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The use of multi-temporal MODIS images with ground data to distinguish cotton from maize and sorghum fields in smallholder agricultural landscapes of Southern Africa a
Mbulisi Sibanda & Amon Murwira
a
a
Department of Geography and Environmental Science, University of Zimbabwe, Harare, Zimbabwe Available online: 31 Jan 2012
To cite this article: Mbulisi Sibanda & Amon Murwira (2012): The use of multi-temporal MODIS images with ground data to distinguish cotton from maize and sorghum fields in smallholder agricultural landscapes of Southern Africa, International Journal of Remote Sensing, 33:16, 4841-4855 To link to this article: http://dx.doi.org/10.1080/01431161.2011.635715
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International Journal of Remote Sensing Vol. 33, No. 16, 20 August 2012, 4841–4855
The use of multi-temporal MODIS images with ground data to distinguish cotton from maize and sorghum fields in smallholder agricultural landscapes of Southern Africa
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MBULISI SIBANDA∗ and AMON MURWIRA Department of Geography and Environmental Science, University of Zimbabwe, Harare, Zimbabwe (Received 9 September 2009; in final form 6 April 2011) In this study, we test whether we can significantly (p < 0.05) distinguish cotton (Gossypium hirsutum L.) fields from maize (Zea mays L.) and sorghum (Sorghum bicolor) fields in smallholder agricultural landscapes of the Mid-Zambezi Valley, Zimbabwe, using a temporal series of 16-day Moderate Resolution Imaging Spectroradiometer – normalized difference vegetation index (MODIS NDVI) data. We test this hypothesis at different phenological stages over the growing season, that is, early green-up onset, late green-up onset, green-peak, early senescence and late senescence. We also statistically compare the rate of change in the greenness of the three crops at the three phenological stages. Results show that we can significantly (p < 0.05) distinguish cotton fields from maize and sorghum fields using 16-day MODIS NDVI data during the late green-up onset as well as during the green-peak stage of the three crops. Our results indicate that cotton can successfully be distinguished from maize and sorghum in spatially heterogeneous smallholder agricultural landscapes using temporal MODIS NDVI.
1.
Introduction
Remote sensing the phenology (e.g. green-up onset, green-peak onset, senescence onset and length of growing season) of different crop types is important even before estimating their specific area coverage (Wardlow et al. 2007), particularly in smallholder agricultural landscapes. A number of remote-sensing studies have distinguished different crops as well as vegetation surfaces based on their phenology using multi-temporal satellite data, particularly from the near-daily satellites such as the National Oceanic and Atmospheric Administration Advanced Very High Resolution Radiometer (NOAA AVHRR) with 1–8 km spatial resolution (Rasmussen 1997, Jakubauskas et al. 2002, Xiaoa et al. 2005) and a radiometric sensitivity of up to 10 bits. However, the commonly used 1 km spatial resolution and sometimes 8 km spatial resolution AVHRR limits the ability of NOAA AVHRR to map complex land-cover types in spatially heterogeneous landscapes. This is a result of mixed pixels that generalize spectral response from multiple land-cover types contained within the 1–8 km footprint (Wardlow et al. 2007). As a result, the coarse spatial resolution makes NOAA AVHRR inappropriate for distinguishing agricultural crops in *Corresponding author. Email:
[email protected] International Journal of Remote Sensing ISSN 0143-1161 print/ISSN 1366-5901 online © 2012 Taylor & Francis http://www.tandf.co.uk/journals http://dx.doi.org/10.1080/01431161.2011.635715
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areas with high spatial heterogeneity such as in smallholder agricultural landscapes in Africa. The recent deployment of advanced optical sensors, such as the Moderate Resolution Imaging Spectroradiometer (MODIS), which are more sensitive to vegetation reflectance than their predecessors, on board the Terra and Aqua satellites may have improved the capacity to separate different crop types, thereby improving croptype mapping in spatially heterogeneous areas. Unlike NOAA AVHRR with 1–8 km spatial resolution and a radiometric sensitivity of 10 bits, MODIS has a moderate spatial resolution of 250, 500 and 1000 m bands, as well as an improved radiometric sensitivity of 12 bits. The combination of the high temporal resolution of a neardaily revisit frequency, a high radiometric resolution, as well as a moderate spatial resolution of 250 m may enable differentiation of different crop surfaces especially in smallholder agricultural landscapes (Chen et al. 2006, Wardlow et al. 2007). The ability to apply remote sensing to distinguish between crop types in smallholder agricultural areas is particularly important as smallholder agriculture constitutes the most predominant land-use practice in many agriculture-based economies of the world (FAO 2006). Thus, in order to make remote sensing useful to a wider user community including planners and conservation organizations, it is important to develop applications tailored for smallholder agricultural landscapes. In this regard, the ability to use freely available moderate resolution remote sensors such as MODIS to monitor areas under different crop types is particularly important and cost-efficient in supporting food security assessment programmes (Funk and Budde 2009). There is a growing interest in the remote-sensing community to develop approaches to distinguish crops using remotely sensed data as well as remotely sensed indices, particularly following the launch of MODIS (Reed et al. 1994, Fuller 1998, Jakubauskas et al. 2002, Sakamoto et al. 2005, Doraiswamy and Stern 2007, Wardlow et al. 2007, Galford et al. 2008, Simonneaux et al. 2008, Boschetti et al. 2009, Funk and Budde 2009). These applications have had various degrees of success. For example, work by Moulin et al. (1998) and Doraiswamy et al. (2004) showed that different crop models may be better understood by combining remotely sensed data with statistical techniques for yield forecasting over large areas based on time-series data. In addition, Xiaoa et al. (2005) successfully used MODIS images for identifying inundation and rice paddy fields in southern China. Jakubauskas et al. (2002) conducted a harmonic/Fourier analysis of a time series of NOAA normalized difference vegetation index (NDVI) data in developing a technique that successfully segregated maize from other crops in large-scale farms of Kansas in the USA. However, it is apparent that most of the MODIS-based studies have focused on large forest areas or large areas of natural vegetation, as well as large commercial farms for crops. Only a few studies have statistically tested whether different crops can be distinguished using multi-temporal MODIS remotely sensed data in smallholder agricultural landscapes such as those of Southern Africa. In this study, we used temporal MODIS-derived NDVI images of the 2007 cropping season (January–June) to test statistically whether and at which phenological stage, for example, green-up onset, green-peak onset or senescence onset, we can significantly (p < 0.05) separate cotton (Gossypium hirsutum L.) from maize (Zea mays L.) and sorghum (Sorghum bicolor) in a smallholder agricultural landscape of the lower Zambezi Valley, Zimbabwe. We also statistically compared the rate of change in the greenness of cotton, maize and sorghum (NDVI values per pixel) as a function of different phenological stages over the growing season.
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2. Materials and methods
The study was conducted in the communal lands of the Mid-Zambezi Valley in Mbire district, Zimbabwe (figure 1). Communal lands are a land category that is characterized by communal land ownership, and they are subdivided into administrative or management units called wards. We focused our study on wards 2, 3 and 9 of Mbire district (figure 1). The study area is located between 30◦ 00 and 31◦ 45 E and 16◦ 00 and 16◦ 30 S. Mbire has a dry tropical climate, with a low and erratic annual rainfall of between 350 and 1300 mm and a mean annual temperature of 25◦ C. The average altitude is 400 m above mean sea level (a.m.s.l.) (CIRAD-Emvt 2002). The soils of the study area vary from eutric leptosols, to tropically leached iron-bearing soils and calcic luvisols. The natural vegetation of Mbire district is mainly deciduous dry savannah, dominated by mopane trees (Colophospermum mopane) (Gaidet et al. 2003). Most human settlements in Mbire district are along the Angwa and Manyame rivers in a wildlife conservation frontier. The wildlife is mainly concentrated in areas that are tsetse infested, while humans are settled mainly in tsetse-free areas (Cumming and Lynam 1997, Murwira and Skidmore 2005). The major human activity in this district is dryland farming of cotton, maize and sorghum, and often results in human–wildlife conflicts. Cultivation of cotton in the Mid-Zambezi Valley increased rapidly during the 1980s after tsetse fly eradication (CIRAD-Emvt 2002). The increase in human population in Mbire district after the eradication of tsetse fly in the 1980s has resulted in the expansion of and intensification of agriculture in the study area (CIRAD-Emvt 2002, Murwira and Skidmore 2005).
ZIMBABWE
30° 25′ 55″ E
30° 41′ 17″ E
16° 0′ 58″ S
16° 0′ 58″ S
30° 10′ 34″ E
CHISUNGA (2) NESHANGWE A (3)
16° 16′ 19″ S
NESHANGWE B (9)
16° 16′ 19″ S
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2.1 Study area
Legend Mbire district ward 2,3 and 9
0 30° 10′ 34″ E
30° 25′ 55″ E
20 Km
30° 41′ 17″ E
Figure 1. The study area covering wards 2, 3 and 9 of Mbire district. Map in geographic coordinates based on the WGS 84 reference spheroid.
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Sampling was conducted in a SEE–NWW orientation following the direction of tsetse eradication, which generally is observed to define the gradient of cropping intensification (Cumming and Lynam 1997). We used stratified random sampling to select the agricultural fields from which data on crop types were collected. Specifically, we used three spatial data layers, that is wards, soil types and agricultural fields, to define sampling strata. Based on these strata, three transects were randomly generated in a geographic information system (GIS) following a SEE–NWW orientation. We selected 25 homesteads that intersected these transects. We then used a handheld global positioning system (GPS) to locate the selected homesteads. A farmer from a selected homestead led us to the agricultural fields where crop data were collected. Figure 2 shows the distribution of sampled fields. As mentioned earlier, crop data were collected in the sampled agricultural fields. In the selected fields, cotton, maize and sorghum stalks were used as indicators of which crop was cultivated in each of the fields. Presence of stalks was used to verify the information provided by the farmer regarding the type of crops grown because the field survey was conducted during the dry season in October. Next, we used a GPS to measure the centre coordinates of each cotton, maize and sorghum field to minimize the factor of mixed pixels. Finally, we manually digitized fields from Google earth satellite data (Keyhole 2007). The Google earth image domain is based on the 2.5 m SPOT panchromatic band (Guo et al. 2010). The digitized fields were later used for distinguishing fields from other land-cover types through the performance of a mask operation in a GIS. 2.3 Remote-sensing data In order to characterize crop phenology, MODIS 16-day NDVI data composites (MOD13Q1) covering the period from early growth to harvesting of all crops, that is from 1 January to 10 June 2007, in the study area were selected and downloaded from the USGS EROS Data Center (https://wist.echo.nasa.gov) (NASA 1999). NDVI is estimated by the following equation:
30° 24′ 0″ E
30° 12′ 0″ E
16° 0′ 0″ S
PNIR − PRED , PNIR + PRED
(1)
30° 36′ 0″ E
16° 12′ 0″ S
An gw
a
Angwa
16° 0′ 0″ S
NDVI =
16° 12′ 0″ S
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2.2 Crop data
Sampled field location Rivers Mbire district 0 2.5 5
30° 12′ 0″ E
30° 24′ 0″ E
10
15
20 Km
30° 36′ 0″ E
Figure 2. The distribution of the sampled field clusters in wards 2, 3 and 9. Map in geographic coordinates based on the WGS 84 reference spheroid.
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where PNIR is the near-infrared (846–885 nm) and PRED is the red (600–680 nm) reflectance for the respective MODIS bands. The NDVI images were then converted from sinusoidal projection to WGS 1984 UTM zone 36 in a GIS. In this study, we used NDVI to distinguish cotton from maize and sorghum because NDVI has been widely proven to estimate vegetation greenness objectively, that is the total concentration of chlorophyll in vegetation (Tucker and Sellers 1986, Reddy et al. 2001, Doraiswamy et al. 2003, Zhang et al. 2003, Sakamoto et al. 2005, Xiaoa et al. 2005). The relationship between NDVI and green biomass is based on the amount of photosynthetically active radiation absorbed by the canopy quantified via this index (Tucker 1979, Tucker and Sellers 1986). NDVI has also been widely used to characterize crop phenology (Jakubauskas et al. 2002, Zhang et al. 2003, Funk and Budde 2009). The MODIS 16-day spectral data composites were improved at the EROS Data Center by applying atmospheric algorithms, cloud removal and bidirectional reflectance distribution function (BDRF) correction (Chen et al. 2006). The algorithms used to generate MODIS composites include maximum value compositing (MVC) developed by Holben (1986) and view angle constrained for preventing selection of off-nadir pixels for bidirectional reflectance (BRDF)-corrected NDVI compositing (Chen et al. 2006). In addition, we used the MODIS 16-day composite data as they have a sub-pixel accuracy of 50 m at nadir (Wolfe et al. 2002). Provisional analysis using ground control points computed by the MODIS team after the most recent update of sub-pixel geolocation error correction indicated a mean geolocation error of 18 m across track and 4 m along scan with standard deviations of 34 and 40 m, respectively (Wolfe et al. 2002). These errors are less than the sizes of our fields, which are at least 7.5 ha, hence making the 250 m pixel sized satellite data appropriate for this study. It is for these reasons that we adopted this NDVI product for this study. 2.4 Analysis of MODIS time series to detect different crops We extracted NDVI values for different crop types derived from GPS surveyed fields from the near planting period to senescence, that is, January–June 2007, by overlaying the field maps of crops with MODIS-derived NDVI maps for each date in the Integrated Land and Water Information System GIS (ITC 2005). We used 19 samples for maize, 25 samples for cotton and 25 samples for sorghum. Table 1 shows the descriptive statistics of the sampled cotton, maize and sorghum field sizes estimated using SPOT images. Next, we used the respective NDVI time series for cotton, maize and sorghum to test whether the three crops could be distinguished based on several steps. First, exploratory statistical data analysis was conducted to test whether NDVI values of
Table 1. Descriptive statistics of cotton, sorghum and maize field sizes estimated using SPOT images.
Crop Cotton Maize Sorghum
Mean area (ha)
Minimum area (ha)
Maximum area (ha)
Standard deviation of area (ha)
Number of pixels
7.55 8.01 15.21
6.27 6.28 6.25
14.38 13.99 30.40
1.74 1.99 8.37
29 30 56
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cotton, maize and sorghum followed a normal distribution based on the Kolmogorov– Smirnov test. Kolmogorov–Smirnov test results indicated that the NDVI data for 1 January to 26 June were not significantly (p > 0.05) deviating from the normal distribution. Since the data did not significantly deviate from a normal distribution and there were no significant differences in NDVI values of crops on different dates, we used analysis of variance (ANOVA) to test whether there were significant (p < 0.05) differences between the mean NDVI values of cotton, maize and sorghum at 16-day time intervals (from the early green-up, up to the late senescence period) in the second stage. Prior to ANOVA tests, Levene’s test was used to test for the equality of NDVI variances for cotton, maize and sorghum at 5% significance level (table 2). We then used Student’s t-test for independent samples as a post hoc analysis for testing whether there were significant (p < 0.05) differences between the mean NDVI values of cotton and maize, cotton and sorghum and maize and sorghum at 16-day time intervals from the early green-up, up to the late senescence period. As a way to further compare the change in NDVI values among the three crops at different phenological stages throughout the season, we computed the change in NDVI values for each crop per pixel per field by calculating the difference between successive 16-day interval NDVI data. We then used a Fourier transformation to test whether and in what form was the change in mean NDVI per pixel between successive image dates for cotton, maize and sorghum as a function of different phenological stages over the growing season using the formula f (x) = a0 +
∞ nπ x nπx + bn sin , an cos L L
(2)
n =1
where f (x) is the best-fit regression line for mean changes in NDVI between successive image dates for cotton, maize or sorghum, a0 is the intercept of the mean change in NDVI between successive image dates for cotton, maize or sorghum, an is the cosine of the coefficient of change in mean NDVI between successive image dates for cotton, maize or sorghum, bn is the sine of the coefficient of change in mean NDVI between successive image dates for cotton, maize or sorghum, L is the number of image days throughout the year, π (pi) is a constant and x is a particular image date. To accomplish this, we fitted regression lines that best described the rate of change in the greenness of cotton, maize and sorghum over the growing season based on exploratory data analysis involving an inspection of scatter plots with NDVI change as the response variable and time of the season as the predictor variable. This was followed by testing whether there was a significant difference in the rates of change in the greenness (NDVI values) of cotton and maize as well as cotton and sorghum by comparing the slope and intercepts of the regression functions fitted on the rate of change in NDVI for maize, cotton and sorghum. If the rate of change in the greenness of cotton and maize as well as sorghum is not significantly different, we would conclude that these crops change phenologically at the same rate. On the other hand, if the test of differences between the intercepts of regression functions fitted on the rate of change in the greenness of sorghum, maize and cotton yielded significant differences, we would conclude that the regression lines describing the form of the rate of change in the greenness of all the three crops were parallel. We then graphically plotted the gradients of change in NDVI values of cotton, maize and sorghum to facilitate interpretation.
F p
1.434 0.246
0.0171 0.0288 0.0184
Cotton Maize Sorghum 3.134 0.050
0.0110 0.0237 0.0105
17 Jan
2.079 0.133
0.0025 0.0064 0.0072
2 Feb
1.424 0.248
0.0032 0.0046 0.0073
18 Feb
2.917 0.061
0.0028 0.0037 0.0066
6 Mar
Note: F is the F statistic and p is the significance at 95% confidence interval.
Levene’s test of homogeneity of variances
1 Jan
Date
2.364 0.102
0.0028 0.0052 0.0039
22 Mar
0.809 0.450
0.0050 0.0033 0.0062
7 Apr
NDVI variances
0.447 0.641
0.0051 0.0070 0.0050
25 Apr
0.533 0.589
0.0053 0.0051 0.0077
9 May
Table 2. NDVI variances of cotton, maize and sorghum across the growing season.
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0.383 0.683
0.0029 0.0055 0.0047
25 May
0.197 0.822
0.0029 0.0030 0.0040
10 Jun
0.050 0.952
0.0035 0.0026 0.0036
26 Jun
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Results of the ANOVA test showed that there are no significant (p > 0.05) differences among mean NDVI values of cotton, maize and sorghum during the green-up onset up to the late senescence stage, that is, from early January to late June, when averaged over all the dates. Results of the Student’s t-test also showed that there were significant (p > 0.05) differences between mean NDVI values of cotton and maize on different dates. However, on 17 January, Student’s t-test results did not show any significant (p > 0.05) differences between mean NDVI values of cotton and maize. In addition, Student’s t-test results showed that significant differences between NDVI values of cotton and sorghum from the green-up onset to the late senescence stage occur only once on 1 January (figures 3 and 4). Results also showed that during this time, cotton, maize and sorghum are approximately a month in the field and are now covering a large proportion of the ground. Furthermore, we observed that differences between cotton and maize begin on 1 January, which is the green-up onset of all the crops. Student’s t-test results confirmed that there are indeed significant (p < 0.05) differences between the mean NDVI values of cotton and maize during this period. Significant (p < 0.05) differences between maize and cotton persist up to 22 March. We observe that this period marks the late green-up onset and the beginning of the green-peak stage of cotton, maize and sorghum. However, results also show that there is no significant (p > 0.05) difference in the mean NDVI values for sorghum and cotton from 17 January to 10 June 2007. There were also no significant (p > 0.05) differences between mean NDVI values of maize and sorghum from 1 January to 29 January. Figure 5 shows that the rates of change in NDVI values for cotton, maize and sorghum through the growing season are each best described by a third-order Fourier
0.7
0.6
NDVI
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3. Results
0.5
0.4
Cotton Maize Sorghum
0.3 1 17 2 18 6 7 January January February February March April Time (image dates)
23 April
9 May
26 June
Figure 3. Mean NDVI profiles of cotton, maize and sorghum for the 2007 growing season. The bars represent the 95% confidence interval.
NDVI
(a)
25 May
Sorghum
ab
Sorghum
ab
Sorghum
a
0.48
0.56
0.64
(k)
0.32
0.40
0.48
0.56
0.64
(g)
Cotton
a
Cotton
a
10 June
Maize
b
7 April
Maize
a
Sorghum
ab
Sorghum
a
0.35
0.42
0.49
0.56
0.63
(l)
0.32
0.40
0.48
0.56
0.64
(h)
0.32
0.48
0.56
0.64
0.40 Maize Sorghum 2 February
ab
0.32 Cotton
b
(d)
0.40
0.48
0.56
a
Cotton
a
Cotton
a
Cotton
a
26 June
Maize
a
23 April
Maize
a
Maize 18 February
b
Sorghum
a
Sorghum
a
Sorghum
ab
Figure 4. Mean NDVI values of cotton, maize and sorghum from green-up onset to late senescence stage. Bars represent the mean NDVI values of cotton, maize and sorghum. Whiskers represent the 95% confidence interval. Mean NDVI values that are not significantly different are denoted by the same letters.
9 May
Maize
b
22 March
Maize
b
Maize 17 January
a
0.32
Cotton
a
Cotton
a
Cotton
a
0.64
0.32
0.48
0.56
0.64
(j)
0.32
0.40
0.48
0.56
0.64
(f)
0.32
0.40
0.48
0.56
0.64
0.32
Sorghum
a
Sorghum
ab
Sorghum
b
0.40
Maize
a
Maize 6 March
b
Maize 1 January
b
(c)
0.40
Cotton
a
Cotton
a
Cotton
a
(b)
0.40
0.48
0.56
0.64
(i)
0.32
0.40
0.48
0.56
0.64
(e)
0.25
0.30
0.35
0.40
0.45
0.50
0.55
NDVI NDVI NDVI
NDVI
NDVI
NDVI NDVI
NDVI NDVI NDVI
NDVI
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1.4
Change in mean NDVI
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1.2
1
0.8
0.6
0.4
0.2 17
33
49
65
81 97 113 Time (16-day intervals)
129
145
161
Figure 5. Change in mean NDVI between successive image dates for cotton, maize and sorghum from 17 January to 10 June 2007 (17 = 17 January, 33 = 2 February, 49 = 18 February, 65 = 6 March, 81 = 22 March, 97 = 7 April, 113 = 23 April, 129 = 9 May, 145 = 22 May and 161 = 10 June).
transformation or hump-shaped curve. Specifically, we observe a positive change in NDVI values per pixel of cotton, maize and sorghum from January to mid-March (table 1), which coincides with the green-up onset of all the three crops. This is then followed by a negative change from mid-March to early June for cotton, maize and sorghum. The decrease in the rate of change in NDVI during the period of mid-March to early June coincides with the senescence of all the three crops. Statistical comparison of the rates of change in the greenness (mean NDVI values) of these three crops during the green-up period, that is, late January to early March, shows that there are significant (p < 0.05) differences between cotton and maize. The rate of change for cotton during the green-up stage is significantly (p < 0.05) higher than the rates of change for maize and sorghum. Slope coefficients (b1 ) of the rate of change in mean NDVI for cotton, maize and sorghum during the green-up period are 0.021, 0.012 and 0.011, respectively (table 3). We also observe a significant (p < 0.05) difference between the rate of change in the greenness of cotton and the rate of change in the greenness of maize as well as sorghum (figure 6). Significant (p > 0.05) differences were observed Table 3. Intercepts, slope coefficients and R2 of the regression lines of the change in mean NDVI values of cotton, maize and sorghum among successive image dates. Crop Cotton Maize Sorghum
p-Value
R2
Constant
b1
b2
b3
0.00 0.00 0.00
0.98 0.99 0.99
0.89 0.314 0.371
0.021 0.012 0.011
0.00 0.00 0.00
1.155 4.58 3.63
Discriminating different crops with temporal MODIS NDVI (a)
(b) 0.03 a
1 0.8
b
b
0.6 0.4 0.2
Change in mean NDVI
Change in mean NDVI
1.2
a 0.02
b
b
0.01
0
0 Cotton
Maize Crops
Cotton
Sorghum
Significant (p < 0.05) differences in the intercepts
Maize Crops
Sorghum
Significant (p < 0.05) differences in coefficient b1
(c) 6 Change in mean NDVI
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4851
b
5
b
4 3
a
2 1 0 Cotton
Maize Crops
Sorghum
Significant (p < 0.05) differences in coefficient b3
Figure 6. Significant (p < 0.05) differences in the intercepts and slope coefficients of the change in mean NDVI between successive image dates for cotton, maize and sorghum. The slope coefficients (b2 ) of the change in mean NDVI between successive dates for maize, cotton and sorghum were equal to 0 and therefore they are not shown in the caption.
again between the rate of change in the greenness (mean NDVI values) of cotton and maize as well as sorghum during the senescence stage, that is, late March to early June 2007. Also, it can be observed that the change in mean NDVI values for cotton during the senescence stage is different from that of maize and sorghum (figure 6). Cotton has a slope of 1.155 compared with a slightly higher slope of 4.58 and 3.63 for maize and sorghum, respectively (table 3). Figure 6 also shows that there were significant (p < 0.05) differences between the slopes and intercepts of the rate of change in the greenness of cotton and maize. We further observed that the level at which the rate of greenness of cotton changes is higher than that of maize as well as sorghum (figures 5 and 6). However, no significant differences were observed between the rate of change in mean NDVI for cotton, maize and sorghum during their green-peak. The slope coefficients (b2 ) of the rate of change in the mean NDVI values for cotton, maize and sorghum are 0.00 (table 3 and figure 6). 4. Discussion The results of this study using NDVI time series derived from MODIS satellite imagery indicate that the best phenological stage at which to distinguish maize and
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sorghum from cotton is during the late green-up period of the three crops as well as during the green-peak stage of these crops. We make a claim that this is explained by the different growing periods of the three crops. Specifically, although cotton, maize and sorghum are grown almost at the same time, cotton has a longer growing period of up to 250 days, while maize and sorghum have a shorter growing period of around 125 days. The relatively shorter growing period of maize and sorghum means that they are senescing earlier (in March) than cotton, which senesces in June. When maize and sorghum are at their senescence stage in March, cotton is still at its green-peak. We, therefore, deduce that when NDVI values of maize and sorghum are decreasing steeply in March, NDVI values of cotton remain relatively higher during the same time, therefore giving an opportunity to distinguish cotton from the two cereals using remotely sensed data. Furthermore, our results indicate that maize can be distinguished from other crops at the green-up stage using remotely sensed NDVI, which is in general agreement with the findings in the USA that determined that maize can be separated from other crops during the green-up stage using NDVI (Doraiswamy and Stern 2007). However, the only difference between the finding of this study and the one in the USA is that in the USA maize could be separated because its rate of green-up was faster than other crops, while in this study, the rate of maize green-up was significantly slower than that of cotton and sorghum. The slower rate of maize green-up in this study may be explained by the low suitability of maize in our study area. Specifically, the Zambezi Valley has a dry tropical climate with low and unreliable annual rainfall of 650 mm on average and high temperatures of up to 40◦ C, which is not an optimal condition for maize but is optimal for drought-resistant sorghum and cotton, which green-up faster than maize under these conditions (CIRAD-Emvt 2002). However, in this study, results indicated that maize could not be distinguished from sorghum from the green-up onset to the senescence stage. This may be explained by the fact that maize and sorghum are both C4 plants belonging to the same grass family; hence they go through similar growth stages. Although we found the same hump-shaped relationship between the rate of change in the greenness of cotton and maize as well as sorghum (which is consistent with normal hump-shaped seasonal crop growth progression from the green-up onset through the green-peak to the senescence), we found a significant difference in the rate of change in the greenness of cotton and maize during the green-up period. Cotton has the highest rate of change followed by sorghum and finally maize. We thus deduce that cotton can be distinguished from maize and sorghum at the green-up onset mainly due to the relatively faster rate of change in the greenness of cotton compared with that of maize and sorghum. However, we could not find a significant difference between the rate of change in the greenness (NDVI values) of cotton and maize as well as sorghum during the senescence period, suggesting that cotton, maize and sorghum have similar rates of senescence but these senesce at different greenness levels. During the early green-up and the late senescence stage, results indicate that there are no significant (p > 0.05) differences in the mean NDVI values for cotton and sorghum based on the MODIS NDVI time series. There are two reasons for this phenomenon, particularly during the senescence period (22 March to 10 June 2007). First, unlike the way maize is harvested, the harvesting of sorghum does not involve complete removal of the sorghum plant but just the seed-bearing part. Second, sorghum tends to reshoot after harvest, thus resulting in high NDVI values. Therefore, due to the non-removal of the sorghum plant during harvesting, as well as the tendency of
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sorghum to resprout, sorghum tends to regain high NDVI values, thereby making it difficult to distinguish sorghum from cotton during the senescence stage. This result is consistent with the findings of Wardlow and Egbert (2002) that sorghum has a gradual decline in NDVI during the senescence stage but maintains a higher NDVI than maize and other crops towards the end of the growing season. The results of this study are an important preamble for further studies seeking to enhance understanding of the spatial distribution of different crop types in relation to changes in farming methods in smallholder agricultural landscapes of Southern Africa. However, we have to caution that the main challenge in using MODIS is the negative effect of cloud cover especially in December and January, which leads to loss of key information relevant for distinguishing different crop types. 5. Conclusion The main objective of this study was to test whether and at which phenological stage we can significantly distinguish cotton from maize and sorghum in smallholder agricultural landscapes of the Mid-Zambezi Valley, Zimbabwe, using a temporal series of 16-day MODIS NDVI data of the 2007 cropping season (January–June). From the results we come up with a number of conclusions. First, we conclude that MODIS NDVI temporal images can be used to distinguish cotton from maize during the late green-up onset as well as during the green-peak stage towards the senescence of the cereal crops. Second, we conclude that cotton cannot be distinguished from maize during the late senescence period of both cotton and maize using NDVI. Third, we conclude that cotton cannot be distinguished from sorghum during the early green-up onset up to the later senescence stage. Finally, we also conclude that the changes in the rate of change in NDVI values of cotton, maize and sorghum pixels throughout the growing season are best described by a quadratic function or hump-shaped curve. The findings of this study are an important preamble to enhancing the understanding of the spatial distribution of different crop types in heterogeneous landscapes for the purpose of supporting food production and monitoring programmes. Acknowledgements This work was conducted within the framework of the Research Platform ‘Production and Conservation in Partnership’ RP-PCP. I thank the Ministère Français des Affaires Etrangères et Européennes for supporting me through the French Embassy in Zimbabwe (RP-PCP grant/CA#2). We would like to recognize the contributions of the Department of Geography and Environmental Science, University of Zimbabwe (Dr M. Masocha, M.D. Shekede, I. Gwitira, F.M. Zengeya and M.M. Matavire). We wish to thank them for their efforts.
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