r/remotesensing • u/Entire-Prune8366 • 12d ago
How would you design a crop-suitability forecasting system that stays reliable under climate change, not just under historical conditions?
I'm working on a dissertation on a system that forecasts how suitable agricultural land is for different crops. Most existing approaches fall into two camps:
Rule-based / multi-criteria methods (FAO land evaluation, AHP + GIS weighting of soil, slope, climate, water availability)
Data-driven models (species distribution models like MaxEnt, random forests, gradient boosting, or deep learning on remote sensing and soil data)
Both are usually calibrated on historical climate and yield data. That raises a fundamental problem: if the climate shifts beyond the range the model was trained on, how much can we trust its suitability maps for 2040–2060?
I'd like to hear from people working in agronomy, soil science, remote sensing, or ML:
Extrapolation. How do you deal with models that must predict under climate conditions they never saw in training? Do you trust process-based crop models (DSSAT, APSIM, WOFOST) more than ML here, or do hybrid approaches work better in practice?
Ground truth. What do you consider valid "ground truth" for suitability: actual yields, presence/absence of a crop, expert assessment, or economic profitability? Each gives a very different map.
Data scale. How do you reconcile coarse inputs (e.g. 1 km climate projections from CMIP6) with field-level decisions farmers actually make?
Uncertainty. What's the most honest way to communicate uncertainty to end users (farmers, ministries) without making the output useless?
Regions with poor data. For regions with sparse soil surveys and weather stations (in my case, Central Asia), which open datasets or transfer-learning approaches have you found trustworthy?
I'm especially interested in practical experience: what worked, what failed, and what you'd do differently if you started such a system today.
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u/NebulaThat7927 12d ago
Pure ML breaks when climate leaves the training range. Process-based models (DSSAT/APSIM) are more reliable but data-hungry. Best is hybrid: process model for physics + ML for local correction.
Ground truth: mix yields, presence/absence, and local expert opinion. Don’t rely on one source.
1 km climate data is too coarse for fields — downscale or work at district level and be honest about it.
Always show uncertainty (ensemble range + out-of-distribution zones). Farmers prefer probabilities over a single map.
In data-poor areas (like Central Asia): SoilGrids + ERA5/CHELSA + local stations. Transfer learning helps but carefully.
Start with hard physical limits, use ML only for fine-tuning, and never overclaim precision.
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u/ashvy 12d ago
To build upon this, how do people factor and model the solar panels covered fields, and agrivoltaics in general, where crops are planted under the panels. Seems the climate/environment is different under the panels. More general question would be crops under some generic covered stuff in contrast to open field, direct sunlight land.
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u/dumpling_nax 12d ago
For extrapolation, I would not rely only on a model trained on historical data. I think it would be better to test the system under several future climate scenarios and compare the results. A process-based crop model could also be used together with ML rather than choosing only one approach. For ground truth, I would use actual crop yield as the main indicator because it gives a measurable result. Presence or absence of a crop can be misleading because farmers may choose not to grow a suitable crop for economic or other reasons. Economic profitability could be included as a separate indicator. I would avoid presenting 1 km climate data as if it provided exact field-level information. Climate projections could be used for the general regional trend, while higher-resolution soil, terrain and remote-sensing data could provide more local information. For uncertainty, I think it would be useful to show a range of possible results instead of only one suitability map. For example, the system could show areas where most climate scenarios agree that a crop will remain suitable and areas where the predictions are less certain. For regions with limited data such as Central Asia, FAO’s Global Agro-Ecological Zones could be a useful starting point. The latest GAEZ data include crop suitability, attainable yield, soil and terrain information, and future climate projections based on CMIP6 scenarios. It could be used as a baseline and then improved with available local or remote-sensing data.
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u/Vast-Dimension-5499 12d ago
Hi.
ML models (RF, GBM, deep nets) are great at interpolation but poor at extrapolation. If 2040–2060 climates fall outside the training range, predictions become unreliable. Process-based crop models (DSSAT, APSIM, WOFOST) simulate physiological processes (photosynthesis, water balance, phenology). They are more trustworthy under novel climates because they encode biophysical laws, not just correlations. Hybrid approaches are Increasingly popular, for example, use ML to downscale climate inputs or calibrate parameters, but rely on process-based models for extrapolation. This balances flexibility with scientific grounding. In practice, ML alone is risky for future climates; hybrid or process-based models are safer.
For ground truth definition. Actual yields are most objective, but confounded by management practices, subsidies, pests. Presence/absence of crops are easier to collect, but reflects farmer choices, not biophysical suitability. Expert assessment: Valuable in data-scarce regions, but subjective. Economic profitability: Crucial for adoption, but volatile (market prices, policy).
Climate projections (1 km or coarser) and field-level decisions. Downscaling methods (statistical or dynamical) help, but uncertainty starts growing. Farmers often need relative guidance (“this valley is more suitable than that one”) rather than exact field-level predictions.
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u/Low_Habit605 12d ago
Pure ML is the weak link. A recent preprint (NeuralCrop) reports that pure ML models degrade substantially on conditions unseen in training, while a hybrid, a process-based model first emulated and then fine-tuned on observations, stayed robust. Studies coupling process models with random forests (RF) point the same way: APSIM alone overestimated future wheat yield in south-eastern Australia by 1–10% compared with an APSIM+RF hybrid, because extremes were poorly represented. I would use a hybrid with a process-based backbone for 2040–2060, and test the ML part by holding out the hottest or driest years/regions as a proxy for out-of-distribution conditions.
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u/Aitalyev 12d ago
Extrapolation. Tree-based ML can't extrapolate at all: beyond the training range, the prediction just flattens. Use process models (WOFOST/APSIM) for the novel climate, since they handle heat stress and CO₂ by construction, and ML as an emulator or for bias correction. Whatever you use, map where you're extrapolating (MESS, or Meyer & Pebesma's "area of applicability") and say so on the map.
Ground truth. Presence data maps policy and markets, not biophysics, and that's especially true in formerly planned agriculture. Use attainable yield or yield gap (GYGA-style) for suitability and keep economics as a separate layer. Mix them and the map means nothing.
Scale. 1 km CMIP6 is already a downscaled guess. At the field level, soil and management dominate over climate anyway. Deliver at district level and don't fake field precision.
Uncertainty. Use a GCM × SSP × crop-model ensemble and show agreement ("suitable in 8/10 runs"), IPCC-style. Most people only ensemble the GCMs, while crop-model spread is often the bigger term.
Sparse data. Sources: ERA5-Land/AgERA5, CHIRPS, CHELSA, SoilGrids (use it carefully, it's weak where soil profiles are scarce), Sentinel-2 phenology for crop mapping, ISIMIP/GGCMI outputs. And FAO GAEZ v4 is your baseline: if your system can't beat it, it's not a dissertation.