S M A r t C a s t : P r e d i c t i n g s o i l m o i s t u r e i - - PowerPoint PPT Presentation

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Feb 06, 2024 238 likes •346 views

S M A r t C a s t : P r e d i c t i n g s o i l m o i s t u r e i n t e r p o l a t i o n s i n t o t h e f u t u r e u s i n g E a r t h O b s e r v a t i o n d a t a i n a d e e p l e a r n i n g f r a m e wo r k . I N S I G H T S

SLIDE 1

I N S I G H T S F R O M S A T E L L I T E S

S M A r t C a s t : P r e d i c t i n g s o i l m o i s t u r e i n t e r p o l a t i o n s i n t o t h e f u t u r e u s i n g E a r t h O b s e r v a t i o n d a t a i n a d e e p l e a r n i n g f r a m e wo r k .

J a m e s F o l e y , S a g a r V a z e , M o h a m e d E l A m i n e S e d d i q , A l e x e y U n a g a e v , N a t a l i a E f r e m o v a , I n t e r n a t i o n a l C o n f e r e n c e o n L e a r n i n g R e p r e s e n t a t i o n s 2 0 2 0 C l i m a t e C h a n g e A I W o r k s h o p : A p r i l 2 6

SLIDE 2

The Problem

Introduction

Climate change will cause a rise in global temperatures but also increase the unpredictability of

weather with more extreme weather events.

Soil moisture is dependent on rainfall and temperature and is a critical component of growth for

almost all arable crops globally.

Non optimal soil moistures can lead to lower yield or in extreme cases entire crop loss so many crop

growers invest in soil moisture monitoring sensors to help inform their irrigation schemes.

Soil moisture sensors provide soil moisture at one specific area but not across an entire area meaning

differences could damage crops unknowingly.

However, irrigation can only occur reactively to changes in soil moisture rather than proactive

reduction of harm.

SLIDE 3

The Problem

Aims

Soil moisture at sensors can be predicted into the future accurately
Satellite imagery can be used and also predicted into the future to derive information across an

entire area.

Accurate interpolation can be done of soil moisture between the sensors.
Deep learning can be used to produce an end to end pipeline of soil moisture prediction across an

entire area.

SLIDE 4

The Problem

Methods

Soil moisture as measured at multiple sensor locations across all depths is predicted forward two

weeks into the future using an encoder decoder LSTM.

Structure of the LSTM encoder decoder model for soil moisture prediction. Soil moisture predictions for a single sensor at all depths.

SLIDE 5

The Problem

Methods

This is then supplemented by using satellite imagery to calculate normalised difference vegetation

index (NDVI) and normalised difference water index (NDWI) across the entire area.

A separate encoder decoder LSTM is used to generate predicted NDVI and NDWI images into the

future

Structure of the LSTM encoder decoder model for prediction of NDVI time series

SLIDE 6

The Problem

Methods

Per pixel NDVI, NDWI and elevation maps are then used with predicted soil moisture at the sensor

locations to interpolate between sensors using a spatial kriging algorithm and create a per pixel prediction of soil moisture across the entire satellite image.

Left) Soil moisture interpolation across the satellite image at 10cm depth. Right) the interpolated soil moisture at all depths with each depth stacked on top of each other.

SLIDE 7

The Problem

Results

Model Training Error Testing Error Value Range Soil Moisture LSTM 0.1% - 1% 0.4% - 2.4% 15% - 60% NDVI LSTM 0.027 0.065 0 - 1 NDWI LSTM 0.014 0.02

0.3 – 0.55
The LSTM predictions of soil moisture perform differently depending on the depth.

Root Mean Squared Error (RMSE) across the 14 days of prediction is highest at shallowest depths decreasing at the lower depths.

SLIDE 8

The Problem

Results

Accuracy varied depending on the depth that was being predicted with kriging scores (a proxy for

accuracy varying between 0 and 1) between 0.82 – 0.97 with and average of 0.93 across all depths.

Interpolated soil moisture at all depths bellow 60cm with each depth stacked on top of each other rotating to show entire area.

SLIDE 9

The Problem

Discussion

Advanced warnings of non optimal soil moistures across an entire area can help to prevent loss of

crops and reduction of yields.

Accurate predictions of soil moisture can provide crop resilience to growers through the construction
f precision irrigation regimes in response to the uncertainty brought about by climate change.
Precision irrigation can be designed to use less water making arable crop growth more sustainable.

SLIDE 10