use of Landsat 7 (ETM+) and 8 (OLI) and also MODIS imagery in order to extract soil adjusted vegetation index (SAVI). The Kcb curves were evaluated against field measured leaf area index (LAI) in 2014-15 growing season. After each Kc curve was modeled, net irrigation requirement (NIR) was calculated on daily and season basis. Results showed that the SWAP approach was weak in estimating the Kcb and Kc curves especially at the late-season stage. The VI approach could properly detect changes in vegetation cover during an entire growing season. But, when it came to Kc curve modelling, the VI approach was limited to the values given in FAO 56. However, the Priestly-Taylor approach compensated for this limitation therefore yielded more sensible trends in Kc curves. Results indicated that the VI approach reduced estimates of NIR of late-planted winter wheat compared with the FAO-recommended approach by 5.37%. The Priestly-Taylor approach resulted 21.72 and 0.32% lower NIR compared with the FAO-recommended approach respectively for early- and late-planted winter wheat. The decrease in NIR from satellite-based approaches derived from more realistic Kc curves during the entire growing season. Overall, making use of the satellite-based approaches could improve water management on regional scales.