Crop yield determination is a crucial function in planning for food security of the population of a district or even of the whole country. Agriculture, as the backbone of many developing economies (especially in Africa), provides a substantial portion of their Gross Domestic Product (GDP). Thus, the possibility to obtain yield estimates with reasonable accuracy prior to harvest is important, since timely interventions can take place in case low yields are predicted.
Analysis of RS data alone, or in combination with other ancillary data (e.g., soil moisture), permits the determination of crop yield prior to harvest period (Dempewolf et al., 2014; Morel et al., 2014). The ability of RS to provide information on crop status and health (through NDVI or LAI) is a key contribution to the estimation of potential crop yield. Two methodological approaches to estimate crop yield with RS data are discussed here.
The first approach is a simplistic one in which an empirical relationship is determined between a vegetation index (e.g. NDVI) and in-situ measurement of yields at harvest time (Bolton and Friedl, 2013; Tucker et al., 1985). Ideally, the acquisition date of the image from which the vegetation index is computed coincides with the time of in-situ yield measurement. However, this is not always the case, thus requiring interpolation of one of two metrics. Other indices such as LAI have also been explored in crop yield estimation.
- Figure 4.12 Empirical relationship between NDVI and yield for an area of wheat fields in Russia (Source: https://faviessaywritings.com/econimages/821.jpg disadvantage of the above approach is that the established relationship is often only valid for the particular field, crop type and RS data acquired on specific date(s). This is because growth seasons differ in precipitation, temperature, crop cultivated, fertilizer application, and other biophysical and management factors. Consequently, no two seasons are the same and consequently the application of the developed relationship may give undesired results. Although the relationship can be made stronger by including more historical data, such result will mostly be suboptimal.
Another approach to crop yield estimation is through crop models. Better predictions can be achieved through models by considering the factors that affect crop growth and yield for a year of interest. Information such as meteorological and climatic data (surface temperature, rainfall, etc.), soil properties and farming practices are combined with spatially explicit RS-derived information such as slope and vegetation indices (NDVI) to model crop growth and eventual estimations of the crop yield (Dorigo et al., 2007 — data assimilation).
Within the STARS project, high resolution soil surveys coupled with hyperspectral remote sensing data were combined to estimate the spatial variability of grain yield prior to harvest period.
Rodrigues et al. (2015) mapped the within-field yield variability in a wheat field in Mexico using high resolution proximal soil sensing and hyperspectral RS data. The proximal soil sensing was carried out using a dual-dipole EM38 Mk2 sensor (Geonics, Mississauga, ON, Canada) conductivity measured simultaneously in the 0–0.75 and 0–1.5 m range. The correlation among yield, apparent soil electrical-conductivity and several narrow-band spectral indices that are known to be related to stress-detection photosynthetic indicators based on pigment were tested. Figure 4.13 presents a map of the within-field spatial variability of yield, which ranged from 4.6 to 8 t ha-1.
Figure 4.13 Spatial variability of wheat yield at the field scale in Mexico (Source: STARS Mexico team).
To understand possible causes of this spatial yield variability, the highest and lowest yield areas were identified on the yield map and corresponding reflectance values (from the hyperspectral image) were extracted for the whole cropping season. Based on this, a plot of the reflectance profile of the highest and lowest yield areas was made (Figure 4.14).
The figure shows that the highest (green lines) and lowest (red lines) yield region display similar spectral behavior from 400 to 770 nm, but differences in the reflectance at each yield level emerge from 770 nm to 840 nm (NIR).
Zarco-Tejada et al. (2005) found similar reflectance behaviour for low and high growth areas of cotton. Such differences in reflectance may open possibilities for nutrient stress diagnosis at the flowering stage while there is still time for crop management, or even at early stages such as GS31.
Figure 4.14 Reflectance profile from the highest/lowest yield areas on a test field across crop cycle (Source: STARS team, CIMMYT, Mexico).
Findings of the study demonstrate the potential of the use of multiple vegetation indices together with proximal soil sensing data in predicting yield spatial variability.
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