# Mapping Smallholder Yield Heterogeneity at Multiple Scales in Eastern Africa
## 4 Discussion  
Our results highlight the potential of using high-resolution satellite imagery for generating smallholder productivity estimates. Overall, by using two or three relatively cloud-free satellite images and growing season mean temperature, we successfully capture about one-third to one-half of the variation in yield measurements. The competitive performance by the uncalibrated approach is noteworthy as this approach does not require any ground data for calibration and, thus, has a greater potential for scale up to broader regions. Although a considerable amount of variability in ground-based yield estimates remains unexplained by our satellite estimates, some of this variability is likely noise in the ground-based estimates, as indicated by the fact that results were better when using district than division aggregates. Moreover, satellite-based estimates do not need to be perfect to be useful for a range of applications [4]. For example, yield differences can be used to evaluate the impact of certain agricultural interventions by governmental programs or nongovernmental organization (NGO) projects. Furthermore, the ability to map yield at field level across a region over time could contribute to our ability to understand the magnitude and determinants of yield gaps, thus enabling more effective transformation in agronomic managements. Several of the multiple refinements tested in this study helped to improve the uncalibrated model performance, among which using MTCI seems to be the most effective option in this region. The consistent outperformance of MTCI compared to other VIs for both the B-K sub-region and the 1 AF region is likely due in part to its greater sensitivity to canopy chlorophyll content [15; 24]. Potentially more important is the fact that MTCI is much less affected by atmospheric conditions because its calculation is based on the difference in reflectance between two nearby bands that will be similarly affected by atmospheric scattering [15]. As is highlighted in Figure 7, GCVI showed obvious patterns associated with cloud edges and haze, whereas MTCI was less affected. Unfortunately, several of the most recent high-resolution satellite sensors (e.g., Planet Scope and Worldview series) lack the red edge bands for calculating MTCI. A second lesson, consistent with the study of the US by reference [8], is that using Biomass\(\times\)HI to derive yields for training was consistently superior to relying on the direct APSIM yield simulation. Figure 7: Snapshots of June 7 image by Sentinel-2 in raw and the derived GCVI and MTCI. A red square highlighted the area where cloud contamination affected the GCVI more than MTCI. The poorer performance by using APSIM simulated yields instead of biomass likely indicates the weaker capability of APSIM in reproducing the temperature effect on grain number and grain filling rate (which jointly determined the end-season yield) than the effect on photosynthesis and biomass accumulation; thus, the more mechanistic simulation of grain number and grain filling adds another layer of uncertainty. Using constant HI works well likely because a substantial fraction of weather-induced HI variability is already captured by the biomass by the time when total kernel numbers is determined (i.e., flowering stage in APSIM), thus, a dynamic HI approach may double-account the weather stress. Employing other crop models is not likely a promising solution, because almost all crop models have no memory of the temperature effects on photosynthesis that will be carried over to yield production [23]. Contrary to our expectation, using updated phenology parameters did not improve the yield estimation, even though the updated parameters resulted in more realistic timing of the peak season. One caveat is that we only tested a set of parameters that were derived in the US Midwest, which may not fit this tropical region.